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SpatialandtemporalspreadofmaizestemborerBusseolafusca(Fuller)(Lepidoptera:Noctuidae)damageinsmallholder...
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Agriculture, Ecosystems and Environment 235 (2016) 105118
Spatial and temporal spread of maize stem borer Busseola fusca
(Fuller)(Lepidoptera: Noctuidae) damage in smallholder farms
Frank T. Ndjomatchouaa,b,*, Henri E.Z. Tonnanga,c, Christophe
Plantampd,Pascal Campagnee, Clment Tchawouab, Bruno P. Le
Rua,f,g
a icipe African Insect Science for Food and Health, P. O. Box
30772-00100, Nairobi, Kenyab Laboratory of Mechanics, Department of
Physics, Faculty of Sciences, University of Yaound 1, P. O. Box 812
Yaound, Cameroonc International Maize and Wheat Improvement Center
(CIMMYT) ICRAF House, Off United Nation, Avenue, Gigiri, P. O. Box
1041, Village Market, 00621,Nairobi, KenyadUniversit de Lyon, 69000
Lyon, Universit Lyon 1, Laboratoire Biomtrie et Biologie Evolutive.
Universit Claude Bernard Lyon 1 Btiment Gregor Mendel,43 bd du 11
novembre 1918, 6922 Villeurbanne Cedex, FranceeDepartment of
Evolution, Ecology and Behavior, Institute of Integrative Biology,
Biosciences Building, University of Liverpool, Crown Street,
Liverpool L697ZB, United Kingdomf IRD/CNRS UMR IRD 247 EGCE,
Laboratoire Evolution Gnomes Comportement et Ecologie, CNRS, Bat.
13, 1 Avenue de la Terrasse, 91198 Gif sur Yvette
Cedex,FrancegUniversit Paris-Sud 11, 91405 Orsay Cedex, France
A R T I C L E I N F O
Article history:Received 30 May 2016Received in revised form 10
October 2016Accepted 12 October 2016Available online 20 October
2016
Keywords:Busseola fuscaSmallholder maize farmsDamage
spreadTemporal patternSpatial pattern
A B S T R A C T
The main purpose of this study was to understand the
spatio-temporal spread of the maize stem borerBusseola fusca
(Fuller) (Lepidoptera: Noctuidae) in smallholder maize farms. The
analysis carried outallowed the establishment of complementary
sampling scheme and analysis that can be applied toinvestigate the
propagation of stem borer damages and extended to other insect
pests. This approachrequires consideration of all plants point
locations, the knowledge on the level of damage and
itscharacterization. Results showed that there was a two-week
interval between occurrence of the peaks ofleaf damage and male
adult moth abundance. The prior role of leaf damages in the farm
infestation by B.fusca is revealed, and an estimate of the mean
transition time between different damage types isprovided.
Furthermore, damaged plants exhibited a local spatial
autocorrelation within a range ofdependence of 0-10 meters; and the
spatio-temporal pattern of B. fusca damage spread evolves as a
spiralaround an initial patch of damaged plants. By assuming a
neighbor configuration of distribution ofdamaged plants nearby
non-damaged, we showed that the inner plants are likely to become
damagedwithin a time period of a week; thus, B. fusca infests farms
in a systematic fashion. Overall, these resultshave useful
implications for improving and optimizing existing field sampling
methods for insect pestdamages. The approaches used in carrying out
the analysis further provided a deep understandinghelpful to
improve integrated pest management (IPM) strategies against stem
borers, and offer IPMpractitioners the opportunity to design,
develop, and implement optimum control methods against B.fusca, an
important pest of maize in Africa.
2016 Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
Agriculture, Ecosystems and Environment
journal homepage: www.elsev ier .com/locate /agee
1. Introduction
The relation between ecological processes (such as
individualdispersal, habitat selection, and spatial damage
distribution
* Corresponding author at: icipe African Insect Science for Food
and Health, P. O.Box 30772-00100, Nairobi, Kenya.
E-mail addresses: [email protected],
[email protected](F.T. Ndjomatchoua).
http://dx.doi.org/10.1016/j.agee.2016.10.0130167-8809/ 2016
Elsevier B.V. All rights reserved.
pattern) is of primary importance in ecology and in
agro-ecologicalsystems, to allow efficient control measures against
insect pests(Mazzi and Dorn, 2012; Vinatier et al., 2011). Yield
losses in cropsare a consequence of the spatial and temporal
dispersal of theinsect pests (Caldiz et al., 2002; Ferguson et al.,
2003; Hughes,1996; van Leeuwen et al., 2000; Winder et al., 2013).
Therefore, fordecades, spatial and temporal dispersion information
about theseharmful organisms has gained great relevance for plant
protectionspecialists and agricultural entomologists (Aukema et
al., 2006;Cocu et al., 2005; Diaz et al., 2012; Emmen et al.,
2004;
http://crossmark.crossref.org/dialog/?doi=10.1016/j.agee.2016.10.013&domain=pdfmailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.agee.2016.10.013http://dx.doi.org/10.1016/j.agee.2016.10.013http://www.sciencedirect.com/science/journal/01678809www.elsevier.com/locate/ageehttps://www.researchgate.net/publication/223684562_Agro-ecological_zoning_at_the_regional_level_spatio-temporal_variation_in_potential_yield_of_the_potato_crop_in_the_Argentinian_patagonia?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/221790632_Interplant_movement_and_spatial_distribution_of_alate_and_apterous_morphs_of_Nasonovia_ribisnigri_Homoptera_Aphididae_on_lettuce?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/223221039_Spatial_distribution_of_pest_insects_in_oilseed_rape_Implications_for_integrated_pest_management?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/222356419_Incorporating_spatial_pattern_of_harmful_organisms_into_crop_loss_models?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/222356419_Incorporating_spatial_pattern_of_harmful_organisms_into_crop_loss_models?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/227636721_Landscape_level_analysis_of_mountain_pine_beetle_in_British_Columbia_Canada_Spatiotemporal_development_and_spatial_synchrony_within_the_present_outbreak?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/262910296_Movement_of_insect_pests_in_agricultural_landscapes?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/236017515_The_spatial_distribution_of_canopy-resident_and_ground-resident_cereal_aphids_Sitobion_avenae_and_Metopolophium_dirhodum_in_winter_wheat?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/229874627_Spatial_autocorrelation_for_identifying_the_geographical_patterns_of_aphid_annual_abundance?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/227663817_Factors_and_mechanisms_explaining_spatial_heterogeneity_A_review_of_methods_for_insect_populations?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/40792120_Yield_loss_in_apple_caused_by_Monilinia_fructigena_Aderh_Rhul_Honey_and_spatio-temporal_dynamics_of_disease_development?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/289409404_Temporal_and_spatial_dynamics_of_Empoasca_fabae_Harris_Homoptera_Cicadellidae_in_alfalfa?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==
106 F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118
Holland et al., 2005; Ishaaya and Horowitz, 2004; Kautz et al.,
2011;Keasar et al., 2005; Kim et al., 2007; Lausch et al., 2013;
MoralGarca, 2006; Perry, 1994; Reay-Jones, 2010; Reay-Jones et
al.,2007; Smith et al., 2004; Thomas et al., 2001; Wang et al.,
2009;Wright et al., 2002; Zimmerman et al., 2004).
