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1.1.! Historical relationships between people, the country and the marine resources ...... 1!1.2.! Human threats to reef ecosystems in Indonesia ........................................................ 2!1.3.! Brief on the obstacles in the management of coral reef resources ............................ 4!
1.3.1.! Past influences in politic and governance on coastal resource management ............................................................................................... 4!
1.3.2.! The influence of economy on reef management and coastal livelihoods .. 5!1.3.3.! Obstacles in the management of Marine Protected Areas ......................... 6!
2.1.! The value of the whole: seamless relationships between reef ecosystems and social systems. ......................................................................................................... 8!
2.2.! Local reefs susceptible to future resilience loss: Case study in Karimunjawa National Park. ........................................................................................................ 10!2.2.1! Reef resource, community and management. .......................................... 10 2.2.2! Synergies of Local scale reef stressors .................................................... 13!
2.2.2.1.! Fishing Pressure: Past and Current ............................................ 15!2.2.2.2.! Physical disturbances to coral reefs ........................................... 15
2.2.2.3.! Increasing demand for fish from tourism .................................. 16!2.2.2.4.! Degrading Water Quality .......................................................... 17!
2.3.! Synthesizing social and ecological perspectives in managing reef resources in KNP. ...................................................................................................................... 18!
3.1.! Background and Objectives .................................................................................... 20!3.2.! Study Area Biophysical Assessments ..................................................................... 21!
3.3.! Ecological Modelling Approach and Conceptual Design ...................................... 28!3.4.! Model Parameterization .......................................................................................... 30!3.5.! Sensitivity Analysis: Method and results. .............................................................. 35!
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3.6.! Graphical analysis: Method and results .................................................................. 37!3.7.! Discussion: Model implication for management and end-users. ............................ 43!
4.3.! Survey Results and Discussions ............................................................................. 52!4.3.1! Response profile and possibilities of response bias ................................. 52!4.3.2! Livelihood flexibility and adaptation ...................................................... 55!4.3.3! Flexibility and adaptation to resource regulation .................................... 59!4.3.4! Local capacity to learn about changes in the environment ...................... 62!4.3.5! Organizational capacity, experience in decision-making and migration . 69!4.3.6! Household and community assets ............................................................ 73!
5.1.! Thesis conclusions .................................................................................................. 79!5.1.1! General conclusions ................................................................................. 79!5.1.2! Specific conclusions related to the ecological study. .............................. 80!5.1.3! Specific conclusions related to the social study. ..................................... 81!5.1.4! Research implication to the general context of natural resource
management. ............................................................................................ 83!5.2.! Thesis limitation ..................................................................................................... 83!5.3.! Directions of future research .................................................................................. 84!5.4.! Recommendations for reef ecosystem management in KNP ................................. 85!
5.4.5! Evaluations and directions related to ecosystem management ................ 86!5.4.6! Socioeconomic-related evaluation and direction ..................................... 87!5.4.7! Stewarding institutional linkage for integrated resilience management in
M$#)(,-(D.<%"#( Table 3.1. Descriptions of benthic substrate categories visually sampled in each quadrant
transect photo. ......................................................................................................... 23 Table 3.2. Benthic state category settings used for simulation input and output based on a
range of percentage of cover proportion data, part of the biophysical assessment in Karimunjawa in August 2008 (see Figure 3.4, Appendix 2). ............................. 31
Table 3.3. Categories of substratum and the conceptual behaviour applied in the simulation. .............................................................................................................. 31
Table 3.4. Options of initial set of parameter values adjusted from the literature of other selected regions. ...................................................................................................... 33
Table 3.5. Summary of parameter sensitivity assessment showing mean relative abundance output using base value and adjusted values. ......................................................... 36
Table 4.1. Summary of types of information gathered through questions developed from social resilience indicators assessed in the survey research ................................... 52
Table 4.2. Responses to questions related to livelihood flexibility and adaptation to hypothetical decline in reef resource conditions. ................................................... 55
Table 4.3. Response to questions related to perception and adaptation to zoning regulations in Karimunjawa National Park. .............................................................................. 59
Table 4.4. Test of associations between responses of perceived resource conditions (Table 4.3) and the related influential causal factors (Table 4.4.). .................................... 64
Table 4.5. Response to questions about past and future changes of fishery and coral reef conditions. All response categories were based on answers given limited to one (n=209). .................................................................................................................. 64
Table 4.6. Response to questions about the dominant activity that influence changes in fishery and coral reef conditions. ............................................................................ 65
Table 4.7. Degree of association between respondents’ level of formal education (No. 2, Table 4.6) and knowledge of key local reef issues (No. 1.b, Table 4.6) where both variables are treated as ordinals ..................................................................... 69
Table 4.8. Response to questions related to three putative local scale reef related issues and level of education. ................................................................................................... 69
Table 4.9. Reponses to questions related to social capacity to organize including involvement in organization, participation in decision-making, and migration status and intention.. ............................................................................................... 70
Table 4.10. Response distribution related to assets such as style of living based household appliances, housing materials, sanitation and individual assets such as approximate monthly income and age group. ......................................................... 76
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Figure 1.1. The Coral Triangle region consists of 6 countries, which are home to some of the most species rich marine habitats in the world. Photo credit: WWF. ....................... 3
Figure 2.1. A conceptual model of reef habitat state trajectory responding to human disturbances over time. ............................................................................................. 9
Figure 2.2. Diagram depicting the relationship between social and ecological arrangements. 10
Figure 2.3. Map of islands and the coral reef areas within Karimunjawa National Park boundaries. (Figure modified from map courtesy of Karimunjawa National Park Agency, Wildlife Conservation Society). ............................................................... 11
Figure 2.4. Conceptual depiction of identified key reef stressors (red boxes) in Karimunjawa that are influencing reef ecosystem processes. ....................................................... 14
Figure 2.5. Undersized reef fishes caught on Karimunjawan reefs. A: Small Parrotfishes (Scarridae) sold at the street market at Karimunjawa village, B: Young Groupers and Parrotfish caught in a protected zone and served as lunch to tourists, C: Young Rabbitfish (Siganidae) served as regular meals in a homestay (guesthouse) in Karimunjawa. ..................................................................................................... 17
Figure 2.6. Direct disposal (A) and untreated (B) domestic waste polluting coastal waters in Karimunjawa. .......................................................................................................... 17
Figure 3.1. Fourteen reef sites surveyed in Karimunjawa and Kemujan island inshore reefs. . 22 Figure 3.2. Mean fish abundance of herbivorous and predatory reef fish for each size-length
categories in sampling sites during preliminary assessments in Karimunjawa, August 2008 ............................................................................................................ 25
Figure 3.3. Mean percentage cover composition of each benthic category per site based on preliminary biophysical assessments in Karimunjawa, August 2008. ................... 26
Figure 3.4. Selection of photos documenting preliminary key findings taken during the baseline survey in August 2008 .............................................................................. 27
Figure 3.5. Conceptual diagram depicting the layout of the benthic community dynamics model and key stressor functions influencing each community group. .................. 28
Figure 3.6. Conceptual diagram of benthic space occupation by living substrate groups assumed in the model. ............................................................................................. 29
Figure 3.7. P1-A timeline projections of three starting point compositions of reef habitat: Good (10% M, 70% C), Moderate (50% M, 30% C) and Poor (70% M, 10% C). 38
Figure 3.8. P1-B timeline projections of three starting point compositions of reef habitats: Good (10% M, 70% C), Moderate (50% M, 30% C) and Poor (70% M, 10% C). 39
Figure 3.9. P2-A colour plot projection of three reef habitats with starting point compositions of: Good (10% M (X-axis), 70% C (Y-axis)), Moderate (50% M, 30% C) and Poor (60% M, 20% C), each marked with white cross-hairs. ................................. 41
Figure 3.10. P2-B projection result of three reef habitats with starting point compositions of: Good (10% M, 70% C), Moderate (50% M, 30% C) and Poor (60% M, 20% C), each marked with white cross-hairs ........................................................................ 42
Figure 4.1. Map of area surveyed in residential areas of Karimunjawa, Kemujan and Parang villages (in colours). ............................................................................................... 51
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Figure 4.2. Pie diagram showing distribution of respondents’ main occupation (n= 209) grouped by relation to reef resources, both directly (A) and (B) and indirectly (C); as well as those not related to marine resource utilisation (D). .............................. 54
Figure 4.3. Photos showing semi-dry red soil ground characteristic in Parang island (A), allowing much tropical fruit to grow like pomegranates, for example (B,C), whereas seaweed drying was visually more common in Kemujan and Karimunjawa area (D,E,F). ..................................................................................... 57
Figure 4.4. Photos showing logging activities, some conducted by ex-fishing families (A,B), including rock breaking to supply construction material (D). These supplementary income strategies could be found in combination for some households (C,E). ..... 58
Figure 4.5. Photos taken in the Karimunjawa Kota area showing a makeshift garbage dumping site (A), unmanaged garbage (B,C), and collected plastic bottles (D). ... 67
Figure 4.6. Photos showing the typical structure of village roads. Main roads constructed with asphalt layering connect Karimunjawa and Kemujan villages (A), a brick pavement road in Parang village (B), whereas in the sub-village areas mostly soil roads still predominate. ........................................................................................... 74
Figure 4.7. Photo showing a gasoline-based generator used by a household to supply an additional period of electricity, however, this was visually uncommon during the survey. ..................................................................................................................... 75
Indonesia is one of six countries within the Coral Reef Triangle - the area with the highest
species diversity of coral reef organisms: about 590 of the world’s 793 known reef-building coral
species (Veron, J 2000; Wilkinson, Clive 2008). The coastal ecosystems in the region provide
resources and livelihoods for millions of people in the coastal and island communities of Indonesia.
Their dependency on reef resources places pressure on reef systems (Cesar, H 1996). These reef
ecosystems support the core of subsistence fishing in Indonesia, as well as other socio-economic
assets such as natural coastal protection, tourism revenue, and aesthetic and cultural values.
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Unfortunately, the condition of coral reefs in Indonesia is in a declining state with almost half of the
reefs now highly threatened (Wilkinson, Clive 2008). Human impacts on coral reefs have altered
these ecosystems through both long-term stresses such as overfishing, domestic, industrial and
agricultural pollution, eutrophication and sedimentation (Dixon 1997; Edinger, EN et al. 1998;
Holmes et al. 2000; Tomascik, T, Suharsono & Mah 1993). Short-term (often acute) threats such as
destructive fishing practices include blast fishing, poison fishing and anchor damage (Pet-Soede, L &
Erdmann 1998).
Figure 1.1. The Coral Triangle region consists of 6 countries, which are home to some of the most species rich marine habitats in the world. Photo credit: WWF.
Similar to other countries in Southeast Asia, these impacts have been correlated with over-
exploitation associated with rapid human population growth in Indonesia in the past 30 years
Anthropogenic stresses in many ecosystems have caused dramatic change in species
composition, which are often almost irreversible (Scheffer, M. 2003; Scheffer, M. et al. 2001). For
coral reefs, strong influence from human activities at local, regional and global scales has reduced the
ecosystem’s capacity to re-establish after disturbances (Hughes et al. 2003; Mora 2008; Pandolfi, JM
2005). In many reef areas, disturbances have caused transition in community compositions from
corals to algae, known as ‘phase shifts’ (Hughes et al. 2003; Hughes, Rodrigues, et al. 2007; Pandolfi,
J 2003). The occurrence of an undesirable phase shifts (Fig. 2.1), where the coral reef system
gravitates irreversibly to a degraded system (Done 1992; Scheffer, M. et al. 2001), has given rise to
studies that address the importance of managing for coral reef resilience (Hughes et al. 2003; Hughes,
Rodrigues, et al. 2007; Mumby, P., Hastings & Edwards 2007; Nyström, Folke & Moberg 2000; West
& Salm 2003).
The definition of ‘resilience’ is often specific to the scientific disciplines in which it is used
(Brand & Jax 2007). As in most coastal and marine ecosystem, humans and coral reef are part of an
socio-ecological system (SES) (Hughes et al. 2005). In this context Walker et al. (2004) described
resilience as “the capacity of a system to absorb disturbance and reorganize while undergoing change
so as to still retain essentially the same function, structure, identity, and feedbacks”. The link between
social and ecosystem resilience is closely linked with the dependency of people on reef resources
(Adger, W. 2000; Folke et al. 2002). As human societies utilize resources from reef systems, it is in
their interest to help sustain the reef’s regenerative capacity to continuously deliver resources and
services that are important for their livelihood (Folke et al. 2004). However, individuals and
institutions within the social systems can differ in their vulnerability to both natural or human-induced
ecological surprises (Adger, WN et al. 2005; Holling 1996). Thus, the social adaptability to maintain
ecological resilience is also affected (Walker, B et al. 2004)
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Figure 2.1. A conceptual model of reef habitat state trajectory responding to human disturbances over time. If severity and frequency of disturbances to some extent impaired reef ecosystem functions, incomplete recovery can occur (brown line). In this case, the reef system is losing its regenerating capacity to human-related stressors such as habitats that are losing its structural complexity (from coral dominated to coral-depauperate state). Managing reef resilience, in this case, is to maintain key ecosystem process so that reef system dynamics are remaining in the ‘desirable’ domain (blue line). This includes managing human-made disturbances in a level where impacts still allow for successful or, at least, prolonged reorganization of reefs. (Figure modified from Palumbi et al. (2008)).
People interact with coral reefs predominantly through fishing (McManus, J. W. 1997; Roberts
1995). Unfortunately, over-harvesting is nearly universal (Jackson, J. 2001; Myers & Worm 2003;
Pauly, D. et al. 2002), partly due to fisheries management treating ecosystem resources as static rather
than dynamic systems (Pauly, D. et al. 2002). Moreover, at local to regional scale, much of planning
of protected areas and actions associated with coral reef fisheries provide inadequate consideration of
the socio-economic context (Christie 2004; Christie et al. 2005; McClanahan, T et al. 2006). Thus,
most reef management policies and regulation that were economically detrimental could be socially
less adaptable and develop negative feedbacks from the community (McClanahan, T.R. et al. 2008;
Mora 2008). Therefore, managing reef resilience requires the perspective of socio-ecological system
(SES) that develops dual positive feedbacks of both maintaining ecosystem functions and processes,
but also the capacity social that are responding and adapting to environmental changes and
management intervention (Folke 2006; Hughes et al. 2005; Mumby, PJ & Steneck 2008) (See Fig.
2.2).
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Figure 2.2. Diagram depicting the relationship between social and ecological arrangements. The coral reef ecosystem provides benefits for the people that are linked to it, such as in fisheries and tourism (1). Concurrently, reef use by people impact on reef ecosystem processes (6), such as modification of reef habitats or reduction in fish population sizes (2). The response of the reef system includes recoverability and resistance to man-made stressors yet ecosystem information received by reef user is partial through reinterpretations such as science or traditional knowledge (3). Collective decisions and actions from the social system, subsequently, generates feedbacks or controls, such as in managing fishing or, protecting and govern reef resource (4); or amplification of an effect, such as further exploitation that could generate detrimental effects to ecosystem, such as overfishing and destructive fishing. In this case, reef resource management is required to be responsive to both ecological changes (3) as well as the socioeconomic aspect that influence decisions and actions of resource user (5). Synthesizing science with the common knowledge of resource users (e.g. the community) could be essential to influence decision whether to intensify, reduce, or stop resource use (1,2,4). Yet, scientific tools to assess biophysical and ecological variables in the context of resilience and appropriate management actions are a challenge (3,4,6). (Figure modified from Nyström (2006)).
The Indonesian government has established fifty National Parks (Dephut 2008) - nine of
which cover an area of 41,129 km2 gazetted as marine national parks (Clifton 2003). Karimunjawa
Islands is one of the nine National Marine Parks in Indonesia (Dephut 2008). KNP (5' 40"-5' 57" S
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and 110' 4"-110' 40" W) lies in the Java Sea, approximately ± 45 nautical miles from Jepara, on the
north coast of Central Java, Indonesia (Fig. 2.3). The park includes 111.625 Ha, which consists of
7.033 Ha of terrestrial and 104.592 Ha of marine areas. Karimunjawa consists of 27 islands that
include 5 inhabited islands of Karimunjawa, Kemujan, Parang, Nyamuk, and Genting. The marine
zonings within the KNP boundaries are buffer, rehabilitation, traditional fishing, aquaculture, tourist,
and a no-take zone. The purpose of the zoning plan was to regulate park utilization mainly for
research, science, education, resource sustainability, tourism and recreation (BTNKJ 2004). There are
five types of characteristic ecosystems in the island group, consisting of low-land tropical forest,
beach forest, mangrove forest, seagrass, and coral reef (BTNKJ 2004).
Figure 2.3. Map of islands and the coral reef areas within Karimunjawa National Park boundaries. (Figure modified from map courtesy of Karimunjawa National Park Agency, Wildlife Conservation Society).
The close proximity of the island group to Java Island, home to more than 60% of the
population of Indonesia (BPS 2005); pose a significant challenge for reef managers to address issues
of balancing demands for reef-generated resources with conservation efforts (Edinger, EN et al.
1998). Karimunjawa National Park (KNP) represents well the population-related reef management
issues that are common in developing countries (Bell et al. 2006), particularly in the Indo-Pacific
where efforts to mitigate local impacts that reduce reef resilience are currently scarce (Hughes et al.
2003). Interestingly, coral biodiversity in KNP still resembles that of other fringing reef regions in
eastern Indonesia (Edinger, EN et al. 1998). Despite historical unsustainable reef fishing practises
such as muro ami (Marnane, Ardiwijaya, Wibowo, et al. 2004), recent trends in the average of coral
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cover were moderate (around 40 – 60% of average for 3 and 10 meters depth, however
inconsistencies of sampling sites and size for each year between 2005-2008 (Ardiwijaya, Kartawijaya
& Herdiana 2007; Ardiwijaya, R. L. et al. 2008; Marnane et al. 2005)). Furthermore, time-series reef
ecological data resource for this region is lacking and recent relative to the establishment of marine
area for conservation with limited collaborative scientific support form NGOs (e.g. Wildlife
Conservation Society and Taka Foundation) (Ardiwijaya, R.L. et al. 2008; BTNKJ 2004).
There are more than 9000 registered residents living in KNP, of which approximately 40%
undertake fishing using a variety of artisanal fishing methods (BTNKJ 2008). Their selectivity in
fishing areas as well as fish targets also vary greatly mainly due to the bi-monsoonal season and local
knowledge to fish aggregation sites (Campbell & Pardede 2006; Yulianto & Herdiana 2006).
Migratory fishers have brought up different preferences in fishing behaviour that has likely resulted
from multi-ethnicity influences (Pet-Soede, L & Erdmann 1998) that originated both from the western
and eastern Indonesian archipelago (e.g. Jawa, Madura, Bugis and Buton tribes (BTNKJ 2004). Each
cultural group tends to have nucleated settlement pattern where those from Kalimanatan and Sulawesi
tend to aggregate in northern part of the region such as Kemujan, and Java in the southern part such as
Karimun island (BTNKJ 2004). Despite cultural heterogeneity, fish and reef resource are utilized as
‘all-access’ as shared as a ’common property’ with customary laws that exclude non-Karimunjawan
from fishing within the region (S. Haryanta, Pers. Comm., 2008).
Because of such social complexity (e.g. community heterogeneity and traditional resource
used with high dependency), further conflict due to reduced social adaptation to management
interventions can be avoided by proactively involving local community as actors that also perform
management task (Armitage, D. 2005). More, as an areas where high sense of resource ownership is
being displayed, current regulative framework of the working policy (2004 zoning) in KNP also needs
to be deliberately consistent with the potential collective decisions existed in the community that may
enhance acceptability of conservation initiatives (e.g. participatory management and customary
marine tenure) (Cinner, J 2005; Elliott et al. 2001; Pomeroy, RS 1995). However, in the recent
rezoning in 2004, extensive consultation with community was undertaken only in the beginning.
Moreover, several years after the zoning enacted low community compliance was observed in a report
by Wildlife Conservation Society (Yulianto & Herdiana 2006). The differing rules, procedures and
values in the community are suggested to have restrained efforts to achieve common consensus for
conservation and limit park manager to promulgate regulation (Rudd 2000; Rudd et al. 2001). On the
other hand, low institutional capacity (e.g. management resource and fund) had also limited practical
efforts to monitor and enforce zones in KNP, and even to communicate with the community (e.g.
warden and patrol boats) (S. Haryanta, Pers. Comm., 2008). Karimunjawa National Park might be just
one of the typical case in developing nations where conservation initiatives are not yet being able to
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reflect local social condition such as knowledge, experience, and their cooperative behaviour (Christie
2004; McClanahan, T et al. 2006). Long-term communication between regulators and the community
was not initially well formulated, thus, community perception and opinion has not yet fully taken
account of conservation interventions strategies. Correspondingly, community-based approaches to
fishery and reef management, such as adopting and cohesively attaching to local customary or
traditional norms in regulation (Satria, Arif, Matsuda & Sano 2006), are still lacking.
Historically, some of the extractive activities in Karimunjawa have been harmful to the reef
ecosystem (see section 2.2.2) and marine zonings areas, including no-take areas; have been regulated
to minimize the impact on reef habitat. However, area restriction established resulted in fishing
displacement such as to inshore subsistence fishers and yet effective livelihood support strategies to
reduce socioeconomic problems that drives unsustainable activities was absent. Correspondingly, how
people utilize resources relates to a multitude of social, cultural, and economic factors that shapes
social perception of resources (Fauzi & Buchary 2002; Marshall, N et al. 2007; Nazarea et al. 1998).
