The DeepFaune initiative: a collaborative effort towards the automatic identification of the French fauna in camera-trap images

Abstract Camera-traps have revolutionized the way ecologists monitor biodiversity and population abundances. Their full potential is however only realized when the hundreds of thousands of images collected can be rapidly classified with minimal human intervention. Machine learning approaches, and in particular deep learning methods, have allowed extraordinary progress towards this end. Trained classification models remain rare however, and for instance are only emerging for the European fauna. This can be explained by the technical expertise they require but also by the limited availability of large datasets of annotated pictures, which are key to obtaining successful recognition models. In this context, we set-up the DeepFaune initiative ( https://deepfaune.cnrs.fr ), a large-scale collaboration between dozens of partners involved in research, conservation and management of wildlife in France. The aim of DeepFaune is to aggregate individual datasets of annotated pictures to train species classification models based on convolutional neural networks, an established deeplearning approach. Here we report on our first milestone, a two-step pipeline built upon the MegaDetector algorithm for detection (discarding empty pictures and cropping the animal) and a classification model for 18 species or higher-level taxa as well as people and vehicles. The classification model achieved 92% validation accuracy and showed > 90% sensitivity and specificity for many classes. Most importantly, these performances were generally conserved when tested on an independent out-of-sample dataset. In addition, we developed a cross-platform graphical-user-interface that allows running the pipeline on images stored locally on a personal computer. In conclusion, the DeepFaune initiative provides a freely available (for non-commercial purposes) toolbox with high performance to classify the French fauna in camera-trap images

Identifying the environmental drivers of corridors and predicting connectivity between seasonal ranges in multiple populations of Alpine ibex ( Capra ibex ) as tools for conserving migration

ABSTRACT Seasonal migrations are central ecological processes connecting populations, species and ecosystems in time and space. Land migrations, such as those of ungulates, are particularly threatened by habitat transformations and fragmentation, climate change and other environmental changes caused by anthropogenic activities. Mountain ungulate migrations are neglected because they are relatively short, although traversing highly heterogeneous altitudinal gradients particularly exposed to anthropogenic threats. Detecting migration routes of these species and understanding their drivers is therefore of primary importance to predict connectivity and preserve ecosystem functions and services. The populations of Alpine ibex Capra ibex , an iconic species endemic to the Alps, have all been reintroduced from the last remnant source population. Because of their biology and conservation history, Alpine ibex populations are mostly disconnected. Hence, despite a general increase in abundance and overall distribution range, their conservation is strictly linked to the interplay between external threats and related behavioral responses, including space use and migration. By using 337 migratory tracks from 425 GPS-collared individuals from 15 Alpine ibex populations distributed across their entire range, we (i) identified the environmental drivers of movement corridors in both spring and autumn and (ii) compared the abilities of three modeling approaches to predict migratory movements between seasonal ranges of the 15 populations. Trade-offs between energy expenditure, food, and cover seemed to be the major driver of migration routes: steep south-facing snow-free slopes were selected while high elevation changes were avoided. This revealed the importance of favorable resources and an attempt to limit energy expenditures and perceived predation risk. Based on these findings, we provided efficient connectivity models to inform conservation of Alpine ibex and its habitats, and a framework for future research investigating connectivity in migratory species

It's time to go: drivers of migration phenology in a mountain ungulate, the Alpine ibex Capra ibex

International audienc

It's time to go: drivers of migration phenology in a mountain ungulate, the Alpine ibex Capra ibex

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

Compensatory recruitment allows amphibian population persistence in anthropogenic habitats

International audienceHabitat anthropization is a major driver of global biodiversity decline. Although most species are negatively affected, some benefit from anthropogenic habitat modifications by showing intriguing life-history responses. For instance, increased recruitment through higher allocation to reproduction or improved performance during early-life stages could compensate for reduced adult survival, corresponding to “compensatory recruitment”. To date, evidence of compensatory recruitment in response to habitat modification is restricted to plants, limiting understanding of its importance as a response to global change. We used the yellow-bellied toad ( Bombina variegata ), an amphibian occupying a broad range of natural and anthropogenic habitats, as a model species to test for and to quantify compensatory recruitment. Using an exceptional capture–recapture dataset composed of 21,714 individuals from 67 populations across Europe, we showed that adult survival was lower, lifespan was shorter, and actuarial senescence was higher in anthropogenic habitats, especially those affected by intense human activities. Increased recruitment in anthropogenic habitats fully offset reductions in adult survival, with the consequence that population growth rate in both habitat types was similar. Our findings indicate that compensatory recruitment allows toad populations to remain viable in human-dominated habitats and might facilitate the persistence of other animal populations in such environments