Several methodologies are employed to describe the spatial
andtemporal distribution of insect pest damage. The most
commonlyused approaches are: tracking the evolution of the centroid
ofinfestation distribution (Diaz et al., 2012; Lausch et al., 2013;
Reay-Jones et al., 2010), analyzing the spatial autocorrelation
ofinfestation (Blackshaw and Hicks, 2013; Bone et al., 2013; Cocuet
al., 2005; Smith et al., 2004), carrying out spatial evaluation
bythe estimate of distance indices (Cocco et al., 2015; Dder et
al.,2015; Holland et al., 2005; Kim et al., 2007; Li et al., 2012;
Reinekeet al., 2011; Schumann et al., 2014; Thomas et al., 2001),
conductingMarkovian chain analysis (Zimmerman et al., 2004),
deployinggeostatistical tools (Keasar et al., 2005; Moral Garca,
2006; Reay-Jones et al., 2010; Rogers et al., 2015; Wright et al.,
2002), usingspatial mapping (Emmen et al., 2004; Fernandes et al.,
2015; Kautzet al., 2011; Massoud et al., 2012; Zhang et al., 2016)
and usingcluster analysis (Aukema et al., 2006). However, results
obtainedfrom these approaches only specified the type of spatial
andtemporal pattern used by the pests during the damage spread.
Ithas been mostly revealed that such pattern is random, with
eitheraggregated or regular structures (Begon et al., 1996;
Vinatier et al.,2011). Although some of the above-mentioned
methodologies forspatial analysis have been applied to various
types of insect pestsand crops (Aukema et al., 2006; Blackshaw and
Hicks, 2013; Boneet al., 2013; Cocco et al., 2013; Cocu et al.,
2005; Dder et al., 2015;Diaz et al., 2012; Emmen et al., 2004;
Fernandes et al., 2015;Holland et al., 2005; Ishaaya and Horowitz,
2004; Kautz et al., 2011;Keasar et al., 2005; Kim et al., 2007;
Lausch et al., 2013; MoralGarca, 2006; Perry, 1994; Reay-Jones,
2010; Reay-Jones et al.,2007; Reineke et al., 2011; Rogers et al.,
2015; Schumann et al.,2014; Smith et al., 2004; Thomas et al.,
2001; Wang et al., 2009;Wright et al., 2002; Zhang et al., 2016;
Zimmerman et al., 2004), nostudies have investigated the
spatio-temporal damage patterns oflepidopteran stem borers using
experimental data from small-holder maize farms.
Maize (Zea mays L.) is the most important staple food in
sub-Saharan Africa, particularly in East Africa (De Groote et al.,
2005;Kipkoech et al., 2006). However, biotic (stem borers, gray
leaf spot,maize streak virus) and abiotic (drought, low soil
fertility) factorsconstrain maize production (Stevens, 2008).
Lepidopteran stemborers are considered to be the most damaging
insect pests ofmaize in Africa (Overholt et al., 2001). In East
Africa, the noctuidBusseola fusca (Fuller) is the most damaging in
the high potentialyield areas, which include the highland tropics
and moisttransitional zones (De Groote, 2002; Ongamo et al., 2006).
FemaleB. fusca moths lay several hundred eggs (in batches of
30100inserted between the sheath and the stem) (Harris and
Nwanze,1992). Larvae hatch after one week and disperse over
neighboringplants using silk strands (Harris and Nwanze, 1992).
After passingthrough five larval instars in 3045 days, all the
while eating theleaves and stems, they pupate in tunnels inside the
plant, oftenafter excavating emergence windows to facilitate the
exit of adultmoths (Overholt et al., 2001). Adults emerge 1020 days
afterpupation. The life cycle is completed in 78 weeks. In its
earlylarval stage, this insect causes foliar damage during the
plant whorlstage which gives an array of small holes when unfolded;
thisdamage is called leaf damage (LD) (Harris and Nwanze,
1992).Destruction of the meristematic tissues causes dead heart
(DH)damage (Bosque-Prez and Mareck, 1998). Furthermore, stemborers
may cause exit holes (EH) damage at the periphery of thestem, to
facilitate the exits of adult moths (Harris and Nwanze,1992).
Damages caused by stem borers at larval stage are
ultimately stunting plant growth and plant death (D)
(Brenire,1971). In high potential areas of Kenya, yield losses of
maize due tostem borer infestations are estimated at between 12%50%
of thetotal production, as a result of leaf feeding, dead heart,
stemtunneling, direct damage to grain, and secondary infection by
stalkrots and lodging (De Groote et al., 2003; Kfir et al., 2002;
Polaszek,1998).
Research on lepidopteran stem borer pests has been carried outin
sub-Saharan Africa for decades (Calatayud et al., 2014; Harrisand
Nwanze, 1992; Kfir et al., 2002). Although the biology andecology
of lepidopteran stem borers have been extensively studiedduring a
long period of time (Calatayud et al., 2014; Harris andNwanze,
1992; Kfir et al., 2002; Polaszek, 1998), to our knowledge,there
are no downscaled studies from smallholder maize farms toassess
spatio-temporal infestation dynamics of B. fusca. Theavailable
information is related only to regional distribution,agro-climatic
preferences and phylogeography over wide scales(Dupas et al., 2014;
Guofa et al., 2001; Harris and Nwanze, 1992;Hauptfleisch et al.,
2014; Le Ru et al., 2006; Mwalusepo et al., 2015;Ongamo et al.,
2006; Overholt et al., 2001; Sezonlin et al., 2006).Understanding
the spatial and temporal dynamics of damagespread constitutes the
basic information for future development ofappropriate pest
management strategies. Most studies concernedwith smallholder
farming have focused on the density and rate ofinfestation at field
level, by selecting randomly a few plants perfield and looking at
the within-plant insect distribution, withoutmaking emphasis on the
level of damage, its characterization andthe insect pest
distribution in the field (Amoako-Atta et al., 1983;Ndemah et al.,
2001; Overholt et al., 1994; Van Rensburg andPringle, 1989). Such
sampling approach presents a considerablebias, because of the
probable failure in capturing the infestationspread in the whole
farm. Selecting only a small number of plantsfailed to reveal the
spatial and temporal dynamic of infestation ofthe pest.
The main purpose of this study is to understand the
spatio-temporal spread of B. fusca damage at smallholder maize
farmlevel. Specific objectives are to: find a relationship between
thetemporal evolution of infestations rate and the flight
dynamicpattern; estimate the probability of infestations and
understandthe transition time between damage types; analyze the
spatialdistribution of the infestation; and identify the spread
pattern ofthe damage in the farm.
