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Biodiversity in two parts: environmental heterogeneity and the maintenance of diversity, and the prioritization
of diversity
by
Caroline Marie Tucker
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Ecology and Evolutionary Biology University of Toronto
Chapter 3 Fire variability, as well as frequency, can explain coexistence between seeder and resprouter life histories........................................................................................... 57
Chapter 5 Unifying measures of biodiversity: understanding when richness and phylogenetic diversity should be congruent........................................................................... 111
5.3.1 Conceptual underpinning of biodiversity measures............................................ 117
5.3.2 Exploring the correlation between metrics ......................................................... 118
5.3.3 Tree structure ...................................................................................................... 118
5.3.4 Spatial structure and abundance distribution ...................................................... 120
5.3.5 Species pool size ................................................................................................. 120
5.4 Conclusions: Securing the place for evolution and rarity in conserving biodiversity ..................................................................................................................... 121
Vepsalaininen 1981), protists (Gause 1934; Caceres 1997), as well as many others. As a result, it
is not surprising that diversity in many systems is dependent on the continued occurrence of
5
environmental variability. For this reason, understanding the mechanisms by which
environmental variability contributes to biodiversity is also valuable for management and
conservation activities.
Part 2: Using large-scale patterns of diversity to inform prioritization
Ecological dynamics are changing globally for a number of reasons. Climate is changing,
including increasing mean temperatures and decreasing precipitation and snowfall (IPCC 2007).
The amount of variability in climate conditions is also changing – the extremes of temperature
and precipitation values are increasing along with overall variation (Karl et al. 1995; Folland et
al. 2002). Changes in disturbance regimes accompany these climatic changes, for example
modifying the frequency, intensity, and extent of fire events (Gillet et al. 2004). Changes in
climate and disturbance regimes, combined with habitat loss and fragmentation have contributed
to a century of species extinctions (Groombridge 1992; Heywood & Watson 1995).
In response to the potential for extinctions, conservation activities include selecting vulnerable or
valuable regions for protection (Myers et al. 2000; Mittermeier & Cemex 2004), managing land
for values such as diversity maintenance, and restoring damaged sites (Hunter Jr 1990; White &
Walker 1997; Grumbine 2002). These activities tend to focus on diversity with a regional lens,
because changes in climate and human activities act at a large scale. In addition, there is a
recognition that “biodiversity” is similarly broad, and encompasses all forms of organismal
variety, from genetic variation to the differences in the richness of higher taxa, and diversity in
ecosystem structure and function in conservation activities (Wilson & Peter 1988). In any
geographical region of interest, spatial patterns of different forms of diversity vary. This makes it
difficult to capture all types of diversity in a single protected area, for example. Combined with
limited funds, this creates the need to prioritize regions and/or types of taxa, a problem described
as the agony of choice (Vane-Wright 1991). By focusing on multiple types of diversity in regions
of interest, researchers can gain important information about the processes at play and informs
prioritization of areas for reserve locations. As a result, the focus of prioritization is becoming
increasingly multidimensional with regards to optimal reserve selection and protection of
diversity (Faith 1992; Rodrigues et al. 2005; Forest et al. 2007; Huang et al. 2011; Tucker et al.
2012a).
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Chapter overviews
In this thesis, I will consider these two broad areas of ecological research, examining first the
mechanisms by which spatial and temporal heterogeneity promotes diversity maintenance in
communities and secondly how spatially variable patterns of biodiversity in biogeographical
regions inform conservation and management activities.
Part 1: The maintenance of diversity in local communities
1) Environmental Variability Counteracts Priority Effects to Facilitate Species Coexistence:
Evidence from Nectar Microbes.
In the first chapter, I explore whether variability in temperature through space and/or time affects
the assembly of floral microbial communities, and further whether it alters the contribution of
priority effects to community assembly. Priority effects have received the majority of attention as
a determinant of species diversity and identity in nectar microbial communities, but natural
communities of nectar yeast and bacteria also experience temperature variation over a wide
variety of scales. In this chapter, I examine the possibility that environmental heterogeneity may
alter the outcome of other mechanisms of diversity maintenance using experimental
manipulations and mathematical models.
2) Community-level Interactions Alter Species’ Responses to Climate Change.
The second chapter focuses on a different scale of temporal variability, the importance of intra-
seasonal partitioning by competing annual plants and the effect of increasing temperatures on
this. To minimize competitive interactions during their growing season, annual plants often
minimize temporal co-occurrence by differentially specializing on particular subsets of
temperature, precipitation and photoperiod conditions during the season. In annual plant
communities structured in this way, climate change may affect the temperature-sensitive timing
of reproduction, and the degree of competition between species in a community. This may have
important implications for studies of shifts in plant phenology in response to global climate
change, because it suggests a constraint–biotic interactions—rarely considered when using
phenological measures as indicators of changing climate.
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3) Fire Variability, as well as Frequency, can Explain Coexistence Between Seeder and
Resprouter Life Histories.
In the third chapter, I use theory and modelling to explore whether temporal variation in fire
occurrences can help to promote coexistence between two life histories of Mediterranean shrubs.
Evidence that such variability in fire events mediates a storage effect would have implications
for fire management plans and the question of whether maintaining natural variation in planned
burns is likely to be important for diversity maintenance.
Part 2: Using large-scale patterns of diversity to inform prioritization
4) Incorporating Geographical and Evolutionary Rarity into Conservation Prioritization.
A variety of mechanisms, including temporal and spatial variability in disturbance and climate,
have led to high levels of angiosperm diversity and endemism in Mediterranean ecosystems. As a
result, all Mediterranean ecosystems are declared biodiversity hotspots (Myers et al. 2000). For
example, in the Cape Floristic Region of South Africa, there are a number of international,
national, and provincially established protected areas that capture a high proportion of the
Proteaceae species in the region. However, other forms of diversity were not considered when
the initial reserves were established. In the fourth chapter, I examine how well existing reserve
networks capture phylogenetic diversity (PD) and biogeographically-weighted evolutionary
diversity (BED), as well as Proteaceae richness, and consider the implications for conservation in
the Cape Floristic Region.
5) Unifying measures of biodiversity: understanding when richness and phylogenetic diversity
should be congruent
Not surprisingly, spatial patterns of species richness often differ from spatial patterns of
evolutionary diversity or functional diversity, because ecological and evolutionary processes do
not occur evenly through space, and since ecological processes contribute differentially to
different types of diversity. In this chapter, I develop a predictive framework to help understand
the conditions under which we expect species richness and evolutionary history in communities
to be differentially or similarly distributed through space, using information about a region’s
evolutionary history and spatial structure.
8
Conclusions
Across these five chapters, common themes include the understanding the mechanisms behind
diversity maintenance in local communities, with a particular focus on environmental
heterogeneity, and the implications of this information for management and conservation
activities. I hope to show that environmental variability is important for ecological processes
such as coexistence because it is ubiquitous, it alters species demographic responses, and human
actions and changing climate are altering drivers of variability. In addition I look at large-scale
patterns of diversity, particularly contrasting patterns of species richness and evolutionary
history, to inform diversity prioritization and conservation. This multi-scale, multi-method
approach allows me to more completely explore these important questions in ecology.
9
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Chapter 1 Environmental Variability Counteracts Priority Effects to Facilitate
Species Coexistence: Evidence from Nectar Microbes
1 1
1.1 Abstract
The order of species arrival during community assembly can affect species coexistence, but the
strength of these effects, known as priority effects, is variable among species and across
ecosystems, and causes of this variation remain unclear. Here we show that environmental
variability can be one such cause. In experiments with nectar-inhabiting microorganisms that
disperse between flowers via pollinators, we manipulated spatial and temporal variability of
temperature and examined consequences for priority effects. If species arrived sequentially,
multiple species coexisted when temperature was variable, but not when it was constant.
Temperature variability prevented extinction of late-arriving species that would have been
excluded due to priority effects if temperature had been constant. In contrast, if species arrived
simultaneously, species coexisted under both variable and constant temperature. These results
suggest that understanding consequences of priority effects for species coexistence requires
consideration of how environmental variability alters the strength of priority effects.
1.2 Introduction
It is now widely recognized that variation in the order of species arrival among sites can drive
local communities to divergent successional trajectories, thereby affecting the coexistence of
species—the phenomenon known as priority effects (Sutherland 1974, 1990; Drake 1991; Chase
2003). However, studies of community assembly have yielded variable results as to the
importance of priority effects (Chase 2003) and identifying the causes of this variation remains
elusive. Although many potential causes have been considered (e.g. Chase 2003; Knowlton
2004; Fukami 2010), one likely cause, environmental variability, has rarely been investigated
despite the considerable interest it has long received as a factor affecting species coexistence
In the case of unstable equilibrium between the two species, the starting concentrations of the
resource and the inhibitor chemical determine whether the yeast and bacteria will coexist or not
(Figure 3A). The resource and inhibitor concentrations in turn depend in part on which species
21
arrives first (Figure 3A, arrows). This is similar to our results in Figure 1, which suggest that
priority effects determine community composition. Temperature variability should reduce
growth rates, and the rate of resource consumption and inhibitor production, promoting
coexistence of Gluconobacter and Metschnikowia (Figure 3B, green arrow). However, if these
two species respond differentially to temperature variability, as suggested by the results (Figure
2C), in which Gluconobacter was more tolerant to changes in temperature than Metschnikowia,
Gluconobacter should gain an advantage from temporal variability (Figure 3B, pink arrow). This
is one likely explanation for the finding (Figure 1) that Gluconobacter, but not Metschnikowia,
gains an advantage from temperature variability.
The finding that variability can impact priority effects emphasizes the need for research into the
underlying mechanisms of priority effects. Combining this knowledge with an understanding of
species’ tolerance of, and responses to, relevant environmental conditions will improve our
ability to predict how priority effects will change if the environment is variable. For example, if
priority effects depend on arrival timing in relation to the type of predators present (which
induces phenotypic changes), variation in predator type or activity through time could reduce the
advantage of arriving at a particular time and weaken priority effects (Hoverman & Relyea
2008). Alternately, in frequently disturbed systems, arrival order during favourable conditions
may be especially important (Palmer et al. 1996) .
Priority effects may have wider-ranging ecosystem-level consequences than just for the structure
of the assembling communities (Fukami et al. 2010). For example, we recently found that
Metschnikowia and Gluconobacter differ in their effects on plant-pollinator mutualism, likely
due to their contrasting effects on the chemical properties of nectar (Vannette et al. 2013). In
combination with the results from the present study, this finding suggests that priority effects in
nectar microbes and the modification of their strength by temperature variability may have
consequences for plant-pollinator interactions. More generally, our results suggest that
consideration of both natural levels of abiotic variability and patterns of propagule arrival is
necessary to understand the causes and consequences of community assembly.
22
1.6 Acknowledgements
We thank Breanna Allen, Melinda Belisle, Nicole Bradon, Daria Hekmat-Scafe, and Pat Seawell
for laboratory assistance. We also thank Marc Cadotte for comments on earlier versions of this
manuscript. The Department of Biology and the Terman Fellowship of Stanford University and
the National Science Foundation (award number: DEB1149600) funded this research.
23
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25
Figures
Figure 1-1. Temporal changes in mean species abundances (± standard errors, n=4
metacommunity replicates), averaged over the paired flowers for each metacommunity,
when species were introduced in different timings in a constant or variable environment.
Simultaneous introductions were carried out on day 0, sequential introductions on days 0 and 2.
Temperature was either held constant (a-c) or spatially and temporally variable (d-f). For results
for either spatially or temporally variable temperature, see Appendix 1-1.
26
Figure 1-2. Characterization of the common species Metschnikowia reukaufii and
Gluconobacter sp.
(a) Effect on mean nectar pH after 36 hours growth (± standard errors, n= 3), (b) Percent decline
in amino acid concentrations in nectar after 36 hours growth (± standard errors, n= 3), (c) Mean
abundance attained after four days of growth at different temperatures
that climate change-induced phenological shifts cannot be fully understood without accounting
for competition. Future studies need to experimentally manipulate the strength of intra- and
interspecific competitive interactions in plant communities and consider how these treatments
alter community responses to warming conditions.
2.5 Acknowledgements
Thanks to Art Weis and Kelly Carscadden for comments on an earlier version of this manuscript.
