Exploring Habitat Selection by Wildlife with adehabitat

Knowledge of the environmental features affecting habitat selection by animals is important for designing wildlife management and conservation policies. The package adehabitat for the R software is designed to provide a computing environment for the analysis and modelling of such relationships. This paper focuses on the preliminary steps of data exploration and analysis, performed prior to a more formal modelling of habitat selection. In this context, I illustrate the use of a factorial analysis, the K-select analysis. This method is a factorial decomposition of marginality, one measure of habitat selection. This method was chosen to present the package because it illustrates clearly many of its features (home range estimation, spatial analyses, graphical possibilities, etc.). I strongly stress the powerful capabilities of factorial methods for data analysis, using as an example the analysis of habitat selection by the wild boar (Sus scrofa L.) in a Mediterranean environment.

Exploring Habitat Selection by Wildlife with adehabitat

Knowledge of the environmental features affecting habitat selection by animals is important for designing wildlife management and conservation policies. The package adehabitat for the R software is designed to provide a computing environment for the analysis and modelling of such relationships. This paper focuses on the preliminary steps of data exploration and analysis, performed prior to a more formal modelling of habitat selection. In this context, I illustrate the use of a factorial analysis, the K-select analysis. This method is a factorial decomposition of marginality, one measure of habitat selection. This method was chosen to present the package because it illustrates clearly many of its features (home range estimation, spatial analyses, graphical possibilities, etc.). I strongly stress the powerful capabilities of factorial methods for data analysis, using as an example the analysis of habitat selection by the wild boar (Sus scrofa L.) in a Mediterranean environment

Estimation of species relative abundances and habitat preferences using opportunistic data

We develop a new statistical procedure to monitor, with opportunist data, relative species abundances and their respective preferences for dierent habitat types. Following Giraud et al. (2015), we combine the opportunistic data with some standardized data in order to correct the bias inherent to the opportunistic data collection. Our main contributions are (i) to tackle the bias induced by habitat selection behaviors, (ii) to handle data where the habitat type associated to each observation is unknown, (iii) to estimate probabilities of selection of habitat for the species. As an illustration, we estimate common bird species habitat preferences and abundances in the region of Aquitaine (France)

Capitalising on Opportunistic Data for Monitoring Species Relative Abundances

With the internet, a massive amount of information on species abundance can be collected under citizen science programs. However, these data are often difficult to use directly in statistical inference, as their collection is generally opportunistic, and the distribution of the sampling effort is often not known. In this paper, we develop a general statistical framework to combine such ``opportunistic data'' with data collected using schemes characterized by a known sampling effort. Under some structural assumptions regarding the sampling effort and detectability, our approach allows to estimate the relative abundance of several species in different sites. It can be implemented through a simple generalized linear model. We illustrate the framework with typical bird datasets from the Aquitaine region, south-western France. We show that, under some assumptions, our approach provides estimates that are more precise than the ones obtained from the dataset with a known sampling effort alone. When the opportunistic data are abundant, the gain in precision may be considerable, especially for the rare species. We also show that estimates can be obtained even for species recorded only in the opportunistic scheme. Opportunistic data combined with a relatively small amount of data collected with a known effort may thus provide access to accurate and precise estimates of quantitative changes in relative abundance over space and/or time

Same habitat types but different use: evidence of context-dependent habitat selection in roe deer across populations

International audienceWith the surge of GPS-technology, many studies uncovered space use of mobile animals and shed light on the underlying behavioral mechanisms of habitat selection. Habitat selection and variation in either occurrence or strength of functional responses (i.e. how selection changes with availability) have given new insight into such mechanisms within populations in different ecosystems. However, linking variation in habitat selection to site-specific conditions in different populations facing contrasting environmental conditions but the same habitat type has not yet been investigated. We aimed to fill this knowledge gap by comparing within-home range habitat selection across 61 female roe deer (Capreolus capreolus) during the most critical life history stage in three study areas showing the same habitat types but with different environmental conditions. Female roe deer markedly differed in habitat selection within their home range, both within and among populations. Females facing poor environmental conditions clearly displayed a functional response, whereas females facing rich environmental conditions did not show any functional response. These results demonstrate how the use of a given habitat relative to its availability strongly varies in response to environmental conditions. Our findings highlight that the same habitat composition can lead to very different habitat selection processes across contrasted environments

Capitaliser sur les données massives opportunistes de biodiversité.

International audienc

Des outils statistiques pour l'analyse des semis de points dans l'espace écologique

La mise en relation d'un ou de plusieurs semis de points avec des cartes de variables environnementales est centrale en Écologie. Les points représentent en général des localisations d'individus d'une ou de plusieurs espèces. Cette thèse présente une démarche pour identifier les variables environnementales qui sont les plus structurantes de la distribution des points. Elle repose sur l'exploration des données dans l'espace géographique et dans l'espace défini par les variables environnementales (espace écologique). L'analyse d'un seul semis et celle de plusieurs semis de points sont considérées. Des collaborations ont permis de développer ou d'améliorer des outils d'analyse spatiale (analyse discriminante sur vecteurs propres du graphe de voisinage) ou écologique (ENFA, analyse K-select, analyse factorielle des rapports de sélection), reposant sur la théorie de l'analyse factorielle. Une bibliothèque de fonctions pour le logiciel R, adehabitat, a été programmée pour faciliter cette démarche d'analyseLYON1-BU.Sciences (692662101) / SudocSudocFranceF

The concept of animals trajectories from a data analysis perspective

The Global positionning system (GPS) has been increasingly used during the past decade to monitor the movements of free-ranging animals. This technology allows to automatically relocate fitted animals, which often results into a high-frequency sampling of their trajectory during the study period. However, depending on the objective of trajectory analysis, this study may quickly become difficult, due to the lack of well designed computer programs. For example, the trajectory may be built by several “parts” corresponding to different behaviours of the animal, and the aim of the analysis could be to identify the different parts, and there by the different activities, based on the properties of the trajectory. This complex task needs to beper formed into a flexible computing environment, to facilitate exploratory analysis of its properties. In this paper, we present a new class of object of the R software,the class “ltraj” included in the package adehabitat, allowing the analysis of animals’ trajectories. We developed this class of data after an extensive review of the literature on the analysis of animal movements. This class of data facilitates the computation of descriptive parameters of the trajectory (such as the relative angles between successive moves, distance between successive relocations, etc.), graphical exploration of these parameters, as well a numerous tests and analyses developed in the literature (first passage time, trajectory partitioning, etc.). Finally, this package also contains numerous examples of animal trajectories, and a working exampleillustrating the use of the package