2. Materials and methods
2.1. Study site and sampling procedure
The farms selected for the study are located in Naivasha, in
theRift Valley region, Northwest of Nairobi. The
geographicalcoordinates are: latitude 04300000 S, longitude
362600900 E,2086 m a.s.l. Busseola fusca is the predominant
lepidopteran maizestem borer at this elevation (Ongamo et al.,
2006). The study wascarried out on private land, after the owners
gave permission toconduct the study on these sites. Six maize plots
(Table 1) withmonoculture farming were selected. The maize plants
wereplanted at regular row/within row space intervals, to
facilitatecounting. The planting date and crop management practices
wereidentical in all selected plots. Data collection consisted of
visualchecking of all plants damaged by B. fusca, and was
conductedweekly during 13 weeks, from the 23 November, 2010 to the
10February, 2011. Coordinates of all sampled maize plants
with/without damages were recorded. Four plant damage types
wereconsidered: LD, DH, EH and D. Furthermore, we placed
twopheromone insect traps around each field to follow the
flightdynamic of B. fusca males. For each plant, we assigned an
ordinalnumber ranging from 1 to the total number of plants within
the
https://www.researchgate.net/publication/231760233_Influence_of_maize_cowpea_and_sorghum_intercropping_systems_on_stem-pod-borer_infestations?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/230819342_Distribution_pest_status_and_agro-dimatic_preferences_of_lepidopteran_stem_borers_of_maize_in_Kenya?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/230819342_Distribution_pest_status_and_agro-dimatic_preferences_of_lepidopteran_stem_borers_of_maize_in_Kenya?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/261107535_Phyloeography_in_continuous_space_Coupling_species_distribution_models_and_circuit_theory_to_assess_the_effect_of_contiguous_migration_at_different_climatic_periods_on_genetic_differentiation_in_Busse?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/232006474_Changes_in_the_distribution_of_Lepidopteran_maize_stemborers_in_Kenya_from_the_1950s_to_1990s?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/230819333_Geographic_distribution_and_host_plant_ranges_of_East_African_noctuid_stem_borers?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/274509631_Ecology_of_the_African_Maize_Stalk_Borer_Busseola_fusca_Lepidoptera_Noctuidae_with_Special_Reference_to_Insect-Plant_Interactions?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/274509631_Ecology_of_the_African_Maize_Stalk_Borer_Busseola_fusca_Lepidoptera_Noctuidae_with_Special_Reference_to_Insect-Plant_Interactions?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/11627000_Biology_and_Management_of_Economically_Important_Lepidopteran_Cereal_Stem_Borers_in_Africa?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/11627000_Biology_and_Management_of_Economically_Important_Lepidopteran_Cereal_Stem_Borers_in_Africa?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/11627000_Biology_and_Management_of_Economically_Important_Lepidopteran_Cereal_Stem_Borers_in_Africa?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/278677039_Predicting_the_Impact_of_Temperature_Change_on_the_Future_Distribution_of_Maize_Stem_Borers_and_Their_Natural_Enemies_along_East_African_Mountain_Gradients_Using_Phenology_Models?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/7326391_Phylogeography_and_population_genetics_of_the_maize_stalk_borer_Busseola_fusca_Lepidoptera_Noctuidae_in_sub-Saharan_Africa?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/32997959_Les_problemes_de_lepidopteres_foreurs_des_graminees_en_Afrique_de_l'Ouest?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/32997959_Les_problemes_de_lepidopteres_foreurs_des_graminees_en_Afrique_de_l'Ouest?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==
Table 1Description of plots used for the experiment. Distances
are given in meters. The dimensions are the maximal repartition
range of plants inside the plot. The six plots covered azone of 7
kilometer square.
Plot Row length Row spacing Dimensions Total number of maize
plants GPS coordinateslatitude, longitude
1 55.00 0.81 51.03 53.93 6547 0.7701, 36.49592 31.54 1.86 150.66
31.57 3914 0.7747, 36.47863 53.10 1.56 123.24 52.14 8146 0.7819,
36.47114 30.50 1.49 120.69 30.50 4974 0.7713, 36.47665 46.80 1.40
74.20 46.80 4884 0.7826, 36.47046 27.82 1.05 153.30 27.82 7314
0.7765, 36.4791
F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118 107
field. In addition, for each of the 4 damage types, we assigned
foreach plant an integer value indicating the week of the
observeddamage. If there was no B. fusca damage observed during
thecollection period, the value given was 0.
2.2. Observation of infestation rate variation and insect trap
catches
To get the weekly variation of LD rate, the differences
betweennumbers of damaged plants during two consecutive weeks
wereevaluated in each plot. Results were normalized so that
theassociated values ranged from 0 to 1. As well, the weekly
meanvalues of adult B. fusca males caught in insect traps were
computedand then, the temporal dynamics of LD rate and the
abundance ofadult males were compared.
2.3. Conditional probabilities for plant infestation type
Because maize was planted at regular row/within row spaceand the
planting date and crop management were identical as wellas the date
of data collection, we decided to compute theprobability that a
randomly selected plant is found dead (D) oncedamage i has
occurred, where i = LD, EH or DH. Next, theconditional probability
that D occurs once i has occurred wascomputed.
2.4. Meantime transition between infestation types
The estimation of the average time span between damage typeswas
done. First, for plant P we recorded the damage type i (i = LD,EH,
DH or D), and the corresponding week of observation. Second,we
checked and recorded the week of occurrence for otherdifferent
damage from i, which we called j. Third, we computed thedifference
between the two weeks to get the time duration for thetransition Pi
! Pj. Fourth, we repeated the same process for othermaize plants to
obtain a mean time transition from a damage typeto another.
2.5. Spatial distribution
2.5.1. Spatial autocorrelationObservations of damage in plants
with different geographical
coordinates may not be uncorrelated. Spatial autocorrelation
maybe positive or negative symbolizing how similar or
dissimilardamage occurs close by. The farm in this context had a
latticestructure made of discontinuous spatial repartition of
maizeplants. The first step in this analysis was to define the
Euclideandistance matrix from x- and y-coordinates of two
individual plantsi and j (dij). Secondly, the spatial relationship
between damagedplants was quantified using the spatial weight
matrix W in whichelements represent the strength of the spatial
structure betweenunits (Anselin et al., 2004; Cliff and Ord, 1973).
This matrix wasused to evaluate the level of spatial
autocorrelation. There are
various ways to define W and the choice of a particular method
toother is subjective. The easiest option is to construct a
binarycontiguity matrix (made of 0 and 1) by specifying the units
that areadjacent (1) and those that are not (0) (Cliff and Ord,
1981).Therefore, the spatial weight matrix wrij is equal to 1 for
dij less thana certain critical distance r and, it is equal to 0
otherwise. Thevariable r is the radius of proximity. The Morans I
coefficient Ir isused to quantify the degree of spatial correlation
betweenneighboring infested plants. The formula used to calculate
MoransI can be found in the literature (Cocu et al., 2005; Jumars
et al.,1977; Moran, 1950; Zuur et al., 2007). In this analysis, the
valueassigned to each plant is a binary index of LD, EH, DH or D (0
fornon-infested and 1 for infested plants). The interpretation
ofMorans I is similar to the correlation coefficient (Zuur et al.,
2007).If the Morans I is null, then the spatial link between
infested plantsat distant locations is null, and there is no
spatial autocorrelation(SA). If the Morans I is positive, then the
contagiousness of infestedplants at distinct locations is
considerable, and the SA is positive. Ifthe Morans I is negative,
then the majority of the infested plantsare not next to each other,
and the SA is negative. The graphicalrepresentation of the Morans I
function of r is called variogram (orcorrelogram). For the Morans I
computation, data were reorgan-ized as follow: for each maize plant
we recorded the spatialcoordinates. Values of LD, EH, DH and D,
were 0 or 1 (1 is forinfested and 0 for non-infested). The process
was repeatedcumulatively for subsequent weeks to obtain a temporal
vario-gram.