44
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48
Figures
Figure 2-1. Rate of allocation to reproduction (i.e 1/Di) for species 1-4. Species’ optimal
temperatures are 19, 20, 21, 22°C, respectively.
! "! #! $! %!
!&!!
!&!"
!&!#
!&!$
!&!%
!&!'
()*+),-./,)01234
5)+,26/7.89)0-::27-.82;0,-.)
Species 1Species 2Species 3Species 4
49
Figure 2-2. Randomly simulated temperatures for 1000 years, shaded area represents the
range of the possible values, the dashed line represents the mean temperature under a)
ambient conditions and b) warming conditions (+2°C).
50
Figure 2-3. Boxplots of Julian day of first flower over 1000 simulated years for four species.
Blue boxes represent ambient temperature conditions (either light blue for no competition
or dark blue for competition) and pink boxes represent warming (+2°C) conditions (light
pink, no competition or dark pink for competition).
Bradstock & Kenny 2003; Brooks et al. 2004; Pausas et al. 2004; Bowman et al. 2009).
59
Determining targets related to fire frequency, intensity, or season, for managed fire regimes in
Mediterranean ecosystems (Gill 1975) is the focus of much research (for example, Gill 1977;
Gill & Bradstock 1997; Richards et al. 1999; McCarthy et al. 2001). This research focuses on the
length of the time between fires (the inter-fire interval) and its relationship to important life-
history events among plant species including maturation, seed bank accumulation, and
senescence. In the case of Mediterranean shrub species, when fires burn too frequently species
may not have time to mature and produce seed, leading to population extirpation (Gill & Groves
1981; Gill & Bradstock 1995; Pausas 2001). When fires occur too infrequently, seed banks of
species that require fire-related cues for germination may be lost (Pausas 2001), thereby limiting
population recruitment.
Variation in the inter-fire interval may also be important in determining the outcome of fire
regimes, but the effect of variation is much less understood (Cary & Morrison 1995; Bradstock et
al. 1996). Work from fire-prone heathlands in Australia suggests that invariant timing of fire
events can be harmful to overall diversity (Keith & Bradstock 1994; Morrison et al. 1995; but
see Wittkuhn et al. 2011), possibly because some mechanisms of coexistence rely on fluctuations
in fire occurrence. However, theoretical work explicitly considering the mechanisms that relate
variation in the fire interval and species diversity is still generally lacking, making it difficult to
determine how much variation should be incorporated into a fire regime to maintain diversity in
an ecosystem (Gill & McCarthy 1998).
The characteristics of present-day fire regimes in Mediterranean ecosystems are important
because species’ life histories are adaptations to historic fire regimes, the result of which is that
the timing and nature of fires determine species’ presences and abundances (Bond et al. 1990;
Bond & van Wilgen 1996; Bond & Midgley 2003; Bond & Keeley 2005). Across different
Mediterranean shrublands, convergent evolution has repeatedly produced woody, evergreen,
sclerophyllous shrub species (Mooney & Dunn 1970). Crown-fires in these shrublands consume
the majority of above-ground biomass, leading to a well-documented trade-off in post-fire
regeneration strategies among shrub species: species either rely on fire-stimulated germination or
post-fire resprouting behaviour (Mooney & Dunn 1970; Bond & Midgley 2003).
60
We hypothesize that variability in the length of the inter-fire interval may be one mechanism by
which fire promotes coexistence among species. In particular, we provide an example of a
possible mechanism – a temporal storage effect – through which variability in the length of the
inter-fire interval could promote species coexistence between an obligate resprouter and obligate
seeder. The storage effect (Chesson & Huntley 1997; Chesson 2000; Adler & Drake 2008) is a
form of temporal partitioning in which competing species show differential recruitment in
response to environmental conditions. There are several conditions required for the storage effect
to act (Chesson & Huntley 1997): 1) species must have differential responses to environmental
conditions including disturbances; 2) there must be covariance between competition and these
environmental conditions, which occurs when one species is favoured over another by particular
conditions; and 3) there must be a mechanism for buffered population growth, allowing species
to persist through unfavourable conditions when interspecific competition is high, by “storing”
fitness from past times when conditions were more favourable. Storage could be a result of long-
lived life history stages such as seed banks or long-lived perennials (Chesson 2000). Although
the focus is usually on fluctuations in the abiotic environment, variability in fire events can also
create a storage effect (e.g.Miller & Chesson 2009 ). Given that shrub species in Mediterranean
systems fulfil the requirements for the storage effect, we develop a model to show that varying
the length of the inter-fire interval could alter the effect of fire regimes on seeder and resprouter
species in Mediterranean ecosystems.
3.3 Materials and methods
3.3.1 Lottery model
We model the storage effect using a simple version of the lottery model (Chesson & Warner
1981). A lottery model considers the division of available sites among species as being in
proportion to their representation in the available pool of recruits (Sale 1977, 1978). Such a
model is useful for space-limited systems, where there are more recruits than there are available
sites for establishment, or to represent stochastic recruitment in systems where species appear
similar in form and function (Hubbell 2001). A simple formulation of the lottery model
represents the proportion of sites occupied by species i as:
61
€
Pi(t +1) =Bi(t)Pi(t)
Bi(t)Pi(t)j=1
jmax
∑ , (1)
where ßi(t) represents the net per capita reproduction species i at time (t) and P(t) represents the
proportion of sites occupied by species i at time (t). Evidence from similar models developed for
both plants and animals, suggest that in general, when there are overlapping generations and
environmental variation, an inferior and superior competitor can coexist (Fagerstrom & Agren
1979; Chesson & Warner 1981).
The lottery model has been used to represent recruitment in Mediterranean shrublands, where
species are often very similar in structure, phenology, and other ecological characteristics usually
associated with niche differentiation (Cowling 1987; Lamont et al. 1991; Bond et al. 1992;
Laurie & Cowling 1994), but given the apparent lack of niches, diversity is perplexingly high.
We are considering Mediterranean systems with obligate resprouters and obligate seeders, which
differ from the traditional formulation of the lottery model. Recruitment and mortality are
strongly tied to fire events, particularly for fire obligate seeders, where all recruitment and total
mortality can be assumed to occur following each fire (Keeley 1986). Because the recruitment of
seeds from obligate seeders occurs immediately following the most recent fire event, and seeder
and resprouter recruitment functions represent a build-up of seeds that depend on the length of
the interval between fires, we treat each time step in the model as a fire event with some
associated inter-fire interval length (f). Each step then ends with a fire leading to recruitment of
the next generation of individuals. The recruitment function represents the number of seeds
available for recruitment at a given inter-fire interval: this is ultimately a function of both species
longevity and seed bank longevity, since it represents the accumulation of the year’s seed
production and all surviving seeds in the seed bank. For obligate seeder species, recruitment
comes from the seed bank formed during the interval between fires. For the purposes of our
model, we will assume that this is a soil-based seed bank, which means that seeds can survive in
the seed bank after the adult plant has died. For the obligate resprouter species, the recruitment
function represents seed production during a given year only: these species do not form seed
banks and seeds tend to be short-lived, and disperse away from the site (Keeley 1986). For
simplicity, we consider sites to be saturated immediately following fire events, so that
62
recruitment of both resprouting species (from seeds produced during the previous year) and
seeding species (from the seed bank accumulated over the time between fires) only occurs during
the post-fire period when mortality makes sites available. Here seeds in the seed bank are
considered to be in the soil and so survive past the death of the plant. As resprouters survive fire
events, we treat this as a situation when one species (resprouters) have overlapping generations,
while the other (seeders) does not.
This model shows the proportion of sites occupied by species i with adult population size Ni(f) at
a given fire (f):
€
Pi(t +1) = (1−δ i( f ))Pi( f ) + δ j ( f ))Pj ( f )j=1∑⎡
⎣ ⎢ ⎢
⎤
⎦ ⎥ ⎥
βi( f )Pi( f )( β j ( f )Pj ( f ))∑
⎡
⎣ ⎢ ⎢
⎤
⎦ ⎥ ⎥ , (2)
where ßi(f) represents the seed bank accumulated by species i over the current interval and P(f)
represents the proportion of sites occupied by species i at the end of the fire interval. Henceforth,
we will use the subscript Sp to represent the resprouter species, and the subscript Se to represent
the seeder species. δ represents mortality caused by a fire event: for the resprouter species this is
can take a range of values between 0 and 1, ranging from no, to total, mortality of adult
resprouters. This value can be a function of the inter-fire interval, or may be represented as a
constant value. For the seeder species, δ is set to 1, representing the total mortality of seeders
following a fire event.
For the seeder species, ßSe(f) represents the seed bank accumulated during the inter-fire period,
which we represent as a Gaussian function of the length of the inter-fire interval. The seeder
species is most common when fire intervals are intermediate, because recruitment is low when
fire intervals are too short to allow time for establishment and reproduction, or too long, causing
seed bank exhaustion (Keeley 1986; Bond & van Wilgen 1996; Schwilk et al. 1997; Pausas
2001).
€
βSe ( f ) = c⋅ e−( f −µ )2
2σ 2 , (3)
63
where µ represents the length of the inter-fire period giving the seeder the highest number of
seeds, f is the length of the inter-fire period, σ corresponds to the width of the function, and c is a
constant representing the maximum seed production. σ represents the degree of tolerance to the
length of the inter-fire interval a species’ recruitment shows – larger values would represent
longer lived seeder species and/or longer lasting seed banks. This allows the model to be
extended to species with differing lifespans or seed bank longevity.
For the obligate resprouter, no seed bank is formed, and recruitment is assumed to include only
those seeds produced in the last year of the inter-fire interval. This number of seeds is assumed to
be a linear function of the length of the inter-fire interval, because resprouter size and seed
production are correlated (Higgins et al. 2008). (Although resprouting ability may be reduced as
the inter-fire intervals decrease (Bond & Midgley 2001)).
βSp(f) = f * a, (4)
where the length of the inter-fire period (f) and a constant level of seed production (a) determine
resprouter seed production. The assumption is that the resprouting species live at least as long as
the longest inter-fire interval (40 years).
3.3.2 A disturbance-based storage effect
The necessary components of the storage effect have been identified as (Chesson 2003):
differences in species’ responses between environments; storage (persistence) through
unfavourable periods; and covariation between environment and competition. We develop a
version of the storage model to account for differences in seeder and resprouter ecology, in
particular, differences in their responses to the length of the inter-fire interval. Variation in
environment is represented here by variability in the timing of fire events, and accordingly in the
length of the inter-fire interval – that is, the number of years between fires. We model this as a
normally distributed random variable:
f = N(mean, variation). (5)
Differences in seeder and resprouter responses to the length of the inter-fire interval are
driven by differences in their life histories. In Mediterranean ecosystems, resprouters are often
64
observed to have lower seed recruitment than seeder species, and being outcompeted by seeders
(Keeley 1986; Burgman & Lamont 1992; Pausas 2001). While there is variation among
Mediterranean ecosystems in seeder and resprouter life histories and in fire regimes, we follow a
general model where seeders dominate at intermediate inter-fire intervals and resprouter at low
and high inter-fire intervals (Burgman & Lamont 1992; Pausas 2001).
Finally, both the seeder and resprouter species can buffer their fitness, either through fire
tolerance and survival of resprouters, or seed bank formation by seeders. Competition among
seeder and resprouter species occurs primarily during the recruitment of seedlings (Yeaton &
Bond 1991; Laurie & Cowling 1994), and once established, adult resprouters may persist for
multiple fire cycles. Hence resprouters that establish during favourable periods can maintain their
populations by persisting through unfavourable periods. Seeds produced by seeder species are
either stored in serotinous seed banks or, particularly in the South Africa and Australia, cached
underground by ants. Comprehensive data on the longevity of these buried seeds is lacking, but
at least some buried seeds from seeder species may remain viable for longer periods of time and
this confers some buffering of fitness (Holmes & Cowling 1997; Auld et al. 2000; Holmes &
Newton 2004; Willis & Read 2007). As stated earlier, we assume soil-based seed banks in this
analysis.
