Anne-Sophie Bonnet-Lebrun, Alexandros A. Karamanlidis, Miguel de Gabriel Hernando, Olivier Gimenez

Transcription

1 Making the most out of different sources and qualities of data in species distribution modeling: an example on the distribution of brown bears (Ursus arctos, L.) in Greece. Anne-Sophie Bonnet-Lebrun, Alexandros A. Karamanlidis, Miguel de Gabriel Hernando, Olivier Gimenez

2 INTRODUCTION the brown bear (Ursus arctos) Distribution: - Holartic - Greece: southernmost distribution in Europe Conservation status: - Globally: Least Concern (IUCN Red List status) - Locally in Europe: small and isolated populations Threats: - Habitat loss and fragmentation - Human-bear conflicts (livestock depredation, agricultural damage ) map its distribution in Greece -> map potential problematic interactions

3 INTRODUCTION Brown bears are difficult to monitor: - Cryptic and solitary - Low density in very large areas -> Build models (Species Distribution Models) -> Use multiple data sources

4 METHODS Species Distribution Models Partial information on the species presence + Probabilities of presence in the overall area of interest Environmental variables

5 METHODS Environmental variables (1km² grain size) - Physical (digital elevation model) Mean slope Cumulated length of rivers in the pixel Altitude

6 METHODS Environmental variables (1km² grain size) - Land cover (CORINE land cover maps) Percentage of forest Percentage of agricultural land

7 METHODS Environmental variables (1km² grain size) - Human-related Distance to the roads Population density

8 METHODS Datasets - Power poles: collection of signs of bear presence (hair deposits, marks, mud smears, scats, tracks, ) -> presence-absence -> systematic

9 METHODS Power poles

10 METHODS Datasets - Opportunistic data: citizen calls when signs of bear presence -> presence-only -> opportunistic => observer bias (more observations in places that are more accessible by more people)

11 METHODS Power poles data Signs at power poles: - Presence/absence = binomial response GLM possibly with spatial autocorrelation (autologistic model; Augustin et al. 1996) Presence/Absence ~ Mean slope (-) + Population density (-) + Rivers length (+) + Mean altitude (+) + % of agricultural land (+)

12 METHODS Opportunistic data -presence-only data inhomogeneous Poisson point process (Warton & Shepherd 2010) - Poisson point process: random process to generate points scattered in space - Intensity: average number of points per unit area Homogeneous Intensity = constant Inhomogeneous Intensity = f(spatial variable)

13 METHODS Opportunistic data -opportunistic data Model observer bias 1. Use both ecological (forest cover, altitude, ) and observer bias (distance to the roads, ) variables to build the model 2. condition on a common level of bias when projecting the model (Warton & Renner 2013)

14 METHODS Opportunistic data Very much alike ANCOVA, propensity score method in biostatistics

15 RESULTS Model based on power poles data Model based on opportunistic data Probabilities of presence Average expected number of observations The results of the two models seem coherent

16 DISCUSSION Outputs of the models: Power poles data (GLM) -> probabilities of presence Opportunistic data (PPM) -> intensities (average number of points per unit area) How to combine the results; reconcile the outcomes? How to evaluate the relative contribution of each dataset? Model selection for each dataset / integrated dataset How to assess the goodness-of-fit of integrated model to the data? Are Poisson Point Processes the way to go?

17 DISCUSSION

18 DISCUSSION ISEC 2014

19 DISCUSSION ISEC 2014

20 Thank you for your attention!

21 Thank you for your attention!