Publications by authors named "Clément Calenge"

10 Publications

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Estimating disease prevalence and temporal dynamics using biased capture serological data in a wildlife reservoir: The example of brucellosis in Alpine ibex (Capra ibex).

Prev Vet Med 2021 Feb 26;187:105239. Epub 2020 Dec 26.

OFB - Office Français de la Biodiversité - Direction de la Recherche et Appui Scientifique - Unité Sanitaire de la Faune, Gap, France.

The monitoring of the disease prevalence in a population is an essential component of its adaptive management. However, field data often lead to biased estimates. This is the case for brucellosis infection of ibex in the Bargy massif (France). A test-and-cull program is being carried out in this area to manage the infection: captured animals are euthanized when seropositive, and marked and released when seronegative. Because this mountainous species is difficult to capture, field workers tend to focus the capture effort on unmarked animals. Indeed, marked animals are less likely to be infected, as they were controlled and negative during previous years. As the proportion of marked animals in the population becomes large, captured animals can no longer be considered as an unbiased sample of the population. We designed an integrated Bayesian model to correct this bias, by estimating the seroprevalence in the population as the combination of the separate estimates of the seroprevalence among unmarked animals (estimated from the data) and marked animals (estimated with a catalytic infection model, to circumvent the scarcity of the data). As seroprevalence may not be the most responsive parameter to management actions, we also estimated the proportion of animals in the population with an active bacterial infection. The actual infection status of captured animals was thus inferred as a function of their age and their level of antibodies, using a model based on bacterial cultures carried out for a sample of animals. Focusing on the population of adult females in the core area of the massif, i.e. with the highest seroprevalence, this observational study shows that seroprevalence has been divided by two between 2013 (51%) and 2018 (21%). Moreover, the likely estimated proportion of actively infected females in the same population, though very imprecise, has decreased from a likely estimate of 34% to less than 15%, suggesting that the management actions have been effective in reducing infection prevalence.
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http://dx.doi.org/10.1016/j.prevetmed.2020.105239DOI Listing
February 2021

Should I stay or should I go? Determinants of immediate and delayed movement responses of female red deer (Cervus elaphus) to drive hunts.

PLoS One 2020 9;15(3):e0228865. Epub 2020 Mar 9.

Direction de la Recherche et de l'Appui Scientifique-Unité Ongulés Sauvages, Office Français de la Biodiversité, Birieux, France.

Hunting can be used as a tool for wildlife management, through limitation of population densities and dissuading game from using sensitive areas. The success of these approaches requires in depth knowledge of prey movement. Indeed, movement decisions of game during hunting may affect the killing success of hunters as well as the subsequent location of surviving animals. We thus investigated red deer movement responses to drive hunts and their causal factors. We studied 34 hunting events in the National Estate of Chambord (France) and thereby provided a fine-scale characterization of the immediate and delayed movement responses of red deer to drive hunts. Red deer responded to drive hunts either by immediately fleeing the hunted area, or by initially remaining before ultimately fleeing after the hunters had departed. A few hours after the hunt, all individuals were located in distant areas (> 2 kilometres) from the hunted area. Immediate flight responses were less common when drive hunts occurred in areas with dense understorey. However, neither beater/dog densities nor site familiarity influenced the immediate flight decision. Following a drive hunt, red deer remained outside the hunted areas for periods twice as long compared to periods when no hunting occurred (34 hours vs. 17 hours). Such knowledge of game movement rates in response to drive hunts may help the development of informed management policy for hunted red deer populations.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228865PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062277PMC
June 2020

Altitude shapes the environmental drivers of large-scale variation in abundance of a widespread mammal species.

Ecol Evol 2020 Jan 14;10(1):119-130. Epub 2019 Dec 14.

Unité-PAD Office National de la Chasse et de la Faune Sauvage Birieux France.

Aim: Habitat quality and heterogeneity directly influence the distribution and abundance of organisms at different spatial scales. Determining the main environmental factors driving the variation in species abundance is crucial to understand the underlying ecological processes, and this is especially important for widely distributed species living in contrasting environments. However, the responses to environmental variation are usually described at relatively small spatial scales. Here, we studied the variation in abundance of a widely distributed mustelid, the European badger (), across France.