2.5.2. Tracking the center of plant infestationObserving
directly the spatial and temporal evolution of plant
infestation is difficult in a maize farm having a
considerablenumber of stems; thus we opted to compute the
iso-barycentre (IB)coordinates of each occurring damage type.
First, we selected thecoordinates of plants with leaf damage, and
then we calculated thecoordinates of the weekly IB without taking
into account resultsobtained from the previous weeks computations.
We obtained theIB coordinates by averaging x- and y-coordinates
corresponding tothe set of newly infested plants observed at each
week, whichallowed us to track the position of the center of
patches formed byinfested plants with time. The IB coordinates at a
week t xt ; yt wascomputed as follows:
xt ; yt Xnti1
xtint;Xnti1
ytint
!; 1
where xti ; yti are the spatial coordinates of a damaged plant i
atweek t, and nt is the total number of damaged plants at week
t.
2.5.3. Model-based cluster analysis: spatial clusteringTo follow
the evolution of the initial shape, density and number
of clusters of infested plants formed with time the
model-basedcluster analysis was used. For a spatial classification
of clusters, the
https://www.researchgate.net/publication/227260508_Detecting_two-dimensional_spatial_structure_in_biological_data?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/227260508_Detecting_two-dimensional_spatial_structure_in_biological_data?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/229874627_Spatial_autocorrelation_for_identifying_the_geographical_patterns_of_aphid_annual_abundance?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/8295498_Notes_on_Continuous_Stochastic_Phenomena?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/40792120_Yield_loss_in_apple_caused_by_Monilinia_fructigena_Aderh_Rhul_Honey_and_spatio-temporal_dynamics_of_disease_development?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/40792120_Yield_loss_in_apple_caused_by_Monilinia_fructigena_Aderh_Rhul_Honey_and_spatio-temporal_dynamics_of_disease_development?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==
Fig. 1. Neighborhood plant configurations (black color) around
the central plant(white color). (a) Representation of the extended
Moore neighborhood types thecentral plant possessing the label
number 1 the rest are labeled randomly from 2 to25. (b)
Representation of the Moore neighborhood type. We consider the
centralplant possessing the label number 1, the rest are labeled
randomly from 2 to 9.
108 F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118
following procedure was adopted: (i) positions of damaged
plantswere assumed implicitly generated by a mixture of
probabilitydistribution function in which different components
representgroups or clusters; (ii) based on the framework developed
in(Banfield and Raftery, 1993), we used Gaussian and
non-Gaussianprobability density (PDF) functions for the clustering.
In addition,orientation, volume, and shape of the cluster were
determinedusing the models summarized in Table 2 (Fraley et al.,
2007). Thesethree features are characteristics of the bottom of the
two-dimensional PDF. The likelihood of each model is estimated via
theexpectation-maximization algorithm (Dempster et al., 1977),which
allows us to assign a Bayesian information criterion (BIC)(Akaike,
1974; Schawrz, 1978) for selecting the best model. Thissection of
the analysis was carried out using the statistical packagemclust of
the software R (R Core Team, 2013).
2.5.4. Spatial and temporal contagion patterns: identification
ofcellular automata rules for the propagation of infestations
Cellular automata (CA) are spatially and temporally
discretesystem characterized by local interaction and
synchronousdynamical evolution (Von Neumann, 1957). Usually, CA is
usedas a modeling approach to mimic the behavior and pattern of
thespread of an infestation. However, our objective was to find
thelikely geometrical configuration (rule) of the spread of
infestationto uninfested maize plants by considering each maize
plant as anelement, which is attributed a state
(infested/non-infested), whichwas assumed to change depending on
the plants state and thestates of the plants in its vicinity.
Hence, CA is useful forinvestigating the spatial pattern of the
nearest neighboring plantsaround a safe central plant that is most
likely the source of thecontagion. The minimal neighborhood for the
CA is estimated viathe application of the algorithm described in
(Sun et al., 2011). Themethodology was structured as follows: (i)
the state of each plantand its neighbors are collected in a maximal
neighborhood radius(initially fixed), then the primary neighborhood
of a plant isformed by 24 maize plants (Fig.1); (ii) the initial
neighborhood wasreduced by selecting the configuration that
minimized thevariance between the CA results and the data; (iii) in
the obtainedneighborhood, the plants without any effect on the
state of thecentral plant are removed; (iv) the BIC is used to
determine theneighboring configuration that has the most
significant impact onthe change of state of the central maize
plant. This approach wasimplemented in Matrix Laboratory (MATLAB,
2010).
3. Results
Due to climatic conditions in Naivasha, B. fusca was
theexclusive lepidopteran maize stem borer found during the
surveyperiods. Subsequently the results presented here only focus
on thisspecie.
Table 2Model identifiers use three letters encoding the
geometric characteristics: Volume-Shape-Orientation. E means equal,
V means varying across clusters and I refers toidentical
orientation (Fraley et al., 2007).
Model identifier distribution
EII SphericalVII SphericalEEI DiagonalVEI DiagonalEVI
DiagonalVVI DiagonalEEE EllipsoidalEEV EllipsoidalVEV
EllipsoidalVVV Ellipsoidal
3.1. Dynamics of infestation
Variation of the number of damaged plants and damage typesper
week for each plot are shown in Fig. 2. It is observed that
thephenomenon is not linear. A detailed observation of LD in plots
3, 5and 6, displayed two notable phases of the occurrence of
damages,especially around weeks 45 and weeks 78. During weeks 45
asudden increase of damaged plants in plots 3, 5 and 6 is
detectedalthough no damage was previously noticed. At weeks 7 and
8,another sudden peak of damage is noted before starting
togradually decrease, as the maturity of the plants approached.
InFig. 3, we showed the variation of the average number of
insectscaught and the number of LD in the six plots. Two peaks of
maleflying activities can be clearly seen during weeks 2 and
6.Subsequent appearances of LD peaks during weeks 4 and 8 can
alsobe noticed. Linking Figs. 2 and 3, we estimated that the time
lagbetween the peak of B. fusca male captured and the observed
peakof LD is approximately 2 weeks.
3.2. Probability and transition time between damages
The conditional probability for a randomly selected plant to
diefollowing different situations is given in Table 3. In plots 1
and 2,the probability for a plant to die given that it has LD is
the highest.For plots 36 those probabilities are null. Table 4
shows the meantransition time between different types of damage.