From this, Karimunjawan reef management needs to be able to acknowledge social perception of
resources, which means understanding both the varying ways of people utilizing the resource and the
relative values to them (Cinner, JE & Pollnac 2004). Evaluating social perception at early stages of
MPA planning and management may help identify areas of conflict and agreement, recognize shared
perceptions, and produce solutions (Cinner, JE & Pollnac 2004; Cocklin, Craw & McAuley ;
McClanahan, T, Castilla, et al. 2009). Specifically, social perception of risk relates to their adaptive
behaviour towards changes (e.g. resource depletion, transitional livelihood) (Adger, WN et al. 2005;
2009). Aside from maintaining the ecological functions of an MPA, therefore, it is critical for KNP
management to gain positive social perception of resource conservation and restrictions while
simultaneously prescribing livelihood-based policies that might be needed to improve local-level
social resilience (e.g. promoting economic growth).
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Coral reefs are in decline worldwide from disturbances that occur both at the global (Hoegh-
Guldberg et al. 2007) and local (Knowlton & Jackson 2008) scales (see also review by (Knowlton
2001; Wilkinson, C 2004). Local stressors that are influenced by a large and growing human
population in coastal areas (Curran et al. 2002) include overfishing (Hughes et al. 2003; Mumby, P.
2006b), physical disturbances (Nyström, Folke & Moberg 2000; Ostrander et al. 2000), and declining
water quality (McCook, LJ 1999). Fishing pressure can impact directly on trophic relations between
fish and benthos (Pinnegar, Polunin & Francour 2002; Steneck, R. S. 1998) and also indirectly on
benthic reef communities (Hughes 1994). In areas where predatory fish stocks are severely depleted
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and recovery potential is low, growing demands for fish food can prompt fishermen to target lower
trophic levels (e.g. herbivorous fishes) (Done 1992; Mcmanus, J. 2000). The loss of herbivorous
fishes reduces the grazing rate on the reef (Mumby, P. 2006b), thereby favouring an increase in algal
biomass which encroaches on corals via space competition, potentially shifting the reef community
towards macroalgal dominance (Hughes, Bellwood, et al. 2007). Increasing sediment and nutrient
loading are factors that can exacerbate such a shift by enhancing algal growth rate (Russ & McCook
1999). In this section, I analyse the role of these environmental stressors in determining reef
functioning, health and state in Karimunjawan reef (See Fig. 2.4).
Figure 2.4. Conceptual depiction of identified key reef stressors (red boxes) in Karimunjawa that are influencing reef ecosystem processes. The arrangement of human activities at local scale brings either desirable and undesirable (blue and red text, respectively) control mechanism to specific processes of the reef. Sustainable human activity can prompt negative controlling feedback both the ecosystem and the community (blue texts), whereas the opposite activity can potentially disrupt reef ecosystem process and risk in livelihood (red texts) (Impact 2). The interaction of coral and algae influence structural complexity that was affected by both physical impact of fishing and coastal pollution, which also determines the aesthetical value of the reef such as for tourism. Fishing can directly affect both habitat and fish species composition, which influences both the habitat condition and the amount of protein output form the reef.
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Being one of the important artisanal fishing areas in the Java Sea region, fishing at the
boundaries of KNP has been intense with clear effects on fish populations and reef habitats. Early
evidence of overfishing in Karimunjawa was observed by Edinger et al. (1998) at few reef sites
surrounding Karimunjawa main island (Kecil and Burung island, Cemara patch, Mrican lagoon).
Furthermore, constraints in departmental authorization (e.g. between National Park Agency and the
Ministry of Marine Affairs and Fisheries) appears to be restricting the regulation of fishing
intervention (e.g. regulation in gear and target fishes) limiting the protection to relatively small no-
take areas (BTNKJ 2004; Dirhamsyah 2006). The no-take zones aims to replenish fish stocks by
establishing enclosures around identified key fish spawning and aggregation sites. However, difficulty
in restricting resource access [much was still considered as part of ‘all-access rights’ (BTNKJ 2004)]
combined with low community support for MPA regulation meant that no take-areas were ineffective
in preventing the decline in fish populations (2008; Ardiwijaya, Kartawijaya & Herdiana 2007; 2008;
Rudd et al. 2001).
Although the intensity of past unsustainable fishing practises (e.g. cyanide fishing and muro-
ami (Marnane, Ardiwijaya, Wibowo, et al. 2004) had receded due to significant decrease in fish catch
felt in years following, small numbers of muro-ami fishermen were still operating. This situation was
likely to be triggered by economic pressure and slow fish-stock replenishment (pers. comm. Sutris
Haryanta, 2008). To date, fishing methods were characterised by effective yet non-selective methods
that operated seasonally (BTNKJ 2004). This means each type of fishing gear used (e.g. hook and
line, spear guns, hand spears, traps, small gillnets and tonda (traditional trolling) are specific only to
catch a certain range of fish families (Ardiwijaya, Kartawijaya & Herdiana 2007). Unfortunately,
currently dominating herbivorous fish families (e.g. Scarridae) were among targets of each of these
methods (Campbell & Pardede 2006); threatening species that control algal biomass and thereby help
provide space for corals to grow. Therefore, it is critical that reef manages in KNP are be able to
implement management remedies to avoid the loss of key functional species of the reef ecosystem.
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An increasing fishing pressure has significantly reduced the reef fish populations around
Karimunjawa Islands (Ardiwijaya, Kartawijaya & Herdiana 2007; Wibowo, Joni T 2006). The fishing
practices have modified the community structure of reef fish (Campbell & Pardede 2006), and have
caused direct physical damage to reef habitats and reef communities (Marnane, Ardiwijaya, Wibowo,
et al. 2004). The persistence of these impacts were also suggested from a recent baseline survey [see
also Maynard et al.(2008)], which indicated that several sites showed signs of macroalgal dominance
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and had low abundance of herbivorous fish irrespective of marine park zoning (e.g. Tengah Kecil
(Tourism zone), Cemara Kecil (Buffer Zone). While past fishing impacts from practises such as
muro-ami are decreasing, recent observations of broken rock anchors on reef crests, fractured coral
colonies and interviews with boat operators suggest that rock anchors are still being used by local
fishermen. This is related to the fact that only a few mooring buoys have been installed within the
park boundaries near Karimunjawa and Kemujan islands. This number is inadequate to support the
large number of fishing boats that operate in these areas. As yet there are no specific regulations
concerning anchoring within KNP, another problem with installing mooring buoys has been the cost,
which together with the absence of standard procedures has prevented their widespread use within the
KNP.
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Tourism development can also contribute to reef health decline (Moberg & Folke 1999) where
increasing demand for reef service, such as sea food products, can lead to intensified fishing efforts
(Maypa et al. 2002). Since the Kartini catamaran was introduced in 2003 to help service the
Semarang-Karimunjawa crossing, there has been an increase in tourism influx to the islands.
Predatory fishes such as coral trout (genus Plectropomus) or emperors (family Lethrinidae) are more
sought after than herbivorous fishes (e.g. Scarridae and Siganidae) and as a consequence, adult
predatory reef fishes are nearly fished out on Karimunjawan reefs (Campbell & Pardede 2006). Some
fishermen target more remote fishing grounds to continue targeting sought-after species despite
greater travel and fuel costs, whereas other fishermen continue fishing on local grounds but with a
greater inclusion of herbivorous fishes. During two field trips to the Islands, I observed daily that
undersized and juvenile reef fish (including Scarrids and Siganids) are used to cater for tourists (see
Fig. 2.5). With the increase in tourist activity, coupled with local demands by a growing local
population, pressure on these particular fish species is likely to increase in the near future
(McClanahan, T et al. 2003; Wilkinson, CR 1999), potentially leading to an uncontrolled growth of
macroalgae within the reef system (Mantyka & Bellwood 2007; Mumby, P. 2006a).
! $*
Figure 2.5. Undersized reef fishes caught on Karimunjawan reefs. A: Small Parrotfishes (Scarridae) sold at the street market at Karimunjawa village, B: Young Groupers and Parrotfish caught in a protected zone and served as lunch to tourists, C: Young Rabbitfish (Siganidae) served as regular meals in a homestay (guesthouse) in Karimunjawa.
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The link between water-quality impacts such as sewage and pollutant outflows and sediment
run-off and coral reef degradation in Indonesia has been well documented (Edinger, EN et al. 1998;
2000; Holmes et al. 2000). Human development in the coastal perimeter is likely to contribute to the
nutrient enrichment of coastal marine areas (Szmant 2002), which can disturb reefs and slow down
recovery (e.g. inhibition of reef growth (Edinger, E et al. 2000; Kleypas 1996), and reduction of
competitive strength of corals against macroalgae (Loya 2004). In many parts of the Karimunjawa
district, domestic sewage is disposed of directly into the marine environment (Fig. 2.6), resulting in
distinct signs of eutrophication - e.g. colouration, odour (Gurel et al. 2005).
Figure 2.6. Direct disposal (A) and untreated (B) domestic waste polluting coastal waters in Karimunjawa.
The concept of resilience has been used in ecosystem studies since the early 1970s (Holling
1973) and has become a popular framework for describing links between socioeconomic and
ecological systems (Folke 2006). Along with its rapid theoretical development (Brand & Jax 2007),
resilience has been a fundamental point in understanding ecosystem behaviour, including coral reef
studies (Bellwood, D et al. 2004; Hughes et al. 2003; Hughes, Rodrigues, et al. 2007). However,
measuring ecosystem resilience is a complex task, particularly to reef managers in developing
regions. This is particularly due to the fact that less specific management guidelines having been
available as well as the need to address the complex processes involved [e.g. species interaction
between trophic structures and functional diversity (Bellwood, DR, Hoey & Choat 2003; Nyström,
Folke & Moberg 2000)]. In this chapter, Karimunjawa National Park (KNP) is used as a case study
area where reefs are dominantly protected by a restrictive policy based on conservation zones.
However, the designation of the latest 2004 marine zoning policy in KNP was a response limited to
the changes and alerts resulting from issues of local fish depletion and the related unsustainable
practices. Only a small protection zone has been designated relative to the total area (BTNKJ 2004).
Currently the KNP conservation framework has not yet included a deliberate ‘systems perspective’
approach or tools that can inform managers about the vulnerability of the reef system to future
environmental changes or hazards in order to find a specific management approach or intervention
that could promote ecological resilience to these disturbances.
This chapter examines the applicability of ecological resilience modelling in providing a
viable basis for evaluating the outcome of different management decisions in KNP, particularly how
local man-made disturbances are affecting the processes of the reef system. The model is built partly
based on the work by Mumby et al. (2007) and uses coral reef data obtained in KNP (Ardiwijaya,
Kartawijaya & Herdiana 2007; Ardiwijaya, R. L. et al. 2008; Marnane, Ardiwijaya, Pardede, et al.
2004; Marnane et al. 2005; Marnane, Ardiwijaya, Wibowo, et al. 2004; Wibowo, Joni T 2006) while
referring to data from relevant literature from other regions. The purpose of the model was to develop
a informative tool useful to managers to assess reef resilience (meeting minutes, KNP, Diponegoro
University and the University of Queensland, 23 September 2008), rather than a tool to accurately
simulate the complex behaviour of reef dynamics. The study was being developed to address several
key questions stemming from the meeting such as:
! %$
1. How can we aid local reef managers to explore possibilities of coral reef habitat response to
different levels of local disturbances given by limited ecological data?
2. How can we aid reef managers to better understand the impact of local disturbances to reef
processes and provide guidance to more resilient future coral reef habitats?
The study was an effort to provide a scientific and practical means of managing reef resilience
to support prioritization of management incentives in KNP. Its focus is on effects of fishing and
associated physical disturbances to the reef system processes, exploring different outcomes of reef
habitat conditions. It is designed to describe options for changes in fishing practices and management
interventions that can help avoid undesirable phase shifts and other semi-permanent ecological
change. The model can also be used as a learning aid where managers can ask questions relating to
the sensitivity of the systems to a range of environmental scenarios with the intent to work out how
management strategies can prevent or mitigate negative outcomes (e.g. herbivorous fish population
management, protection priority of vulnerable reef habitat).
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In August 2008, we (S. Afatta, K. Anthony, J. Maynard, L.F. Anggraini and D. Haryanti)
conducted a set of field surveys within the marine zonings at KNP. Its purpose was to get a
description of the recent condition of reef habitat within and outside the KNP zonings as well as
partial indications of reef resilience based on fish assemblage (e.g. certain fish trophic species
depletion due to exploitation (Dulvy, Freckleton & Polunin 2004)) and reef benthic structure (e.g.
presence of key functional reef fish associated with benthic algal cover (Bellwood, DR, Hughes &
Hoey 2006)). Surveys took place at 14 in-shore reef sites associated with Karimunjawa and Kemujan
main islands (Fig. 3.1). The reef sites selected were indiscriminate of park marine zonings. It include
both areas that are protected and unprotected from fishing activity within the National Park
boundaries. The sites were selected reef areas considered as favourable in-shore fishing grounds for
residents of the near dwellings in Karimunjawa and Kemujan main islands (pers. comm. Sutris
Haryanta, National Park Field Officer; Pak Darman, local resident and skipper, 2008). Another less-
populated region of Parang island district in the National Park boundaries was not surveyed due to
weather and logistical constraint at the time of survey. The surveys are (1) rapid visual census of the
abundance and distributions of reef fish; (2) fine-scale surveys of benthic groups and substrate
quality; (3) photographic surveys of visual signs of human impacts and resilience indicators.
! %%
The fish survey used three repetitions of timed-swims which each covered an approximately
50 x 5 meter belt transect in both the reef crest (3-5m depth) and reef slopes (7-9m depth), covering a
sample of approximately 750m2 per site (English, Wilkinson & Baker 1997). Reef fish were visually
identified and noted for their size-length in situ based on family groups of predators (e.g. Serranidaes
or ‘Groupers’, Lethrinidae or ‘Wrasses’, Lutjanidae or ‘Snappers’) and herbivores (e.g. Scarridae or
‘Parrotfishers’, Siganidae or ‘Rabbitfishes’) (Allen et al. 2003). Each group was divided into three
size-length visual estimation categories of 0 to 10, 10 to 20, and 20 to 30 centimetres. Fish abundance
for each size-length category per site was calculated from the mean total abundance of the three timed
swims (Fig. 3.2).
Figure 3.1. Fourteen reef sites surveyed in Karimunjawa and Kemujan island inshore reefs.
For the benthic substrate surveys in the reef crest habitat (depth of 3 – 5 m) of each site, we
took 3 sets of 10 digital photos of 1 x 1 m quadrate transects. The quadrate transects was placed
haphazardly with approximately 5 meters distance in-between. The sampling covers approximately 30
m2 of reef crest area per site. Coral environment within the fore reef habitat (depths of 5 – 15 m) was
not surveyed because SCUBA diving permission in the conservation zones was unable to be obtained
until the time of survey. The purpose was to obtain representative data of the benthic substrate
! %&
composition and condition of each site. For each quadrate transect photo, 36 random points of interest
(RPI) were digitally overlaid within quadrate frame (36 points/m2) using CPCe (Coral Point Count
with Excel Extension) software (Kohler & Gill 2006). The designated number of RPI sampled per
quadrate was for the purpose of data processing efficiency, yet, acquiring a reliable quantitative
description of each benthic category cover (1080 RPI within a ± 30 m2 belt transect per site) (Dumas
et al. 2009). The proportion of each benthic categories sampled within the total RPI sampled (36) was
treated as the relative percentage of cover for each quadrate. Microsoft Excel software was used to
calculate descriptive statistics and develop graphs. Major benthic categories visually observed were
Hard Corals; Non-Scleractinian Corals; Grazing Invertebrates; Other Invertebrates, Fleshy
Macroalgae; Other Macro/micro-algae; Settable Substrate; and Sand, Silt, and Sediments (For
descriptions see Table 3.1).
Table 3.1. Descriptions of benthic substrate categories visually sampled in each quadrant transect photo.
Benthic substrate Description Hard Coral
Scleractinian corals grouped by their growth forms such as Branching, Encrusting, Foliose, Massive, Mushroom, Sub-massive and Tabulate corals. (Veron, J 2000).
Other Corals Non-scleractinian corals such as Soft Corals (Xenia sp., Sarcophyton sp.), Fire Corals (Millepora sp.) (Veron, J 2000).
Grazing Invertebrates Sea urchins (Diadema sp.).
Non-grazing Invertebrates
Sponges (Encrusting, ‘Upright’ sponges growth forms) and Other Invertebrates (such as Sea Stars and Sea Cucumbers).
Fleshy Macroalgae Seaweeds or ‘Upright’ macroalgae >10mm height of local genera such as Caulerpa sp., Turbinaria sp., Sargassum sp., Padina sp. (BTNKJ 2008; Diaz-Pulido & McCook 2008).
Other Macro/micro-algae
Algaes that facilitate coral settlements and reef construction and cementation, which includes sub-categories of: Crustose Coralline Algaes and Calcareous Algaes (e.g. Halimeda sp.); visually distinct clumps of Filamentous Turf Algaes, including those overlaying other substrates on top of either living/dead corals or rubbles). (Diaz-Pulido & McCook 2008).
Settlement (‘space’) Providing Substrates
Recently dead corals (corals that have lost icoloration and have distinct early signs of algae overgrowth), Rocks (dead corals with deformed colony structure) and Rubbles.
Unsuitable substrate Trapped sediment, sand/silts including those overlaying other substrata.
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At the time of survey, fish survey showed a relatively low mean abundance of predatory reef
fishes (less than 10 counts) in all size-length categories considering the extent of the area surveyed.
Among the higher trophic group, Labridae (Wrasses) was the commonly found family with the
presence of economically desirable families such as Caesioneidae (Fusselliers) and Serranidae
(Groupers) being almost non-existent throughout the area surveyed. For herbivorous reef fish, such as
the Scarridae (Parrotfishes) and Siganidae (Rabbitfishes) families, the highest abundance (ranging
! %'
from ±35 – 40 fishes) was found in two buffer zone sites (Sintok East and Tanjung Gelam) and the
least incidence in other sites varied from 0 to 20 (Fig. 3.2). Additionally, a time-series survey from the
Wildlife Conservation Society (WCS) in Karimunjawa had described a trend in the decline of both
herbivorous abundance and biomass since the latest zoning regulations were enacted in 2004 to 2008
(Ardiwijaya, R.L. et al. 2008). Moreover, the majority of herbivorous reef fish, such as the Scaridae
family, were from 10 to 20 cm in size-length. This is relatively lower than the general adult size
lengths of Scarrids, such as the locally common Scarrus ghobban that normally ranges between 30-50
cm (Ardiwijaya, R. L. et al. 2008; BTNKJ 2008; FishBase 2010). From this, I suspected that past in-
shore reef fishing activity might have contributed to the decline of the higher reef fish trophic, such as
locally preferred predatory reef fishes. As they become rare, on the lower trophic, functional species
such as herbivorous reef fish that are important to maintain algal growth on the reef are becoming
more favourable to the fishers.
Results from the benthic survey showed that other than Tanjung Gelam, hard coral cover at all
sites varied from around 40% – 60% of cover (Fig. 3.3). Most of these sites were likely
counterbalanced with a proportion of suitable substrate for settlement such as dead corals and rubble
that were also varied in its cover proportion. Fleshy macroalgae were in low abundance across all
sites with the highest less than 10% of cover in Cemara Kecil. This included other coexisting
macro/micro-algae such as a substantial proportion of calcareous algae for instance Halimeda sp. and
sp.) barrens (e.g. Legon Boyo, Legon Tole, Tanjung Gelam), and encrusting sponges (e.g. Tanjung
Gelam).
Due to the one-time nature of the survey, the past trajectory of the growth of the living
substrates (e.g. seasonality of algae) was not explained; therefore, inferences about resilience
indications based on community dynamics are strictly limited here. However, using live hard coral
cover as a proxy for habitat quality, variation occurred irrespective of any of the site protection levels
of zoning. This suggests that there are other ecological aspects that are involved in regulating
community structure and resilience such as herbivory, nutrient level, and site connectivity (McCook,
L et al. 2009; Smith, JE, Smith & Hunter 2001; Sotka & Hay 2009), which have not been fully
incorporated in past management approaches. At the time of survey, fleshy macroalgae cover was
relatively low compared to hard coral, which indicates there may be other herbivores that compensate
for the functions of the threatened herbivorous reef fishes (Aronson & Precht 2000) as well as effects
from the nutrient factor (McCook, LJ 1999). The presence of rocks and fragments of recently dead
corals (for description of terms refer to Table 3.1) also indicate intensive past disturbance to the
surveyed sites of either natural or human-influenced origin.
! %(
Figure 3.2. Mean fish abundance of herbivorous and predatory reef fish for each size-length categories in sampling sites during preliminary assessments in Karimunjawa, August 2008. Sites in each habitat (reef crest and slope) are clustered to their designated zonings (B=Buffer, C=Core/No-take, P=Protection, TU=Tourism Use, TF=Traditional Fishing). Mean abundance measures were calculated from three replicates of 50 x 5m belt transects. Colours represent three size length categories of <10, 10-20, and >20 cm in blue, red and green, respectively.