For simplicity’s sake, we model a generic obligate resprouter and obligate seeder species with a
soil-based seed bank in an ecosystem with similar fire regimes as those found in the Cape
Floristic Region of South Africa (CapeNature & SANBI 2008). Although this is necessarily a
simplification of the actual relationship between seeders and resprouters and fire (and it ignores
species-specific differences), it is sufficient to highlight how fluctuations in fire occurrences
could promote long-term persistence of these life histories.
3.3.3 Numerical simulations
We chose to simulate a co-occurring obligate seeder and obligate resprouter species in a
system where the mean length of the inter-fire interval ranged between 0–40 years and varied by
between 0–15 years (see Appendix 3-1 in Supporting Information for R code). This represents a
realistic range of values for the Cape Floristic Region of South Africa (CapeNature & SANBI
2008), but the specific values are less important than the necessity that the requirements of the
65
storage effect be met, and any Mediterranean ecosystem could have been modelled provided the
life histories of species and their relationship to historical fire regimes were understood. The total
number of available sites in a community was set to 1000, and initial starting populations were
set in accordance to invasion analysis: i.e. the invading species had a starting population of 1
individual, and the resident a starting population of 999. The invader was considered the species
with the fewer seeds available for recruitment for each length of the inter-fire interval, when
variability in length of the inter-fire interval is zero, given the parameter values used for a and c
(see below).
We repeated the simulations 1000 times at each combination of inter-fire interval (for lengths
between 0 and 40 years) and variation (from 0 to 15 years), a total of 600,000 simulations. It
should be noted that regimes with short periods between fires and high variability are unlikely to
be observed in nature. For each simulation, we recorded the proportion of the community
occupied by resprouters and seeders after 1000 time steps. We calculated the probability of
coexistence at each combination of inter-fire length and variability as the number of runs per
1000 in which seeders and resprouters persisted together after 1000 time steps. Persistence was
defined as occupying at least 1 site in the community after the 1000 time steps. Throughout the
results, where we refer to “coexistence” we imply this definition of long-term persistence, rather
than analytical coexistence.
3.3.4 Parameter value selection
The numbers of seeds available for recruitment at time each fire event were set to c = 8000
(Equation 3) for seeders and a = 50 (Equation 4) for resprouters. c is equivalent to the
accumulated seed bank available for recruitment for seeders; this seed bank is modelled to be
largest when the fire-return interval (µ) is 20 years (Figure 3-1), which is equivalent to saying
that the combination of seeder life span and seed bank longevity result in the greatest number of
seeds at 20 years. We examine the effects of changing the seeder seed recruitment function, to
account for different species lifespans or seed bank longevity: however, the results of our
simulation do not fundamentally change (Figure S1). Resprouters are likely to have far fewer
seeds available for recruitment (Bond & Midgley 2001) (one record from the CFR found that
resprouters produced between 9.7% to 88.0% of the number of cones produced by seeder
66
species (Higgins et al. 2008)), and resprouters do not form a lasting seed bank (Keeley 1986).
Given these parameter values, resprouters are invaders for sites with fire frequencies less than
~33 years. Resprouters were invaders for sites with frequencies between those values. Resprouter
fire mortality (δSp) was set to 0.25 (i.e. 75% survival). We examine the sensitivity of our model
to the difference in the number of seeds available for recruitment between seeders and
resprouters (see below).
3.3.5 Sensitivity of the model to parameter values
One essential question is how important is the difference in the seeds available for recruitment
for seeders and resprouters. We examined how altering the number of seeder and resprouter
seeds available changed the likelihood of coexistence at different inter-fire intervals. We expect
that there should be a relationship between the amount of variation that allows coexistence, the
differences in seeds available for recruitment, and the amount of storage that the species have
(Chesson 2000). To explore this relationship, we simulated all combinations of parameters of
length of inter-fire period variation (f ∈ [0,40]), buffering (δSp ∈ [0.1,0.9]), and the difference in
the number of seeds available for recruitment between seeder and resprouter (c ∈ [3000,10000],
a ∈ [10,300]) and recorded the corresponding minimum variation in length of inter-fire period
required for coexistence at each combination of these.
An important point is that the storage effect should not function in the absence of some form of
storage or buffering that allows species to maintain their populations through unfavourably short
or long inter-fire intervals. For example, if the resprouter species are no longer able to survive
fire events, variability in the length of the inter-fire period should not promote coexistence of the
seeder and resprouter species. We removed buffering of resprouter fitness by setting δSp to 0, so
that no adult resprouters survive fire events. We then repeated the simulations 1000 times at each
combination of length of inter-fire period (for fire frequencies between 0 and 40 years) and
variation (for values ranging from 0 to 15 years). For each simulation, we recorded the
proportion of the community occupied by resprouters and seeders after 1000 time steps.
67
3.4 Results
3.4.1 Coexistence with non-variable fire return:
When there is no variability in the length of the interval between fires, there is a small range of
fire frequencies where the seeder and resprouter species are expected persist (Figure 3-2A,
greyed regions 2, 4). These regions reflect the length of the inter-fire period that minimize the
difference in recruitment between seeders and resprouters and allow persistence under the lottery
model. However, for the majority of fire frequencies only one of the two species is predicted to
persist when variability is set to 0 (Figure 3-2B, regions 1, 3, 5).
3.4.2 Coexistence with variable fire return
When variability in the length of the inter-fire period is incorporated, persistence of seeders and
resprouters can occur in regions where exclusion occurred in the absence of variation (Figure 3-
2B, 1–5). For example, in region 3 (Figure 3-2B–3) where the seeder species excluded the
resprouter species when variability is zero, increased variation means that the resprouter species
periodically has high recruitment, which, combined with buffered population growth, allows its
population to coexist with the seeder species. In contrast, higher variability can decrease the
ability of the seeder to persist (region 4), by increasing the number of unfavourably long inter-
fire intervals. Ultimately, the likelihood that the seeder and resprouter species coexist is
determined by the interaction between the length of the inter-fire interval (and implicitly, its
relationship with the number of seeder and resprouter seeds available for recruitment) and the
variability in this length, which interacts with buffering ability (Figure 3-3). When variation in
the length of inter-fire period is 0 in this plot, the red regions of coexistence are equivalent to the
grey areas in Figure 3-2a. There is a high probability of coexistence of the seeder and resprouter
species across the widest range of fire frequencies when the variability is ~8.5 years. In fact,
when variation is this high, the resprouter and seeder species coexist across nearly all inter-fire
intervals below 30 years.
When adult mortality of the resprouter species was set to 1, so that there was no storage of fitness
between generations for that species, variability in length of inter-fire period did not increase the
region over which resprouters and seeders could coexist (Figure 3-4).
68
3.4.3 Influence of parameter values on coexistence
The values of a and c that we chose appear to be less important to the outcome of our model than
the overall difference in the number of seeds available for recruitment between seeders and
sprouters. Figure 3-5 suggests that there is a relationship between the size of this difference in
seed number and the mortality resprouters experience during fire events, and the corresponding
amount of variation necessary for coexistence. When seeders have more seeds available for
recruitment, greater variability in the inter-fire interval is necessary for the resprouters to coexist.
When resprouter mortality is low, resprouters are able to maintain sites and more effectively
compete, so less variability is required for their coexistence with seeders. When resprouter
mortality is higher, greater variability is required for coexistence. The initial choice of parameter
values (a and c) for the resprouter and seeder species is not as important as having the essential
components of the storage effect present, i.e. variation in length of inter-fire period and buffering
of fitness.
3.5 Discussion
We found that variability in the length of time between fires can greatly increase the likelihood
of coexistence between species with obligate seeder and obligate resprouter life histories. This
trade-off (between seeder and resprouter life histories) is common in Mediterranean ecosystems.
In many ecosystems, recurrent fires are necessary to maintain community composition and
diversity (Cowling & Campbell 1980; Keeley 1986), in part because disturbance creates
opportunities for temporal niche differentiation (Bonis et al. 1995; Buckling et al. 2000). In such
situations, invariant fire return intervals would be likely to reduce diversity by removing
temporal niches for differentiation among species. However, achieving a balance between risk
reduction through fire management and diversity maintenance may be difficult, especially when
it is unclear which aspects of natural fire regimes must be retained for diversity maintenance. For
example, maintaining an appropriate mean return interval between fires but neglecting variability
in the return interval could lead to a reduction in diversity, if coexistence depends on temporal
fluctuations in fire events.
Historically, fires regimes were both spatially and temporally variable. Fire regimes in
Mediterranean ecosystems were initiated by lightning strikes (prior to human habitation) and
69
initiation was probabilistic, dependent on the combination of suitable weather and fuel conditions
in addition to the initial spark (Keeley et al. 1989; Keeley & Fotheringham 2003). In fact, most
aspects of fires were likely much more variable in the past (Keeley et al. 2005). Plant species in
Mediterranean ecosystems show clear adaptations that allow post-fire regeneration (seed banks,
resprouting ability), and can provide a buffering mechanism against some variability in fire
return intervals. It may be that managed fire regimes should account for the historical variability
in fire return in a region and the life history traits of species present that have evolved in
response to it. Although there have been few empirical studies looking at the relationship
between variability in the length of the inter-fire interval and diversity, Morrison et al. (1995)
found that variability in the length of the inter-fire interval is associated with increased diversity
of both fire sensitive and fire tolerant species, similar to the expectation of a storage effect.
Although we did not explore the effect of variability over multiple spatial scales, both temporal
variability and spatial variability in the length of the inter-fire period could be important in these
regions. The combination of both temporal rescue of populations via storage, and spatial rescue
via seed dispersal could concurrently act to maintain diversity in fire prone ecosystems (Miller &
Chesson 2009). While our results show that the coexistence of resprouter and seeder species may
even be possible in the absence of variability, in situations with multiple (>2) species, variability
may be an important coexistence mechanism.
The exact shape of the relationship between resprouter and seeder seed recruitment, and the
length of fire return interval in different Mediterranean regions will differ from our model
(Bellingham & Sparrow 2004), since different fire regimes have different selective effects on the
relationship between seeder or resprouter fitness and the fire return interval (for example, in the
Californian chaparral some obligate seeders may reestablish even after 100+ years between fires
(Keeley 1986)). However, the seed recruitment curves implicitly encompass a number of life
history traits, including seed bank longevity and species lifespan, making them flexible across
different species and ecosystems where these traits may vary in complex ways. Our model is also
flexible in terms of parameter values (degree of buffering, shape of the relationship between
seeder and resprouter fitness and fire), and only requires that the components of the storage
effect be present. It is of particular importance that buffering must be present, since systems
where species show little ability to tolerate unfavourable conditions will do poorly when
70
variation is increased. Further, the storage effect, modelled here to explain a two-species
interaction, could explain the coexistence of multiple seeder and resprouter species, if these
species are differentiated along additional axes relating to fire conditions (intensity) and/or
specialized within the seeder or resprouter response, or even partitioned along other aspects of
the biotic and abiotic environment.
3.5.1 Management implications
For high-diversity Mediterranean regions, the specific mechanisms by which disturbance can
contribute to and promote coexistence have important management implications. In most fire-
prone systems, species have evolved to historical fire regimes and it is highly probable that
historical fire regimes were variable. In these systems, even if there is an absence of species-
specific information about fire responses, it should be assumed that fire is an important aspect of
species coexistence. In these cases, we argue, management programs need to consider the
variability, as well as frequency, in fire events. The storage effect may be a fundamentally
important coexistence mechanism in these systems, and management activities that remove
variability in fire occurrence could ultimately result in population declines and extinctions. Thus
it is increasingly important to develop mechanistic models of the relationship between diversity
maintenance and fire in these species-rich, fire-prone systems. However, the value of variability
in managed fire regimes must be balanced against the higher fuel loads that result from longer
than average inter-fire intervals, and the increased risk of large, high-intensity fires which put
human communities and property at risk. It will remain important to optimize risk management
against the ecological gains of incorporating variability into fire regimes in Mediterranean
ecosystems.
71
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76
Figures
Figure 3-1. Conceptual model showing the number of seeds available for recruitment (β i,
Equation 2) as a function of the length of the inter-fire interval (f) for a generic seeder (red)
and resprouter (black) species. c=8000 and a=50. See Materials and methods for further
details on parameterization.