The time spansare similar for plots 1 and 2 and, longer for plot 5.
In plot 4, thetransitions are faster compared to the other plots.
Plots 3, 4 and 6have a shorter transition time between damages. The
transitionLD ! DH seems to be the fastest. Overall, if all the
plots areconsidered as a unique field, the results would
demonstrate thatthe transition time between different types of
damage is notuniform, which means the phenomenon is stochastic.
3.3. Spatial scattering of leaf damage with time
The weekly evolution of spatial and temporal
autocorrelation(Morans I) for the LD is depicted in Fig. 4. For
plots 2, 3, 5, and 6 theinfestation is strongly spatially
correlated for a very low radius ofproximity between infested
plants, but over time, the spatial linkbetween damaged plants
became more and more considerable.The spatial correlation distance
for damaged plants inside thesefour plots did not exceed 10 m. A
considerable spatial autocorrela-tion was observed between damaged
plants within a radius ofproximity exceeding 10 m in plot 1 and 4.
These results help us to
https://www.researchgate.net/publication/265505693_Model-Based_Gaussian_and_Non-Gaussian_Clustering?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/5142972_Model-Based_Methods_of_Classification_Using_the_Mclust_Software_in_Chemometrics?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/5142972_Model-Based_Methods_of_Classification_Using_the_Mclust_Software_in_Chemometrics?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/49661344_Fast_Rule_Identification_and_Neighborhood_Selection_for_Cellular_Automata?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/221995817_Maximum_Likelihood_from_Incomplete_Data_Via_EM_Algorithm?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/271196714_New_Look_at_Statistical-Model_Identification?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==
Fig. 2. Differences in infested plants densities between two
consecutive weeks. The computation has been done for the four
infestation types, leaf damages (LD), death hearth(DH), exit hole
(EH) and death (D). For plot 4 see the annex.
F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118 109
understand the level of similarity and dependence between
thepoint locations of damaged plants. Fig. 5 shows the weekly
trackingof spatial iso-barycentre (IB) for LD plants. The initial
IB position israndom; the others are located around the former IB.
After the firstcycle of infestation, we observed a general trend
towards an IBfurther from the previous recorded during the first
cycle. Indeed, a
spatio-temporal pattern of infestation is clearly visible: a
spiral-like pattern of LD at the beginning, which goes farther from
theorigin point with time.
Spatial clustering analysis was carried only on plots 3, 5 and
6where the damage was at the initial stage of plants growth. Fig.
6displays the initial spatial classification of LD plants. The
spatial
Fig. 3. (a) Mean values of number of adult B. fusca caught
weekly with pheromonetraps at. (b) Mean values of plants with the
leaf damage (LD) infestation recordedweekly. (1a) and (2a)
represent the peaks observed before theapparition of theinfestation
peaks (1b) and (2b) respectively. The bars represent the
standarddeviation error of the mean.
Table 4Mean time in weeks for transition between infestation
types. It is given in theformat: mean time standard error of the
mean (SEM). The symbol () means thatsuch transition was not
observed. For the plot 4 and 5 see the annex.
Mean time SEMTransition Plot 1 Plot 2 Plot 3 Plot 6LD ! DH 6.34
0.12 6.42 0.17 6.75 1.60 6.49 0.11DH ! D 7.14 0.30 7.45 0.31 7.03
0.53 LD ! D 7.17 0.28 7.07 0.31 7.03 0.53 LD ! EH 7.23 0.08 8.28
0.16 7.77 0.20
110 F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118
clusters generated by patches of infested plants are dissimilar
interms of the number of damaged plants at the start of
observation;however the gap reduced as it approaches the last
weeks. In plot 3,we initially observed a small number of damaged
and scatteredplants; whereas plot 5 and 6 engorged big number of
damagedplants. The cluster shape made by damaged plants is
typicallyellipsoidal. With time, two phases were observed; during
the firstphase, the number of clusters remained almost the same but
theshape of the initial clusters and their respective size (number
ofplants) changed gradually. During the second phase, new
clustersarose possibly from the fragmentation of the initial
clusters ratherthan the creation of isolated new entities.
3.4. Understanding plant contagion patterns
According to Table 5, the LD dynamics in plot 1, 2 and 4
followeda cellular automata law. It is demonstrated that, if four
plants areinfested following the Moore neighborhood of contagion
pattern,the plant at the central position is most likely to be
infested duringthe subsequent week. However, in plot 3, 5 and 6 the
algorithmreduced the initial neighborhood to the central cell,
indicating thefailure to estimate the neighborhood in these cases.
Such resultsare justified by the non-linear and perhaps chaotic
behavior of thespread of the infestations of pests within plants at
field level. Asummary of all the spatial results are provided in
Table 6.
Table 3Conditional probability for a plant to die following
different cases. P(D|i) is aprobability that a plant died, given
that it has an infestation i. LD = leaf damage,EH = exit hole, DH =
dead heart and D = dead.The symbol () means null probabilityand the
symbol [ stands of or.
Probabilities
Events Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 Plot 6
PDjLD 0.0116 0.0337 0 0 0 0PDjEH[ DH 0.0082 0.0303 0 0 0 0PDjEH[
DH[ LD 0.0102 0.0330 0 0 0 0
4. Discussions
4.1. Sampling scheme
Developed sampling schemes for assessing lepidopteran stemborer
pest infestations in maize farms are often focusing
onchecking/collecting at random a few plants infested/uninfested
bythe larvae without taking into consideration the precise
pointlocation of the plants within the field (Amoako-Atta et al.,
1983;Ndemah et al., 2001; Overholt et al., 1994; Van Rensburg
andPringle, 1989). In our study, all the plants had a precise
geo-referenced position in each field. We did not used
destructivesampling, as the dissection of plants can interfere with
the spreadof the larvae and the oviposition distribution of the
females in theplots. In addition, farmers perceive destructive
sampling asunacceptable, and it is time consuming and expensive
(Nyropet al., 1999). The presence/absence infestation data
collected fromvisual inspection of external and comprehensive signs
of insectpest damages in the plants may be more appropriate.
Particularattention was given to leaf damage because young larvae
are oftenresponsible for this type of damage and it occurs mainly
on youngmaize plants; therefore making it easy to be used as a
proxy forrelating the spread of the infestation. In addition, leaf
damage hasalso been reported as an important factor in contributing
to yieldlosses in maize farms (Kfir et al., 2002; Reddy and Sum,
1991).
