9th international symposium on wild boar and others suids, hannover 2012 factors influencing wild...

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


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



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



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)


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.


PREDICTORS

MaxEnt is a program for modelling species distribution from presence-only data→ minimizing the entropy between two probability density, presence & background


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


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


Hunting data

Response curve of distance to forest variables



Damage data

Response curve


Damage data - Response curves

Habitat

Cover fields

Potato fields

Road density


Both dataset


Both dataset

Response curves

Road density

Distance to forest


Model evaluation

Classical – ROC curve analysis

AUC


Model projection


Model projection

Comparison with known presence of wild boar


Model projection« Hunting model »

« Damage model »


Model projection« Both model »

« Damage model »


Model projection

How to fix a probability threshold to create a presence/absence map?

→ Theoritically: maximizing sensitivity while minimizing specificity [Philips 2006]


Model projection


→ BUT to conservative approach! (175 km² of predicted area vs. already 250 km² of presence area)


Model projection


→ BUT to conservative approach! (175 km² of predicted area vs. already 250 km² of presence area)

0

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0,1 0,2 0,3 0,4 0,5

threshold value

pred

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ea (k

m²)

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damage_km hunting_km both_km damage_pc hunting_pc both_pc


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


Model projection



Model projection


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 …?


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.


Thank you for your attention

P. Taymans

9th international symposium on wild boar and others suids, hannover 2012 factors influencing wild...

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