! %)
Figure 3.3. Mean percentage cover composition of each benthic category per site based on preliminary biophysical assessments in Karimunjawa, August 2008. Reef crest habitat of each sites are clustered into their designated zonings (B=Buffer, C=Core/No-take, P=Protection, TU=Tourism Use, TF=Traditional Fishing). Cover composition percentage and mean point count was measured from 3 sets of 10 quadrate transects, where 36 random points of interest were sampled for each transect (nRPI=360 x 3). Sintok East was excluded from the site comparison due to missing 4 quadrate transect photo samples.
From visual documentation during the survey, most herbivorous reef fish schoolings
encountered were relatively small in size-length (Fig. 3.4, B), while documenting larger fishes was
relatively uncommon (Fig. 3.4, A). Regarding pressures from human activity, grazing reef fishes,
such as parrotfish, were found to have local economical value as a fish catch, however, their role as a
local fishing commodity either for subsistence or trade was not explored further (Fig. 3.4, C).
Furthermore, building materials such as concrete leftovers were commonly used as anchors (Fig. 3.4,
D) in the subsistence fishing activity (e.g. handlines, traps) which could cause direct physical damage
to living coral (Fig. 3.4, E). Habitats suggested to be in a poor state of resilience were found in several
sites, though they were patchy, with features such as signs of macroalgae overgrowth (Fig. 3.4, F, e.g.
Legon Tole) and barren except for sea urchins (Fig. 3.4, G, e.g. Cemara Kecil).
! %*
Figure 3.4. Selection of photos documenting preliminary key findings taken during the baseline
survey in August 2008. Presence of large sized (above 30cm) Parrotfish was rare (A), where most of the schoolings were relatively small-sized (B). For specific reasons, herbivorous reef fish, such as the Scarridae family group are a local fish target (C). An example of a rock anchor used (D), and one that was left by fishermen, with demolished coral underneath (E). Sign of resilience loss in some sites where macroalgae (F) and sea urchin domination were apparent (G).
Supported by the above preliminary findings, though they did not provide substantial
interpretation of the past biophysical trend of the sites, it was apparent that there has been a series of
key local stressors that has not yet been given full attention in the local reef management process.
Issues include: (i) overfishing that is threatening the lower fish trophic level such as the herbivorous
reef fishes and (ii) direct anchoring on the reef bed such as the use of rock anchors as an additional
threat of physical damage to the living benthic substrate. These two issues were then further analysed
using mathematical modelling, paying attention to their impact on the reef benthic configuration and
relevant remedial action in management. As mentioned earlier in Chapter 2, from the field survey we
found visual signs of (iii) nutrient enrichment and sedimentation presumably due to coastal pollution
from village household waste and sewage (see section 2.2.2.4, Fig. 2.6). However this particular
threat to reef was not explicitly addresses in the modelling study, thereby it remains as limitation of
our modelling investigation (see Model Output Limitation, section 3.4). The reason was due to both
limitation in survey logistical constraints and secondary data to obtain recent information related to
the water quality of the study area (i.e. sediment rate, nutrient concentration). Yet, this does not
negate the fact that the control of threats related to domestic pollution from segregated dwellings
should also be the priority and a tractable issue relevant to the management focus of the park agency.
Figure 3.5. Conceptual diagram depicting the layout of the benthic community dynamics model and key stressor functions influencing each community group. Two benthic groups of hard coral and fleshy macroalgae interact and compete for benthic space, which subsequently determines the main composition of the reef. Grazer loss due to overfishing leads to reduced mortality of macroalgae, thus growth and survivorship is enhanced whereas physical damage from rock anchoring causes the opposite effect.
The model incorporates a non-spatial projection of key benthic groups in response to a range
of environmental variables. The simulated coral-algae interaction assumes that space (turf)
availability of a reef quadrate is constant and equals 100% (Fig. 3.6). The quadrate space can be
occupied by several types of substratum in which its proportions are results of mathematical
! %,
calculation using differential equations. It quantifies the relative abundance of three major space
occupants of the reef benthic community: Hard Coral (C), Fleshy Macroalgae (M), and Turfs (T) in
differential equations (Eq. 1, 2, 3) using a modified mathematical approach suggested by Mumby
(2006). New colonization is due to growth or recruitment into free space (Eq. 3, Table 3.2).
!Figure 3.6. Conceptual diagram of benthic space occupation by living substrate groups assumed in
the model. Space is assumed to be a mix of settable substrate such a recently dead coral, rubble and rock for which turf algae provides foundation layers for the settling substratum. The model generalized three groups of substratum to be simulated, these are hard coral, fleshy macroalgae and other substrates.
The disturbance functions (e.g. grazing loss on macroalgae, hard coral mortality) were
expressed as non-dimensional functions as well as demographic responses for each community group
(e.g. the relative abundance of coral and macroalgae). The mathematical foundation is based on a
Lotka-Volterra model of inter-specific competition (Gotelli 2001). The dynamics of macroalgae is
determined by its capacity to overgrow coral in competition (!M-C MC) and the remaining proportion
after grazing (1 - dGM), assuming its natural establishment in any bare space/turf colonization (!M-
TMT). As for hard corals, new colonies may also establish themselves into free space (assuming
recruitment / survivorship) (!C-TCT) with hard coral loss from mortality: natural (grazer forage bite)
combined with damage from rock anchoring (dP+AC) and space for growth due to extirpation of
macroalgae (- !M-C MC). Consistent with Mumby (2007), the model can be expressed as:
(1)
! &-
(2)
(3)
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The model was parameterized using conditional modifications of data from the literature in
other regions such as the Red Sea, the Caribbean, Jamaica, the Great Barrier Reef, and Curaçao
(Dutch Antilles) combined with disturbance scenario settings that approximate both the local
biological and social-economic environments of Karimunjawa. From the literature of different
regions, I set up probabilistic values for each variable by selection of the best-available species type
as the closest representation to each benthic substrata, which involved converting data values and
units to the spatial and temporal dimension of the model (for details for benthic components, model
conditional settings and cited literature refer to Tables 3.2 and 3.3).
Spatial and temporal dimension - Modifying the work by Mumby et al. (2006), the
simulation model represents a square space that approximates 0.25 m2 of the reef benthic area that can
be occupied by a mixture of substrata (see Table 3.3). For each time step of simulation, the interaction
of these benthic groups was iterated up to one thousand times so that consequent means and variance
can be calculated regarding benthic occupant proportions (relative abundance). The simulation
updated the composition of the cell in 6 month time steps because of the bi-seasonal variation of
Karimunjawan waters (western and eastern monsoons, (BTNKJ 2004)).
Space occupants – In the simulation, three benthic groups of hard coral, fleshy macroalgae,
and turf algae were determined as the major dynamic occupants in terms of their proportion of
occupied space within the cell. The relative abundance (proportion in space/cell) of the first two
groups would be used as a quantitative measure of a reef habitat condition or status. I set up five
putative habitat categories to help define starting conditions (input setting) and projected benthic
composition (simulated output), using the maximum and minimum value of benthic group percentage
cover proportions from the 2008 biophysical assessment data (Table 3.1). Benthic substrate categories
and their conceptual behaviours in the model were initially listed to determine which benthic
components were included or excluded in the simulation (Table 3.2). Afterward, a literature review
was conducted to define the parameter values of ecological and environmental variables (Table 3.3).
! &$
Table 3.2. Benthic state category settings used for simulation input and output based on a range of percentage of cover proportion data, part of the biophysical assessment in Karimunjawa in August 2008 (see Figure 3.3, Appendix 2).
Habitat State Proportion in space ( x 100% )
Hard Coral Fleshy Macroalgae
Poor 0 - 0.23 0.35 - 0.7
Moderate > 0.23 - 0.46 > 0.23 - 0.46
Good > 0.525 - 0.7 0 – 0.23
Table 3.3. Categories of substratum and the conceptual behaviour applied in the simulation.
Substratum Description Reef state determinants / Major reef occupiers
Scleractinian coral (hard corals) (e.g. ‘Tabulate’ growth forming coral such as Acropora spp. to represent spawning corals, and non-branching growth form coral such as Porites spp. for brooding corals)
Hard corals here include Scleractinian corals which are divided into two groups based on their growth forms to represent different reproduction strategies of the species, thus differing mortality, growth, and survivorship (see Table 3.3). Two groups of hard corals were initially defined only for referential purposes in literature exploration to place values on the range of growth and recruitment; however, hard coral would be treated as a single input in the simulation. Fleshy macroalgae refers to those of >10mm in height excluding calcareous articulated ones such as Halimeda sp.
Fleshy macroalgae (e.g. Caulerpa sp., Padina sp.)
Minor reef occupiers
The group includes non-scleractitian corals (e.g. soft corals, fire corals), calcareous macroalgae groups (e.g. Halimeda sp.). The group could randomly reduce the availability of settlement space proportion, however, this effect is indirectly included in parameters related to coral-macroalgae interaction.
Settlement / space providers
Cropped algae (‘Pro-resilience’ algae such as Filamentous Algae, Crustose Coralline Algae, Short Turfs)
These substrates provide settlement space for major reef benthic occupiers. The presence of bare space can initiate early settlement space for epilithic algae to lay the foundation for settlement. Initiated from cropped fleshy macroalgae if a grazing effect is present. Bare space (e.g. dead coral, rock, rubble)
Unsettleable and ungrazable substratum The group includes sand, sediment layers, and silt, which are ignored in the simulation.
Natural mortality – Fleshy macroalgae loss in each time step was assumed due to the
probability of seasonal die-off in each year. Data from the island of Seid Seikh, in the Red Sea
indicated that macroalgae cover decline could reach to 30% in 6 month period (Ateweberhan,
Bruggemann & Breeman 2006). Therefore, this was the maximum value set. This study was chosen
because there was the least differentiation in sea surface temperature variability compared with the
2006; Putri 2005). For hard coral, the level of mortality occurring in addition to macroalgal
overgrowth could reach 2% of the colony per time interval (based on pubescent corals (three species)
in the Caribbean (60-250 cm2 in size) (Bythell, Gladfelter & Bythell 1993)).
! &%
Disturbance mortality – Algal removal due to fish grazing was simulated based on a
predicted maximum value of 30% of the reefs being maintained in a cropped state per 6-month period
[modelled from Long Cay (LC), the Caribbean]. This value was selected seeing that there was a
relatively small difference in the recorded maximum parrotfish biomass from the Long Cay region
(1998 data, ~220 gr/25m2) compared to Karimunjawa (WCS 2007 data; ~231 gr/25m2, average from
43 reefs) (Ardiwijaya, R. L. et al. 2008; Mumby, P. 2006b). The worst case overfishing scenario
assumed that the herbivorous fish population has been reduced by 5% in a 6 month period (Mumby,
P. 2006a). However, this assumption excludes the possibility that the dominant grazing species in LC
such as Sparisoma viride (epilithic algae consumer (EC)) and Scarus vetula (coral excavator (CE))
could have a three to four-fold mean foraging capacity (bite size, bite rate) compared to species in
KNP such as Scarus rivulatus (EC/CE) and Scarus ghobban (EC) (Bellwood, D. & Choat 1990;
Bruggemann, Van Oppen & Breeman 1994).
Hard coral loss was simulated as the product of the probability of anchor hit and the
proportion of loss (extent of damage). The maximum value for risk of an anchor impact was a product
of probability based on two conditions. Firstly, the average of the maximum fishing days was 6 days a
week (~86% of each 6-month time step) and secondly, around 60% of fishermen conducted near-
shore artisanal fishing (e.g. using handline, reef net, muroami, spear, trap) (survey of 102 respondents
representing households (Wibowo, Joni T. 2006)). The relative percentage cover loss of hard coral
due to physical impact by anchors is based on a simulation experiment of a moderate-sized boat
anchor falling into a hard coral by Marshall (2000). Marshall (2000) mentioned “the relative
percentage of loss was measured as the absolute change of planar area of a coral colony divided by
the initial planar area before physical treatment”. The overall mean of planar damage was modified
as the probability of maximum hard coral loss (dANC) due to damage, whereas for massive corals it
was 5% (dANC=0.05) damage and 100% (dANC=1) for branching corals. Various intensity of
anchoring was applied for the simulation by adjusting the value of fishing trips and randomizing the
probability of hit and proportion of loss.
Space occupation (growth and recruitment) – The probability that fleshy macroalgae (M)
fill a space that is occupied by turf (T) is due to the product of growth rate of M and the competitive
strength of hard coral (C) over T. Cropped algae (recently grazed) have an up to 100% probability of
overgrowing space if there was zero grazing for the next one year and up to 70% with zero grazing for
the next 6 months [e.g. genus Lobophora sp. (De Ruyter van Steveninck, E & Breeman 1987)].
Another caged experiment from Orpheus Island, Great Barrier Reef, showed that for an
approximately six-month duration, the absence of grazing fish above ~15 cm size/length led to a ~
20% increase of algal cover. Macroalgae growth rate over cropped algae (space) was simulated
assuming macroalgae can overgrow living coral (Hughes, Rodrigues, et al. 2007). Macroalgae can
! &&
overgrow coral at the rate of 0.0004 m2 per 6 month, converted from an overall mean of 8 m2 per
annum (Mumby, P., Hastings & Edwards 2007; Nugues & Bak 2006).
The probability that C fills a space that is occupied by T is the product of the growth rate of C
and the competitive strength of C over T. The probabilistic value of coral occupying space in the
model was based on the adjusted recruit density per cell (n / space) in one six-month (t) cohort of
~0.1 individuals / 0.25 m2 for brooding corals (e.g. Porites sp.) and at least ~0.07 individuals / 0.25 m2
which was significantly lower for spawning corals (e.g. Agaricia sp.) (Mumby, P. 2006b). The
simulation allows hard coral to overgrow fleshy macroalgae (Jompa & McCook 2002). It is also
simulates hard coral growth using constant values for the lateral extension rate of brooding corals (4 x
10-5 m2/6-month) and a slightly faster rate for spawning corals (45 x 10-5 m2/6-month) based on
median rates calculated from 6 coral species (genus Poritidae and Agariciidae) from the Caribbean
region (Mumby, P., Hastings & Edwards 2007). These values were then treated as a range, which
would be randomly assigned during computation.
Macroalgae-Coral Interaction – For each time step, the model calculates the natural
probability of macroalgae interacting with corals which was calculated using an equation developed
by Mumby by best-fitting the data of Hughes in Jamaica (Hughes 1994; Mumby, P. 2006b). The
prevalence effect of coral was also applied when local hard coral cover is high (! 50%), thus the
coral-algal competition reduces the rate of macroalgal growth by up to 25% (De Ruyter van
Steveninck, ED, Van Mulekom & Breeman 1988; Jompa & McCook 2002; in Mumby, P. 2006b).
Hard coral recruit survivorship was reduced by 50% if macroalgae occupies more than 60% of the
proportionate space, based on studies of Lobophora sp. and Dyctiota sp. (Box & Mumby 2007)).
While there is no comparable data in KNP, this finding would appear relevant because of the
incidence of these benthic groups in KNP.
Table 3.4. Options of initial set of parameter values adjusted from the literature of other selected regions. Input values were set either as fixed, a range, or as an equation in which some would be set as benchmark values in the sensitivity analysis (See Table 3.4).
Symbol Model Component Input Value Options Reference
Macroalgae Mortality
Macroalgae mortality
Baseline natural macroalgae mortality
(Ateweberhan, Bruggemann & Breeman 2006)
Macroalgae mortality due to grazing
(Ardiwijaya, R.L. et al. 2008; Mumby, P. 2006a, 2006b)
Coral Mortality
Coral mortality
dGRmax = ~ 0.3dGRmin = ~ 0.05
dC
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Symbol Model Component Input Value Options Reference
Natural coral mortality
(Bythell, Gladfelter & Bythell 1993)
Probability of risk of anchor hit to coral
PANC(MAXIMUM) = 0.86 x 0.6 = 0.516 ! (~0.5) (Wibowo, Joni T 2006)
Proportion of coral loss due to anchoring (Marshall, PA 2000)
Colonization of macroalgae into space
Space occupation by macroalgae
(De Ruyter van Steveninck, E & Breeman 1987)
Macroalgae lateral growth
Proportional growth relative to individual cell size:
If the growth relativity to space size is ignored, then as a ratio:
(Mumby, P., Hastings & Edwards 2007; Nugues & Bak 2006)
Colonization of coral into space
Space occupation by coral (recruitment) (Mumby, P. 2006b)
(De Ruyter van Steveninck, ED, Van Mulekom & Breeman 1988; Jompa & McCook 2002; in Mumby, P. 2006b)
Effect of macroalgae on corals (Box & Mumby 2007)
Model limitations, strengths and applications – The model presented in this chapter is not a
full representation of the complex process in the reef system, despite the fact that it was designed to
describe the health of a reef affected by local scale disturbance variables. For example, some
ecological aspects in the regulation of the benthic community were excluded, such as bottom up
forces of nutrient flux, that might simultaneously affect herbivory (Lapointe 1999; Littler, Littler &
Brooks 2009; McCook, LJ 1999), as well as external recruitment related to site connectivity.
Regarding the spatial magnitude and intensity of simulated disturbance, scenarios of coral mortality
!SP"M min = 0.2!SP"M max = 0.7
PANC
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due to anchoring, for example, may not represent the actual situation that is unknown. This was due to
insufficiency of socioeconomic datasets that could fine-calibrate input parameter values such as
fishing catch effort and selectivity to specific species group (Jennings & Polunin 1997; McClanahan,
T 1995) as well as the local boating profiles (McManus, John W., Rodolfo B. Reyes & Cleto L.
Nañola 1997; Saphier & Hoffmann 2005).
In this model, the presence of other benthic taxa such as sponges, soft corals, and benthic
invertebrates (including other forms of algae) that may render alternatives of benthic composition by
gaining space from coral or macroalgae loss (McManus, JW & Polsenberg 2004; Norström et al.
2009) was not involved in simulated benthic community interactions. Moreover, reef system changes
due to perturbations such as from disease, hurricane, mass bleaching and outbreaks have not yet been
considered in the disturbance variable. Thus, specifically, as benthic communities are dynamic, the
output of the model does not imply that the composition of the simulated major benthic group is the
predominant qualitative indicator of the possible reef condition in the future, nor that the presence of
macroalgae is the main descriptor of the extent of disruption of the reef (Bruno et al. 2009; Mumby,
PJ 2009). For these reasons, in general, the applicability of the model output may not be simply
transferrable to other regions or sites or time periods. Likewise, selected and defined parameters were
specific to reef management issues in KNP.
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A parameter sensitivity test was conducted to assess which parameters contribute most to
output variability. Regarding the primary use of the model as a predictor, this is important since
model validity is assessed by knowing which parameters in the model are more or less influential, as
well as which are redundant to the model outcome (Jørgensen & Bendoricchio 2001). This step was
done after the set of parameter value options was defined (see Table 3.3). To achieve this, a one-
factor-at-a-time (OAT) sensitivity analysis method was chosen, based on reasons of practicality for
both ease of calculation and graphical interpretation of results (Frey & Patil 2002; Hamby 1995). The
OAT test basically examines the relative change of model output by systematically perturbing
parameter values by a fraction of their base value (for the OAT summary see Table 3.4). For this
assessment, parameter input values were set either as a single value or range of maximum and
minimum values used as a benchmark for gradual adjustment. Each state variables were tested both
with a single base input value and the adjustment values set either as the standard deviation (+20%
and -20% SD) or one-third add of quartile division for range value (1st distance) (for full OAT results
see Appendix 3). The percentage difference of relative abundances in base output value relative to
from the adjusted values is defined as the ‘Sensitivity Index’ (Hamby 1995). In the study, all
mathematical calculations and graphical plotting were generated by executing programming script
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using MATLAB® R2008b software (The MathWorks™) (for M-File simulation scripts refer to
Appendix 4).
Table 3.5. Summary of parameter sensitivity assessment showing mean relative abundance output using base value and adjusted values. State variable sets using single base value were adjusted based on ±20% of SD. Those using range base values (maximum or minimum) were adjusted based on one-third adds. M and C is for Macroalgae and Coral output, respectively. In the computation, a randomizing effect was applied for certain state variable types. The computation was based on 100 iterations of a 6-month time step projection of 35% each of C and M as the starting condition. (See Appendix 3 for the complete assessment).
State variables Adjustments Parameter Input Value
Output Variables
Rel. abundance
Sensitivity Index (%)
Space Occupation by Macroalgae
Base (Min.)
0.2 M 0.155 C 0.310
1st distance 0.325 M (t+1) 0.155 0.003 C (t+1) 0.310 0.000
Space Occupation by Coral
Base (Min.)
0.07 M 0.176 C 0.307
1st distance 0.3025 M (t+1) 0.176 0.000 C (t+1) 0.307 0.001
Macroalgae Grazing
Base (Max.) 0.3 M 0.176 C 0.307
1e
0.2375 M (t+1) 0.197 12.459 C (t+1) 0.307 0.000
Baseline Macroalgae Mortality
Base
0.3 M 0.176 C 0.307
±20% SD
0.36 M (t+1) 0.155 11.960 C (t+1) 0.307 0.000
Macroalgae Lateral Growth
Base
0.0004 M 0.155 C 0.310
±20% SD
0.00048 M (t+1) 0.155 0.004 C (t+1) 0.310 0.000
Baseline Coral Mortality
Base
0.02 M 0.176 C 0.307
±20% SD
0.024 M (t+1) 0.176 0.000 C (t+1) 0.306 0.455
Coral Lateral Growth
Base (Min.)