4.2. Infestation rate dynamic
Considering that B. fusca egg development is completed during810
days at 2520 C (Khadioli et al., 2014), the first flight
periodstarted at least 10 days before the first set of leaf damage,
andended in all plots at least 10 days before the end of the 5th
week.This suggests an absence of B. fusca female oviposition during
days1015 days around week 56 as no damage was recorded in week 7in
all plots. The second peak of damages is observed on all
plotsbetween weeks 7 and 8. No exit holes were noticed in all
plotsduring the second peak; which suggest that majority of eggs
laidwithin this period of time was caused by females from
otherlocalities. The observation of a fixed time lag between
theoccurrence of peaks of LD and adult male abundance in
pheromonetraps is in accordance with previous studies (Coop et al.,
1992;Hillier et al., 2004; Masetti et al., 2015; Millar et al.,
2002; Moriet al., 2014; Qureshi and Ahmed, 1991; Riedl and Croft,
1974;Thming et al., 2011; Tobin and Whitmire, 2005). The abundance
ofmales in pheromone-baited traps was significantly correlated
toobserved damages in studies (Coop et al., 1992; Hillier et al.,
2004;Masetti et al., 2015; Millar et al., 2002; Mori et al., 2014;
Qureshiand Ahmed,1991; Riedl and Croft,1974; Thming et al., 2011;
Tobinand Whitmire, 2005). Additionally, the spacing of two weeks is
theB. fusca egg development time, after which it emerges to
larvastage and starts damage. It has been reported that B. fusca
prefer tolay eggs in young maize plants (Calatayud et al., 2014;
Harris andNwanze, 1992; Kfir et al., 2002); however, the
continuous
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romone-baited_trap_capture_larval_abundance_damage_and_flight_phenology?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/264717413_Relationships_among_male_Coleophora_deauratella_Lepidoptera_Coleophoridae_pheromone-baited_trap_capture_larval_abundance_damage_and_flight_phenology?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/249449990_Monitoring_seasonal_population_fluctuation_of_spotted_and_spiny_bollworms_by_synthetic_sex_pheromones_and_its_relationships_to_boll_infestation_in_cotton?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/249449990_Monitoring_seasonal_population_fluctuation_of_spotted_and_spiny_bollworms_by_synthetic_sex_pheromones_and_its_relationships_to_boll_infestation_in_cotton?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/249449990_Monitoring_seasonal_population_fluctuation_of_spotted_and_spiny_bollworms_by_synthetic_sex_pheromones_and_its_relationships_to_boll_infestation_in_cotton?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/262002147_Risk_assessment_of_pea_moth_Cydia_nigricana_infestation_in_organic_green_peas_based_on_spatio-temporal_distribution_and_phenology_of_host_plant?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/262002147_Risk_assessment_of_pea_moth_Cydia_nigricana_infestation_in_organic_green_peas_based_on_spatio-temporal_distribution_and_phenology_of_host_plant?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/274509631_Ecology_of_the_African_Maize_Stalk_Borer_Busseola_fusca_Lepidoptera_Noctuidae_with_Special_Reference_to_Insect-Plant_Interactions?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/11627000_Biology_and_Management_of_Economically_Important_Lepidopteran_Cereal_Stem_Borers_in_Africa?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/11627000_Biology_and_Management_of_Economically_Important_Lepidopteran_Cereal_Stem_Borers_in_Africa?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/283593398_Spread_of_gypsy_moth_Lepidoptera_Lymantriidae_and_its_relationship_to_defoliation?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/283593398_Spread_of_gypsy_moth_Lepidoptera_Lymantriidae_and_its_relationship_to_defoliation?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/283593398_Spread_of_gypsy_moth_Lepidoptera_Lymantriidae_and_its_relationship_to_defoliation?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/259379438_Determination_of_Economic_Injury_Level_of_the_Stem_Borer_Chilo_Partellus_Swinhoe_in_Maize_Zea_Mays_L?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==
Fig. 4. Spatial and temporal autocorrelation for leaf damage
(LD) on plants. The computation has been done for a radius of
proximity (r) from 1 m to 20 m. The color bar on theright side of
each figure represents the spatial autocorrelation level. For plot
1 and 4 see the annex.
F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118 111
infestation pattern observed in this study suggests that
ovipositionon older plants is possible.
Although the main goal of this study was to examine the
spatialand temporal spread of maize stem borer B. fusca under
fieldconditions, we though necessary to comprehend the sequence
ofplants infestations by estimating the conditional probabilities
ofeach damage type causing death of the plant. A higher mortality
ofmaize plants with LD was observed. However, the stem
tunnelingwhich is logically expected to occur after LD and before
DH, D, EHwas not considered here. Moreover, the data collection
protocol ofthe present study was unsuitable for assessing stem
tunneling,which effect is a major cause of grain yield reduction in
maize(Ajala and Saxena, 1994). It might be convenient to sample
anddissect a few plants to assess and account for the incidence
oftunneling.
The mean transition time from a type of damage to anotherdamage
type was different from one plot to another. This suggestseither a
variability of the phenological stage of the plants due to
thedifference in sowing time in each plot and/or the
heterogeneouscharacteristics of the soil and nutrients managements,
which weredifferent from one plot to another. Variability might be
also due tothe phenological stage of the stem borer larvae and the
differencein nutritional quality of the plants. Temperatures
difference may aswell play a part in the discrepancy.
4.3. Spatial and temporal scattering of infestation
An increase of the spatial correlation with time implies
anoverall trend toward a single gradient. The results of
independenceof damaged plants noticed via negative spatial
autocorrelations insome plots are likely an artifact and not a
biological phenomenon.
The variogram is expected to depict no spatial autocorrelation
forsome distances (Blackshaw and Hicks, 2013; Bone et al., 2013;
Cocuet al., 2005; Smith et al., 2004). During data collection,
indepen-dent samples must be taken at random at different locations
sothat each sample has an equal chance to be selected (Pedigo,
1999).However, infested plants separated by a small radius of
proximity(less than 10 m) were considerably correlated. Therefore,
suchevidence of a spatial link between infested plants leads to
theconclusion that sequential plant samples may not be a
properrepresentative of a statistically independent sample
procedure,thus violating the assumptions of random sampling in the
field(Pedigo,1999). In improving traditional sampling schemes
devotedto lepidopteran stem borer infestation through a selection
ofsamples far enough from each other to ensure total
independence,this finding should be taken into account.
