Biology subjects

Decors, A.

Publications and source records attributed to Decors, A..

3 recordsLinked to original sources

bioRxiv2025

What if wildlife health surveillance was not just for veterinarians? - Opportunistic use of population monitoring by camera traps for syndromic surveillance of the Eurasian lynx (Lynx lynx)

Fifty years after the reintroduction of the Eurasian lynx Lynx lynx to the Vosges and Swiss Jura mountains, the species remains in danger of extinction in France. The main threats it faces are habitat fragmentation, high anthropogenic mortality (mainly vehicle collision but also illegal killing) and low genetic diversity, but little is known about the importance of disease. Camera trapping surveillance provides crucial information for understanding population health status but includes many biases. To be more efficient, surveillance must combine several modalities. This work first presents the development of a protocol for clinical data collection by camera trapping, intended for biologists in charge of the populations monitoring. This method was then applied to the 3574 events, i.e. 270 identified individuals, present between 1997 and 2020 in the lynx photo identification database of the Wolf-Lynx Network (Reseau Loup-Lynx) of the French Biodiversity Agency (Office Francais de la Biodiversite). Seventeen percent of the lynx studied showed at least one change indicative of disease. The most common changes were skin disorders. The others were body condition, ocular or locomotor changes, lack of auriculas and rarely respiratory, behavioral, or digestive troubles. The latter changes were concentrated at the end of the study period, which also corresponded to the period with the most camera traps and therefore the most data. Finally, these observations complemented postmortem surveillance of the French lynx population carried out by the French network of epidemiological surveillance (Reseau SAGIR).

ecology↗

bioRxiv2025

Annotating opportunistic camera-trap images with conditions ofrecording, for the disease surveillance of Eurasian lynx (Lynxlynx)

The French population of the Eurasian lynx (Lynx lynx) is small and fragmented. Any emerging disease would endanger it even further, so health surveillance is crucial. Currently, health monitoring relies on lynx carcass surveillance. In parallel, the Eurasian lynx population is being monitored since 1997 through a large network of observers in different regions and this trove of camera-trap images could allow for the opportunistic detection of clinical signs. Camera traps have been used for a very long time in ecology and, more recently, in epidemiology to study e.g. sarcoptic mange. However, the quality of the images from camera traps varies, the details of the animals body are more or less clearly visible. This work examines how the quality of the images relates to the ability to detect cutaneous changes and abnormal body conditions. Different factors affect image quality and the detection of changes: intrinsic camera parameters like the type and settings of the camera trap, extrinsic factors like the external lighting conditions or the position of the animal in relation to the camera. In our data set, clearly visible cutaneous changes were associated with a different set of factors than visible abnormal body conditions. The camera-trap conditions currently used for ecological monitoring of the lynx are sufficiently diverse to allow for the general surveillance of abnormal health signs. However, for monitoring specific health signs, the camera settings as well as the shooting context should be optimized to ensure the best possible sensitivity and specificity of the detection.

ecology↗

bioRxiv2024

Relevance of the synergy of surveillance and populational networks in understanding the Usutu virus outbreak within common blackbirds (Turdus merula) in Metropolitan France, 2018

Usutu virus (USUV) was first isolated in Africa in 1959 and has since spread to and through Europe with a typical enzootic mosquito-bird cycle. In France, it was first detected in birds in 2015, but during summer 2018 the spread of USUV was particularly significant throughout the country, killing mainly common blackbirds (Turdus merula) and to a lesser extent great grey owls (Strix nebulosa), among other captive and non-captive wild bird species. Previous studies of USUV in France have focused on reconstructing pathways of introduction, but not on structural aspects of virus spread within the country. Data (RT-PCR of geolocated dead birds) on this 2018 outbreak were collected through both an event-based wildlife network named SAGIR and the health surveillance of the French-speaking Association of Zoo Veterinarians (AFVPZ). In addition, common blackbird populations could be monitored through another network (REZOP). Statistical analysis (spatial, temporal, spatiotemporal and environmental determinants) of the SAGIR and AFVPZ network data helped to highlight the early appearance of separate large clusters of USUV cases in mid-July 2018, the subsequent diffusion into smaller and secondary clusters at the end of August 2018, and a meanwhile enlargement of the first clusters with an increase in the number of cases. High human density (top 10.5% densest areas in France) and wetland concentration (top 19.3% most likely wetland areas) were significant factors in USUV case locations. Using generalised additive mixed models on REZOP data, we also highlighted the decline in common blackbird population trends in areas with medium and even more with high USUV pressure (areas defined based on SAGIR-AFVPZ data) following the 2018 outbreak (respectively -7.4% [-11.4; -3.9]95% and -15.7% [-16.2; -9.1]95%). A large area (radius [~]150 km) in the centre and centre-west of France, and smaller areas in the south-east, north and north-east of France (each with a radius [~] 50 km) were particularly affected. We conclude on the importance to work with synergistic networks to assess infection spread in wild bird species, as well as the negative impact of an emerging arbovirus. The responsiveness of such a network system could be improved by automating alerts.

ecology↗