77
Figure 3-2. A. Mean inter-fire intervals for which coexistence or exclusion between seeder
and resprouter species is expected, when the length of the inter-fire interval is invariant.
c=8000 and a=50. 5 regions of inter-fire intervals are highlighted; grey regions indicate
where long-term persistence is predicted.
B. 1–5: Relationship between variability in the length of the inter-fire interval and
coexistence for the five regions from Figure 2A. Points represent the average proportion of
sites in a community occupied by the seeder (red) and resprouter (black) species at a site,
calculated from 1000 replications for each value of fire variability. Error bars represent the
standard deviation.
78
79
Figure 3-3. The probability of coexistence between the seeder and resprouter species, as a
function of both the length of the inter-fire interval and variation in the fire return interval.
Cells are color-coded in a gradient from blue to red, representing the probability of coexistence
(from 0 to 1) occurring at a given combination of fire return interval and variation. c = 8000 and
a = 50; see Materials and methods for details on the calculation.
80
Figure 3-4. The probability of coexistence between seeders and resprouters when there is
no storage for the resprouter species (i.e. δ = 1), as a function of the length of the inter-fire
interval and variation in the length of the inter-fire interval.
Cells are color-coded in a gradient from blue to red, representing the probability of coexistence
(from 0 to 1) occurring at a given combination of fire return interval and variation.
81
Figure 3-5. The interaction between the number of seeds available for recruitment and
resprouter mortality (δ), and their effect on the minimum amount of variation in the inter-
fire interval necessary for coexistence.
Recruitment is calculated as a function of the length of the inter-fire interval, as in equations (3)
and (4), with f ∈ [0,40], buffering (p2 ∈ [0.1,0.9]), and c ∈ [3000,10000], a ∈ [10,300].
82
Appendices
Appendix 3-1. R code for the disturbance-based storage model
###Basic model, proportion occupied vs inter-fire interval length####
a=50
c=8000
f=seq(from=1,to=40) # range of inter-fire interval lengths
intervals=1000 #number of fire intervals
y=0.9 #germination rate
mat=matrix(NA,ncol=4,nrow=1) #holds output information
To calculate biogeographically weighted evolutionary distinctiveness of a species, one can
partition phylogenetic branch lengths by descendant species’ abundances or by the number of
populations or in our case, the number of occupied grid cells. We calculate BED as
BED(T ,i) = λenee∈q(T ,i,r )
∑ (2)
where ne is the number of grid cells in which a species is present, below branch e, in the set
q(T,i,r), which includes the branches connecting species i to the root r of tree T. (Cadotte and
Davies (2010) provide a detailed description and graphical representation of how this metric
partitions internal branches). The metric BEDT is then the summation of the BED values of all
species in a site, thus sites with species that are narrowly distributed will have higher BEDT than
sites with widely distributed species.
BED shares with an alternative measure, phylogenetic endemism (Rosauer et al. 2009b), the
partitioning of internal branches by range size. However, phylogenetic endemism weights
internal branch lengths by the union of subtending ranges and splits evolutionary distinctiveness
among populations of different species if their ranges overlap. As a consequence, the
phylogenetic endemism metric does not sum to PD as BED does (e.g. via BEDT), which makes it
difficult to compare PE and PD. We also calculated species richness weighted by range size
using the BSR metric, which is equivalent to weighted endemism (Williams et al. 1994; Linder
1998; Rosauer et al. 2009b) :
€
BSR =1nii
∑S∑ (3)
92
where i is the species, s is the number of species in the cell, and n is range size (in our case, the
number of cells occupied).
4.3.6 Metrics with genera-level tree
For all metrics (species richness, PD, BEDT, and BSR), we repeated the calculations at the level
of genus rather than species with the genera-level tree and site data aggregated across species at
each site. We examined the concordance among species richness, PD, BSR and BEDT with
Pearson correlation coefficients. We did not correct for spatial autocorrelation directly because
autocorrelation tends to strengthen the perceived relation between spatially structured variables,
whereas we were interested in departures between the various metrics; hence, our comparisons
are conservative.
Maps representing the distribution of species richness, PD, and BEDT values (all standardized at
mean[SD]=0 [1]) across the Cape Floristic Region were constructed in arcEditor (ESRI,
Redlands, California) with 8 quantile intervals from blue to red. The mapped values were derived
from the most conservative tree (low), where species lacking sequence data were included at low
evolutionary diversity position.
4.3.7 Reserve representation indices
We used a weighted index to compare how the prioritization of areas outside the current reserve
system differed when applying phylogenetic and species-diversity metrics. We weighted species
as an inverse function of the number of sites in which they were present were contained in
reserves. For species richness, this calculation was similar to BSR, except that the number of
sites in reserves was used instead of range size. We used a modification of the BEDT metric to
calculate the representation of phylogenetic richness in reserves. In this case, however, we
partitioned phylogenetic branch lengths by the number of sites a species occupied in the reserve
system, rather than by range size. Results were mapped for the Cape Floristic Region, using 8
quantile intervals from blue to red. The mapped values illustrated standardized (mean 0, SD 1)
values derived from the most conservative (low) tree, in which species lacking sequence data
included at low evolutionary diversity position.
93
To explore further the relation between evolutionary distinctiveness and range size, we plotted
one against the other (including only species for which sequence data were available)
4.4 Results
The cells analyzed in the Cape Floristic Region contained between 2 and 26 Proteaceae species
(mean [SD]=5.28 species [3.83]). Pearson’s correlation coefficients among the 3 metrics were
calculated separately for metrics from the no added species, lowest and highest evolutionary
distinctiveness and genus-level trees (Fig. 4-1). For the 4 trees, PD was strongly correlated with
species richness (r = 0.68-0.85), whereas the correlation between BEDT and species richness was
much weaker (r = 0.18-0.56). PD was similarly correlated with BEDT (r = 0.26-0.53). All
correlations were positive, but BEDT was distinct from the other metrics (Fig. 4-1).
The positioning of species missing phylogenetic information in the trees had little effect on the
relation between metrics, regardless of whether these species were added in positions of low or
high evolutionary distinctiveness (Fig. 4-1). However, metrics calculated with a tree resolved to
the level of genus resulted in different patterns of correlation than the equivalent values
calculated from the species-level tree. There was a stronger correlation between BEDT and PD (r
= 0.45) and BEDT and species richness (r = 0.37) and a weaker correlation between species
richness and PD (r = 0.67) for genus-level than for species-level trees.
To determine whether the distribution of BEDT values resulted only from the inclusion of
information on range size, independent of phylogeny, we compared BEDT with our metric BSR,
which incorporates both species richness and range size, but not phylogenetic branch lengths.
The correlation between the BSR and BEDT metrics was weak (r = 0.21-0.22, depending on how
species with missing sequence data were incorporated) (Fig. 4-1), which indicated BEDT’s
differential performance was the result of a nonrandom distribution of range size in relative to
evolutionary history. In addition, BSR was not correlated with species richness (r = -0.21).
Species richness and PD had similar spatial distributions. However, PD was less concentrated
than species richness such that many regions had moderate levels of phylogenetic diversity
whereas species richness tended to have high values in relatively fewer locations. The map of
BEDT showed far fewer areas of high diversity than the maps of either species richness or PD
94
and highlighted sites in the south and southwest as having higher diversity and thus higher
priority for conservation.
Species with high evolutionary distinctiveness tended to have smaller ranges, whereas a subset of
species with low evolutionary distinctiveness had very large ranges (Fig. 4-3b). However,
species’ range sizes were not correlated with relatedness (Blomberg’s Κ= 0.18, p>0.05), which
indicated close relatives did not tend to have similar range sizes.
The representation of species and phylogenetic diversity in the current reserve system differed
greatly. The correlation between the species and phylogenetic representation indices was not
significant (r = 0.17). Furthermore, the spatial distributions of these representation indices across
the region were strikingly different (Appendix 4-3). Few areas had a high concentration of
underrepresented species, and those that did were primarily near the southern border of the
region. There were numerous areas with high levels of underrepresented phylogenetic richness
near both the south and northeastern edges of the region.
4.5 Discussion
It is necessary to determine whether and when phylogenetic and species diversity represent
complementary or comparable information. We found that alternative metrics of diversity
emphasize different areas within the Cape Floristic Region of having high diversity, and that this
disconnect may provide additional information for conservation planning, such as in the selection
of areas for augmenting an existing reserve network. For example, the spatial distribution of our
multivariate metric, biogeographically weighted evolutionary distinctiveness (BEDT), which
incorporated both evolutionary distinctiveness and regional species rarity, departed from the
distribution of more traditional diversity metrics and highlighted additional areas (e.g., areas in
the southern edge of the region) that might be considered for protection, because they represent
areas with species that are both relatively distinct and rare compared the regional species pool.
Metrics accounting for evolutionary history may help identify sites overlooked by diversity
metrics that focus on species richness (Polasky et al. 2001; Forest et al. 2007). Divergence
between phylogenetic diversity (measured using PD) and species richness may be sizeable in
only particular cases, for example, when evolutionarily distinct species have narrow geographic
95
distributions and occur in species-poor sites (Rodrigues et al. 2005). Here, we found that
phylogenetic diversity and species richness are, unsurprisingly, strongly correlated in the Cape
Floristic Region. Nonetheless, their mapped values indicated species richness was concentrated
in fewer sites, which underrepresented the more spatially extensive distribution of phylogenetic
diversity, particularly in the eastern Cape Floristic Region. Significant differences among the
metrics were evident in the Cape Floristic Region even in the absence of a highly unbalanced
phylogeny or structured species distribution. Relatively few Proteaceae in the region have very
large or very small range sizes, and the phylogenetic tree we used was not greatly unbalanced (Ic
= 0.067, which is not significantly different than an equal-rates Markov null model [Ic = 0]. We
therefore suggest that even modest departures in tree shape or structuring of the distribution of
species ranges may therefore result in realized differences among metrics. In particular,
phylogenetic metrics and species richness can be decoupled when the focus is on sites within an
extensive region because sites may contain greatly different subsets of the species pool, which
would alter the shape of the tree (Cadotte & Davies 2010).
Forest et al. (2007) examined phylogenetic diversity for the entire Cape Floristic flora at a
coarser spatial grain than our analysis. Our results for patterns of divergence between species
richness and evolutionary diversity were similar to theirs. However, because Forest et al.
examined genus-level rather than species-level richness, it is possible their estimates of
phylogenetic diversity are biased downward because branching relations among species within
genera were not included. When we compared the relation between phylogenetic diversity and
species richness at the genus level, we observed an approximately 10% drop in correlation
strength compared with the species-level analyses, which may suggest the coarser taxonomic
resolution overestimated the mismatch between phylogenetic diversity and species richness.
Despite this difference in spatial and taxonomic resolutions between studies, our results were
generally congruent: phylogenetic diversity and species richness covaried closely when
considered in the absence of spatial context, but departed significantly in their spatial
distribution.
Metrics such as biogeographically weighted evolutionary distinctiveness, which combines
evolutionary diversity and rarity into a single measure of diversity, may allow a more holistic
approach to conservation prioritization. Nonrandom relations between evolutionary
96
distinctiveness and range size can produce rankings different from those of any single input
variable. Comparing biogeographically weighted evolutionary distinctiveness and
biogeographically weighted species richness allowed us to determine whether differences
between biogeographically weighted evolutionary distinctiveness and phylogenetic diversity
resulted simply from the incorporation of range-size information (in which case
biogeographically weighted evolutionary distinctiveness and biogeographically weighted species
richness should have a similar relation to the relation between species richness and phylogenetic
diversity) or from more complex relations between range size and evolutionary distinctiveness.
We found that biogeographically weighted evolutionary distinctiveness differed from the
similarly range size-weighted biogeographically weighted species richness, which suggests
evolutionarily distinct species tended to have more restricted geographical distributions. The
relation between evolutionary distinctiveness and range size could be of interest for species
conservation. Species that are evolutionarily distinct are of high conservation value (Crozier
1997), and species with small ranges have greater risk of extinction (Purvis et al. 2000a; Purvis
et al. 2000b; Cardillo et al. 2005). Although species-based prioritization schemes that
incorporate range size more likely emphasize these species, such schemes cannot differentiate
between species with small ranges that are not evolutionarily distinct and species that have small
ranges and are more evolutionary distinct (Davies et al. 2011).
