Modelling spatial distributions of small mammal assemblages and dog space utilization

Mary Woodcock Kroble
Monday 9 November 2009
Date: 28 April 2010
Time: 4:00 pm

Speaker: Amélie Vaniscotte (Department of Chrono-environment, University of Franche-Comté)


My current work aims at modelling the distributions and spatial behaviours of vertebrate terrestrial species with principal goal the control of possible impacts of anthropogenic disturbances on ecosystem dynamics and human health of local populations. My thesis focused on the transmission ecology of Echinococcus multilocularis, a parasite responsible for fatal zoonosis when ingested by humans. I analysed the distributions of host species for this parasite in Chinese remote areas (Sichuan province), at different scales and levels of biological organisations: i) the spatial distributions of small mammal assemblages on the basis of presence/absence data sets collected on the field and, ii) the nocturnal space occupancy for some individual domestic dogs based on data collected using GPS collars.

In order to summarise the great diversity observed in sampled habitats and trapped species, we first defined small mammal assemblages by reducing the redundancy that was found in the habitat classes chosen as explanatory variables for the multinomial model fitted to the trapping success observations. The spatial distributions of assemblage presences/absences were then modelled along environmental gradients extracted from satellite images such as elevation, slope and vegetation indices. We obtained that Multiple Adaptive Regression Splines provide lower bootstrapped classification error rates than GLM’s or linear or non-linear discriminant analysis. Finally, while predictions from locally trained models were not transferable on study sites distant from one hundred km, a regional classification of assemblages, trained on the whole regional data set, provided low predictive errors. Finally, analysis of dog nocturnal trajectories versus spatial distributions and contaminations counts of canid faeces allowed to assess and quantify the predominant contribution of dog over fox in the environmental contamination and to localise areas of high transmission risk, close to human habitations. Dogs spend the majority of the day near their owners’ houses but also travel excursive paths outside the mean activity area of the village dog populations where small mammal presence indices were the most frequent.

Based on this first experience in statistical and spatial modelling of eco/epidemiological data and the encouraging results obtained using recent statistical methodology, I seek to go on working on the interface between field data collection alongside with ecologists and the development of pertinent statistical approaches in close collaboration with statisticians. Furthermore, the developing availability of large data sets of multiple species presences/absences, consisting of a collection of several field surveys or of atlas data, along with satellite images actually enhances model building and evaluation on large spatial ranges at which effects of global environmental changes (climate warming or landscape disturbances) on population distributions and biodiversity can be observed. Concerning the analysis of individual trajectories, I am currently working on the modelling of dog movements using dynamic models based on Brownian motion and Levy flights. One interesting expected result would be to identify model switching behaviours in dog nocturnal trajectories. ”

Share this story