9th international symposium on wild boar and others suids, hannover 2012 factors influencing wild...
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9th International Symposium on Wild Boar and others Suids, Hannover 2012
Factors influencing wild boar presence
in agricultural landscape: a habitat
suitability modelling approachKevin Morelle
Lejeune Philipppe
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Wild boar (Sus scrofa) populations have increased worldwide
In parallel, distribution of the species has enlarged, out of forest habitat → plasticity of the species can explain partly the phenomenon
Ability to make « home range shift » [Keuling et al. 2009]
Consequently, agricultural areas have become new « home » for wild boar, providing cover and food
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Cultural cycle offers cover all over the year for wild boar
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Why modelling distribution?
Habitat management policy [Park at al. 2003]
Conservation planning [Park at al. 2003]
Species invasion [Evangelista et al. 2008]
Forecast distribution (climate change…)
Risk mapping - damage [Saito et al. 2012]
- disease transmission [Nexton-Cross et al. 2007]
→ Give informations on environmental correlates influencing the patterns of distribution of a species
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Situation in Belgium
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What are main drivers of wild boar distribution in these agricultural landscape?
1 - identifying environmental variables that explain seasonal distribution of the species
2 - defining habitat suitability map in agricultural landscape
3 - extrapolate the best model to the north of Wallonia
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
We used Condroz as study site to buildour model
• agricultural area with patchily distributed forest • « recently » (10-30 y) colonized by wild boar
STUDY AREA
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2 « presence » datasets : agricultural damages & hunting records• covering same period (2009-2010)• differences within year (april-october vs. october-december)
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
DATASETS
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Set of 18 predictors defining habitat, agricultural cover, topography and human presence
cell size of 300m (and landscape metrics) were derivated using R packages raster (Hijmans), SpatStat (Baddeley) and dismo.
Environmental predictors are represented as raster thematic layers.
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
PREDICTORS
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MaxEnt is a program for modelling species distribution from presence-only data→ minimizing the entropy between two probability density, presence & background
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
MODELING TECHNIQUE: MaxEnt [Phillips et al. 2006]
From Elith et al. (2011)
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Training data: to fit the modelTest data : to evaluate the predictive ability of the model (20%)Background sample of 2000 points ~ # hunting/damage records
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
MODELING TECHNIQUE: MaxEnt [Phillips et al. 2006]
Model evaluation
receiver operating characteristic (ROC) - Area under curve (AUC)
→ measure of the prediction success
→ ROC curve is obtained by plotting all true positive values (sensitivity fraction) against their equivalent false positive values (1-specificity fraction)
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Hunting data
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
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Hunting data
Response curve of distance to forest variables
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Damage data
Response curve
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Damage data - Response curves
Habitat
Cover fields
Potato fields
Road density
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Both dataset
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Both dataset
Response curves
Road density
Distance to forest
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model evaluation
Classical – ROC curve analysis
AUC
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
Comparison with known presence of wild boar
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection« Hunting model »
« Damage model »
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection« Both model »
« Damage model »
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
How to fix a probability threshold to create a presence/absence map?
→ Theoritically: maximizing sensitivity while minimizing specificity [Philips 2006]
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
How to fix a probability threshold to create a presence/absence map?
→ BUT to conservative approach! (175 km² of predicted area vs. already 250 km² of presence area)
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
How to fix a probability threshold to create a presence/absence map?
→ BUT to conservative approach! (175 km² of predicted area vs. already 250 km² of presence area)
0
400
800
1200
1600
2000
0,1 0,2 0,3 0,4 0,5
threshold value
pred
icte
d ar
ea (k
m²)
0%
20%
40%
60%
80%
100%
over
lap
true
pre
senc
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damage_km hunting_km both_km damage_pc hunting_pc both_pc
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
Current species range could increase up to 535 km² if wild boar occupies all the areas predicted as suitable by the MaxEnt model
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
Current species range could increase up to 1116 km² if wild boar occupies all the areas predicted as suitable by the MaxEnt model
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DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
Current species range could increase up to 879 km² if wild boar occupies all the areas predicted as suitable by the MaxEnt model
35 km
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Factors’ analysis
Distribution model show differences in environmental covariates between
→ autumn/winter: decrease in cover/food in agricultural plain + acorn
availability: switch to forest habitat after crop harvesting
→ spring/summer: intensive use of fields providing cover & food
BUT…reliability of presence model for a highly mobile species? How to take into account movement ability of the wild boar?
Model prediction/projection
Prediction show that range could increase into suitable clustered patches
→ now hunting pressure is high and maintain population low, but …?
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
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ReferencesEvangelista, P. H., S. Kumar, T. J. Stohlgren, C. S. Jarnevich, A. W. Crall, J. B. Norman Iii, and D. T. Barnett. 2008. Modelling invasion for a habitat generalist and a specialist plant species. Diversity and Distributions 14:808-817.
Mateo-Tomás, P. and P. P. Olea. 2010. Anticipating Knowledge to Inform Species Management: Predicting Spatially Explicit Habitat Suitability of a Colonial Vulture Spreading Its Range. PLoS ONE 5:e12374.
Newton-Cross, G., P. C. L. White, and S. Harris. 2007. Modelling the distribution of badgers Meles meles: comparing predictions from field-based and remotely derived habitat data. Mammal Review 37:54-70.
Park, C.-R. and W.-S. Lee. 2003. Development of a GIS-based habitat suitability model for wild boar Sus scrofa in the Mt. Baekwoonsan region, Korea. Mammal Study 28:17-21.
Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231-259.
Saito, M., H. Momose, T. Mihira, and S. Uematsu. 2012. Predicting the risk of wild boar damage to rice paddies using presence-only data in chiba prefecture, Japan. International Journal of Pest Management 58:65-71.
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
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Thank you for your attention
P. Taymans