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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
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
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Cultural cycle offers cover all over the year for wild boar
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
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Situation in Belgium
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
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
2 « presence » datasets : agricultural damages & hunting records• covering same period (2009-2010)• differences within year (april-october vs. october-december)
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
DATASETS
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
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)
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)
Hunting data
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Hunting data
Response curve of distance to forest variables
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Damage data
Response curve
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Damage data - Response curves
Habitat
Cover fields
Potato fields
Road density
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Both dataset
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Both dataset
Response curves
Road density
Distance to forest
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model evaluation
Classical – ROC curve analysis
AUC
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
Comparison with known presence of wild boar
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection« Hunting model »
« Damage model »
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection« Both model »
« Damage model »
DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT
Model projection
How to fix a probability threshold to create a presence/absence map?
→ Theoritically: maximizing sensitivity while minimizing specificity [Philips 2006]
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)
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
e
damage_km hunting_km both_km damage_pc hunting_pc both_pc
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
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
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
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
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
Thank you for your attention
P. Taymans