1. 9th International Symposium on Wild Boar and others Suids, Hannover 2012 Factors influencing wild boar presence in agricultural landscape: a habitat suitability modelling approach Kevin Morelle Lejeune Philipppe

2. 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

3. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Cultural cycle offers cover all over the year for wild boar

4. 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

5. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Situation in Belgium

6. 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

7. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT We used Condroz as study site to build our model agricultural area with patchily distributed forest « recently » (10-30 y) colonized by wild boar STUDY AREA

8. 2 « presence » datasets : agricultural damages & hunting records covering same period (2009-2010) differences within year (april-october vs. october-december) DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT DATASETS

9. 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

10. 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)

11. Training data: to fit the model Test 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)

12. Hunting data DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT

13. Hunting data Response curve of distance to forest variables DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT

14. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Damage data Response curve

15. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Damage data - Response curves Habitat Cover fields Potato fields Road density

16. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Both dataset

17. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Both dataset Response curves Road density Distance to forest

18. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Model evaluation Classical – ROC curve analysis AUC

19. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Model projection

20. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Model projection Comparison with known presence of wild boar

21. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Model projection « Hunting model » « Damage model »

22. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Model projection « Both model » « Damage model »

23. DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT Model projection How to fix a probability threshold to create a presence/absence map? → Theoritically: maximizing sensitivity while minimizing specificity [Philips 2006]

24. 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)

25. 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)

26. 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

27. 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

28. 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

29. 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

30. References Evangelista, 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

31. Thank you for your attention P. Taymans