The movements of the focal area (centroid) associated
withinfestations might reflect shifts in the spatial and
temporalpatterns of adult and larval dynamics during insect
damageprocess. In literature, centroid infestation tracking was
used tomonitor the movement of Eoreuma loftini with pheromones
baitedtraps throughout the Texas rice belt (Reay-Jones et al.,
2007), tocheck the spatial evolution of Nasonavia ribisnigri after
release offew adults at the center of a lettuce field (Diaz et al.,
2012), andquantifying spatio-temporal infestation patterns of Ips
typogra-phus in the Bavarian Forest National Park through dead
woodpatches surveillance (Lausch et al., 2013). These studies
providedan approximate speed of leading edge of rice borer
infestationmovement (Reay-Jones et al., 2007), ability of the aphid
to spreadfrom a source plant of release over time in a green
house(Diaz et al., 2012) and spatio-temporal dispersion patterns of
barkbeetle damages on trees on a wide area scale over years
https://www.researchgate.net/publication/290791657_Interrelationship_among_Chilo_partellus_Swinhoe_damage_parameters_and_their_contribution_to_grain_yield_reduction_in_maize_Zea_mays_L?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/257496322_Distribution_of_adult_stages_of_soil_insect_pests_across_an_agricultural_landscape?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/236455471_A_GIS-Based_Risk_Rating_of_Forest_Insect_Outbreaks_Using_Aerial_Overview_Surveys_and_the_Local_Moran's_I_Statistic?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/221790632_Interplant_movement_and_spatial_distribution_of_alate_and_apterous_morphs_of_Nasonovia_ribisnigri_Homoptera_Aphididae_on_lettuce?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/221790632_Interplant_movement_and_spatial_distribution_of_alate_and_apterous_morphs_of_Nasonovia_ribisnigri_Homoptera_Aphididae_on_lettuce?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/43260929_Dispersal_and_Spatiotemporal_Dynamics_of_Asian_Longhorned_Beetle_Coleoptera_Cerambycidae_in_China?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/229874627_Spatial_autocorrelation_for_identifying_the_geographical_patterns_of_aphid_annual_abundance?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/229874627_Spatial_autocorrelation_for_identifying_the_geographical_patterns_of_aphid_annual_abundance?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/236216904_Spatio-temporal_infestation_patterns_of_Ips_typographus_L_in_the_Bavarian_Forest_National_Park_Germany?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/6436329_Movement_of_Mexican_Rice_Borer_Lepidoptera_Crambidae_Through_the_Texas_Rice_Belt?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/6436329_Movement_of_Mexican_Rice_Borer_Lepidoptera_Crambidae_Through_the_Texas_Rice_Belt?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==
Fig. 5. Temporal evolution of the isobarycentre (IB) for leaf
damaged infested plants. The integer values represent the
corresponding week. The position of the number is thespatial
position of the IB. The IB from the first and second cycle are
colored in blue and red respectively. For plot 4 and 5 see the
annex. (For interpretation of the references tocolour in this
figure legend, the reader is referred to the web version of this
article.)
112 F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118
(Lausch et al., 2013), respectively. A similar method was
appliedhere, but with regular monitoring of all plants within the
fields andprecise damage types were captured. Such consistency in
theexperiment helped us to detect that spatial and temporal
patternof B. fusca infestations had a geometrical design under a
form of aspiral around initial patches of damaged plants.
Identifying suchan infestation pattern can be difficult or
impossible with previousapproaches (Diaz et al., 2012; Lausch et
al., 2013; Reay-Jones et al.,2007).
It has been reported that some females insects such as
Deliaradicum on cabbage plants (Baur et al., 1996), Eurosta
solidaginis onlate golden rod plants (Craig et al., 2000),
Lygocoris pabulinus onpotato plants (Groot et al., 2003) and
Anthocoris nemorum onapple/pear trees (Sigsgaard, 2004), oviposit
where conspecificshave already oviposited. The contrary has been
reported forTrichoplusia ni on cotton plants (Landolt, 1993),
Narnia femorata oncactus plants (Miller et al., 2013), Heliothis
virescens on tobacco
plants (De Moraes et al., 2001) and Pieris rapae on crucifer
plant(Sato et al., 1999). In the present study, results of spatial
clusteringsuggest that B. fusca females tend to oviposition patches
of maizeplant already infested; and thus, exhibit the fisrt
behavorialcharacteristic. The homogenization of distribution inside
eachcluster might be due to overcrowding during plant host
selection(Schoonhoven et al., 2005; Thompson and Pellmyr,
1991).Furthermore, a strong instability (drastic changes in
clustersshapes and number of plants inside each spatial cluster)
during theevolution of initial damage distributions might be due to
acombination of adult moth oviposition and larval
interplantmovements (Calatayud et al., 2014; Harris and Nwanze,
1992;Van Rensburg et al., 1987).
Spatial pattern dynamics that insect pests generated can beused
to improve sampling strategy, site-specific pest management,and
sowing distribution. However, when agricultural practices
areundertaken, the precise spatial and temporal dynamics of the
https://www.researchgate.net/publication/221790632_Interplant_movement_and_spatial_distribution_of_alate_and_apterous_morphs_of_Nasonovia_ribisnigri_Homoptera_Aphididae_on_lettuce?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/227142786_Preference_for_plants_damaged_by_conspecific_larvae_in_ovipositing_cabbage_root_flies_Influence_of_stimuli_from_leaf_surface_and_roots?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/12053020_Caterpillar-induced_nocturnal_plant_volatiles_repel_conspecific_females?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/230333390_Oviposition_preference_of_Lygocoris_pabulinus_Het_Miridae_in_relation_to_plants_and_conspecifics?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/281771434_Pieris_rapae_Lepidoptera_Pieridae_females_avoid_oviposition_on_Rorippa_indica_plants_infested_by_conspecific_larvae?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/274509631_Ecology_of_the_African_Maize_Stalk_Borer_Busseola_fusca_Lepidoptera_Noctuidae_with_Special_Reference_to_Insect-Plant_Interactions?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/249432697_The_influence_of_host_plant_variation_and_intraspecific_competition_on_oviposition_preference_in_the_host_races_of_Eurosta_solidaginis?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/236216904_Spatio-temporal_infestation_patterns_of_Ips_typographus_L_in_the_Bavarian_Forest_National_Park_Germany?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/236216904_Spatio-temporal_infestation_patterns_of_Ips_typographus_L_in_the_Bavarian_Forest_National_Park_Germany?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/227838144_Oviposition_preference_of_Anthocoris_nemorum_and_A_nemoralis_for_apple_and_pear?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/296353059_Evolution_of_oviposition_behavior_and_host_preference_in_Lepidoptera?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/226441022_Effects_of_host_plant_leaf_damage_on_Cabbage_Looper_moth_attraction_and_oviposition?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/231750377_Ecology_of_the_maize_stalk_borer_Busseola_fusca_Fuller_Lepidoptera_Noctuidae?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/249967859_Conspecific_and_Heterospecific_Cues_Override_Resource_Quality_to_Influence_Offspring_Production?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/6436329_Movement_of_Mexican_Rice_Borer_Lepidoptera_Crambidae_Through_the_Texas_Rice_Belt?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==https://www.researchgate.net/publication/6436329_Movement_of_Mexican_Rice_Borer_Lepidoptera_Crambidae_Through_the_Texas_Rice_Belt?el=1_x_8&enrichId=rgreq-80e54525e315c07168090c2ac1354ae5-XXX&enrichSource=Y292ZXJQYWdlOzMwOTMxOTIwNDtBUzo0NDkyODQzMzczNDQ1MTJAMTQ4NDEyOTE0MTA2Mg==
Fig. 6. Spatial clustering of plants with LD infestation for
plot 6 at week 4 (a), 9 (b), 11 (c) and 13 (d). Each color
represents a spatial cluster. The ellipsoids/circles represent
thebottom of two-dimensional probability density functions (PDF).
The shapes are chosen according to the Bayesian information
criteria. The centers represent the centroid ofthe cluster
distributions. The dot outside the circular represents plants
positions with weak probabilities compared to the center of the
PDF. For plot 3 and 5 see the annex.