0.00016 M 0.176 C 0.307
±20% SD
0.000165 M (t+1) 0.176 0.000 C (t+1) 0 0.000
Anchor Hit Probability
Base
0.5 M 0.155 C 0.254
±20% SD
0.6 M (t+1) 0.155 0.000 C (t+1) 0.254 0.183
Anchor Damage
Base (Min.)
0.05 M 0.155 C 0.308
1st distance 0.2875 M (t+1) 0.155 0.000 C (t+1) 0.299 0.121
PANC
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Calculations were made to assess the extent to which changes in the input parameters would
affect the relative abundances’ divergence from their baseline values. This was due to the focus of the
model in exploring the behaviour of the simulated system rather than estimating threshold values that
trigger output variation. Based on the sensitivity index, variables related to coral lateral growth were
considered non-sensitive to the output compared to other state variables (SI=0.000). This might also
be due to limitation in converting growth value data from selected literature, which has a different
measurement period to that of the modelled time-step, though unit conversion has been made to
match spatial and temporal scales. In this case, the two-dimensionality of the model requires data of
lateral metrics for growth in a 6-month time step. As the primary consideration was selecting taxa that
might closely represent the general local benthic group for Karimunjawa, coral and microalgae data
with similar specified measurements was not found. The model was more sensitive to changes in
input parameters to macroalgae (in terms of grazing) than for corals (in terms of anchoring
disturbances). Accordingly, this would be taken into consideration when setting values for
disturbance combination scenarios in the model’s implementation. In general, the nominal approach
of the test showed that the stressor function has a greater influence on the model output than the
space-coral-algae interaction function.
TN`N a0.G>$'.%(.&.%=#$#1(_")>,+(.&+(0"#*%)#(
In this analysis, graphical methods were used to visualize how disturbances of grazing loss due
to overfishing and coral damage due to anchoring are shifting the benthic community composition.
Two types of output data projections were used. The first was a projection of the benthic composition
change over time (P1) using the Monte Carlo (James 1980) approach (Figure 3.7, 3.8). The second
was a projection of the benthic composition frequency over time (P2) based on the Poisson
distribution (Consul & Jain 1973) approach (Figures 3.9, 3.10). For each projection, the first
disturbance setting applied was a scenario of the effect of three levels of grazing loss (P1-A, P2-A in
Figures 3.7, 3.9); the second looked at the effect of three levels of anchoring in a high grazing
situation (P1-B, P2-B, in Figures 3.8, 3.10), each setting was applied to three categories of reef
habitat. Habitat composition was projected on a decadal basis, which was relevant to the practicalities
of management in Karimunjawa (e.g. 15 and 30 year projections).
Reef categories were set at starting benthic compositions of ‘Good’ (10% M, 70% C),
‘Moderate’ (50% M, 30% C) and ‘Poor’ (70% M, 10% C). Grazing losses were set at three decreasing
levels of 20% (High), 10% (Moderate) and 5% (Low). Anchoring levels were set at 0.5 / 30% (Low),
0.4 / 37% (Moderate) and 0.5 / 58% (Low), for which the numerical values in each set refer to the
probability of hit and the proportion of damage, respectively. These were randomly generated as
pseudo-random (MATLAB® R2008b software (The MathWorks™))
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For a P1-A projection setting (Fig. 3.7), the gradual reduction of fleshy macroalgae grazing by
herbivorous reef fish affected the change of coral-algae composition over time. In the simulation, a
‘Good’ site that represents a coral-dominated reef habitat was found to be less impacted even by
grazing loss down to 5% (Fig. 3.7: Box A, B, C). The increase of grazing for a moderate habitat could
lead to an earlier sign of macroalgae control reduction (Fig. 3.7: Box D, E, F). As habitat quality was
further decreased from moderate to poor there is a much more drastic and earlier decline (Fig. 3.7:
Box G, H). Despite those transitions when macroalgae surpassing coral abundance in the shift was not
well visualized, in terms of the benthic community interaction, reduction in coral survivorship whilst
macroalgae is increasing was distinctly visualized. (Fig. 3.7: Box D, E, G). Among all site categories,
the recovering site (Poor) was found to be more sensitive, because of the ‘High’ grazing level that
was required to maintain coral survivorship (Fig. 3.7: Box G).
Figure 3.7. P1-A timeline projections of three starting point compositions of reef habitat: Good (10%
M, 70% C), Moderate (50% M, 30% C) and Poor (70% M, 10% C). Three scenarios of macroalgae grazing loss due to fishing in proportions of 20% (High), 10% (Moderate) and 5% (Low). Each time-step is the mean of outputs from 250 iterations of the 30-year projection (X-axis). The relative abundance (Y-axis) plot for coral and macroalgae is represented by red and green dots, respectively, with 95% confidence interval lines. The baseline mortality for both benthic groups is set as fixed using tested base values (Table 3.4). However, due to non-sensitivity, space occupation and growth parameter values were set as a random selection within the value range used in the OAT test.
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Figure 3.8. P1-B timeline projections of three starting point compositions of reef habitats: Good (10% M, 70% C), Moderate (50% M, 30% C) and Poor (70% M, 10% C). The grazing scenario was set as ‘best’ (20%). Anchoring levels were set at 0.5 / 30% (Low), 0.4 / 37% (Moderate) and 0.5 / 58% (Low), for which for which the numerical values in each set refer to the probability of hit and the proportion of damage, respectively. Each time-step is the mean of outputs from 250 iterations of a 15-year projection (X-axis). The relative abundance (Y-axis) plot for coral and macroalgae is represented by red and green dots, respectively, with 95% confidence interval lines. The baseline mortality for both benthic groups is set as fixed using tested base values (Table 3.4). However, due to non-sensitivity, space occupation and growth parameter values were set as a random selection within the value range used in the OAT test.
Using the P1-B projection setting, in general, Figure 3.8 depicts how short-term but frequent
physical disturbance by anchor use has affected coral survivorship at all site categories. In the best
grazing conditions and a shorter timeframe (fifteen years), the anchoring scenarios caused earlier
coral decline compared to the grazing effect in the P1-A setting (for comparison see Fig. 3.7, 3.8: Box
B) as well as the period required for macroalgae to start gaining over coral proportion (Fig. 3.7, 3.8:
Box E). In the P1-B projection, a high grazing level was shown to have a controlling effect to further
increase the level of macroalgae throughout the course of proportionate coral decline due to anchoring
impacts (Fig. 3.8: Box E, F, G, H). A similar projection setting was generated for ‘moderate’ and
‘low’ grazing scenario however, results were not presented considering the anchoring scenarios, on
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the overall, generated similar yet, more deleterious effect to the trajectory to hard coral cover and the
opposite for macroalgae.
For the P2 setting, a colour contour plot (Anthony, 2008) was used to visually compare the
stochastic distribution of benthic composition between site categories. The first colour plot, using a
P2-A setting, generally showed that habitat composition tended to be distributed away from a coral
dominated state as grazing loss increased and habitat quality (coral-algae starting composition) was
lowered. In a shorter the ten-year simulation, for ‘Good’ reef, the increase of overfishing scenario did
not disrupt the trajectory towards a ‘healthy’ reef state as most of the occurrences of overfishing were
distributed in coral dominated reef areas (Fig. 3.9, Box A, D, G). However, a transitional trajectory
was observed when the reef site quality was lowered to ‘Moderate’, which is explained by the
distribution of benthic composition being altered partially towards ‘Moderate’ events as grazing loss
increases (Fig. 3.9, Box B, E, H). Moreover, for a ‘Poor’ site, the transitional trajectory was also
prompted to be at a high grazing level (Fig. 3.9, Box C), then the distribution of benthic composition
moved towards a coral-depauperate boundary as grazing decreased (Fig. 3.9, Box F, I).
Compared with the impact of grazing loss from P2-A, the P2-B projection result showed that
anchoring disturbance caused a more intense but dispersed perturbation of the distribution of benthic
composition at all site categories, even at a low level (Fig. 3.10, Box D, E, G, H). Similar to the result
from the P1-B projection, an increase of anchoring intensity affects coral replenishment by way of
reducing the likelihood of the reef to establish a higher hard coral cover state (for a ‘Moderate’ site,
Fig. 3.10, Box B, E, H). Among site categories, this situation occurs more prominently for a ‘Poor’
site as the distribution of low-coral cover was consistent as the anchoring level increased (Fig. 3.10,
Box C, F, I).
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Figure 3.9. P2-A colour plot projection of three reef habitats with starting point compositions of:
Good (10% M (X-axis), 70% C (Y-axis)), Moderate (50% M, 30% C) and Poor (60% M, 20% C), each marked with white crosshairs. Three scenarios of macroalgae grazing loss due to fishing were applied to the areas: 20% (High), 10% (Moderate) and 5% (Low). Each contour plot cell represents specific benthic relative abundance compositions relative to the axis. Each cell colour describes the frequency of occurrence of specific habitat compositions out of 250 simulated iterations, within a 10-year timeframe. The baseline mortality for both benthic groups is set as fixed using tested base values (Table 3.4). However, due to non-sensitivity, space occupation and growth parameter values were set as a random selection within the value range used in the OAT test.
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Figure 3.10. P2-B projection result of three reef habitats with starting point compositions of: Good
(10% M, 70% C), Moderate (50% M, 30% C) and Poor (60% M, 20% C), each marked with white crosshairs. The grazing scenario was set as High (20%). Three scenarios of coral mortality due to anchoring were applied with the probability of hits and the proportion of damage set at 5%~30% (Low), 40%~37% (Moderate) and 50%~58% (High).
Global climate change has caused severe problems for coral reef ecosystems; yet, human
impacts at local scales are still driving the decline of reef habitats around the world (Carpenter et al.
2008; Hoegh-Guldberg et al. 2007; Knowlton & Jackson 2008). The impact of overexploitation of
reef resources and unsustainable extractive activities have predominantly occurred in less-developed
coastal regions (Bell et al. 2006); such is the case in Karimunjawa National Park (KNP), Indonesia
(see Chapters Two and Three). Key functional reef species, such as herbivorous reef fish which are
locally declining, require better management (Mumby, PJ & Steneck 2008). In this case, achieving a
locally sustainable use of reef resources would also require the integration of both social and
ecological perspectives within the local reef management framework (Hughes et al. 2005; Walker, B
et al. 2002). Initially, this would require an understanding of local socioeconomic variables and at
least some understanding of the key drivers of fish decline, such as local livelihood dependencies on
key functional reef species, before socially adaptable remedies can be developed. Such remedies
would also benefit strongly from participation of the local people in marine reserve management.
(Christie 2004; Cinner, Joshua E. et al. 2009; Pollnac, R et al. 2010; Webb, Maliao & Siar 2004).
Fishermen have mentioned that economically profitable fish species have declined, including
those that are a rare commodity for both inshore reef commercial and subsistence fishers (Field Notes,
Biophysical Survey, August 2008). Biological data to support the allegation was minimal, although
some research has been undertaken (Ardiwijaya, R.L. et al. 2008; Campbell & Pardede 2006)
In the Karimunjawa islands, a National Park Agency (KNPA) has been appointed under the
decree of the Ministry of Forestry of Indonesia to administer a conservation area. This included
developing a multiple-use zoning policy that was established between 2004 and 2005. In the early
phase of the policy’s enactment, protection of areas of reef fish spawning and aggregation sites was
the ecological focus of the zonings and a social participation study was also undertaken (BTNKJ
2004).
However, within the management processes (planning, action, monitoring, and evaluation
(Schreiber et al. 2004)), limited resources meant that social research supporting biophysical data,
social research and monitoring have not yet been taken into account due to limitations in management
resources. This suggests that indicators of gradual social change and social transfiguration might have
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been ignored and, therefore, are absent from the learning process of stakeholders (e.g. the local
community, reef managers, and state agencies).
In sum, key information regarding both local characteristics of resource users and resource
conditions has been missed, whereas in a data-poor situation such as Karimunjawa this is essential to
develop management precautions at the very least (Adger, WN et al. 2002; Johannes 1998). Thus,
there is a current paucity of information for stakeholders (the park agency, state agencies, NGOs) to
evaluate relative resilience of local resource users. This is true for evaluating the social consequences
of both future resource changes and different marine reserve policy options (Alcala & Russ 2006;
Bunce et al. 1999). From this basis, several key questions has set the point of departure of the
research. They were:
1. How does the local community perceive and adapt to past and future changes of reef resources
by referring to factors that could indicate social resilience?
2. What are the knowledge gaps in developing locally adaptable reef management measures for
the sustainability of both the livelihood of the local community and the reef ecosystem?
YNQN _")>,+,%,3=(
YNQNO D>",0")$'.%(-0.4"7,0:(
Social resilience, as defined by Adger (2000), is “the ability of groups or communities to cope
with external stresses and disturbances as a result of social, political or environmental changes”. For
reef resource dependent communities such as in the Karimunjawa islands, social stability and the
communities’ response to resource changes predominantly influenced the local livelihoods (Pollnac,
RB & Pomeroy 2005; Pomeroy, RS, Katon & Harkes 2001; Pomeroy, RS et al. 2006). In traditional
coastal communities, perceptual learning processes, rather than scientific ones, predominate how the
social system responds to ecosystem signals; which then informs actions related to the process of
utilization of ecosystem goods and services (Berkes, F, Colding & Folke 2000; Kurien 1998).
Thus way communities collectively interact with their environment can influence their
decisions and activity, for example, whether to settle in a particular area, and for how long, or how to
gain their livelihoods (Adger, WN 1997; Adger, WN et al. 2002). These choices can have either
favourable or detrimental impacts on natural resources, such as coral reef systems.
Understanding the social aspect within marine resource management is essential since
reducing threats to the reef ecosystem means managing human behaviour (Hughes et al. 2005;
Nyström, Folke & Moberg 2000). Moreover, integrating the social perspective within resource
management can increase the efficacy of conservation policies that are often restrictive (e.g. area
closure) in the management of small scale fisheries in less developed regions in particular (e.g. in the
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Indo-Pacific (Aswani & Hamilton 2004; Lowry, White & Christie 2009; McClanahan, T et al. 2006),
and the Western Indian Ocean (Cinner, Joshua E. et al. 2009). Cinner et al. (2009) and Marshall &
Marshall (2007) assess four factors that describe social resilience at the local level: capacity to learn,
flexibility and adaptation, capacity to organize and the nature of assets of the community. Survey
results and discussions related to each of the factors are included in specific sections in this chapter.
Observing ecological and biological changes in reef ecosystems can take a long time and
confounding this are the interpretation gaps between scientist-managers and the local community,
which are often apparent (Kainer et al. 2009). One of the potential benefits of assessing capacity to
learn is that resource managers may identify an error of judgment by the community as they perceive
changes in the ecosystem, from which the specific scientific research study can then be developed to
retrieve specific information that both supports social expectations and local conservation objectives
(Kainer et al. 2009). As for the community flexibility and adaptation, stakeholders related to reef
resource management can identify critical gaps in securing and stabilizing local livelihoods, thus
providing momentum for the community to reconfigure whilst abating unsustainable socioeconomic
activities as well as adapting to resource regulations (Smit & Wandel 2006). For policy makers,
assessing organizational capacity will be a critical point of reference prior to improve community
trust, knowledge sharing and mobilizing the community toward proactive participation in resource
governance, rather than toward resistance (Lebel et al. 2006). Lastly observing assets could aid
stakeholders at the regional or national level to pinpoint and contribute to reinforcing assets that are
critical for the local community to cope with ‘surprises’ (market failure, reef resource loss, natural
disasters), thus, avoiding socioeconomic pauperization that forces unsustainable resource exploitation
(Adger, WN et al. 2005; 2002).
YNQNQ ?*0H"=(.&+(.&.%=#$#(4")>,+(
The survey research was undertaken at Karimunjawa National Park from 14 - 21 April 2010
covering the three administrative villages of Karimunjawa, Kemujan and Parang (Fig. 4.1). Social
information was collected with the aid of a questionnaire form (Appendix 5) in sessions held with
individual community members. Respondents were selected either through snowball sampling (e.g.
respondent identification via personal reference of a previously identified respondent) or haphazardly
from sidewalk encounters, as housing areas were built in the proximity of the village main roads.
Each participant was required to answer a list of structured semi-close-ended questions, which allow
respondents to select between the pre-defined answers or give their personal answer (Rea, Parker &
Allen 2005). The design purpose was both for survey efficiency in terms of time, yet, minimizing
response bias as different comprehension of the question responses was highly likely for respondents
of different cultural and educational backgrounds, (Groves et al. 2009). Prior to the survey, local
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research assistants from the Taka Foundation in Semarang performed a pre-test on the questions in
terms of their wording, rephrasing, and sequence as well as holding an internal discussion for
agreement on transliteration for Javanese comprehension. Respondents were allowed to read
questions and write answers by themselves or questions were narrated and answers noted by the
interviewer. The latter was applied to situations when participants were limited in their Javanese
language, illiterate, unable to write or simultaneously conducting an activity.
Figure 4.1. Map of area surveyed in residential areas of Karimunjawa, Kemujan and Parang villages (in colours).
Each respondent was required to answer a similar set of questions related to the social
indicators assessed (Table 4.1, Appendix 5), which generally covered information related to
individual perception and knowledge about natural resources and their management, personal
opinions, past living experiences, demography, age, income, general knowledge and education.
Observational data such as the materials use in residents’ housing was obtained from reconnaissance
surveys, these included floors, roofing, walls and sanitation. Public facilities and infrastructure was
also recorded. The coding, response data entry, and statistical computation were conducted using
Microsoft" Excel" and IBM# PASW# software. Questionnaire response data was categorized as
nominal and ordinal data, which was treated as dichotomous and ranked variables, respectively (Rea,
Parker & Allen 2005) (Appendix 6). The distribution of each response category was determined as
relative frequencies (in percentages) proportional to the total value sampled from each question
(Agresti & Finlay 2009). Monotonic association between nominal variables was measured using Phi (
r! ) and Contingency coefficients. The former applies for two dichotomous variables and the latter
applies more than two (Healey 2008). A rank-biserial coefficient was used for nominal–ordinal
associations (Cureton 1956; Newson 2008) and Goodman and Kruskal’s Gamma ( r" ) for ordinal-
ordinal associations (Healey 2008). These coefficients take account of the distribution of the two
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categorical variables and values ranges from "1 to +1, where ±1 indicates perfect agreement or
disagreement, and 0 indicates no association. Statistical results will also be related with respondent
comments noted from discussions during questionnaire sessions (from here on referred to as ‘notes’,
for the summary see Appendix 7).
Table 4.1. Summary of types of information gathered through questions developed from social resilience indicators assessed in the survey research
Information Variables Socioeconomic variables related to asset
Age group Wealth based on income level Household appliances and materials for housing Level of education
Perception related to capacity in learning
Past to present fishery condition Future fishery condition Primary cause of change in fishery condition Past to present coral reef condition Future coral reef condition Primary cause of change in coral reef condition Knowledge of existing zoning regulation Types, functions and location of zoning acknowledged Knowledge of zones where fishing is restricted Knowledge of proposed key local reef issues Knowledge of 'climate change'
Perception related to livelihood flexibility and adaptation
Livelihood adaptation to hypothetical fishery decline Livelihood adaptation to hypothetical coral reef decline
Perception related to flexibility and adaptation to resource governance
Acceptance of zoning regulation Acceptance of no-take zone Preference of fishery regulation method
Preference in communication / learning efforts
Preferred communication media / method Preferred location for communication
Experience in migration and decision making
Migration based on origin Purpose of migration Period of live spent in Karimunjawa Planned period of time to live in Karimunjawa in the future Involvement in decision making, local organization
Involvement in decision making for natural resource regulation.
The survey represented opinions of individuals aged 18 years and above, comprising of people
whose main occupation related to utilization of goods and services of marine resources (direct and
indirectly reef related, ±55%) and not reef related (±45%) (Fig. 4.2). A total of 209 respondents were
selected randomly from around 9000 residents from 2400 households registered in the Karimunjawa
district (BPS 2006; BTNKJ 2008). The response rate was 100% as all respondents have voluntarily
participated in a questionnaire interview session, which consisted of male respondents (68%) in a
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higher proportion than female respondents (32%). Age wise, the adult group (above 20 years old,
88%) was higher than the youth group (ranging from 18-20 years old, 12%).
Difficulties in obtaining a representative ratio of gender and age groups relative to the
available demographic data was due to interviews being ethically plausible only during the daytime.
This age/gender matrix was also influenced by the varying working schedule of individuals and
households. Opinions represented in the survey are, therefore, contingent on the size of population
sampled. Therefore, inter- and intra-community quantitative comparisons such as comparisons
between villages and comparisons between gender groups, respectively, were not explored in the
survey analysis.
Despite population representativeness and zero non-response of the survey response, response
bias was yet unavoidable. This might include factors such as underrepresentation and social
desirability that existed due to the difference in gender and age composition of the respondents
(Wright & Marsden 2010). Specific gender and age group have particular tendency of preference and
insincerity that may prompt answers or comments that were based on what respondent believe is
socially desirable (Wright & Marsden 2010). Thus, a dominant gender or age respondent group may
bring undesirable influence the overall survey result. In this survey, we presume that social
desirability can occur on areas of information that were likely sensitive such as personal income and
earnings, compliance with regulations, and knowledgeability of culturally regarded activities like
fishing. Response to this information could, therefore, be inflated or deflated on respondent's
interpretation, which implies that opinions gathered in the survey may not well-represent specific age
or gender group in Karimunjawa.