Location: The whole metropolitan France.

Methods: We used (a) direct detections of 9,439 dead and living badgers, from 2006 to 2009, to estimate badger relative abundance in 703 small agricultural regions of metropolitan France and (b) a Bayesian modeling approach to identify the main environmental determinants influencing badger abundance.

Results: Despite a continuous distribution of badger in France, we found large variation in badger abundance between regions, explained by environmental factors. Among a set of 13 environmental variables, we demonstrated that badger abundance in lowlands (<400 m a.s.l.) was mostly driven by biotic factors such as potential food resources (earthworm abundance and fruit crops) and forest fragmentation. Conversely, in mountainous areas, abiotic factors (i.e., soil texture and climate) drove the variation in badger relative abundance.

Main Conclusions: These results underline the importance of mapping the abundance of wildlife species based on environmental suitability and highlight the complexity of drivers influencing species abundance at such large spatial scales. Altitude shaped the environmental drivers (biotic vs. abiotic) that most influenced relative abundance of a widespread species. In the case of badger, such abundance maps are crucial to identify critical areas for species management as this mustelid is a main wild vector of bovine tuberculosis in several countries.
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http://dx.doi.org/10.1002/ece3.5851DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972803PMC
January 2020

Many, large and early: Hunting pressure on wild boar relates to simple metrics of hunting effort.

Sci Total Environ 2020 Jan 3;698:134251. Epub 2019 Sep 3.

Office National de la Chasse et de la Faune sauvage, DRE-Unité Ongulés Sauvages, Monfort 01330, Birieux, France. Electronic address:

Wild boar populations have increased dramatically over the last decades throughout Europe and in France in particular. While hunting is considered the most efficient way to control game populations, many local conflicts persist after the hunting period due to remaining high densities of wild boar despite the large number of animals culled every year. Therefore, increasing the efficiency of hunting is a timely issue. Herein, we assessed how hunting effort can be measured, and we determined whether the hunting effort carried out by hunters explains the observed hunting pressure. We measured the characteristics and results of all hunts that occurred in the experimental forest of Châteauvillain-Arc-en-Barrois (Northeastern France), and we modelled the number of animals culled as a function of the hunting effort, measured by the number of beaters, hunters, and dogs, as well as the size of the hunting area. We also accounted for variables suspected to affect the hunting efficiency achieved with a given effort, such as time of day (AM/PM), the month during which hunting occurred. We found that more posted hunters, larger hunted areas, and hunts carried out early in the season, i.e. before February, increased the number of culled animals. Our model can be used by wildlife managers to adjust hunting effort in order to reach the hunting pressure expected to meet management objectives.
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http://dx.doi.org/10.1016/j.scitotenv.2019.134251DOI Listing
January 2020

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

Sci Rep 2018 03 23;8(1):5102. Epub 2018 Mar 23.

Office National de la Chasse et de la Faune Sauvage, Unité Ongulés Sauvages. Direction de la Recherche et de l'Expertise, 85 bis Avenue de Wagram, 75017, Paris, France.

With 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.
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http://dx.doi.org/10.1038/s41598-018-23111-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5865119PMC
March 2018

Capitalizing on opportunistic data for monitoring relative abundances of species.

Biometrics 2016 06 23;72(2):649-58. Epub 2015 Oct 23.

CESCO, UMR 7204, MNHN-CNRS-UPMC, CP51, 55 rue Buffon, 75005 Paris, France.

With the internet, a massive amount of information on species abundance can be collected by 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 article, 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 makes it possible 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 in 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 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.
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http://dx.doi.org/10.1111/biom.12431DOI Listing
June 2016

The spatial distribution of Mustelidae in France.

PLoS One 2015 26;10(3):e0121689. Epub 2015 Mar 26.

Office national de la chasse et de la faune sauvage, Direction des études et de la recherche, Saint Benoist, BP 20. 78612 Le Perray en Yvelines, France.