F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118 113
damages are usually not well known (Wang et al., 2009).
Moreover,most of the existing statistical techniques used in
similar agro-ecological frameworks than the present study are not
accurateenough to detect a precise plant-to-plant contagion
patterns(Aukema et al., 2006; Blackshaw and Hicks, 2013; Bone et
al., 2013;
Table 5Results obtained after estimations of the neighborhood
configuration of the cellular auto3.7. The number represents the
number of plants in the neighborhood (including the c
Plots Initial number ofneighbors
Number after reduction made duringstep (ii)
Numberstep (iii)
Plot 1 25 25 4 Plot 2 25 25 4 Plot 3 25 25 1 Plot 4 25 25 4 Plot
5 25 25 1 Plot 6 25 25 1
Cocco et al., 2013; Cocu et al., 2005; Dder et al., 2015; Diaz
et al.,2012; Emmen et al., 2004; Fernandes et al., 2015; Holland et
al.,2005; Ishaaya and Horowitz, 2004; Kautz et al., 2011; Keasar et
al.,2005; Kim et al., 2007; Lausch et al., 2013; Moral Garca,
2006;Perry, 1994; Reay-Jones, 2010; Reay-Jones et al., 2007;
Reineke
mata (CA). Step (ii), (iii) and (iv) are briefly describe in the
text, in the methodologyentral cell).
after reduction made during Maximal Bayesian information
criteria after step(iv)
441411
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Table 6A summary of the purpose of applying each spatial
analysis symbols x and U stand for no and yes respectively.
Method Purpose of the method Main result
Evaluates with time thespatial link betweeninfested plants
located atdifferent positions
Tracks the positions ofthe center of patchesformed by
infestedplants with time
Follows the evolution of the initialshape, the density and the
numberof clusters of infested plantsformed with time
Estimates the rulesby which plants getinfected
throughneighbors
Morans' I U x x x The spatial correlation between infestedplants
is considerable at a radius of 10 m
Centertracking
x U x x The spatial shift of the patterns created byan infested
plant moves with time in a formof a spiral from an origin point to
otherparts of the plot
Modelbasedclusteranalysis
x x U x The initial shape and number of spatialclusters remain
stable during a certainperiod of time and, later disaggregate
tocreate new clusters
Cellularautomata
x x x U A safe plants surrounded by four infectedplants is most
likely to become damaged inthe next subsequent week
Symbols x and U stand for no and yes respectively.
114 F.T. Ndjomatchoua et al. / Agriculture, Ecosystems and
Environment 235 (2016) 105118
et al., 2011; Rogers et al., 2015; Schumann et al., 2014; Smith
et al.,2004; Thomas et al., 2001; Wang et al., 2009; Wright et al.,
2002;Zhang et al., 2016; Zimmerman et al., 2004). The reasons
reposedon the fact that the methodologies used in previous
studiesfocused on two main aspects: analyzing the count-variance
ofsamples without taking into account the spatial point
locations(indices of dispersion, aggregation and clumping indices,
Taylorpower law, quadrat-variance methods, probability
distributionmodels); and/or focusing on count-variance on a few
samples andtheir locations (semi-variography, spatial
autocorrelation analysis,local spatial statistics) (Dale et al.,
2002; Ishaaya and Horowitz,2004; Liebhold and Gurevitch, 2002;
Moran, 1950; Perry et al.,2006, 2002; Vinatier et al., 2011;
Wiegand and Moloney, 2014). Togain a considerable amount of
information from spatial data, theapplication of several
methodologies is recommended (Perry et al.,2002). Previous studies
failed to report some of our findingsbecause they usually applied a
single method (Aukema et al., 2006;Blackshaw and Hicks, 2013; Bone
et al., 2013; Cocco et al., 2013;Cocu et al., 2005; Dder et al.,
2015; Diaz et al., 2012; Emmen et al.,2004; Fernandes et al., 2015;
Holland et al., 2005; Ishaaya andHorowitz, 2004; Kautz et al.,
2011; Keasar et al., 2005; Kim et al.,2007; Lausch et al., 2013;
Moral Garca, 2006; Perry, 1994; Reay-Jones, 2010; Reay-Jones et
al., 2007; Reineke et al., 2011; Rogerset al., 2015; Schumann et
al., 2014; Smith et al., 2004; Thomas et al.,2001; Wang et al.,
2009; Wright et al., 2002; Zhang et al., 2016;Zimmerman et al.,
2004). In choosing to use several approachesthat have not been used
in the literature before (such as centroidtracking and spatial
clustering in order to track easily the shift ofspatial patches and
their relative stability, respectively), thecurrent study was more
precise. In addition, the cellular automatamethod was used in our
study to investigate the likely arrange-ment of infested plants to
contaminate healthy maize plants, goesbeyond the classical
analyses.
4.4. Advanced in movements ecology of B. fusca
As an attempt to advance the knowledge on movement ecology,the
quantification and qualification of the delocalization pattern
ofthe damages spread of B. fusca with time were studied. Weexamined
whether the infestation is initiated from an origin pointfrom which
it spreads to other locations to create a big cluster ofplants
damages, or whether multiple damages of plants
eruptedsimultaneously in different locations to create clusters,
which
merged with time. Through the record of point location
forindividual infested plants at each week and their
respectivecentroid, it is noticed that the shift of the focal point
moves as aspiral centered around the first set of damaged plants.
Theformation of new spiral during the second cycle of damages
couldbe explained by the ability of B. fusca to distinguish patches
oflarvae/eggs and oviposit in different zones. Practically,
whileprobing for more host plant and flying toward adjacent stems,
thepest female is showing preference to the areas with less density
ofinfection, thus spreading their offsprings more widely by
creating anew spiral at a distant radius. This result corroborates
thehypothesis stating that a lepidopteran is able to adopt a
regularpattern during host selection (Thompson and Pellmyr,
1991).
We observed that the selection of the first plants to be
infestedby the B. fusca female is likely to be random; subsequently
theneighbors to the infected plants are most likely to be infected.
Inaddition, it is noticed that the initial infestations
occurredsimultaneously in numerous plants. The enhancement of
thespatial expansion of damage clusters can be explained by the
pestlarvae short-range movement ability to migrate from a maize
plantto another (Calatayud et al., 2014). This displacement is
consideredas a survival mechanism; because of competition, the
larvae of B.fusca are likely to die in a highly infested maize stem
(Ntiri et al.,2016). Although B. fusca females have the ability to
fly over severalkilometers (Campagne et al., 2015; Dupas et al.,
2014), it wasobserved that the number of plants infested within a
clusterincrease progressively and, the shape of the cluster was
relativelymaintained with time; this can be explained by the
ability oflepidopteran to visit patches of plants already infested
by theirconspecifics (Thompson and Pellmyr, 1991). The
competitionamong B. fusca female for the same set of host plants
duringoviposition may be further considered as a key factor
thatinfluences the appearance of the patterns observed in this
study.
4.5. Implications for pest management
4.5.1. OverviewControl of negative impa