Dr. Marek Giergiczny – Using advanced choice models to study animal behaviour

  • Date:

Assistant Professor, Department of Economic Sciences, University of Warsaw

Using advanced choice models to study animal behaviour

Wednesday 18th January 2017

Abstract:
Recent developments in positioning technology have led to new opportunities for investigating resource selection by animals but also new challenges related to the development of proper tools for the analysis of large amounts of information. The two currently most prominent approaches in ecology are Resource Selection Functions (RSF) and Step-Selection Function (SSF).  Resource Selection Functions (RSFs) are used to model habitat selection by animals using data from GPS locations. A RSF is defined as any statistical model deployed to estimate the relative probability of selecting a resource unit versus alternative possible resource units, which in most applications to date has been logistic regression. Another powerful modelling approach in ecology is the Step-Selection Function (SSF), which has been developed to estimate resource selection by animals moving through a landscape (Fortin et al., 2005). The main advantage of using an SSF rather than RSF is that SSFs may better model choices animals make as movement is included and as it constrains selection and availability, which enables association of parameters of movement rules with landscape features.

The main contribution of our paper is to use the state of the art choice modelling approaches in  modelling RSF and SFF. We make use of GPS locations collected within the GLOBE project which aimed to study brown bear behaviour in Poland and Sweden. Within this research project 1.5 million GPS locations for 150 individual bears were collected over a period of 11 years. In our work we have made use of these data and built Multinomial Logit (MNL), Latent Class (LCM) and Mixed Logit (MMNL) at both individual and sample levels. We also incorporate a large amount of interactions with bear-specific characteristics such as age, gender and number of cubs. A variety of different characteristics are used to describe the alternatives, such as road density and building density, land cover area for different types (barren land, forest, shrub land etc.), forest age, terrain ruggedness, and vegetation index.

Our work shows that there is a substantial amount of inter-bear preference heterogeneity among studied animals. Our results clearly show that the current practice in ecological applications assuming that animals have similar behaviour is too restrictive. Our analysis shows that using more advanced discrete choice models gives a much deeper understanding of brown bear behaviour and yields much better predictions of SSF which is a very promising tool in ecology, wildlife management and conservation. We think that our study will propagate the use of more advanced choice models in ecology.