Other metrics of phylogenetic diversity also incorporate measures of extinction risk (Redding &
Mooers 2006; Isaac 2007; Faith 2008a), but they rely on inferred estimates of extinction risk that
require detailed species data such as probability of extinction. In contrast, biogeographically
weighted evolutionary distinctiveness requires information only on range size (or abundance, or
population numbers, etc.), which is perhaps the most widely available type of species data. We
suggest biogeographically weighted evolutionary distinctiveness might therefore have much
greater practicality, especially for less well-described clades. Species prioritization rankings are
often developed at global or national scales, which means sites may be assigned a high priority
that may not contain the rarest species at the scale of interest.
A common criticism of alternative diversity metrics is that they are sensitive to the calculations
and the weighting scheme used to construct them. Although this criticism is valid, in fact
weighting schemes are implicit in all reserve-selection approaches (e.g., measures of species
97
richness simply assume all species have equal weights). Alternative weighting schemes allow
one to make explicit those aspects of biological diversity that are valued and further encourages
debate over what aspects of biological diversity should be valued. In addition, all metrics depend
on the calculations used to construct them. Comparing alternative metrics and performing
sensitivity analyses (here, differing phylogenetic construction methods) makes it clear that there
are differences in the distribution of biological diversity that are worth considering and that are
due to more than choice of metric construction alone.
The Protea Atlas Project (Rebelo 2001) produced one of the most detailed surveys of species
occurrence in the world (presence or absence of approximately 330 species over 36,000 sites).
These data are being used to guide reserve selection and predict how range sizes, locations, and
extinction risks will change as temperatures increase (Lombard et al. 2003; Midgley et al. 2003;
Bomhard et al. 2005). Proteaceae species in the Cape Floristic Region currently receive
considerable protection; the majority of species occur in at least one reserve. Our analyses are
primarily an illustration of how the distribution of evolutionary history can differ from the
distribution of species richness. Our results suggest that sites near the southern edge of the region
contain species that have high levels of evolutionary distinctiveness and limited ranges, but are
not assigned high conservation-priority rankings on the basis of species richness or phylogenetic
diversity. These areas in the south and southwest representing sites high in rare and evolutionary
distinct species may relate to the presence of lowland fynbos, which is restricted to areas
between the coast and interior mountains and has different vegetation than other fynbos types.
Many species in this region have small ranges, and mountains and coastal areas may form
barriers to dispersal. The resulting negative relationship between evolutionary distinctiveness and
range size that resulted in this area is important to consider because it means that in some cases
the species that capture the greatest evolutionary diversity will also be the species most
vulnerable to extinction. Further, modest differences in the distribution of phylogenetic and
species richness in the region may suggest different conservation scenarios for protecting
phylogenetic versus species richness and may lead to different conclusions regarding the future
positioning of protected areas.
98
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Figures
Figure 4-1. Pearson correlation coefficients showing the strength of the relationships
among species richness, phylogenetic diversity (PD), and biogeographically weighted
evolutionary distinctiveness (BEDT) metrics for Proteaceae in the Cape Floristic Region,
South Africa (none, species lacking sequence data not included; low, species lacking
sequence data included at low evolutionary diversity position; high, species lacking
sequence data included at high evolutionary diversity position; genera, resolved only to the
level of genus).
The inset shows the Pearson correlation coefficients between the 2 range-weighted metrics
(biogeographically weighted evolutionary distinctiveness and biogeographically weighted
species richness (BSR)) for the none, low, and high trees.
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Figure 4-2. Proteaceae diversity of 311 species in the Cape Floristic Region on the southern
tip of Africa, diversity is measured using (a) species richness, (b) phylogenetic diversity,
and (c) biogeographically weighted ecological distinctiveness, where (b) and (c) were
calculated using the low tree, where species lacking sequence data were included at low
evolutionary diversity position.
All diversity measures are scaled with mean 0 and SD 1. Colors are scaled over 8 quantile
intervals from blue (low diversity) to red (high diversity).
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Figure 4-3. (a) The relation between biogeographically weighted evolutionary
distinctiveness (BEDT) and range size (calculated as the square-root transformed number
of cells occupied by the species) and (b) distribution of range size for the ‘none’
phylogenetic tree, where Proteaceae species lacking species data are not included.
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Appendices
Appendix 4-1. Phylogenetic tree of the CFR Proteaceae, constructed using sequences from
Genbank.
(Martin & Dowd 1991; Martin & Dowd 1993; Nickrent & Soltis 1995; Hoot & Douglas 1998;
Hoot et al. 1999; Parkinson et al. 1999; Qiu et al. 1999, 2000; Fishbein et al. 2001; Barker et al.
2002; Mast & Givnish 2002; Moisen & Frescino 2002; Soltis et al. 2003; Barker et al. 2004;
Kim et al. 2004; Mast et al. 2004; Reeves et al. 2004; Pharmawati et al. 2005; Qiu et al. 2005;
Qiu et al. 2006; Redding & Mooers 2006; Wright et al. 2006; Chase et al. 2007; Worberg et al.
2007; Holmes et al. 2008; Lahaye et al. 2008; Mast et al. 2008; Ford et al. 2009; Group 2009;
Royas-Jimenez et al. 2009; Sauquet et al. 2009; Wang et al. 2009; Gillman et al. 2010; Qiu et al.
2010; Valente et al. 2010).
105
106
Appendix 4.1 References for phylogeny
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3. Nickrent DL & Soltis DE (1995) A comparison of angiosperm phylogenies from nuclear 18S rRNA and rbcL sequences. Annals of the Missouri Botanical Garden 82(2):208-234.
4. Hoot SB & Douglas A (1998) Phylogeny of the Proteaceae based on atpB and atpB/rbcL intergenic spacer region sequences Australian Systemic Botany, 11(4), 301-320.
5. Parkinson CL, Adams KL, & Palmer JD (1999) Multigene analyses identify the three earliest lineages of extant flower plants. Unpublished.
6. Qiu YL, et al. (1999) The earliest angiosperms: evidence from mitochondrial, plastid and nuclear genomes. Nature 402:404-407.
7. Hoot SB, Magallon S, & Crane PR (1999) Phylogeny of basal eudicots based on three molecular data sets: atpB, rbcL, 18S nuclear ribosomal DNA sequences. Annals of the Missouri Botanical Garden 86:1-32.
8. Qiu YL, et al. (2000) Phylogeny of basal angiosperms: analyses of five genes from three genomes. International Journal of Plant Sciences 161(S6):S3-S27.
9. Fishbein M, Hibsch-Jetter C, & Hufford L (2001) Phylogeny of Saxifragales (angiosperms, eudicots): analysis of a rapid, ancient radiation. Systematic Biology 50(6):817-847.
10. Moisen GG & Frescino TS (2002) Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling 157(2-3):209-225.
11. Mast AR & Givnish TJ (2002) Historical biogeography and the origin of stomatal distributions in Banksia and Dryandra (Proteaceae) based on their cpDNA phylogeny. American Journal of Botany 89(8):1311-1323.
12. Barker NP, Weston PH, Rourke JP, & Reeves G (2002) The relationships of the southern African Proteaceae as elucidated by internal transcribed spacer (ITS) DNA sequence data. Kew Bulletin 2002.
13. Soltis DE, et al. (2003) Gunnerales are sister to other core eudicots: implications for the evolution of pentamery. American Journal of Botany 90(3):461-470.
14. Mast AR, Jones EH, & Havery SP (2004) An assessment of the DNA sequence evidence for the paraphyly of Banksia with respect to Dryandra (Proteaceae). Unpublished.
15. Reeves G, Barraclough TG, Rebelo AG, Fay MF, & Chase MW (2004) Molecular phylogenetics of African Protea: evidence from DNA sequences and AFLP markers for a Cape origin. Unpublished.
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16. Barker NP, Vanderpoorten A, Morton CM, & Rourke JP (2004) Phylogeny, biogeography, and the evolution of life-history traits in Luecadendron (Proteaceae). Molecular Phylogenetics and Evolution 33(3):845-860.
17. Kim S, Soltis DE, Soltis PS, Zanis M, & Suh Y (2004) Phylogenetic relationships among early-diverging eudicots based on four genes: were the eudicots ancestrally woody? Molecular Phylogenetics and Evolution 31(1):16-30.
18. Qiu YL, et al. (2005) Phylogenetic analyses of basal angiosperms based on nine plastid, mitochondrial, and nuclear genes. International Journal of Plant Sciences 166(5):815-842.
19. Pharmawati M, Yan G, & Finnegan PM (2005) The conservation of mitochondrial genome sequence in Luecadendron (Proteaceae). Unpublished.
20. Wright S, Keeling J, & Gillman L (2006) The road from Santa Rosalia: a faster tempo of evolution in tropical climates. Proceedings of the National Academy of Sciences of the United States of America 103(20):7718-7722.
21. Qiu YL, et al. (2006) Reconstructing the basal angiosperm phylogeny: evaluating information content of mitochondrial genes. Taxon 55(4):837-856.
22. Worberg A, et al. (2007) Phylogeny of basal eudicots: insights from non-coding and rapidly evolving DNA. Organism Diversity and Evolution 7(1):55-77.
24. Mast AR, Willis CL, Jones EH, Downs KM, & Weston PH (2008) A smaller Macadamia from a more vagile tribe: inference of phylogenetic relationships, divergence times, and diaspore evolution in Macadamia and relative (tribe Macadamieae; Proteaceae). American Journal of Botany 95(7).
25. Holmes GD, Blacket MJ, James EA, & Hoffmann AA (2008) Molecular phylogenetic analysis of the Grevillea aquifolium (Proteaceae) group of species. Unpublished.
26. Lahaye R, et al. (2008) DNA barcoding the floras of biodiversity hotspots. Proceedings of the National Academy of Sciences of the United States of America 105(8):2923-2928.
27. Sauquet H, et al. (2009) Contrasted patterns of hyperdiversification in Mediterranean hotspots. Proceedings of the National Academy of Sciences of the United States of America 106(1):221-225.
28. Wang W, Lu AM, Ren Y, Endress ME, & Chen ZD (2009) Phylogeny and classification of Ranunculales: Evidence from four molecuar loci and morphological data. Perspectives in Plant Ecology, Evolution, and Systematics 11:81-110.
29. Ford CS, et al. (2009) Selection of candidate coding DNA barcoding regions for use on land plants. Botanical Journal of the Linnean Society 159(1):1-11.
30. Group CPW (2009) A DNA Barcode for Land Plants. Unpublished.
31. Royas-Jimenez K, Vindas-Rodriguez M, & Tamayo-Castillo G (2009) Evaluation of three chloroplastic markers for barcoding and for phylogenetic reconstruction purposes in native plants of Costa Rica. Unpublished.
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32. Gillman LN, Keeling J, Gardner RC, & Wright SD (2010) Faster evolution of highly conserved DNA in tropical plants. Unpublished.
33. Valente LM, et al. (2010) Diversification of the african genus protea (proteaceae) in the cape biodiversity hotspot and beyond: equal rates in different biomes. Evolution 64(3):745-760.
34. Qiu YL, et al. (2010) Angiosperm phylogeny inferred from sequences of four mitochondrial genes. Journal of Systematics and Evolution 48:391-425.
35. Redding DW & Mooers AO (2006) Incorporating evolutionary measures into conservation prioritization. Conservation Biology 20:1670-1678.
Appendix 4-2. Graphical representation of how a species, D, lacking sequence data, would
be positioned on the phylogenetic tree, based on branch lengths, relative to its congeners A,
B, and C with sequence data.
a) The species tree based only on species with sequence data; b) the high tree with D in a
polytomy with its most evolutionarily distinct congener A, and c) the low tree with D in a
polytomy with its most evolutionarily distinct congeners (B and C).
‘none’ ‘high’ ‘low’
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Appendix 4-3. Reserve representation index for 311 species of Proteaceae in the Cape
Floristic Region, a biodiversity hotspot on the southern tip of Africa. The maps illustrate
prioritization of a, species diversity or b, phylogenetic diversity, outside of reserve sites:
Phylogenetic or species diversity is scaled by degree of representation within the existing
reserve network species to highlight remaining areas with less represented phylogenetic or
species diversity (see Methods).