As an example, young adults with lesser attachment and experience with fishing might have
different expectation of future employment opportunities and alternatives than older adults. Moreover,
gender difference of adult household member, combined with cultural stereotyping, could have also
inflated or played down response related to sensitive information that might expose their household
social status or family condition (e.g. education, income, main occupation, household materials)
(Bernardi 2006). To minimise this particular response behaviour, a 'consent sheet' was being
introduced to respondent in each of the beginning of survey session (See Appendix 5). Moreover,
noted respondent comments were also used in the analysis, in combination with questionnaire data, to
improve the reliability of interpretation of opinions collected in the survey.
! ('
Figure 4.2. Pie diagram showing distribution of respondents’ main occupation (n= 209) grouped by
relation to reef resources, both directly (A) and (B) and indirectly (C); as well as those not related to marine resource utilisation (D).
Around half of the Karimunjawa residents conduct fishing (BTNKJ 2008). At each of the
surveyed villages primary piers that also operate as primary fish landing sites were available. At the
time of survey, tourism development was most prolific at Karimunjawa village where accommodation
and tourism services were the highest registered (BTNKJ 2008). At the time of survey, seaweed
farming was a visually widespread practice, particularly in Kemujan area where some households
have relied on it for their main income. In contrast some residents of Parang had migrated from the
area: a large number of the youth and young adults were on a temporary leave to work collectively in
cities in Java (pers. comm., Head of Parang village and one of the Heads of Rukun Tetangga (smallest
neighbourhood system) of Kemujan village). These general observations in livelihood activities might
indicate some differentiation of coping strategies within the community as well as possibilities of
social instability together with a newer industry (seaweed production) that could improve stability
(Adger, WN 1999; 2002). Social resilience can be explained through a variety of indicators (Adger,
W. 2000). Therefore, analysis and discussion following this chapter focuses on the context of the
sustainability of local livelihoods and community dependency on reef resources in Karimunjawa.
! ((
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Flexibility and adaptation in livelihood was assessed by exploring individual responses to a
hypothetical decline in fish and coral reef conditions associated with decisions related to livelihood
mobility and diversity (Adger, W. 2000; Cinner, JE & Pollnac 2004) (Table 4.2). Information was
gained regarding the individuals’ adaptation to park regulations by questioning the respondents’ level
of knowledge and understanding of the marine zonings, approximately five years after it was enacted.
Additionally questions were asked to determine flexibility to resource regulations based on their
acceptance of zoning regulations and preferences about fishery regulations (Cumming et al. 2005)
(Table. 4.3). Interviewers agreed that the term ‘decreasing resources’ would mean a reduction to half
of what the respondent could obtain regularly, such as the weekly or biweekly fish catch, or a visual
indication of reef habitat, such as coral cover. To some respondents who were non-fisher s (crop
farmers, housewives, youths) and fishers, the coral reef was less tangible than fish. They included
individuals who have not experienced much direct visual observation of coral reef and their judgment
was derived from news or stories passed on from those who had more visual underwater experience.
Table 4.2. Responses to questions related to livelihood flexibility and adaptation to hypothetical decline in reef resource conditions. Responses in numbers 1 and 2 were based on one answer per respondent (n=209), number 3 was based on a maximum of three answers for any respondent who in number 2 chose not to stay in their current occupation (n=118).
No Information, Response Category Distribution
1 Effect of 50% decrease of fishery condition to main occupation
Stay with current occupation 46.38% Find alternative or transitional occupation 48.31% Emigrate 0.48% No answer 4.83%
2 Effect of 50% decrease of coral reef condition to main occupation
Stay with current occupation 48.33% Find alternative or transitional occupation 41.15% Become involved in conservation activity 3.35% Emigrate 0.48% No answer 6.70% 3 Preferred alternative / transitional occupation Aquaculture 37.60% Crop / land-based farming 24.00% Informal, small business 13.60% Salaried employment 13.60% Tourism 4.80% Fishing 2.40% No answer 2.40%
! ()
In conditions of resource scarcity, the respondent would mostly decide to stay in a particular
occupation or find an additional occupation (No. 1,2, Table 4.2). Such a condition was typical of the
flexibility of most rural fishing communities in South-East Asia where despite strong occupational
attachment, occupational multiplicity may concurrently exist to cope with uncertainty, particularly of
the natural resources themselves (Allison & Ellis 2001; Bailey & Pomeroy 1996). Here, the term
occupation was defined as one particular activity that the respondent would rely on as source of
regular income. Extractive-based occupations predominate in Karimunjawa where the larger portion
of the community were registered as both fishers and farmers (BPS 2006) and yet occupational
heterogeneity was identified in the survey (Fig. 4.2). The survey was of insufficient duration to fully
understand how the community is managing the risk of resource uncertainty; however, these results
could be signs of risk spreading across livelihood activities (Adger, WN 1999). Moreover, the variety
of economic goals and orientations could also be associated with different strategies in livelihoods:
either diversifying or specializing in occupations (Smith, L, Khoa & Lorenzen 2005). For example,
we found that individuals with a strong attachment to a specific occupation, often the case of a full-
time and commercial enterprise such as fishing, could be in touch with another household member
who can obtain supplementary income from part-time (e.g. seasonal or migratory) jobs and
subsistence level occupations. For flexible individuals, on the other hand, the motive behind
occupational diversification might be strictly related to the diminishing condition of the natural
resources as assets, which could interfere with income security at either the individual or the
household level (McManus, J. W. 1997; Pomeroy, RS et al. 2006). This was illustrated by interview
comments stating the importance of having several occupations in the family during the particular
time when fish were considered scarce (No. 4, Table 4.5, r! = -0.084), particularly as the monsoonal
weather pattern was getting unpredictable and close-range fishing in inshore reef areas was yielding
less.
To benefit the rehabilitation process of the natural resources, the literature has suggested that
the arrangement of livelihood diversification has to develop to the extent where people’s dependence
to natural resources is reduced (Bailey & Pomeroy 1996; Cinner, JE & Bodin 2010; Cinner, J. E.,
Daw & McClanahan 2009; Pomeroy, RS et al. 2006). Based on the type of alternative occupation
preferred by respondents, at the time of survey, the community was likely to be reliant on both coastal
and terrestrial natural resources (No. 3, Table 4.2). Non-fishing household members such as the
women favoured terrestrial activities such as crop farming, either for subsistence or to meet small-
trade demands. In Parang village, for example, its typical red soil and flat ground allows planting in
almost all seasons, of crops such as corn and leguminous plants, and tropical fruit (A, B, C, Fig 4.3).
For this reason several Parang locals thought crop and fruit farming to be more promising than
seaweed farming which was more susceptible to the ais-ais disease, compared with inhabitants of
! (*
other village areas. At the two other villages we found seaweed drying was visually more common.
The Petinggi-s (head of village) commented that the practice had been growing for around five years
prior to the survey. To some families it has provided substitution of income loss for those who have
not survived much from commercial fishery (D, E, F, Fig 4.3).
Despite this apparent need to spread income sources through the substitution of occupations,
the livelihood mobility was relatively inward as the source of production was confined to local natural
resources (Gordon 1954). Overexploitation, (e.g. due to unsustainable fishing, forest clearing, and
coastal modification) or restrictions (e.g. area closure, species restrictions, and harvest regulations)
lead to a lack of assets that people could depend on for their livelihoods. Therefore, without
alternative assets expecting the community to suspend or exit from fishing would be unrealistic
(Cinner, J. E., Daw & McClanahan 2009; Smith, C & McKelvey 1986).
Figure 4.3. Photos showing semi-dry red soil ground characteristic in Parang island (A), allowing
much tropical fruit to grow like pomegranates, for example (B,C), whereas seaweed drying was visually more common in Kemujan and Karimunjawa area (D,E,F).
The overexploitation of resources itself could also promote conflict of both the use and
management of resources, particularly when there is a high competition for an ‘open-access’ marine
resources area such as in Karimunjawa (Berkes, F 1985; Pomeroy, R et al. 2007). Related to this, we
identified a number of elderly male respondents in Karimunjawa and Kemujan who have stopped
fishing, some of whom mentioned that the decision was due to a reduced tolerance to working
conditions that reduced fishing capability and being commercially outcompeted by other fishers. At
the time of survey, a large portion of this group have become involved in part-time carpentry, also
carrying out wood collecting and every so often employing members of their family (Fig.4.4).
However, most have experienced a new occupational risk of conflicts related to park regulations
rather than from the environment itself, since targeted resources such as trees were within the Forest
! (+
Protection Zone. Shortly before the survey, we noted that conflicts took place between wood
collectors and the park authority due to cases of violation of tree species and the forest-zoning
perimeter. As well there was a dispute about misplacement of the forest-zoning marker on private
ground that disregarded the owners’ rights. This case demonstrates that highly extractive livelihoods
if not counterbalanced with non-natural-based livelihood access may prompt successive exhaustion of
natural resources, such as the transition from coastal to island forest extraction. Furthermore, resource
managers overlooking the erosion of livelihood options concurrent with natural asset degradation
could further diminish user interest in participating in conservation since social conflicts could
strongly interfere.
Figure 4.4. Photos showing logging activities, some conducted by ex-fishing families (A,B), including
rock breaking to supply construction material (D). These supplementary income strategies could be found in combination for some households (C,E).
Regarding the need of current zoning regulations in KNP to secure resource tenure (e.g. fish
population management (see Chapter 3)) in addition to managing resource access (e.g. zoning policy
(BTNKJ 2004)), the adverse impact of shifting resource use patterns may need to be compensated by
non-natural-based capital asset use such as incentives of non-extractive but still reef-related
livelihoods (e.g. tourism) (Cruz-Trinidad, Geronimo & Aliño 2009; Fabinyi 2010). As an example, at
Karimunjawa kota area, where tourism services were nucleated (BTNKJ 2008), there were
households that already had a considerably high attachment to tourism as the main income
substituting fishing (pers. comm., Head of Village / Hamfah Homestay owner, 2010). However,
poorly developed local tourism could place more pressure on the reef system via increasing local
demand for reef fish (see Chapter 3).
This may indicate that an alternative livelihood portfolio may be necessary to allow a larger
segment of the community to cease or withdraw from fishing to provide the temporal or species
restrictions required for the replenishment of specific fish populations (see Chapter 3). However,
! (,
tourism itself can also bring adverse impact to reef sustainability if it is poorly managed since it could
indirectly increase local demand for fish (See Chapter 3). We also find that the proportion of the
community engaged in tourism is also less than those in extractive activities (Fig. 4.2). In this case,
the alternative occupations incorporated may not necessarily be attractive, not only in income
comparability, but also in their characteristics (e.g. risk, job satisfaction, culture) (Pollnac, RB &
Poggie 2008; Pollnac, RB, Pomeroy & Harkes 2001; Sievanen et al. 2005). In contrast aquaculture
such as seaweed farming was found to be more lucrative than tourism related activities such as
providing a boating service (No. 3, Table 4.2). However, resolving to remain dependent on an
extractive socioeconomic scheme also has a cost to the ecology. Despite the potential of seaweed
farming to reduce fishing efforts, there are economic uncertainties related to the risk of disease, loss
of demand and consequent price drops, as well as environmental issues that may stem from
unsustainable farming practices and disruptions of ecosystem functions (Bergman, Svensson &
Öhman 2001; Crawford, B 2002; Ólafsson, Johnstone & Ndaro 1995; Sievanen et al. 2005).
Adaptation to marine resource regulations was assessed through questions related to the
knowledge of areas where fishing restrictions are applied in the Zona Inti (no-take zone) (No.1, Table
4.3), level of knowledge and understanding of marine zonings (No.2, 3, Table 4.3); acceptance of
zoning regulations (No. 4, 5, Table 4.3); as well as flexibility based on their preference of regulations
related to fishery (No. 6, Table 4.3).
Table 4.3. Response to questions related to perception and adaptation to zoning regulations in Karimunjawa National Park. Each information type has different sample sizes (as indicated) and respondents could only give one answer in 1 (n= 209), 2 (n = 173), and 3 to 6 (n=81). Numbers 2 to 6 were from structured questions where some responses were associated with one another (refer to table footnotes).
No Information, Response Category Distribution
1 Knowledge of any areas where fishing restrictions are applied (Zona Inti)
Yes, I know 38.94% No, I do not know 61.06% 2 Number of zoning types acknowledged * No-Take Zone / Zona Inti 31.03% Protection Zone/ Zona Perlindungan 30.34 Aquaculture-Use Zone/ Zona Pemanfaatan Budidaya 20.00 Tourism-Use Zone / Zona Pemanfaatan Pariwisata 17.93 Four types 20.83% Three types 12.50% Two types 19.44% One type 45.83% None 1.39% 3 Level of understanding of zoning ** Know both zoning function and location 16.75%
! )-
No Information, Response Category Distribution Know either only location or function 22.01% Know only zoning type 61.24% 4 Acceptance of current zoning regulations overall ** Agree 76.25% Do not agree 15% No answer 8.75% 5 Compliance to Core Zone (‘No take’) ** Agree 58.54% Do not agree 36.59% Not sure / Do not know 4.88% 6 Preferred alternative regulation in fishery *** Periodic / seasonal closure 22.58% Gear-related regulation 70.97% Regulation of non-Karimunjawan fishers 6.45% * = Participants that knew about zoning regulations
** = Relative to the type of zoning that the participant knew
*** = Participant does not agree to ‘no take’
Our findings suggest that individual awareness was persistently low (<50%) relative to the
results from a survey by the Wildlife Conservation Society in 2006 (with a lower number of
respondents, n=157, (Wibowo, Joni T 2006)). Furthermore, more than half of respondents did not
know about the presence of no-take areas (No.1, Table 4.3), yet the majority of respondents have
resided in the area before the 2005 park re-zoning (No.7, Table 4.9). Among the four types of zoning
questioned about, Zona Inti and Zona Perlindungan (Protection/Buffer Zone) were the types most
were familiar with, however, the majority did not know the zoning locations and functions (No.2, 3,
Table 4.3). Nevertheless, the majority (±76%) would agree to a zoning scheme (No. 4, Table 4.3),
suggesting that a lack of awareness of park regulations, rather than defiance, might have resulted in
low compliance.
Moreover, the lack of enforcement due to the low surveillance capacity of the park agency
might also contribute to the situation. This is particularly relevant as boats were seen fishing or taking
tourists to no-take areas whenever the patrolling rangers were out of sight (field obs., 2008, 2009,
2010). Studies in Indonesia and the Philippines by Crawford, et al. (2004) and Pomeroy (1995),
respectively, imply that surveillance is just one of the factors that influence community compliance
with natural resource management where inter-community institutions, external programs and
organizations, rights and ownership (e.g. resource tenure and customary management), and
demographic factors (e.g. poverty, population pressure and human capacity) could influence
compliance. Despite there having been a participative process during the 2004 re-zoning planning
process (BTNKJ 2004), since then communication and engagement between the park agency and the
! )$
community has not been particularly effective and previous community participation in the resource-
related decision making process was low (No.5, Table 4.9)).
In the case where management resources are limited, investing in communication might bring
more benefit than improving resources for surveillance as this could indirectly result in reducing costs
in enforcement by bridging collective action in resource management (Hanna, Folke & Maler 1996).
Yet, facilitating inter-community institutions would also depend on the cohesiveness of the
community, which will be discussed in section 4.3.5. Nevertheless, incorporating an effective
communication strategy between resource managers and users at stages of both the management
planning and the management process could increase community familiarity with the roles of
stakeholders. It could also encourage enhanced comprehension of the nature and purposes of the
regulations as well as building trust, as the concern and needs of the community are being taken into
2006; Thorburn, CC 2000). The versatility of the local economy in Karimunjawa is exemplified by
the variety of economic goals and orientations complementing livelihood mobility and resource use
change (see section 4.3.3). Because of this, the learning process of this heterogeneous community
might have been fragmented in terms of their ecological reinterpretation and knowledge transmission
(Reyes-García et al. 2007). This might have also eroded traditional values that were potentially
conservationist (i.e. traditional taboos (Cinner, J. E. & Aswani 2007). Moreover, the manifestation of
! )'
LEK itself may not necessarily have to be accurate since drastic changes were noticed after they
happened (Moller et al. 2004). Subtle changes, on the other hand, in the decreasing variety and
number of reef fish have been unnoticed.
Table 4.4. Test of associations between responses of perceived resource conditions (Table 4.3) and the related influential causal factors (Table 4.4.).
No. Associated Categories Coef. Value Approx. Sig. Level of Assoc.
4 (1) Occupational adaptation: need supplementary occupation (2) Past fishery condition: declining
r! -0.084 0.224 Weak (Value ! 0.3)
Table 4.5. Response to questions about past and future changes of fishery and coral reef conditions. All response categories were based on answers given limited to one (n=209).
No Information, Response Category Rel. Frequency 1 Past to current fishery condition Declining 49.52% Improving 22.60% Average 20.19% No response 7.21% Uncertain 0.48% 2 Past to current coral reef condition Worsening 49.52% Improving 18.27% Average 13.94% No answer 18.27% 3 Future fishery condition in 5 years Less fish 38.49% Depends on fishing activity 27.44% More fish 10.23% Same 8.84% Depends on park regulations 5.12% Depends on economic conditions 0.93% Depends on natural events 2.79% Depends on stakeholders 0.47% No answer 1.07%
! )(
No Information, Response Category Rel. Frequency 4 Future coral reef condition in 5 years Better 36.02% Depends on conservation 25.12% No answer 18.01% Worse 8.06% Depends on natural events 7.11% Average 4.74% Depends on stakeholders 0.95%
Table 4.6. Response to questions about the dominant activity that influence changes in fishery and coral reef conditions. All response categories were based on answers given more than one (Number 1, n=238; 2, n=219).
No Information, Response Category Rel. Frequency
1 Activity that has the most influence on the changes in fishery conditions
Fishing 46.21% Natural events 25.00% Park regulations 11.74% Legal issues 5.68% No answer 10.23% Land-based practices 0.76% Economic situation 0.38%
2 Activity that has the most influence on the changes in coral reef condition
Fishing* 54.59% Natural events 15.75% Boating (anchoring, grounding) 14.29% Water pollution (sewage and waste) 9.52% Park regulation 3.10% Other 1.47% No response 1.10% *) Including destructive fishing 16.49%
With regard to community adaptation to regulations, local acceptance and participation in
resource management were suggested to be at a higher level when local conservation strategies adopt
the integration of LEK with conventional science (Johannes 2002b; McClanahan, T et al. 2006;
Ruddle 1998). Theoretically, members of a culture would never understand that there are limits to the
resources they are relying on until they have experienced the effects of this and yet, the learning
process of the LEK often involves reinterpretation of many possible extremes of resource conditions
over a long period and in a small area (Aswani & Hamilton 2004; Drew 2005; Silvano & Valbo-
Jørgensen 2008). For this case, science could provide short-term, but more objective and spatially
wider, tests of the background mechanism of the ecosystem (i.e. ecological modelling (see Chapter
3)); which could therefore identify key changes, set thresholds, and encourage management
intervention to alter or avoid the extremes (Knowlton & Jackson 2008; Mumby, PJ & Steneck 2008).
! ))
This has been partially demonstrated in 2004 during a rezoning planning process in KNP when local
information of fish spawning and aggregation (SpAg) sites obtained from local fishermen have helped
determine priority sites (BTNKJ 2004). This also shows that the process of obtaining LEK itself is
participative, where policy makers and scientists are involved with the representatives of the local
community, thus, having the potential of building communication to grow local trust and support.
Despite the rationale of LEK’s contribution to local resource management, a recent review
(Cinner, J. E. & Aswani 2007) questions whether the LEK could intentionally develop an ethical
orientation towards local conservation. Even communities with strong local ecological knowledge
that perform customary management debated whether they were actually meant to conserve resources
(Cinner, Joshua et al. 2006; Cinner, J. E. & Aswani 2007). The survey identified signs of cognitive
learning of environmental impacts and environmental consequences of human behaviour within the
community, however, it was likely that the identified values and perceptions about key local reef
issues in Karimunjawa (see Chapter 3) had not yet developed an attitude promoting conservation.
This corresponds to several findings. Firstly, very few local respondents were well informed
about the four local reef issues asked about in the survey. Individually at each issue in the survey, the
highest response related to awareness of coastal pollution, yet this comprised less than half of the total
responses (39%) and most respondents (±60%) only knew about fewer than two issues (No. 1, Table
4.8).
Secondly, both internal and external socioeconomic factors have likely influenced community
initiatives in conservation. Internally, potentially conservationist local customs might have not yet
been translated into conservationist practices. For example, a number of respondents commented that
coastal littering was disrespectful to nature and also brought ‘bad luck’. However, this has resulted in
waste disposal in the terrestrial environment that was considered to be ‘less sacred’. Residents would
resort to conducting landfills and burning waste, as waste collecting activities or services were absent
in most of the village neighbourhoods. Correspondingly, this might explain the biased comments from
respondents in areas other than Karimunjawa Kota (the part of Karimunjawa village where most of
the administrative offices are located) who stated household waste in Karimunjawa did not contribute
much to coastal pollution. Externally, tourism growth was also associated with the increase of waste;
particularly inorganic waste (e.g. plastics, Styrofoam) such as was the case in Karimunjawa Kota. In
the area, despite presence of disposal bins and garbage collectors, and comments about past garbage
relocation to Java from Karimunjawa Kota, the team visually found scenes of garbage piled in the
proximity of village houses, partially converted mangroves and beach areas. Concurrently, we also
identified plastic bottle collecting, by only a few residents, for sale and reuse purposes. However,
whether this was motivated by conservation or merely commercial reasons was unknown. Here, we
! )*
suggest that external intervention may be essential to promote a community ethic of conservation,
particularly when the community is in transition due to development influences such as tourism.
