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

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 build our 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 ( ) 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 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)

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)

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

References Evangelista, P. H., S. Kumar, T. J. Stohlgren, C. S. Jarnevich, A. W. Crall, J. B. Norman Iii, and D. T. Barnett Modelling invasion for a habitat generalist and a specialist plant species. Diversity and Distributions 14: Mateo-Tomás, P. and P. P. Olea Anticipating Knowledge to Inform Species Management: Predicting Spatially Explicit Habitat Suitability of a Colonial Vulture Spreading Its Range. PLoS ONE 5:e Newton-Cross, G., P. C. L. White, and S. Harris Modelling the distribution of badgers Meles meles: comparing predictions from field-based and remotely derived habitat data. Mammal Review 37: Park, C.-R. and W.-S. Lee Development of a GIS-based habitat suitability model for wild boar Sus scrofa in the Mt. Baekwoonsan region, Korea. Mammal Study 28: Phillips, S. J., R. P. Anderson, and R. E. Schapire Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: Saito, M., H. Momose, T. Mihira, and S. Uematsu Predicting the risk of wild boar damage to rice paddies using presence-only data in chiba prefecture, Japan. International Journal of Pest Management 58: DISCUSSIONRESULTSMETHODOBJECTIVESCONTEXT

Thank you for your attention P. Taymans