Measures are scaled with mean 0 and standard deviation 1; colors are scaled over eight quantile
intervals from blue to red, and increase as the degree of underrepresented diversity in a site
increases. The current system of reserves is shown in green. See text for additional details.
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Copyright Acknowledgements
Tucker, C.M., Cadotte, M.W., Davies, T.J., Rebelo, A.G. 2012. The distribution of biodiversity:
linking richness to geographical and evolutionary rarity in a biodiversity hotspot. Conservation
Biology, Volume 26, No. 4, 593–601
2012 Society for Conservation Biology
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Chapter 5 Unifying measures of biodiversity: understanding when richness
and phylogenetic diversity should be congruent
5 5
5.1 Abstract
Aim: Biogeographical theory and conservation valuation schemes necessarily involve assessing
how biodiversity is distributed through space, and ‘biodiversity’ encapsulates many different
aspects of biological organization and information. While biogeography may try to explain
biodiversity patterns, successful conservation strategies should attempt to maximize different
aspects of diversity. Ultimately, diversity patterns are the product of evolutionary history, and
research and conservation efforts seek to understand the unequal distribution of evolutionary
history. For conservation efforts, results have been inconsistent as to whether species richness
provides sufficient surrogacy for evolutionary history. Here we provide a conceptual framework
allowing for the direct comparison of taxonomic richness and phylogenetic diversity, both in
terms of their mechanistic relationship, and the relationship between their spatial distributions.
Location: Global
Methods: We present a framework that relates regional species richness, phylogenetic diversity,
biogeographically weighted evolutionary distinctiveness, and biogeographically weighted
species richness. Further, we use simulations to illustrate how the size of the species pool,
topological patterns within the phylogeny, and autocorrelation in spatial distributions affect the
correlation among metrics.
Results: In regions that include both recently diversified groups and ancient species poor
lineages, large species pools and low spatial autocorrelation, the correlation between biodiversity
measures is lower than regions with low richness, balanced phylogenetic trees and high spatial
autocorrelation.
Main conclusions: We can now understand and predict when regional richness and phylogenetic
diversity should be strongly correlated. This congruency is the product of evolutionary and
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ecological processes that determine species pool membership and community assembly. Further,
in regions where species richness is not expected to be congruent with phylogenetic
distinctiveness, re-examining how existing reserve networks protect the multiple aspects of
biodiversity is critically important.
5.2 Introduction
Global patterns of biological diversity reveal stark contrasts. Some regions contain thousands of
species in relatively small areas, whereas elsewhere there may only be a few species over
extremely large areas. Understanding this inequality in the distribution of species has been the
focus of the creative energy of numerous scientists (e.g. MacArthur & Wilson 1967; Gaston &
Blackburn 2000) and has served as the basis of global conservation prioritization (Myers et al.
2000; Fleishman et al. 2006). The recognition that the term diversity is not synonymous with
species richness, but instead encompasses organismal variety at all levels, from genetic variation
to the differences in the richness of higher taxa, and includes the diversity in ecosystem structure
and function (Wilson & Peter 1988), has led researchers to measure the spatial distribution of
different aspects of diversity (Faith 1992; Forest et al. 2007; Devictor et al. 2010; Huang et al.
2011; Tucker et al. 2012a). Such comparisons aim to understand the biogeographical relationship
between different facets of diversity. This type of research has been motivated, in part, by the
fact that historically reserves have not focused on aspects of diversity beyond richness and
endemism. Therefore it is reasonable to examine the efficacy of existing reserves in protecting
other facets of biodiversity (Devictor et al. 2010; Huang et al. 2011; Tucker et al. 2012a). In
addition, comparing different biogeographical distributions of diversity allows researchers to
potentially infer different mechanisms generating and maintaining different aspects of diversity.
For example, studies examining latitudinal gradients of species richness often infer the influence
of climate on speciation rates (Weir & Schluter 2007), whereas biogeographical studies that
focus on genetic diversity often find that vicariance or natural barriers are critically important
(Kuo & Avise 2005).
There is a long history of measuring and mapping patterns of species richness across
biogeographical regions throughout the world (Wallace 1876; Whittaker 1954; Preston 1960;
Whittaker 1960; Stevens 1989). As the importance of alternative forms of diversity is
documenting patterns of other measures of diversity such as phylogenetic and functional
diversity become an important exercise. For diversity and conservation research, having a precise
estimate of ecological or functional diversity is beneficial. However, ecologically-meaningful
functional diversity is often difficult to quantify due to a lack of comprehensive trait information
for species in a region, or an incomplete understanding of how traits correspond to ecological
differences.
A related measure that is used as a surrogate for functional diversity is that of phylogenetic or
evolutionary diversity, which quantifies the amount, distribution or evenness of evolutionary
information contained within species assemblages. There are a number of ways to measure
phylogenetic diversity in communities (Webb et al. 2002; Cavender-Bares et al. 2009; Cadotte et
al. 2010b), but methods that quantify either the amount of evolutionary history or the
evolutionary distinctiveness of a set of species are most appropriate to examine spatial patterns
of diversity (Faith 1992; Isaac 2007; Cadotte & Davies 2010; Davies & Cadotte 2011). The most
often used measure is Faith’s (1992) phylogenetic diversity (PD), which is the sum of all
phylogenetic branch lengths connecting species together. Evolutionary distances are often
correlated with potential multidimensional phenotypic differences among species (Vane-Wright
et al. 1991; Faith 1992). There are many subtleties associated with this assumption, including the
degree of phylogenetic conservatism among traits and the degree that trait divergence follows
Brownian motion evolution. Specific traits and lineages often fail to meet these assumptions and
some researchers have found functional diversity and phylogenetic diversity vary independently
(Safi et al. 2011). Regardless, researchers often use phylogenetic information to represent
unknown aspects of species ecologies or simply as a representation of similarities in the
information contained within their genomes. To this end, a number of studies have examined the
spatial distribution of phylogenetic diversity and delineate sites with disproportionately high
phylogenetic diversity (Moritz 2002; Rodrigues & Gaston 2002; Forest et al. 2007; Devictor et
al. 2010; Tucker et al. 2012a).
On its own, species richness is not ecologically meaningful, and considering other forms of
diversity which capture species differences becomes important. With a particular focus on
conservation, a number of studies have questioned the efficacy of richness as a surrogate for
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other types of diversity and have called for more multifaceted approaches to conservation
(Crozier 1997; Bonn & Gaston 2005; Fleishman et al. 2006; Devictor et al. 2010; Davies &
Cadotte 2011). Studies that examine the congruence between species (or generic) and
phylogenetic diversity have been inconsistent. For example, Devictor et al. (2010) found a large
spatial mismatch between the species, functional and phylogenetic diversity of birds across
France; these measures were congruent in some areas, and incongruent in others, possibly
depending on the history of the regional species pool in each area. They found that phylogenetic
and functional diversity were underrepresented in the current reserve network, relative to species
richness. Two papers which compared the spatial distribution of generic or species diversity in
the Cape Floristic Region of South Africa (Forest et al. 2007; Tucker et al. 2012a) similarly
found evidence of spatial incongruence between species richness and phylogenetic diversity.
Conversely, several studies found that phylogenetic diversity and taxonomic diversity to have
similar spatial distributions: for example, Rodrigues and Gaston (2002) found that phylogenetic
and generic richness of birds in northwest South Africa showed high spatial congruence, and
reserve site selection was complementary. Perez-Losada and colleagues (2002) found little
difference in conservation priorities for Chilean freshwater crabs, regardless of whether species
richness or phylogenetic diversity was considered (though Faith & Baker 2006 raise doubts about
these results). Similar conclusions were made regarding Ozark crayfishes (Crandall 1998). This
marked variation in the observed relationship between species richness and phylogenetic
diversity appears to makes it difficult to draw conclusions regarding the relationship between
these measures.
The relationship between phylogenetic diversity and species diversity depends on the
phylogenetic topology and the geographic distribution of species (Rodrigues et al. 2005). For
example, in regions with large, diverse species pools, particularly in the case of randomly
accumulating species, phylogenetic diversity increases at a similar rate as species richness, and
thus phylogenetic diversity is likely to be highly correlated with species richness (Fjeldsa 1994;
Mace et al. 2003). This suggests that a framework predicting the degree of correlation expected
between different measures of diversity could make an important contribution to our
understanding of the biogeographical distribution of diversity.
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While there has been substantial effort to measure alternative aspects of diversity, there is a
surprising dearth of studies that explicitly incorporate abundances into phylogenetic metrics of
any kind (but see: Cadotte et al. 2010c; Scheiner 2012). Given the importance of species range
sizes and abundances for understanding basic biogeographical processes as well as their role in
extinction risk, this is an area that deserves further study. One method of weighting richness by
abundances, here referred to as ‘biogeographically-weighted species richness’ (BSR)1, which
sums the inverse of the range sizes, or number of sites or populations of all species at a site or in
a region as: , where S is the number of species at a specific site and ni is the number
of sites (or populations or range size) that species i occurs at over the larger region (Crisp et al.
2001; Rosauer et al. 2009b). Thus BSR is small if a site contains species with large ranges, and is
large if the site has many range-restricted species. A measure like BSR may show quite different
patterns than non-range size related measures of diversity, especially if rich sites
disproportionately contain large-ranged or abundant species (Rosauer et al. 2009a; Tucker et al.
2012b).
Measures of phylogenetic diversity may also provide additional information when they
incorporate range-size. When Isambert and colleagues (2011) examined phylogenetic diversity
patterns in Malagasy national parks, they found that phylogenetic diversity was negatively
correlated with numbers of endemic species, as these endemics are the product of recent species
radiations in Madagascar. Abundance information is straightforward to incorporate into
phylogenies, because stopping a phylogenetic tree at the species level is arbitrary, and a tree can
be resolved to the individual or population level by extending the tree via adding further tips
(Cadotte et al. 2010c). (In cases where additional genetic information is not available for
individuals or populations, intraspecific tips can still be added as uninformative polytomies). As
a result, the evolutionary distinctiveness of a species would explicitly account for the numbers of
individuals or populations, and therefore a measure of extinction risk. Several weighted
1 Crisp and colleagues referred to this metric as ‘weighted endemism’ (WE) and we refer to it as BSR to make the terminology comparable to the other measures in this paper and because endemism is a scale dependent measure with specific connotations.
BSR = 1nii=1
S
!
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phylogenetic diversity metrics have been proposed that explicitly incorporate species abundances
or range sizes into prioritization schemes (Rosauer et al. 2009b; Cadotte & Davies 2010). There
are other useful measures that use extinction risk (Redding & Mooers 2006; Faith 2008b) or
IUCN species ranks (Isaac 2007) to weight phylogenetically-based prioritization. IUCN ranks
and extinction risk are used because of the availability and accessibility of this data and the fact
that such conservation ranks are based on abundance and range size.
An example of a metric that combines evolutionary distinctiveness with abundances is the BED
metric (Cadotte & Davies 2010), which partitions internal branches in a phylogenetic tree by the
range or population size of the subtending taxa: , where ne is the number
of grid cells in which a species is present, below branch e, of length λ , in the set q(T,i,r), which
includes the branches connecting species i to the root r of tree T. (Cadotte and Davies [2010]
provide a detailed description and graphical representation of how this metric partitions internal
branches –see also Fig. 1d). It should be noted that how abundances are calculated (e.g., number
of sites occupied versus geographical extent versus total number of individuals) can affect BED
values and their interpretation, and researchers should be cognizant of the potential implications
of their measure of rarity (Rabinowitz 1981). Species with long branches and low abundances or
ranges are weighted highly (i.e. distinct and rare), while species that share the majority of their
genetic heritages with many other species, and have high abundances receive less weight. As a
result, in a biogeographic setting, BED highlights sites containing species that have greater
extinction risk and also have few close relatives.
5.3 Unifying biodiversity measures
Given seemingly contradictory results from empirical studies (Rodrigues & Gaston 2002; Forest
et al. 2007; Devictor et al. 2010; Tucker et al. 2012a), reconciling results from different
biodiversity metrics, and further, predicting how these differing metrics will relate is clearly
necessary. There have only been a few studies published that investigate the effect of
phylogenetic topology and abundance distributions on the relationship between phylogenetic and
species based metrics of diversity (Rodrigues et al. 2005; Schweiger et al. 2008), and there
remains a need for frameworks relating phylogenetic diversity with species richness (whether
!