Figure 4.5. Photos taken in the Karimunjawa Kota area showing a makeshift garbage dumping site
(A), unmanaged garbage (B, C), and collected plastic bottles (D).
Thirdly, we found LEK that represent a specific process of the reef ecosystem, however, how
disruptions to the process could lead an acute loss to the resources has likely been overlooked or
ignored by the community. For example, some fishermen interviewed commented that by intuition
most have realized the importance SpAg sites for future replenishment of fish population based on
their past experiences of knowing and fishing in those areas, rather than on information given by park
staff. However, SpAg sites are favourable fishing ground rather than a purposively sacred or
safeguarded area. Some who have the information would keep it undisclosed. Likewise, the grazing
behaviour of herbivorous reef fishes such as cemadar (parrotfishes) on bits of latoh (Caulerpa sp.,
including other fleshy seaweeds attached to coral), as well as bronang (rabbitfishes) on krangkam
(also seaweeds) was familiar to most seaweed farmers and subsistence small-scale fishers often
working in shallow reef-adjacent waters. However, most were not aware of the functional role of
these targeted species in the survivorship of coral by controlling macroalgae abundance (see Chapter
3). This corresponds to comments stating that the consequence of the exhaustion of the particular
species to the coral reef was not yet a concern for fishermen. This was supported by the belief that
water movement (e.g. wave force) would eventually remove fleshy seaweed from corals, which was
also considered a sign of an incoming baratan. Similarly, some admitted that anchoring directly on
the reef would damage living coral; however, most denied the potential for severe damage if the
! )+
conduct intensified. This was based on the knowledge that “anchor damage does not hit coral roots”,
and also “the living part of coral will replenish and coral will grow back again just like trees”.
From the above findings, I suggest that LEK in Karimunjawa has been able to identify some
patterns of the ecosystem, particularly extreme events (e.g. bad weathers, depleting fish). However,
this understanding, particularly of the reef, has likely been developing related to efforts in resource
extraction rather than for any deep understanding of the ecological process as such. Here, science can
give further understanding of the ecological process and causal links of human–resource interaction
which has likely been missed by LEK (Moller et al. 2004). Science also has a role in providing a
narrower biophysical perspective, by detecting relatively short-term signals or average patterns. This
provides information about the mechanism behind fluctuations of resources (e.g. ecological
modelling, see Chapter 3).
From the perspective of psychology, the local community may not have identified an acute or
chronic loss of reef resources. To overcome socioeconomic stress most would have likely adapted by
gaining from other resources pools rather than building a positive attitude towards resource
preservation (Hobfoll 2001). This may have placed the local community in a more vulnerable
situation as the resources are becoming depleted. Because of the attachment of the community to their
own land and because in KNP there is limited access to scientifically derived data regarding resource
depletion, it is highly worthwhile to incorporate the experience of the local resource users. Such an
approach is practical and can be cost-effective for awareness building as it could invite participation
and partnership between resource managers and the community (Johannes 1998). Thus, community
involvement in the management process and assessment of resources can build mutual learning where
transfer of knowledge can be broader and more autonomous compared to the existing formal
education.
The majority of respondents (±55%) only finished elementary school, followed by one third
who attended or finished high school, and a few finishing tertiary education (No. 2, Table 4.5). This
hierarchy was similar to the latest Karimunjawa District monographic data recorded in 2006, (BTNKJ
2008). As noted, the formal school curriculum related to environmental education and park awareness
has been applied in junior high school grades, thus, age groups involved are limited. The teaching,
performed by park staff, was fairly recent (around the past five years) and has not been intensive due
to limitations in staffing resources and the segregated locations of the schools. The children and
teenagers were in the age group who would have likely experienced this school curriculum related to
natural conservation and park awareness. These findings suggest that current involvement in formal
education might have a minimal influence on individual knowledge and awareness of local
environmental issues, presumably among adults in particular (r" = 0.360, Table 4.7).
! ),
Table 4.7. Degree of association between respondents’ level of formal education (No. 2, Table 4.6) and knowledge of key local reef issues (No. 1.b, Table 4.6) where both variables are treated as ordinals
No. Associated Categories Coefficient Value Approx. Sig. Assoc.
1 (1) Level of formal education (2) Level of knowledge of key issues
r" 0.360 0.000 Weak (Value ! 0.3)
Table 4.8. Response to questions related to three putative local scale reef related issues and level of education. Response no. 1 was based on answers given about up to four categories (n=249), Response no. 2 was limited to one (n=209)
No Information, Response Category Rel. Frequency
1.a Knowledge of human impact on reef ecosystem processes
Targeting fish spawning and aggregation sites can threat fish stock recuperation (n=71). 28.51%
Physical damage from anchoring can potentially severely reduce live coral cover (n=54). 10.04%
Loss of herbivorous reef fish may trigger uncontrolled macroalgae growth, covering corals (n=26). 22.09%
1.b Know all four issues 0.49% Know three issues 10.24% Know two issues 26.34% Know one issue 30.24% Do not know any of the local issues 32.68% 2 Level of education Elementary School 55.29% Junior High 17.31% Senior High 18.75% Higher Degree Education 3.37% Did not attend school 5.29%
The survey assessed organizational capacity based on responses regarding individual
participation in local community groups or organizational structures (No. 1,2, Table 4.9) as well as
knowledge sharing through the decision making process, particularly related to resource regulation
(No. 3,4, Table 4.9) (Armitage, D. 2005; Olsson, Folke & Berkes 2004). Living experience and
attachment to place was also individually measured through questions related to migration such as
origin, livelihood reason for migration, and living period in Karimunjawa (Adger, WN et al. 2002)
(No. 5,6,7,8, Table 4.9).
! *-
Table 4.9. Reponses to questions related to social capacity to organize including involvement in organization, participation in decision-making, and migration status and intention. Response to nos.1, 3, 4, 5, 7 & 8 were based on answers given limited to one (n=209). Samples were smaller for responses to nos. 2 (n=117) and 6 (n=66) as they were associated to responses in nos. 1 and 5, respectively (see table footnotes).
No Information, Response Category Distribution 1 Involvement in organization Involved 55.98% Not involved 44.02% 2 Number of organizations involved in * Less than 3 organizations 88.89% More than 3 organizations 11.11%
3 Involvement in decision-making process in the community
Yes 30.62% No 69.38%
4 Involvement in decision-making related to natural resource regulation
Yes 14.83% No 85.17% 5 Migratory status Born in Karimunjawa islands 68.42% Coming from outside of Karimunjawa islands 31.58% 6 Reason for migration** Fishing 26.87% Salaried employment 14.93% Crop / Land-based farming 13.43% Sea farming (aquaculture, gleaning) 11.94% Informal/Small-scale business 11.94% Family matters 7.58% Tourism 7.46% To sell marine products 4.48% 7 Length of time living in Karimunjawa Less than 5 years 7.21% 5 to 10 years 3.85% 10 to 15 years 12.02% More than 15 years 76.92% 8 Planned length of time to stay in Karimunjawa Less than 5 years 5.14% 5 to 10 years 6.07% More than 10 years 83.64% No answer 5.14%
* = Based on respondents who are involved in organizations
** = Based on respondents who are immigrants
The survey found that around half (55%) of the sampled respondents were participating in an
organizational activity at a local level (No. 1, Table 4.9). In general, the organizational experience
identified was not considerably associated to resource conservation. At the time of survey, many of
! *$
the respondents who considered themselves to be involved in organizations were associated with
government structures such as holding village administrative unit positions (e.g. Rukun Tetangga,
Rukun Warga) and religious activities (e.g. at mosques, mushalla-s). From comments made by
householders who are not the interviewees, the team identified activities associated with occupations
and conservation. This includes a fisherfolk association called Sinar Bahari based at Karimunjawa
Kota and Kelompok Pelestarian Penyu / KPP (sea turtle conservation group) based in Kemujan
village. Sinar Bahari’s group objective was to “unite local fishermen’s voices” (pers. comm.,
Founder of Sinar Bahari, 2010), which has likely been initiated as a advocacy platform particularly
for settling disputes with external fishers (Suara Merdeka 2002). As for KPP, a member of the
community spent part of his occupational time voluntarily nursing the initial turtle group and hatching
baby sea turtles collected and donated by other community members (pers. comm. KPP Initiator,
2010).
These findings indicate that there is variety of actors and interests within the community
which influences whether individuals or groups would potentially develop a collective decision such
as about resource conservation or other objectives (Agrawal & Gibson 1999). On the contrary, a
relatively heterogeneous Karimunjawan community (in terms of livelihood and cultural background)
might have found difficulty in making a collective decision about conserving resources (Nielsen &
Vedsmand 1999). Nevertheless, in the past, the role of external institutions was found to be effective
in mediating relationships and fostering communication. For example, between 2004 and 2005,
several community groups were initiated by a local NGO at each village as community resource
centres (Kelompok Swadaya Masyarakat / KSM-s). These have been able to gather community
opinion during the participative process for park rezoning during that period. (BTNKJ 2004; TAKA
2004).
Nevertheless, at the time of survey, the community might have considered the extent of
biological production (both marine and terrestrial resources) to be ‘sufficient’, including the
availability of alternative resources (Berkes, F & Jolly 2002). Presumably, this has brought less
resource-related adaptive pressure (e.g. subsistence security) that might have reduced the interest in
collective decision making, consequently resulting in less participation in conservation-related
interaction and practices by individuals or groups (Berkes, F & Jolly 2002; Laumann, Galaskiewicz &
Marsden 1978). We also suggest that there has been an extent of population movement related to
opportunities, reducing dependency on resources although there was also considerably strong
attachment to place. For example, seasonal emigration is more preferable to the youth and young
adults. Some commented that there has been increasing interest in cash income opportunities outside
Karimunjawa, such as labour hire jobs in Java, as well as temporary emigration for educational
reasons. The survey found that most respondents have settled for more than 15 years in Karimunjawa
! *%
(76%), 68% were Karimunjawan-born, while the rest were migrants who came to Karimunjawa for
various occupational reasons (Nos. 4, 5, 6 & 7 Table 4.9). A strong attachment to the particular area
was apparent as the majority (83%) decided to continue to reside there for the next decade or more
(No. 8, Table 4.9). However, some comments noted, particularly from middle to mature adults,
showed that most have past seasonal job experience in urbanized areas in Java. The reason to stay in
Karimunjawa was for security, in order to avoid to unpleasant urban issues (e.g. criminal or
residential problems). Less economic stress from a less demanding social lifestyle was also reported
as a reason to live in Karimunjawa.
For these reasons, facilitation by an external institution may be required to build platforms for
community interaction to develop adaptive capacity and promote knowledge sharing and decision-
making in response to resource change. Respondents who have experience in participating in the
decision-making process were fewer (31%) than those involved in organizations, and even less were
involved directly in resource management (Nos. 3 & 4, Table 4.9). Respondents who have been
involved in meetings and discussions related to park management went infrequently (once or twice)
and for short periods (less than a week’s participation). Some commented that only a minor
representation of the community was invited by the authority (mostly during the 2004-2005 rezoning
process). This comprised those who had a role in the village administrative unit and religious
activities; participation was less proactive and feedback to community members was very minimal.
Despite this situation, there was interest expressed about becoming involved in a communal
platform to share ideas and knowledge, as commented particularly by housewives and the youths.
Several women commented on their interest on participating in a community-based women’s program
such as the PKK (Pedoman Kesejahteraan Keluarga, a village-level women’s volunteer group) and
Kelompok UKM (Usaha Kecil Menengah / small to medium sized industry groups) which some
thought were only available on mainland Java. A youth group at the Nyamplungan sub-village in
Karimunjawa expressed interest in building a local youth group, “… here, young people are eager to
be trained… and the limitation of resources, from knowledge teaching (training) up to tools and
equipment, limits them (us) in realizing their (our) initiatives.... because the adults are less sensitive
than we are of the changes, not only the sea, but how the community is developing”.
This participation in group interaction may indicate that horizontal linkage (across space)
within the community may need to be reinforced prior to establishing vertical linkage across levels of
organizations (e.g. from local community, park managers, and NGOs to higher agencies and
authorities) (Berkes, F 2002). The process of mediation between social and ecological system
management may also be more productive when communities are focused as institutions on a set of
needs and interests and rules-in-use have been defined (Ostrom 1990). When there are no clearly
! *&
defined community objectives and environmental issues are attached to issues of value, equity and
social justice, the community will hold less interest even in mutual collaborative approaches with
external institutions to managing resources (Ludwig 2001).
As indigenous knowledge and institutions in Karimunjawa are weak (compared to indigenous
island communities in eastern Indonesia (Satria, Arif, Matsuda & Sano 2006; Thorburn, CC 2000),
building local community institutional capacity is essential to develop local consensus and
stewardship among community members in resource management. This is in addition to the need for
network building and facilitating the flow of information and knowledge (Crona & Bodin 2006; Hahn
et al. 2006; Scheffer, M et al. 2002). Correspondingly, this will likely challenge the local government
structure in providing the political space for community institutions to have a functional role in the
management and regulation of natural resources (Folke, Colding & Berkes 2003; Leach, Mearns &
Scoones 1999). This might include putting local to regional decentralization of decisions in resource
management, where the legal practice allows consideration of community aspirations and equity and
promotes local participation (Satria, A & Matsuda 2004).
Moreover, as issues of non-compliance were discernible in Karimunjawa (see section 4.3.3),
facilitation of the capacity of the resource manager unit to build trust with the community is essential
to ensure continuous participation in protecting the ecosystem. Thus, the process of bridging local
actors and external stakeholders itself is a nonmonetary investment of social incentive (e.g.
developing trust, identifying common interest, resolving conflict) (Hahn et al. 2006).
By developing local interaction platforms such as community organization, the community
can be better prepared in the event of a resource crisis as community members may well have already
assembled to learn, share knowledge, and develop coping strategies for the risk they will be exposed
to (Armitage, DR et al. 2008; Maarleveld & Dangbégnon 1999). Furthermore, such platforms could
also improve the relationship between resource users by acting as a hub in mediating user conflict and
building perceptions of local resources problem (Berkes, F 2009). Thus, collective action in solving
problems can also minimize overexploitation or degradation of openly accessible resources that could
result from dissimilarities in the thinking of individuals or groups in responding to uncertainty about
resources (Rammel, Stagl & Wilfing 2007).
YNTN` ],*#">,%+(.&+(',44*&$)=(.##")#(
Asset-related information was collected both at the community level, through reconnaissance
and interviews with a key informant in each village, such as either the Petinggi (the Head of Village)
or other administrative authorities. This information was also gained through interviewing and noting
comments at the household and individual levels. Measured indicators include community
infrastructure and facilities, household appliances and housing materials, personal income and age
! *'
group (Table 4.10) (Cinner, J, Fuentes & Randriamahazo 2009; Cinner, JE & Pollnac 2004;
McClanahan, T.R. et al. 2008).
At the community level, physical assets such as facilities and infrastructure related to health,
education, and access were present, however, the number varied for each village district. A hospital
was not available, which makes locals rely mainly on the puskesmas (the community health centre) in
each village and the posyandu (the community-based clinic posts in sub-villages). The puskesmas was
only available in Karimunjawa village and the number and distribution of the posyandu were
consistent with the village population, the lowest number being in Parang, and the highest in
Karimunjawa (BPS 2006).
Schools were available starting from kindergarten up to junior high, which were either state
schools or Islamic schools at each village, with an exception of a fishery high school in Karimunjawa
village. In terms of access, asphalt layered roads were present, however, only for the main roads of
the villages, whereas at dukuh (sub-village) areas and Parang village the main roads were still only
partially paved or made of dirt (Fig. 4.6). We found main fish landing ports in each village, while the
ferry and catamaran services linking the region to Central Java Province were only at Karimunjawa
Harbor. Kemujan, and particularly Parang village (as separate islands), have the least access to the
external community as well as lower progress in development, because of their relative lack of mass
transport and even vehicles.
Figure 4.6. Photos showing the typical structure of village roads. Main roads constructed with
asphalt layering connect Karimunjawa and Kemujan villages (A), a brick pavement road in Parang village (B), whereas in the sub-village areas mostly soil roads still predominate.
At the household level, one specific concern was on productivity-related assets related to
electricity were limited in Karimunjawa. In each village, the main supply of electrical power relies on
a state-owned diesel-powered generator. However, the daily operating period was still limited in the
daytime, excluding Karimunjawa Kota area (yet, 20% of respondents did not have electricity at all
(No.1, Table 4.10)). Community members who can afford to obtain a gasoline-based portable
generator get electricity during the daytime (Fig. 4.7). Availability of energy also affects
! *(
infrastructure such as communications where, for example, the absence of constant electrical power
required for landline telephone service limits this service to Karimunjawa Kota alone. However, at the
time of survey, most people relied on a cellular phone service that covers all three villages, yet, there
were extra costs with purchasing the mobile phone unit and power supply.
The majority (>80%) owned essential household appliances that could support cooking,
transportation and entertainment (No.1, Table 4.10). Nevertheless, most have commented that the
limited electricity has hampered respondents from owning essential electrical appliances that might
enhance sanitation in terms of water supply (e.g. water pumps) and food storage (e.g. refrigerators),
including equipment that can support home industry activities (e.g. food processors).
Figure 4.7. Photo showing a gasoline-based generator used by a household to supply an additional
period of electricity, however, this was visually uncommon during the survey.
Financially, the majority (99%) of respondents has an average monthly household income of
up to A$600 (No.6, Table 4.10), which is below the 2009 national gross domestic product (A$2.224
(IMF; 2009)). Yet, with this extent of income, most houses sampled have been made of rigid
materials such as cement or wood based walls, cemented or tiled floors and a tiled roof (No.2, 3, 4,
Table 4.10; A, B, C, Fig. 4.7). However, some health and sanitation concerns were evident, as a few
houses had a dirt floor and asbestos walls. The toilet location, referred to as the area for bathing,
laundry and personal hygiene was mostly separated from the house rather than indoors (No.5, Table
4.10). This was likely because of how people obtain water, as most have to build wells to obtain
groundwater, or use piping to channel higher-ground aquifer water.
! *)
Table 4.10. Response distribution related to assets such as style of living based household appliances, housing materials, sanitation and individual assets such as approximate monthly income and age group. (Response No. 1 was based on answers given more than once (n=209); Response Nos. 2, 3, 4 & 5 were limited to one (n=209).
Type of information, Response category, Distribution (%) 1. Possession of household appliances Gas stove 87.56% Water tank 26.32% Electricity 80.38% Water pump 18.18% Television 77.03% Electric generator 14.83% Vehicle 70.33% Refrigerator 10.53% Audio player 44.50% Computer 9.09% Video player 36.84% Internet access 3.83% Electric fan 30.62% 2. Roof Material 3. Wall material Thatch 1.44% Cement/Brick 57.42% Metal 8.61% Wood/Plywood 28.71% Tile 89.95% Bamboo 1.44% Asbestos 3.35% 4. Floor Material 5. Sanitation Cement 39.71% Indoor toilet 31.58% Tile 44.50% Outdoor toilet 65.07% Soil 7.66% No toilet 3.35% Wood 8.13% 6. Average monthly income* 7. Age group (in years) < A$ 120 67.31% 18 - 20 11.54% A$ 120 - 600 32.21% 20 – 30 26.92% A$ 600 - 1200 0.00% 31 – 40 27.88% > A$ 1800 0.48% 41 – 50 20.19% > 50 13.46% * = Exchange rate of 1 Australian Dollar for approximately 8,000 Indonesian Rupiah
Despite being a low-income community, in general, the Karimunjawan households might have
already established a situation Reardon & Vosti (1995) described as ‘welfare poverty’ as basic
material needs such as housing, food, and entertainment have been fulfilled, although this may be at
minimal levels. However, ‘investment poverty’ (Reardon & Vosti 1995) may also be likely to occur
as there were limitations on human-made assets, although these assets were required to substitute or
complement current income generating activities that were highly dependent on natural assets.
The lack of physical assets for production, such as electricity, for example, might have
impeded or delayed the local community progressing towards alternative livelihoods. Consequently,
individuals or households would have avoided non-extractive investment because of their limited
ability to obtain a higher cost item such as a portable generator set for electricity during the daytime.
Despite this dynamic of livelihood activities being complex and although involvement in alternative
livelihoods may not necessarily be linked to relieving pressure on resources (Sievanen et al. 2005),
! **
both biophysical (Chapter 3) and social (Chapter 4), findings of this research imply that competition
for local natural resources, particularly reef-related, was already high.
The community requires prompt action to diversify assets that could generate less resource
dependant income (see section 4.3.2, (Ellis 2000; Pomeroy, RS et al. 2006)). Yet, the lack of
alternatives for subsistence living or income generation could result in more unsustainable use of
resources, as user competition is higher. In addition, the community is still strongly attached to
extractive occupations (see section 4.3.2, (Pretty 2003; Pretty & Ward 2001)). In this case, it is argued
that reducing the vulnerability of the community would likely depend on external intervention such as
from policymakers or donors. This may include in setting up poverty alleviation strategies, parallel
with the resource management framework, to improve productive asset holdings of the community.
