4 research outputs found
Estimating abundance of a recovering transboundary brown bear population with capture-recapture models
Integrating opportunistic and structured non-invasive surveys with spatial capture-recapture models to map connectivity of the Pyrenean brown bear population
International audienceConnectivity, in the sense of the persistence of movements between habitat patches, is key to maintain endangeredpopulations and has to be evaluated in management plans. In practice, connectivity is difficult to quantifyespecially for rare and elusive species. Here, we use spatial capture-recapture (SCR) models with an ecologicaldetection distance to identify barriers to movement. We focused on the transnational critically endangeredPyrenean brown bear (Ursus arctos) population, which is distributed over Spain, France and Andorra and isdivided into two main cores areas following translocations. We integrate structured monitoring from cameratraps and hair snags with opportunistic data gathered after depredation events. While structured monitoringfocuses on areas of regular bear presence, the integration of opportunistic data allows us to obtain information ina wider range of habitat, which is especially important for ecological inference. By estimating a resistanceparameter from encounter data, we show that the road network impedes movements, leading to smaller homeranges with increasing road density. Although the quantitative effect of roads is context-dependent (i.e. varyingaccording to landscape configuration), our model predicts that a brown bear with a home range located in anarea with relatively high road density (8.29 km/km2) has a home range size reduced by 1.4-fold for males and1.6-fold for females compared to a brown bear with a home range located in an area with low road density (1.38km/km2). When assessing connectivity, spatial capture-recapture modeling offers an alternative to the use ofexperts’ opinion when telemetry data are not available
Estimating abundance of a recovering transboundary brown bear population with capture-recapture models
International audienc
The DeepFaune initiative: a collaborative effort towards the automatic identification of Europeanfauna in camera trap images
Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed aclassification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly,which allows us to continuously add new species to the classification model