!
BED(T,i) ="enee#q(T ,i,r)
$
117
they are weighted by abundance or not). Comparing the spatial distributions of biodiversity
measures informs conservation decision-making because incongruence between measures
highlights how different aspects of diversity (species richness, evolutionary history, geographical
rarity) are differentially distributed through space. It also provides an opportunity to understand
why patterns of diversity vary among biogeographic regions. In the following, we present a
conceptual unification of these measures, and then explore the effects of 1) tree structure, 2)
spatial structure, 3) species pool size on the relationship between diversity metrics.
5.3.1 Conceptual underpinning of biodiversity measures
It is relatively straightforward to compare counts of the number of species with Faith’s
phylogenetic diversity. Metrics based on species richness (SR) implicitly assume that species are
all equally weighted (weight of 1). This is synonymous to a phylogenetic tree where the
phylogenetic relationships are removed and the tip to root distance is equal to 1 for an ultrametric
tree (Fig. 1a) –that is, a star phylogeny where all terminal branches originate from a single
polytomy (Helmus et al. 2007). If an informative ultrametric phylogeny is also scaled with a tip
to root length of 1 (Fig. 1b) then the more distantly related the individual species, the closer the
value of PD is to SR. The alternative scaling method would be to multiply species richness by
the real tip to root distance from the phylogeny. Regardless of the scaling method, PD will
diverge from SR as the tree becomes increasingly imbalanced and as the mean nearest neighbour
distance decreases. Thus in regions with incongruent site rankings between PD and SR, we
should expect less balanced evolutionary relationships among species.
When we weight the branches by species abundances or range sizes for BSR or BED (Fig. 1c, d),
then there is a second axis to compare. BED can deviate from SR due to topology, abundance or
their combined effect. Thus BED must be compared to both PD and BSR in order to draw
conclusions about the mechanisms that affect diversity distributions. Like the relationship
between SR and PD, when the phylogeny is relatively balanced and has long terminal branches,
the expectation is that BSR and BED give similar values. Both BSR and BED sum to their
unweighted counterparts when each species value is multiplied by its abundance, for example:
or PD is approximated by: where S is the number of species or !
!
PD = ni " BEDii
S
#!
!
n ni " BEDii
S
#
118
terminal tips in the phylogeny and n is a measure of abundance. Thus, if abundance lacks
variation (i.e., all species have roughly equivalent abundances) then PD and site summed BED
values are highly correlated.
5.3.2 Exploring the correlation between metrics
The four biogeographical measures of diversity considered here (Fig. 5-1) can vary from one
another depending on the topology of the phylogeny and the geographical ranges sizes or
abundances of species. We now ask how variation in these aspects can affect the strength of the
correlation between metrics. To do this, we simulated thousands of trees and abundance
distributions (see Appendix 5-1 for full methodology) and compared the four diversity metrics.
Specifically, we assess whether variation in topology, the strength of the spatial autocorrelation
in species occupancy patterns and species pool size have consequences for the strength of the
relationship between richness and phylogenetic measures of diversity.
5.3.3 Tree structure
If all species are equally related in a polytomy or star phylogeny (i.e. all species have identical
amounts of unshared evolutionary information), with tip to root branch lengths equal to 1, then
SR and PD are equivalent (e.g., Fig. 1). When a tree’s topology diverges from that of a star
phylogeny (as is common), so that information is no longer symmetrically distributed through
clades and/or through time (see Fig. 2), we can expect systematic changes in the relationship
between SR and PD.
In trees with proportionally more information in the terminal branches –that is, when there are
few recent radiations (Fig. 2)--SR and PD should be highly correlated. A star phylogeny is the
extreme of this situation, in which internal branches are minimized so the ratio between branch
number and species number approaches one, at which point SR and PD are equivalent (Fig. 3).
This suggests that in communities with species from anciently diverged lineages (Hawkins et al.
2006; Lopez-Fernandez & Albert 2011) or where community assembly selects distantly related
species (Webb 2000; Webb et al. 2002), we would expect stronger correlations between SR and
PD. Conversely, when trees have long internal branches and many short terminal branches
Symmetrical trees may be more likely in regions in which rates of extinction and speciation are
relatively similar or more stable, such as in the tropics (Hawkins et al. 2006; Weir & Schluter
2007).
The relationship between the two abundance-weighted metrics (BSR and BED) is also dependent
on the shape of the phylogenetic tree. A symmetrical tree with long internal branches yields a
stronger correlation between BSR and BED (Fig. 4a-i,iii). This is because short terminal
branches (recent radiations) minimizes the variation in evolutionary diversity, so that BSR and
BED are more similar. In addition, the strength of the correlation between range size and
evolutionary distinctiveness alters the correlation between BSR and BED (Fig. 4a-ii). When
range size and evolutionary distinctiveness are negatively correlated, i.e. rare species do not tend
to be distinct and vice versa, the correlation between BSR and BED is stronger. This is because
the relationship between BSR and BED is weakened when rare species also tend to be distinct
and so receive high BED values, causing BED values to diverge from the abundance weighted--
but not phylogenetically informed--BSR metric.
Similarly, the abundance weighted BED metric should have predictable relationships with PD
and SR depending on the topology of the phylogenetic tree. In addition to the shape of the tree,
the distribution of the abundance information in relation to the phylogenetic branch lengths
changes the relationship between BED and PD. The correlation between BED and PD should be
strongest under those conditions that minimize the importance of the abundance weighting (as
previously, when there is a negative correlation between range size and evolutionary
distinctiveness)(Fig. 4b-ii), and when the phylogenetic tree has long terminal branches and high
symmetry (Fig. 4b-i,iii).
120
5.3.4 Spatial structure and abundance distribution
The spatial structure of species ranges in a region can alter the expected relationship between the
different types of diversity. We examined the role of spatial structure in species’ ranges, in
particular the likelihood that conspecifics be present in neighbouring sites. High autocorrelation
in species presences’ tends to result in small, compact ranges, whereas low autocorrelation
results in patchy but larger ranges. This spatial structure can create variation in the spatial
distribution overall of species richness. High autocorrelation could be reflected in the clumped
distribution of tropical tree species, for example, while in other forests species might be highly
dispersed, representing a system with low autocorrelation in species presences (Condit et al.
2000). The correlation between metrics tends to be lowest when there is low spatial
autocorrelation in species presences (Appendix 5-2, A). When spatial autocorrelation is low, the
distribution of evolutionarily distinct species is more uneven through space, meaning that some
sites may contain more phylogenetic information despite containing fewer species, and this
weakens the relationship between the different metrics.
The distribution of species abundances should also affect the relationship between the SR, PD
and the abundance weighted BED and BSR metrics. When the relative abundance distribution is
uniform (e.g. each abundance is equally likely to be observed), the correlation between
abundance-weighted BED and BSR with SR and PD metrics should be highest. As the
abundance distribution reflects the more realistic scenario in which most species have low
abundances, and increasingly few species have high abundances (often represented with a log-
normal distribution), abundance-weighted and non-abundance-weighted metrics will diverge.
5.3.5 Species pool size
The number of species in the regional species pool impacts the strength of the correlations
between metrics. When species pools are small, the correlation between PD and SR is stronger,
since communities contain relatively few species and proportionally more of the species pool;
this means that the subtree for that community is relatively depauperate and the importance of
tree shape is minimized (Appendix 5-2, B). Only for relatively large regional pools, above about
80 species, do sites with very low SR-PD correlations regularly appear. PD will always be highly
correlated with SR for regional pools with relatively few species.
121
While species pool size has important consequences for PD-SR correlations, it is much less
consequential for metrics that incorporate species range sizes or abundances. The effect of the
abundance distribution or the degree of autocorrelation in species occupancy patterns is critically
important for the abundance-weighted metrics and appears to mask any effect of the pool size.
5.4 Conclusions: Securing the place for evolution and rarity in conserving biodiversity
If we are to conserve the diversity of life on Earth, then biodiversity conservation is an
invaluable endeavour. It necessarily involves emphasizing or accommodating multiple priorities
including social and economic valuations (Meffe & Viederman 1995), the functioning of
ecosystems and accounting for the services they provide (Chan et al. 2006), and the preservation
of the diversity of life. Conservation efforts have focused on numerous aspects of diversity and
have produced conflicting priorities (Fleishman et al. 2006). Species diversity, composition,
rarity and evolutionary distinctiveness are three important aspects of diversity that are often
considered, and conceptual approach that provides a meaningful way to compare differing
aspects of diversity is of value. While incongruities in biodiversity metrics can highlight
additional sites to protect in a conservation network (Forest et al. 2007; Devictor et al. 2010;
Tucker et al. 2012a), understanding how and why metrics diverge is important for larger scale
conservation schemes, as well as informing our basic understanding of the evolutionary and
ecological processes generating patterns of biodiversity. With a priori knowledge about several
aspects of diversity, such as basic information about the evolutionary topology, species pool size
or how species are distributed through space; one can predict whether different metrics should be
weakly or strongly correlated (Fig. 5). This in turn would inform the types of diversity that
should be prioritized in conservation assessments, as well as inform hypotheses about the
processes behind the origin and maintenance of diversity in a region.
Two studies that conclude that SR and PD are highly correlated, and thus recommend using SR
as a surrogate for PD (Rodrigues et al. 2005; Rodrigues et al. 2011b), can be contextualized
given our understanding of how topology and species distributions affect SR-PD correlations. In
one of these studies, which examines the surrogate value of SR for PD using an artificially
simulated set of species and phylogenetic data (Rodrigues et al. 2005), the species pool chosen
122
was quite small –about 16 species. Given the influence of pool size on the strength of the
correlations (Supplementary figure 1 ), we would expect that there would be a high correlation.
This highlights an important message, that when the number of species being evaluated for
conservation is relatively small number, and especially if they are all members of a single clade
(e.g., bumblebees, seahorses, etc.), then finding sites that maximize richness is sufficient to meet
multiple conservation priorities.
In the second study, which examines how well sites selected for species richness also protect
global mammal phylogenetic diversity (Rodrigues et al. 2011a) also finds high surrogate value in
SR. Because Rodrigues and colleagues examined an extremely large species pool of 5258
mammal species globally, the expectation should be for a low correlation between SR and PD,
although results become more variable as species pool size increases (Supplementary figure 1). It
could be that for mammals, SR is an efficacious surrogate for PD. Alternately, other aspects of
the Rodrigues et al. study may lead to a higher correlation. Their phylogeny relied on a backbone
supertree and many species were added as polytomies, and polytomies necessarily increase the
SR-PD correlation. Further, the spatial information that they were able to obtain was at a very
coarse resolution with cells corresponding to approximately 23,000 km2. This scale, which likely
contains many species and phylogenetic branches, but would also undoubtedly mask subtle
spatial patterns of species occupancy, autocorrelation and rarity. We have shown that spatial
patterns of occupancy are quite important, and we haven’t assessed the consequences of
aggregating spatial patterns into larger scales, but lumping together would increase the SR-PD
correlation. While the study by Rodrigues and colleagues (Rodrigues et al. 2011b) has important
value for global conservation, the scale of this study may be mismatched to the finer scales that
many managers focus on.
Widening the focus of conservation programs to account for multiple aspects of biodiversity is a
worthy goal, but given the limited resources available for conservation and the lack of consensus
about multiple forms of diversity, different measures of diversity have not often been used in
biodiversity assessments. One approach to rectifying this is to develop a clearer understanding of
how different measures of biodiversity relate to each other in a region. Here we have attempted
to reconcile inconstant findings on congruencies among different diversity. In regions where
123
species richness is not expected to be congruent with phylogenetic distinctiveness, re-examining
how existing reserve networks protect the multiple aspects of biodiversity is critically important.
124
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Figures
Figure 5-1. Comparison of the four types of biogeographical diversity metrics that use
different types of information.
When only species presence/absence information is available the similarity of (a) species
richness and (b) phylogenetic diversity depends on the deviation of the phylogeny from equal
relatedness. Adding abundance or occupancy information to either richness ((c)
biogeographically-weighted species richness or phylogenetic diversity ((d) Biogeographically-
weighted evolutionary distinctiveness) weights individual tips by t relative abundances. In this
schematic, the tip to root distance (λ ) is set to 1, but this value can be the actual distance from
the phylogeny, in which case, corrected richness is SR x λt . Lambdas with numeric subscripts
are branch lengths and n is the abundance or range size of species.