These include fixed assets (e.g. tools and equipment for small-scale industry) in addition to human
assets (e.g. education) and social assets (e.g. establishment of community institutions, see section
4.3.5). Yet, as physical access to market is costly (due to the distance from mainland Java) and this
could cause failure (e.g. in the case of seaweed farming), these interventions will also need to include
market channels to avoid discouragement of non-extractive diversification.
Suitable development strategies include not only development of the physical assets
mentioned above, but also capacity building to promote innovation and adherence to market grades
and standards (e.g. via training workshops). Assistance and simplification of licensing and regulatory
requirements for small-rural-scale industries to join the regional market is also needed. Undertaking
investment in community assets is beyond the role and capability of the current resource management
institution (e.g. KNPA). Thus agencies and stakeholders will likely rise to the challenge of cross-
sectoral institutional coordination. Specifically this involves prescribing site-specific actions and
policies including improvement of local physical and social infrastructure for natural resource
management of Karimunjawa.
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In general, the purpose of this chapter was to produce evidence illustrating the association of
local livelihood dynamics with natural resources. The varying perceptions of individuals on both the
condition and behaviour of the natural resources have influenced the way livelihood-related risks are
being perceived. Accordingly, this also affects the local community’s socioeconomic decisions and
attitudes that could either contradict or support natural resource management and conservation
initiatives.
Livelihood-enhancing decisions in Karimunjawa have still been confined to a strong reliance
to natural resource exploitation. This suggests progressively weak social resilience, as there are
inadequate livelihood alternatives to reduce future vulnerability because of resource exhaustion.
! *+
Improving the local adaptive capacity of the Karimunjawan community demands, therefore,
sustainability of both the livelihoods and the natural resources.
Hence, this apparent need for the diversification of local livelihood arrangements to
compensate for the reduction of natural resource use means that this research has implications for the
evaluation of the resource management framework in KNP, which will be discussed at length in
Chapter 5.
*,
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In general, this thesis has achieve its aims by demonstrating the importance of identifying key
processes of both social and ecological system for improving or maintaining both socio-economic
needs of local community and the complexity of coral reef habitats.
In Chapter 3, the ecological modelling approach has facilitated the exploration of habitat
response to scenarios of local disturbance in Karimunjawan reef given the limitation of native
ecological data and resources to conduct rigorous biophysical measurements. The modelling output
also provided less-scientific stakeholders an informative approach to understand the complex
ecosystem processes related to the effect of local unsustainable in-shore fishing practices on coral-
algae interactions related to fish grazing in particular. In Chapter 4, social assessments of the local
community have provided an in-depth evaluation of the relative resilience of resource users,
particularly in terms of the capacity of the Karimunjawan people to adapt their livelihoods to changes
in reef-related resources they depend upon. The survey findings showed that an alternative livelihood
portfolio is an essential development aspect for the Karimunjawan community to allow suspension of
or withdrawal from extractive activities, and thus, the effectiveness of the regulatory interventions and
policy measures related to the activities.
The research initiative was building partly form inputs gathered from meetings and
discussions in the end of 2008, which involves park management staffs and academics relevant to the
conservation of Karimunjawa region. In the same period, a rapid ecological survey was conducted to
obtain recent information of biophysical condition and resilience indicator of the reef. Being in the
middle of the community, the surveys has also partly revealed recent key threats to the reef from the
local socioeconomic activities that were affecting health of local reef system. The ecological
modelling study was later carried out from early to mid 2009, generally, as an approach to analyse
and predict, but not estimate, future impact of these key threats to the reef. A social survey followed
later in early 2010 where a 6-month travel warning due to bombing in Jakarta took place in between.
The survey was generally meant to explore the likelihood of the community to mitigate and adapt to
future resource changes given to their recent perception and socioeconomic capacity. For each of the
ecological and social studies, the preliminary results has been communicated to the KNP and other
related governmental institutions through written reports and meetings. Disseminating scientific
results to general audience was also a notable experience. Less-scientific stakeholder seldom gave
unexpected response, such as dissatisfaction or disapproval by the member of the institution. This was
! +-
due to the informed scientific analysis might inherently articulate the faults or mistakes of the
Reducing the impact of unsustainable livelihood activities in Karimunjawa is indirectly
associated with efforts in lowering the local economy’s high reliance on reef resources. Promoting a
transitional economy such as through alternative livelihood provision, ultimately, will require explicit
socioeconomic interventions. This would include, for example, building physical assets such as
community infrastructure and non-natural-based capital assets such as access to markets, aid and
economic programs. On the other hand, the administrative role of the KNPA limits its institutional
capacity exclusively to implicit social intervention such as community engagement for training and
! +,
capacity building. Anything apart from that is beyond the administrative function and responsibility
of the KNPA (The Ministry of Forestry Decree No. P.19/Menhut-II/2004, Forestry Law No. 41/1999
Article 56).
Therefore, the KNPA’s capacity to engage with stakeholders is also indirectly related to the
mitigation of livelihood-related threats to the reef. In this case, the institutional linkage is cross-scale,
with objectives not only of the establishment of support mechanisms to the park management process,
but also deliberately influencing local resilience agendas to conform with the governance functions in
regional or national institutions and other state agencies. In practice, other than the well-established
scientific collaboration with NGOs (e.g. Wildlife Conservation Society, Taka Foundation) and
academics (Diponegoro University, the University of Queensland), KNPA can take a leading role in
improving the coordination of state agencies responsible for the integrated development of
Karimunjawa Island. The expected outcome is to assimilate local ecosystem management priorities,
for example, with an island development program, governed by regional or national state agencies.
Moreover, local participation is also to support management process, which may include up to
enforcement and surveillance measures. Correspondingly, this will likely challenge the local
government structure in providing the political space for community institutions to have a functional
role in the management and regulation of natural resources. This might include putting local to
regional decentralization of decisions in resource management, where the legal practice allows
consideration of community aspirations and equity and promotes local participation. Nevertheless, the
process of bridging local actors and external stakeholders itself is a nonmonetary investment of social
incentive (e.g. developing trust, identifying common interest, resolving conflict).
,-
!2I2!28@2?(
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7 2.
33
0.00
0.
00
0.00
C
Taka
Mal
ang
14.6
7 15
.33
1.67
0.
00
0.00
0.
33
P
C
emar
a K
ecil
2.00
17
.00
12.0
0 0.
00
3.33
0.
00
TU
In
dono
or
11.0
0 8.
33
0.00
0.
33
0.67
0.
00
Men
jang
an K
ecil
5.33
10
.67
2.33
0.
67
6.67
0.
00
Teng
ah K
ecil
15.6
7 18
.67
3.00
0.
33
2.67
0.
33
TF
Le
gon
Boy
o 13
.00
2.67
0.
00
0.33
2.
00
0.00
Le
gon
Lele
15
.00
16.6
7 2.
00
3.00
2.
33
0.33
Le
gon
Tole
4.
67
2.00
0.
00
0.33
2.
33
0.00
M
eric
an
2.67
9.
00
1.00
0.
67
0.67
0.
33
Tela
ga N
orth
5.
33
11.6
7 1.
00
0.67
2.
33
0.00
U
jung
Lem
u 6.
67
13.6
7 0.
33
0.00
1.
00
0.67
R
eef S
lope
B
S
into
k E
ast
9.33
35
.33
17.3
3 0.
67
6.33
3.
00
Sin
tok
Sou
thW
est
3.33
12
.33
1.67
0.
00
0.67
0.
00
Tanj
ung
Gel
am
11.6
7 10
.00
1.67
0.
33
1.00
0.
33
C
Ta
ka M
alan
g 3.
67
8.00
0.
33
0.33
0.
00
0.00
P
Cem
ara
Kec
il 4.
00
7.33
1.
50
0.00
0.
00
0.00
TU
Indo
noor
14
.33
10.6
7 4.
67
0.67
1.
33
0.67
M
enja
ngan
Kec
il 1.
67
0.33
0.
67
0.00
0.
00
0.00
Te
ngah
Kec
il 0.
00
0.67
0.
00
0.33
0.
33
0.33
TF
Lego
n B
oyo
0.00
3.
33
0.00
0.
00
1.33
0.
00
Lego
n Le
le
3.67
12
.67
2.00
0.
00
0.67
0.
00
Lego
n To
le
2.33
0.
33
0.00
0.
00
0.33
0.
00
Mer
ican
19
.33
15.0
0 4.
33
0.67
0.
67
0.33
Te
laga
Nor
th
17.6
7 10
.00
3.00
0.
33
2.33
0.
33
Uju
ng L
emu
1.00
7.
67
0.33
0.
33
0.33
0.
00
Upp
er S
lope
Jela
mun
Lag
oon
1.00
21
.00
1.00
0.
00
0.00
0.
00
Appendix 1. Visual census data of reef fish abundance
Ben
thic
sub
stra
te p
oint
cou
nt d
ata
of 3
0 (1
x 1
m) q
uadr
ate
trans
ect p
hoto
take
n fr
om 1
3 si
tes.
Eac
h si
te s
umm
ariz
es 1
080
(30
x 36
) ide
ntifi
catio
n po
ints
.
Site
Nam
e
Ben
thic
Cat
egor
y
Cem
ara
Kec
il Ta
ka M
alan
g Ta
njun
g G
elam
S
UM
S
ME
AN
S
D
SE
IN
DE
X
SU
MS
M
EA
N
SD
S
E
IND
EX
S
UM
S
ME
AN
S
D
SE
IN
DE
X
HA
RD
CO
RA
L
609.
00
20.3
0 9.
40
1.72
0.
84
677.
00
22.5
7 10
.49
1.91
1.
42
186.
00
6.20
9.
33
1.70
1.
28
Bra
nchi
ng C
oral
(CB
) 42
0.00
14
.00
9.36
1.
71
0.26
20
2.00
6.
73
7.73
1.
41
0.36
91
.00
3.03
6.
59
1.20
0.
35
Enc
rust
ing
Cor
al (C
E)
0.00
0.
00
0.00
0.
00
0.00
18
.00
0.60
2.
11
0.39
0.
10
6.00
0.
20
0.66
0.
12
0.11
Fo
liose
Cor
al (C
F)
10.0
0 0.
33
1.03
0.
19
0.07
14
.00
0.47
1.
48
0.27
0.
08
1.00
0.
03
0.18
0.
03
0.03
M
assi
ve C
oral
(CM
) 0.
00
0.00
0.
00
0.00
0.
00
107.
00
3.57
8.
17
1.49
0.
29
56.0
0 1.
87
4.77
0.
87
0.36
M
ushr
oom
Cor
al (C
FU)
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
1.00
0.
03
0.18
0.
03
0.03
S
ub-m
assi
ve C
oral
(CS
) 19
.00
0.63
3.
47
0.63
0.
11
7.00
0.
23
0.68
0.
12
0.05
2.
00
0.07
0.
37
0.07
0.
05
Tabu
late
Cor
al (C
T)
151.
00
5.03
9.
00
1.64
0.
35
289.
00
9.63
11
.66
2.13
0.
36
25.0
0 0.
83
3.48
0.
64
0.27
N
ON
-HA
RD
CO
RA
L
5.00
0.
17
0.75
0.
14
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
M
illep
ora
(CN
S)
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
S
oft C
oral
(CS
F)
5.00
0.
17
0.75
0.
14
0.00
2.
00
0.07
0.
37
0.07
0.
00
0.00
0.
00
0.00
0.
00
0.00
S
EA
UR
CH
IN
0.00
0.
00
0.00
0.
00
0.00
10
.00
0.33
0.
71
0.13
0.
10
22.0
0 0.
73
2.33
0.
43
0.10
S
ea U
rchi
n (O
IN)
0.00
6.
00
5.00
IN
VE
RTE
BR
ATE
S
0.00
0.
00
0.00
0.
00
0.00
2.
00
0.07
0.
25
0.05
0.
03
5.00
0.
17
0.53
0.
10
0.03
E
ncru
stin
g S
pong
es (O
SP
) 0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Inve
rtebr
ates
(OIV
) 0.
00
0.00
0.
00
0.00
0.
00
4.00
0.
13
0.43
0.
08
0.05
0.
00
0.00
0.
00
0.00
0.
00
Not
Ava
ilabl
e (O
NA
) 0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Upr
ight
Spo
nges
(OS
U)
89.0
0 2.
97
5.16
0.
94
0.35
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
FL
ES
HY
MA
CR
OA
LGA
E
260.
00
19.0
0
90
.00
PR
O-R
ES
ILIE
NC
E A
LGA
E
254.
00
8.47
6.
81
1.24
0.
22
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Cal
care
ous
Mac
roal
gae
(MA
C)
0.00
0.
00
0.00
0.
00
0.00
2.
00
0.07
0.
25
0.05
0.
00
0.00
0.
00
0.00
0.
00
0.00
C
rust
ose
Cor
allin
e A
lgae
(CR
U)
6.00
0.
20
0.48
0.
09
0.00
17
.00
0.57
1.
19
0.22
0.
00
90.0
0 3.
00
6.33
1.
16
0.00
Fi
lam
ento
us T
urf A
lgae
(TU
R)
9.00
38
.00
496.
00
SE
TTLE
AB
LE S
UB
STR
ATE
9.
00
0.30
0.
60
0.11
0.
06
38.0
0 1.
27
2.50
0.
46
0.16
4.
00
0.13
0.
43
0.08
0.
08
Rec
ently
Dea
d C
oral
(CD
C)
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
D
efor
mat
ed d
ead
cora
l (O
RN
) 0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
49
2.00
16
.40
14.5
9 2.
66
0.30
Tu
rf co
vere
d ru
bble
(OR
U)
117.
00
3.90
3.
92
0.72
0.
00
368.
00
12.2
7 10
.09
1.84
0.
04
202.
00
6.73
10
.07
1.84
0.
35
Turf
cove
red
mal
form
ated
dea
d co
ral (
OR
C)
0.00
0.
00
83.0
0
U
NS
ETT
LEA
BLE
SU
BS
TRA
TE
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
83.0
0 2.
77
5.58
1.
02
0.23
S
and
(OS
D)
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
S
edim
en/S
ilt C
over
(OS
I) 60
9.00
20
.30
9.40
1.
72
0.84
67
7.00
22
.57
10.4
9 1.
91
1.42
18
6.00
6.
20
9.33
1.
70
1.28
TO
TAL
SU
BS
RTA
TE S
AM
PLE
D
972
750
882
Appendix 2. Quadrate transect data of benthic composition survey.
S
ite N
ame
B
enth
ic C
ateg
ory
In
dono
or
Mer
ican
Te
laga
- N
orth
S
UM
S
ME
AN
S
D
SE
IN
DE
X
SU
MS
M
EA
N
SD
S
E
IND
EX
S
UM
S
ME
AN
S
D
SE
IN
DE
X
HA
RD
CO
RA
L
355.
00
11.8
3 10
.10
1.84
1.
30
756.
00
25.2
0 8.
01
1.46
1.
30
292.
00
9.73
8.
45
1.54
1.
43
Bra
nchi
ng C
oral
(CB
) 94
.00
3.13
4.
70
0.86
0.
35
312.
00
10.4
0 11
.61
2.12
0.
37
122.
00
4.07
4.
85
0.89
0.
36
Enc
rust
ing
Cor
al (C
E)
13.0
0 0.
43
1.41
0.
26
0.12
22
.00
0.73
3.
83
0.70
0.
10
9.00
0.
30
0.70
0.
13
0.11
Fo
liose
Cor
al (C
F)
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
M
assi
ve C
oral
(CM
) 61
.00
2.03
3.
31
0.60
0.
30
96.0
0 3.
20
3.94
0.
72
0.26
72
.00
2.40
2.
99
0.55
0.
35
Mus
hroo
m C
oral
(CFU
) 0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Sub
-mas
sive
Cor
al (C
S)
4.00
0.
13
0.43
0.
08
0.05
0.
00
0.00
0.
00
0.00
0.
00
4.00
0.
13
0.73
0.
13
0.06
Ta
bula
te C
oral
(CT)
17
1.00
5.
70
10.0
9 1.
84
0.35
26
5.00
8.
83
12.7
4 2.
33
0.37
66
.00
2.20
4.
97
0.91
0.
34
NO
N-H
AR
D C
OR
AL
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Mill
epor
a (C
NS
) 0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Sof
t Cor
al (C
SF)
1.
00
0.03
0.
18
0.03
0.
00
0.00
0.
00
0.00
0.
00
0.00
10
.00
0.33
0.
92
0.17
0.
00
SE
A U
RC
HIN
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Sea
Urc
hin
(OIN
) 47
.00
33.0
0
15
9.00
IN
VE
RTE
BR
ATE
S
46.0
0 1.
53
3.75
0.
68
0.18
32
.00
1.07
1.
68
0.31
0.
24
158.
00
5.27
5.
74
1.05
0.
33
Enc
rust
ing
Spo
nges
(OS
P)
1.00
0.
03
0.18
0.
03
0.01
0.
00
0.00
0.
00
0.00
0.
00
1.00
0.
03
0.18
0.
03
0.01
In
verte
brat
es (O
IV)
0.00
0.
00
0.00
0.
00
0.00
1.
00
0.03
0.
18
0.03
0.
02
0.00
0.
00
0.00
0.
00
0.00
N
ot A
vaila
ble
(ON
A)
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
U
prig
ht S
pong
es (O
SU
) 1.
00
0.03
0.
18
0.03
0.
00
19.0
0 0.
63
1.79
0.
33
0.00
2.
00
0.07
0.
25
0.05
0.
00
FLE
SH
Y M
AC
RO
ALG
AE
17
.00
5.00
29
.00
PR
O-R
ES
ILIE
NC
E A
LGA
E
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
C
alca
reou
s M
acro
alga
e (M
AC
) 3.
00
0.10
0.
55
0.10
0.
00
0.00
0.
00
0.00
0.
00
0.00
25
.00
0.83
1.
82
0.33
0.
00
Cru
stos
e C
oral
line
Alg
ae (C
RU
) 14
.00
0.47
1.
61
0.29
0.
00
5.00
0.
17
0.53
0.
10
0.00
4.
00
0.13
0.
35
0.06
0.
00
Fila
men
tous
Tur
f Alg
ae (T
UR
) 61
.00
82.0
0
19
4.00
S
ETT
LEA
BLE
SU
BS
TRA
TE
11.0
0 0.
37
1.19
0.
22
0.11
61
.00
2.03
5.
30
0.97
0.
20
9.00
0.
30
0.70
0.
13
0.11
R
ecen
tly D
ead
Cor
al (C
DC
) 0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Def
orm
ated
dea
d co
ral (
OR
N)
50.0
0 1.
67
2.41
0.
44
0.19
21
.00
0.70
1.
34
0.25
0.
19
185.
00
6.17
6.
20
1.13
0.
34
Turf
cove
red
rubb
le (O
RU
) 39
5.00
13
.17
7.86
1.
44
0.33
18
4.00
6.
13
6.19
1.
13
0.30
21
2.00
7.
07
5.89
1.
08
0.36
Tu
rf co
vere
d m
alfo
rmat
ed d
ead
cora
l (O
RC
) 21
5.00
62
.00
201.
00
UN
SE
TTLE
AB
LE S
UB
STR
ATE
19
4.00
6.
47
8.91
1.
63
0.35
6.
00
0.20
0.
61
0.11
0.
08
1.00
0.
03
0.18
0.
03
0.01
S
and
(OS
D)
21.0
0 0.
70
2.28
0.
42
0.10
56
.00
1.87
3.
88
0.71
0.
31
200.
00
6.67
8.
60
1.57
0.
35
Sed
imen
/Silt
Cov
er (O
SI)
355.
00
11.8
3 10
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1.84
1.
30
756.
00
25.2
0 8.
01
1.46
1.
30
292.
00
9.73
8.
45
1.54
1.
43
TOTA
L S
UB
SR
TATE
SA
MP
LED
69
6
95
7
87
7
S
ite N
ame
B
enth
ic C
ateg
ory
Te
ngah
Le
gon
Boy
o Le
gon
Tole
S
UM
S
ME
AN
S
D
SE
IN
DE
X
SU
MS
M
EA
N
SD
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E
IND
EX
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UM
S
ME
AN
S
D
SE
IN
DE
X
HA
RD
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RA
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427.
00
14.2
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87
1.80
1.
44
591.
00
19.7
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21
1.50
1.
22
630.
00
21.0
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18
1.68
1.
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Bra
nchi
ng C
oral
(CB
) 22
0.00
7.
33
6.04
1.
10
0.34
12
8.00
4.
27
5.63
1.
03
0.33
29
0.00
9.
67
8.17
1.
49
0.36
E
ncru
stin
g C
oral
(CE
) 39
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1.30
5.
31
0.97
0.
22
69.0
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30
2.93
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53
0.25
9.
00
0.30
0.
65
0.12
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06
Folio
se C
oral
(CF)
21
.00
0.70
1.
95
0.36
0.
15
7.00
0.
23
0.68
0.
12
0.05
34
.00
1.13
3.
88
0.71
0.
16
Mas
sive
Cor
al (C
M)
28.0
0 0.
93
2.18
0.
40
0.18
33
7.00
11
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8.28
1.
51
0.32
16
7.00
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57
6.03
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M
ushr
oom
Cor
al (C
FU)
0.00
0.
00
0.00
0.
00
0.00
1.
00
0.03
0.
18
0.03
0.
01
0.00
0.
00
0.00
0.
00
0.00
S
ub-m
assi
ve C
oral
(CS
) 14
.00
0.47
1.
85
0.34
0.
11
40.0
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33
4.47
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82
0.18
12
.00
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0.
97
0.18
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08
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late
Cor
al (C
T)
92.0
0 3.
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6.17
1.
13
0.33
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00
0.27
1.
05
0.19
0.
06
118.
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3.93
8.
15
1.49
0.
31
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N-H
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AL
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00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
12
.00
0.40
2.
19
0.40
0.
00
Mill
epor
a (C
NS
) 0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
12
.00
0.40
2.
19
0.40
0.
00
Sof
t Cor
al (C
SF)
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
SE
A U
RC
HIN
0.
00
0.00
0.
00
0.00
0.
00
77.0
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57
3.60
0.
66
0.29
36
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1.20
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93
0.53
0.
21
Sea
Urc
hin
(OIN
) 3.
00
2.00
2.
00
INV
ER
TEB
RA
TES
3.
00
0.10
0.
31
0.06
0.
03
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Enc
rust
ing
Spo
nges
(OS
P)
0.00
0.
00
0.00
0.
00
0.00
2.
00
0.07
0.
25
0.05
0.
02
1.00
0.
03
0.18
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03
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In
verte
brat
es (O
IV)
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
N
ot A
vaila
ble
(ON
A)
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
1.00
0.
03
0.18
0.
03
0.01
U
prig
ht S
pong
es (O
SU
) 12
.00
0.40
1.
10
0.20
0.
18
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
FLE
SH
Y M
AC
RO
ALG
AE
20
7.00
0.
00
1.00
P
RO
-RE
SIL
IEN
CE
ALG
AE
17
4.00
5.
80
6.74
1.
23
0.06
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
C
alca
reou
s M
acro
alga
e (M
AC
) 20
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0.67
1.
60
0.29
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
0.00
0.
00
Cru
stos
e C
oral
line
Alg
ae (C
RU
) 13
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0.43
0.
97
0.18
0.
00
0.00
0.
00
0.00
0.
00
0.00
1.
00
0.03
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18
0.03
0.
00
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men
tous
Tur
f Alg
ae (T
UR
) 21
6.00
49
.00
12.0
0
S
ETT
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BLE
SU
BS
TRA
TE
13.0
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43
1.41
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26
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18
0.03
0.
01
0.00
0.
00
0.00
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00
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R
ecen
tly D
ead
Cor
al (C
DC
) 61
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2.03
7.
77
1.42
0.
28
44.0
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51
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10
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Def
orm
ated
dea
d co
ral (
OR
N)
142.
00
4.73
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62
1.76
0.
37
4.00
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13
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2.
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25
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cove
red
rubb
le (O
RU
) 22
8.00
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60
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1.
11
0.34
13
3.00
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43
5.63
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22
2.00
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rf co
vere
d m
alfo
rmat
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ead
cora
l (O
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00
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TTLE
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LE S
UB
STR
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0.
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00
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00
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d (O
SD
) 0.
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0.00
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edim
en/S
ilt C
over
(OS
I) 42
7.00
14
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9.87
1.
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1.44
59
1.00
19
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8.21
1.
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63
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TO
TAL
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AM
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865
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enja
ngan
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gon
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into
k –
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thw
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Site
Nam
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Cat
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SU
MS
M
EA
N
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S
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S
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S
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D
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IN
DE
X
SU
MS
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SD
S
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IND
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S
ME
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D
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IN
DE
X
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. 66
9.0
22.3
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92
524.
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1.
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CB
41
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57
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42
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14
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44
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C
0.00
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00
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17
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97
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20
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11
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21
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0.25
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11
76.0
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3.81
0.
70
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17
0.58
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20
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00
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0.
00
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0.
00
0.00
0.
00
11.0
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CT
214.
00
7.13
9.
57
1.75
0.
36
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10
0.55
0.
10
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13
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ON
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00
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88
0.56
32
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00
0.00
0.
00
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20.0
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3.65
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76
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30
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RC
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00
0.00
0.
00
0.00
0.
00
0.00
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00
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00
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00
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00
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00
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26
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1.
80
0.33
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17
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03
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38
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P
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00
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00
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00
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00
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00
0.00
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00
0.00
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00
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00
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00
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00
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00
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00
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O
NA
0.
00
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00
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00
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00
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00
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00
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00
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00
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42
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35
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34
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7.
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0.57
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00
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00
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00
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0.00
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00
0.00
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00
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00
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00
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RN
71
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35
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37
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26
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91.0
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4.44
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81
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38
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58
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97
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30
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34
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C
53.0
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0
13
3.0
UN
S. S
UB
ST
6.00
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20
0.81
0.
15
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00
0.00
0.
00
0.00
0.
00
26.0
0 0.
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2.45
0.
45
0.20
32
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1.07
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0.50
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18
ISD
47
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1.57
3.
24
0.59
0.
33
16.0
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53
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22
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10
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37
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1.
09
0.33
O
SI
669.
0 22
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11.2
3 2.
05
0.92
52
4.0
17.4
7 9.
93
1.81
1.
44
661.
0 22
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10.4
1 1.
90
1.08
52
8.0
17.6
0 10
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1.97
1.
27
TOTA
L 99
1
70
3
10
22
825
Appendix 3. Model parameter sensitivity test results. Sensitivity index (SI) was measured as the proportion of the adjusted output value change relative to the ‘base’ output values. Values printed in bold indicates the SI of the affected benthic group output. Each parameter test was from 100 simulation and 1-year (t+1) runs. Parameter Name Adjustments Input
Value Output Rel.
abundance (t+1)
Sensitivity Index (%)
Min (Base) 0.2 M 0.155 C 0.310
Adjusted 1 0.325 M (t+1) 0.155 -0.003 C (t+1) 0.310 0.000
Adjusted 2 0.45 M (t+1) 0.155 -0.007 C (t+1) 0.310 0.000
Adjusted 3 0.575 M (t+1) 0.155 -0.010 C (t+1) 0.310 0.000
Max 0.7 M (t+1) 0.155 -0.014
Probability of Macroalgae Occupying Space
C (t+1) 0.310 0.000 -40% SD 0.00024 M (t+1) 0.155 -0.008
C (t+1) 0.310 0.000 -20% SD 0.00032 M (t+1) 0.155 -0.004
C (t+1) 0.310 0.000 Base 0.0004 M 0.155
C 0.310 +20% SD 0.00048 M (t+1) 0.155 0.004
C (t+1) 0.310 0.000 +40% SD 0.00056 M (t+1) 0.155 0.008
Baseline Macroalgae Lateral Growth
C (t+1) 0.310 0.000 Min (Base) 0.07 M 0.176
C 0.307 Adjusted 1 0.3025 M (t+1) 0.176 0.000
C (t+1) 0.307 0.001 Adjusted 2 0.535 M (t+1) 0.176 0.000
C (t+1) 0.307 0.001 Adjusted 3 0.7675 M (t+1) 0.176 0.000
C (t+1) 0.307 0.002 Max 1 M (t+1) 0.176 0.000
Probability of Coral Occupying Space
C (t+1) 0.307 0.003 Min (Base) 0.00016 M 0.176
C 0.307 Adjusted 1 0.000165 M (t+1) 0.176 0.000
C (t+1) 0.307 0.000 Adjusted 2 0.00017 M (t+1) 0.176 0.000
C (t+1) 0.307 0.000 Adjusted 3 0.000175 M (t+1) 0.176 0.000
C (t+1) 0.307 0.000 Max 0.00018 M (t+1) 0.176 0.000
Baseline Coral Lateral Growth
C (t+1) 0.307 0.000 Reduction Effect of Random 'Other 'Presence
100 Iteration Random
value from 0 - 1
No effect to C and M composition
Parameter Name Adjustments Input
Value Output Rel.
abundance (t+1)
Sensitivity Index (%)
-40% SD 0.18 M (t+1) 0.218 23.921 C (t+1) 0.307 0.000
-20% SD 0.24 M (t+1) 0.197 11.960 C (t+1) 0.307 0.000
Base 0.3 M 0.176 C 0.307
+20% SD 0.36 M (t+1) 0.155 -11.960 C (t+1) 0.307 0.000
+40% SD 0.42 M (t+1) 0.134 -23.921
Baseline Macroalgae Mortality
C (t+1) 0.307 0.000 Min 0.05 M (t+1) 0.263 -49.835
C (t+1) 0.307 0.000 Adjusted 1 0.1125 M (t+1) 0.241 -37.376
C (t+1) 0.307 0.000 Adjusted 2 0.175 M (t+1) 0.219 -24.917
C (t+1) 0.307 0.000 Adjusted 3 0.2375 M (t+1) 0.197 -12.459
C (t+1) 0.307 0.000 Max (Base) 0.3 M 0.176
Macroalgae Grazing Loss Scenario
C 0.307 -40% SD 0.012 M (t+1) 0.176 0.000
C (t+1) 0.310 0.911 -20% SD 0.016 M (t+1) 0.176 0.000
C (t+1) 0.309 0.455 Base 0.02 M 0.176
C 0.307 +20% SD 0.024 M (t+1) 0.176 0.000
C (t+1) 0.306 -0.455 +40% SD 0.028 M (t+1) 0.176 0.000
Baseline Coral Mortality C (t+1) 0.302 -1.822
-40% SD 0.3 M (t+1) 0.155 0.000 C (t+1) 0.284 11.877
-20% SD 0.4 M (t+1) 0.155 0.000 C (t+1) 0.277 8.890
Base 0.5 M 0.155 C 0.254
+20% SD 0.6 M (t+1) 0.155 0.000 C (t+1) 0.254 -0.183
+40% SD 0.7 M (t+1) 0.155 0.000
Anchor Hit Probability C (t+1) 0.253 -0.518
Min (Base) 0.05 M 0.155 C 0.308
Adjusted 1 0.2875 M (t+1) 0.155 0.000 C (t+1) 0.299 -3.121
Adjusted 2 0.64375 M (t+1) 0.155 0.000 C (t+1) 0.281 -8.960
Adjusted 3 0.9109375 M (t+1) 0.155 0.000 C (t+1) 0.269 -12.678
Max 1 M (t+1) 0.155 0.000
Anchor Damage
C (t+1) 0.266 -13.562
Appendix 4. Programming scripts in MatLab language used for benthic composition simulation and sensitivity assessment
Script for differential equation of coral-algae interaction using ‘equationmumby.m’ file (Courtesy of Mumby, P.J. 2006):
function Y = equationmumby (A,T,mort,MtoC,CtoT,MtoT) % Definition: Function called 'Mumby equation' accepts inputs of: % - A (Abundance), % - T (Available space), % - mort (Mortality), % - MtoC (Macroalgae and coral interaction), % - CtoT (Coral occupying space), % - MtoC (Macroalgae occupying space), %% Differential equation for Macroalgae (1) Y(1) = A(1)*( (MtoC*A(2)) + (1-mort(1)) + (MtoT*T) ); % Definition: % Macroalgae abundance at (t) = Addition due to competition + Reduction due to grazing + Addition due to space colonization % Y(1) = Macroalgae abundance at t. % A(1) = Macroalgae abundance at t-1. % A(2) = Coral abundance at t-1 %% Differential equation for COral (1) Y(2) = A(2)*(CtoT*T + (1-mort(2)) - MtoC*A(1)); % Definition: % Macroalgae abundance at (t) = Addition due to competition + Reduction due to disturbance + Addition due to space colonization % Y(2) = Coral abundance at t. % A(1) = Macroalgae abundance at t-1. % A(2) = Coral abundance at t-1
Script for Projection 1 using ‘plot2monte_iter’ file (Anthony, K.R.N.; Taruc, S.A.K.):
% risk_anchor_hit = 0; % 0.5 % mortality_coral_anchor = 0; % 0.05 to 1 % macroalgae_coral_interaction = Please scroll and set in Set competition interaction parameter below %% Set starting composition Mzero = macroalgae_starting_composition; Czero = coral_starting_composition; A = [Mzero;Czero]; T = 1-sum(A); %% Set n of scenario for SC = 1:scenario %% Set n of year time = year * 2; for t= 1:time for I = 1:iteration % mortality_macroalgae_baseline = 0.18 + (rand*(0.3-0.18)); %~0.3 % mortality_coral_baseline = 0.012 + (rand * (0.028 - 0.012)); %~0.02 probability_macroalgae_occupying_space = 0.2 +(rand*(0.7-0.2)); %0.2 / 0.7 rate_macroalgae_occupying_space = 1; %0.0004; macroalgae_occupying_space = probability_macroalgae_occupying_space * rate_macroalgae_occupying_space; probability_coral_occupying_space = 0.07 + (rand*(1-0.07)); % 0.07 / 1 rate_coral_occupying_space = 1; %0.00018; coral_occupying_space = probability_coral_occupying_space * rate_coral_occupying_space; %% Set macroalgae baseline mortality mMb = mortality_macroalgae_baseline; %% Set macroalgae mortality due to grazing % if SC == 1 mMg = mortality_macroalgae_grazing; % elseif SC >=2 % mMg = mortality_macroalgae_grazing - (0.05 * SC); % end mMtot = mMb + mMg; %% Set coral basline mortality mCb = mortality_coral_baseline; %% Set coral mortality due to anchor damage %% For ONE anchor scenario
% risk_anchor_hit = 0; % ~0.5 % mortality_coral_anchor = 0; % 0.05 / 1 % pmCa = risk_anchor_hit * rand; % mCa = mortality_coral_anchor * rand; %% For 9 increasing anchor scenario % if SC == 1 % pmCa = 0.3; % elseif SC >= 2 % pmCa = 0.3 + ((0.6-0.3) * SC/scenario); % end % % if SC == 1 % mCa = 0.05; % elseif SC >=2 % mCa = 0.05 + ((1-0.05) * SC/scenario); % end %% For fix set of 1st 2nd and 3rd anchor scenario if SC == 1 pmCa = 0.3; elseif SC == 2 pmCa = 0.4; elseif SC == 3 pmCa = 0.5; end if SC == 1 mCa = 0.05; elseif SC == 2 mCa = 0.37; elseif SC == 3 mCa = 0.58; end mCtot = mCb + (pmCa * mCa); %% Set mortality vector mort = [mMtot; mCtot]; %% Set competition / interaction parameter % Macroalgae overgrowing coral MtoC = (0.83 * (exp(-0.0012* A(2,t))) * A(1,t)); % Macroalgae occupying free space MT = macroalgae_occupying_space; % Coral occupying free space CT = coral_occupying_space; % Competition effects: Coral to macroalgae if A(2,t) >= 0.5 MtoT = 0.75 * MT; else MtoT = MT; end % Competition effects: Macro algae to coral if A(1,t) < 0.6 CtoT = 0.5 * CT; else CtoT = CT;
end % Calculates Macroalgae / Coral Abundance at t+1, repeated it as much as iteration setting. A(:,t+1,I) = equationmumby(A(:,t),T(t),mort,MtoC,CtoT, MtoT); % Function - alculates the amount of turfs (T) based on the amount of corals and algae - total is M+C+T T(t+1) = 1-sum(A(:,t+1)); %% Summarize iteration data (MEAN AND STD) % Average meanA = mean(A,3); % Average each elements of A troughout iteration. % Standard Deviation stdevA = std(A,0,3); % Standard deviation each elements troughout iteration stdevMac = [ (meanA(1,:) + stdevA(1,:)) ; (meanA(1,:) - stdevA(1,:))]; stdevCor = [ (meanA(2,:) + stdevA(2,:)) ; (meanA(2,:) - stdevA(2,:))]; end % loop for iteration end % loop for time %% See multiple scenario values % Grazing Graz_Scen(SC)= [mMg]; % Anchoring Anc_Scen_Prob (SC) = [pmCa]; Anc_Scen_Mort (SC) = [mCa]; %% Set multi rows and colums if scenario == 1 rows = 1; columns = 1; elseif scenario == 3 rows = 3; columns = 3; elseif scenario >= 4 rows = sqrt(scenario); % So that rows and columns are relative to the scen. columns =sqrt(scenario); end %% Set subplot labelling subplot(rows,columns,SC); %% Generate Macroalgae and Coral plot plot(meanA(1,:),'og','MarkerEdgeColor',[0 0.4980 0], 'MarkerFaceColor',[0 0.4980 0], 'markersize', 5); hold on % to generate another plot on the same axes, without erasing the previous plot, until the hold off command is issued plot(meanA(2,:),'oy','MarkerEdgeColor',[0.6824 0.4667 0], 'MarkerFaceColor',[0.6824 0.4667 0], 'markersize', 5); % %% Smooth plot line (Linked to file: smoothLine.m) % For Macroalgae x = 1:time+1; ymac = meanA(1,:); [x,ymac] = smoothLine(x,ymac,10);
hold on; plot(x,ymac,'-g'); % For Coral x = 1:time+1; ycor = meanA(2,:); [x,ycor] = smoothLine(x,ycor,10); hold on; plot(x,ycor,'-m'); % Plot 95% confidence limits % For Macroalgae (STD+) x = 1:time+1; ysdM = stdevMac(1,:); [x,ysdM] = smoothLine(x,ysdM,10); hold on; plot(x,ysdM,'-b'); % For Macroalgae (STD-) x = 1:time+1; ysdM = stdevMac(2,:); [x,ysdM] = smoothLine(x,ysdM,10); hold on; plot(x,ysdM,'-b'); % For Coral (STD+) x = 1:time+1; ysdC = stdevCor(1,:); [x,ysdC] = smoothLine(x,ysdC,10); hold on; plot(x,ysdC,'-m'); % For Coral (STD-) x = 1:time+1; ysdC = stdevCor(2,:); [x,ysdC] = smoothLine(x,ysdC,10); hold on; plot(x,ysdC,'-m'); % %% Modify X and Y Axis xlabel ('TIME (YEARS)'); ylabel ('REL. ABUNDANCE (%)'); xlim ([2 time]); % Scale fit X axis label to designated range ylim ([0 1]); % Scale fit Y axis label to designated range set(gca,'XTick',(0 : 10 : time),... %Set label increments 'YTick', (0 : .1 : 1 ),... 'XTickLabel', {0: 5 : year},... %Set label scaling to manual by user 'YTickLabel', {0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100}); end % loop for scenario
Script for Projection 2 using ‘plot3contour’ file (Anthony, K.R.N.; Taruc, S.A.K.):
mCa = 0.58; elseif SC == 7 mMg = mortality_macroalgae_grazing - (0.05 * 3); pmCa = 0.3; mCa = 0.05; elseif SC == 8 mMg = mortality_macroalgae_grazing - (0.05 * 3); pmCa = 0.4; mCa = 0.37; elseif SC == 9 mMg = mortality_macroalgae_grazing - (0.05 * 3); pmCa = 0.5; mCa = 0.58; end mMtot = mMb + mMg; mCtot = mCb + (pmCa * mCa); %% Set mortality vector mort = [mMtot; mCtot]; %% Set competition / interaction parameter % Macroalgae overgrowing coral MtoC = (0.83 * (exp(-0.0012* A(2,t))) * A(1,t)); % Macroalgae occupying free space MT = macroalgae_occupying_space; % Coral occupying free space CT = coral_occupying_space; % Competition effects: Coral to macroalgae if A(2,t) >= 0.5 MtoT = 0.75 * MT; else MtoT = MT; end % Competition effects: Macro algae to coral if A(1,t) < 0.6 CtoT = 0.5 * CT; else CtoT = CT; end % Calculates Macroalgae / Coral Abundance at t+1, repeated it as much as iteration setting. A(:,t+1,I) = equationmumby(A(:,t),T(t),mort,MtoC,CtoT, MtoT); % Function - alculates the amount of turfs (T) based Zon the amount of corals and algae - total is M+C+T T(t+1,I) = 1-sum(A(:,t+1,I));
%% Prepare plot coordinate (KEN VERSION) if A(1,t+1,I) < 1/cell A(1,t+1,I) = 1/cell; end if A(1,t+1,I)> 1; A(1,t+1,I)= 1; end if A(2,t+1,I) < 1/cell A(2,t+1,I) = 1/cell; end if A(2,t+1,I)> 1; A(2,t+1,I)= 1; end M = round(A(1,t+1,I)*cell); C = round(A(2,t+1,I)*cell); Z(M,C) = Z(M,C)+1; end % loop for iteration end % loop for time %% COLOR CONTOUR PLOTTING %% Set multi rows and colums rows = sqrt(scenario); % So that rows and columns are relative to the scen. columns =sqrt(scenario); subplot(rows,columns,SC); pcolor(Z'); hold on x1 = [ 0.0, cell + cell/30, cell + cell/30 ]; y1 = [ cell + cell/30, 0.0, cell + cell/30 ]; fill ( x1, y1, 'w') hold on plot (x1,y1, 'w') %% Set subplot labelling xlabel ('FLESHY MACROALGAE (%)'); ylabel ('HARD CORAL (%)'); axis square set(gca,'XTick',(0 : cell/5 : cell),... %Set label increments 'YTick', (0 : cell/5 : cell),... 'XTickLabel', {0,20,40,60,80,100},... %Set label scaling to manual by user 'YTickLabel', {0,20,40,60,80,100}); %% See multiple scenario values % Grazing Scen_Graz(SC)= [mMg]; % Anchoring Scen_Anc_Prob(SC) = [pmCa]; Scen_Anc_Mort(SC) = [mCa]; end % loop for scenario
! Appendix 5. Survey questionnaire form in English and Bahasa !
THE UNIVERSITY OF QUEENSLAND Global Change Institute, Gehrmann Labs (Bld. 60) St. Lucia, Brisbane, QLD 4072, Australia Phone: +61 (21) 3365 9555, Fax: +61 (7) 3365 4755
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