129
Figure 5-2. Examples of the range of tree topology simulated.
Trees vary in the distribution of information among species (x-axis), which is manifested as the
degree of symmetry in dichotomous branching, and the distribution of information over time (y-
axis), which is seen in the proportion of total branch length accounted for by internal versus
terminal branches.
130
Figure 5-3. Spearman’s correlation (r) between species richness (SR) and phylogenetic
diversity (PD) as a function of tree topology.
131
Figure 5-4. A) Spearman’s correlation (r) between biogeographically-weighted species
richness (BSR) and biogeographically-weighted evolutionary distinctivness (BED), as a
function of tree topology and species range sizes. B) Spearman’s correlation (r) between
phylogenetic diversity (PD) and biogeographically-weighted evolutionary distinctivness
(BED), as a function of tree topology and species range sizes.
132
Figure 5-5. The expected correlation between species richness (SR) and phylogenetic
diversity (PD) as a function of tree topology, species pool size and spatial autocorrelation.
133
Appendices
Appendix 5-1. Simulation methods.
We randomly generated different a series of phylogenetic trees, communities, and species pools,
allowing us to alternately examine the effects of tree topology, spatial autocorrelation in species
richness, and species pool size on the correlations between different diversity metrics.
To examine the effects of tree topology we used the R package ape (Paradis et al. 2004; R
Development Core Team 2009) to randomly generate ultrametric trees; for each tree, the rate of
character evolution through time was manipulated to either increase the proportional length of
terminal branches, or proportionally increase the length of the internal branches. Therefore
10,000 trees with varying symmetry were initially randomly generated, and from each, 100 new
trees were simulated with sequentially slowed or increased rates of character evolution, resulting
in a set of new trees with the proportion of total branch length from terminal branches ranging by
units of 0.01 from 0.01 to 0.99. Total branch length remained constant for all trees. 100,000 trees
resulted from this procedure, and for each we recorded the Colless index (Ic), a measure of
symmetry which compares the absolute difference between the sizes of the left and right clades
at each node on the tree, and the proportion of the total branch length contributed by the terminal
branches. Ic values were normalized, so that comparisons across different size trees could be
made.
A 10x10 matrix representing 100 communities or sites within a region was generated. The
regional species pool was initially set to 100 species; local communities ranged in richness up to
30 species. Spatial autocorrelation was initially set at 0.5, meaning that there was a 0.5
probability that a community contains a given species, if that species is present in a neighbouring
community. The four diversity metrics (SR, BSR, PD, and BED) were then calculated for each
tree-region combination using the R package ecoPD (Regetz et al. 2009), allowing us to explore
the effects of tree topology while holding community structure constant.
To explore the effects of spatial structure, we generated regions with 100 communities, having
100 species, and varied the strength of autocorrelation from 0.1 to 0.9. This meant that the
134
probability of conspecifics being present in neighbouring sites varied from low (0.1) to high
(0.9). Regions with each level of autocorrelation were replicated 100 times each.
To look at the effects of the size of the regional species pool, we generated a tree having
symmetry and terminal branch lengths similar to the mean value calculated across our initial
40,000 trees, which had 400 tips. We then generated a matrix of 100 communities, having from
30 to 400 species. The tree was randomly pruned to have the same number of tips as there were
species in the regional pool; in total we looked at 40 different sized species pools, and replicated
each species pool size 100 times.
Appendix 5-2. A) Effect of spatial autocorrelation in species occupancy on the correlation
between the four biodiversity metrics; B) Effect of regional species pool size on the strength
of the correlation between the four biodiversity metrics.
A B
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Copyright Acknowledgements
Tucker, C.M. and Cadotte, M.W. 2013. Unifying measures of biodiversity: understanding when
richness and phylogenetic diversity should be congruent. Diversity and Distributions. DOI:
Conclusions: Accounting for diversity in a changing world
Understanding global biodiversity—both the mechanisms that promote and maintain diversity
and the contribution of diversity to ecosystem services—informs management and conservation
in vital ways. Habitat loss, climate change, and species invasions all contribute to high rates of
contemporary species extinctions, and combining ecological theory, particularly with a focus on
mechanisms, with ecological applications is necessary to successfully support conservation
activities. The ecological literature is replete with mechanisms through which species
coexistence can be allowed (and thus species diversity promoted). Reconciling these mechanisms
with the possible effects of changing climate and human actions on their efficacy is necessary
and still incomplete. For example, global warming will lead to changes in both the mean
temperature and precipitation, and possible effects on species extinctions, range shifts, invasion
success, and biome shifts have been explored (Stachowicz et al. 2002; Thomas et al. 2004;
Thomas et al. 2006; Colwell et al. 2008; Chen et al. 2011). Temperature and precipitation
extremes and overall variability will also change with warming, and the implications of changes
in variability regimes have received less attention, although ecological theory suggests that
environmental variability is also an important driver of species coexistence (Warner & Chesson
1985).
In this thesis, I provided theoretical and experimental evidence that environmental heterogeneity
affects species coexistence directly and also indirectly through its effects on other coexistence
mechanisms. Further, I suggested particular conservation and management actions that would be
improved if environmental heterogeneity were considered during planning. In addition, I
provided evidence that, regardless of the mechanisms producing and maintaining spatial patterns
of species, phylogenetic, or functional diversity, they tend to be spatially variable in their
distributions creating a need to explicitly prioritize diversity conservation and reserve selection.
Themes found throughout this thesis include the use of ecological theory to inform conservation,
broadening conservation activities to include multiple types of diversity, and recognizing the role
for environmental variability.
In the first three chapters, I explored the question of how environmental heterogeneity alters
expectations for species coexistence, mechanisms of diversity maintenance, and management
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activities. In the first chapter, “Environmental Variability Counteracts Priority Effects to
Facilitate Species Coexistence: Evidence from Nectar Microbes”, I manipulated communities
of nectar bacteria and yeast species to explore whether temperature variability through space and
time altered the assembly of nectar microbe communities. Because nectar-dwelling communities
typically experience temperature variability through space and time, I hypothesized that
commonly-studied assembly mechanisms such as arrival order might interact with temperature
variability. A fully crossed design of temperature variability treatments (spatial, temporal, or
spatiotemporal) and arrival order (yeast first, bacteria first, or concurrent arrival) indicated that
variability and arrival order interacted to determine the end state of the community. In particular,
models suggested that temporal variability in temperature decreases the strength of priority effect
mechanisms such as habitat modification and resource consumption. Temporal variability in
temperature gave an advantage to temperature-tolerant bacterial species, such that they were
more likely to be present in communities that assembled in temporally variable conditions.
Ultimately these results provide a reminder that community assembly is a complex process
affected by multiple mechanisms. Studying only a single mechanism in isolation will limit our
ability to extend results to the complexities of real communities. Indeed, a key limitation of
laboratory microcosms is that they simplify the wide range of conditions likely to be important in
natural systems (Carpenter 1996; but see Srivastava et al. 2004). If a study of two mechanisms
(priority effects and temperature variability) alters expectations for community assembly, the
natural systems may not be easily understood from simplistic studies.
Annual plant species partition seasonal environmental variation to minimize competitive
interactions, leading to successional patterns of flowering and reproduction. Temperature cues
underlie most such phenological displays, and so the link between temperature and phenology is
used to track changes in global climate. However, observational data suggest that advances in
flowering time are highly variable. In “Community-level Interactions Alter Species’
Responses to Climate Change”, I used simple models of plant development to show that
mismatches between temperature regime and species’ optimal flowering temperatures occur with
warming, but competitive interactions can constrain species from closely tracking changes in
climate. Understanding the mechanisms by which species partitioning seasonal variation in
temperature here informs models of the effects of climate change on plant communities. This
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model provides a template for one mechanism by which competition could introduce variation
into observations of flowering time. However, this is only one pathway by which biotic
interactions could interact with warming temperatures. Facilitative interactions or mutualisms
(e.g. pollinators) could introduce constraints on phenological shifts that counteract those driven
by competition, for example. Natural communities include both annual and perennial species,
and perennial species’ fitnesses and flowering times depend on conditions occurring in more than
a single year.
In “Fire Variability, as well as Frequency, can Explain Coexistence Between Seeder and
Resprouter Life Histories”, I connected ecological theory–coexistence driven by variability in
fire occurrences—with management considerations–the timing and nature of managed fire
regimes that is optimal for diversity maintenance. Recent evidence from Australian shrublands
suggests that modern invariant fire regimes are associated with declines in diversity. Where
species have long histories of adaptation to particular disturbance regimes (such as fire regimes
in Mediterranean hotspots), changes away from natural regimes could disrupt mechanisms of
coexistence. Model results enforced this line of thought: a disturbance-mediated storage effect
could explain the coexistence of competitively unequal shrubs in Mediterranean shrublands,
however this mechanism required that fire events vary in occurrence. Too high or too low
variability and diversity would decline. This suggested that fire management activities that
ignore variability in fire events miss an important component of diversity maintenance. To
determine if this general model has relevance for more specific shrub communities, it will still be
necessary to parameterize the model for specific shrub species and fire regimes. Observational
data from regions that have received differing fire regimes can also provide insight into the
importance of variability in fire occurrence.
The final two chapters provide observational and theoretical evidence that the spatial distribution
of different forms of biodiversity tend to be incongruent and this creates a need to explicitly
consider and prioritize each type of diversity in conservation activities. Researchers have argued
since the 1990s (Faith 1992; UN 1992) that all forms of diversity, not just species richness, have
intrinsic and extrinsic value, but most reserve-selection exercises and applications are species-
focused or habitat-focused. One outcome of this is that other forms of diversity such as a region’s
evolutionary history are not well protected. This proved true in the Cape Floristic Region of
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South Africa, for which I showed in “Incorporating Geographical and Evolutionary Rarity
into Conservation Prioritization” that although Proteaceae species are well protected by
existing international, national, provincial, and regional protected areas, phylogenetic diversity
and range-restricted evolutionary distinctiveness is poorly protected. This proved true regardless
of the degree of resolution of the Proteaceae phylogeny. While showing that alternate forms of
diversity are not captured by extant reserves is an important first step, ultimately political and
economic limitations will determine where future reserves are placed in the Cape Floristic
Region.
The spatial divergence of different forms of diversity can provide insight into the ecological and
evolutionary processes structuring communities as well as informing diversity prioritization. It
places increased pressure on managers however, to obtain and understand information about
multiple types of diversity. Surveys of species richness, for example, tend to be more often
available than evolutionary history, and more easily interpreted. In the final chapter, “Unifying
measures of biodiversity: understanding when richness and phylogenetic diversity should
be congruent”, I provide some insight into this problem by demonstrating that the spatial
congruence between measures such as species richness and phylogenetic diversity is predictably
related to evolutionary history and spatial extent of species’ ranges. When species are anciently
diverged, there are relatively few species, or species ranges are large and disjoint, phylogenetic
diversity and species richness tend to agree, suggesting that a “one-size fits all” conservation
plan will be effective. However, in many biodiversity hotspots, diversification rates vary through
time, species pools are large (hence the initial desire to protect the region) and species are often
range-limited or endemic. In these situations, explicitly considering multiple forms of diversity
separately may be necessary.
The findings presented in this thesis attempt to connect ecological theory with applications for
management and conservation. Ecology has the duty to integrate ecological knowledge and
theory with real-world applications, and it can at times be difficult to understand and express the
connections between highly generalized theory and highly specific real world problems. When
the connections depend on models and/or highly controlled laboratory experiments (as they do in
Chapters 1, 2, 3 & 5), it is likely that experimental work in the field and tests of observation data
will be necessary to test whether the suggested mechanisms tend to be important in natural
140
systems and whether they are altered by interactions with other mechanisms. However, models
of constraints on phenological shifts or coexistence promoted by fire variability provide clearly
testable hypotheses and as such play an important role in combining theory and application.
Future directions require both that we understand how theory applies to natural ecosystems, and
further than relevant knowledge is transferred to managers and policy makers so that it can be
meaningfully applied in the real world.
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