We estimated the spatial distribution of 6 Mustelidae species in France using the data collected by the French national hunting and wildlife agency under the "small carnivorous species logbooks" program. The 1500 national wildlife protection officers working for this agency spend 80% of their working time traveling in the spatial area in which they have authority. During their travels, they occasionally detect dead or living small and medium size carnivorous animals. Between 2002 and 2005, each car operated by this agency was equipped with a logbook in which officers recorded information about the detected animals (species, location, dead or alive, date). Thus, more than 30000 dead or living animals were detected during the study period. Because a large number of detected animals in a region could have been the result of a high sampling pressure there, we modeled the number of detected animals as a function of the sampling effort to allow for unbiased estimation of the species density. For dead animals -- mostly roadkill -- we supposed that the effort in a given region was proportional to the distance traveled by the officers. For living animals, we had no way to measure the sampling effort. We demonstrated that it was possible to use the whole dataset (dead and living animals) to estimate the following: (i) the relative density -- i.e., the density multiplied by an unknown constant -- of each species of interest across the different French agricultural regions, (ii) the sampling effort for living animals for each region, and (iii) the relative detection probability for various species of interest.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121689PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4374970PMC
February 2016

Bayesian modelling of hunting data may improve the understanding of host-parasite systems: wild boar diseases and vaccination as an example.

J Theor Biol 2014 Feb 21;343:32-43. Epub 2013 Nov 21.

Office national de la chasse et de la faune sauvage, Micropolis, F-05000 Gap, France. Electronic address:

Wildlife diseases are often studied using hunting data. In such studies, inferences about diseases are often made by comparing raw disease prevalence levels, ignoring complications like stochasticity in recruitment. We carried out a field trial to study the effectiveness of oral vaccination of wild boar (Sus scrofa) against classical swine fever (CSF) in the Vosges mountains (Northeastern France) for 3 years (2008-2010). Since August 2004, hunters had carried out three vaccination sessions per year in spring, summer and autumn. During our study period, we determined whether each wild boar hunted in our study area was immunized or not against CSF. We used a Bayesian approach to model the changes in the proportion of vaccinated animals in the population of young animals (i.e., <12 months old). This approach allowed to disentangle the effects of the birth peaks (leading to a decrease) and of both the vaccination sessions and natural infection (leading to an increase) on this proportion. We thus inferred, at the individual level, the probability that a non-immunized animal became vaccinated after a particular session. There was a high between-year variability in the effectiveness of the vaccination: the observed patterns were similar in 2008 and 2010, but 2009 was characterized by an overall greater effectiveness of the vaccination. Within a particular year, the spring vaccination session was more effective than the autumn session, probably because of the higher food availability in autumn that render the vaccination places less attractive to the animals. The vaccination effectiveness was rather low in summer, except in 2009, probably because of higher age identification error this year. This model also highlighted an immunisation of animals occurring outside vaccination periods, which suggests either the presence of the CSF virus in our study area, or the consumption of the vaccine outside the vaccination sessions. Finally, we observed a high spatial variability of the probability of vaccination. The effectiveness of the vaccination was indeed strongly related to both the distribution of the forests and the distribution of the vaccination places in our study area. This study highlights an optimal vaccination effort of 1.25 places per km(2) to maximize the proportion of immune wild boar in that area.
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http://dx.doi.org/10.1016/j.jtbi.2013.11.011DOI Listing
February 2014

Wildlife tracking data management: a new vision.

Philos Trans R Soc Lond B Biol Sci 2010 Jul;365(1550):2177-85

To date, the processing of wildlife location data has relied on a diversity of software and file formats. Data management and the following spatial and statistical analyses were undertaken in multiple steps, involving many time-consuming importing/exporting phases. Recent technological advancements in tracking systems have made large, continuous, high-frequency datasets of wildlife behavioural data available, such as those derived from the global positioning system (GPS) and other animal-attached sensor devices. These data can be further complemented by a wide range of other information about the animals' environment. Management of these large and diverse datasets for modelling animal behaviour and ecology can prove challenging, slowing down analysis and increasing the probability of mistakes in data handling. We address these issues by critically evaluating the requirements for good management of GPS data for wildlife biology. We highlight that dedicated data management tools and expertise are needed. We explore current research in wildlife data management. We suggest a general direction of development, based on a modular software architecture with a spatial database at its core, where interoperability, data model design and integration with remote-sensing data sources play an important role in successful GPS data handling.
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http://dx.doi.org/10.1098/rstb.2010.0081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894960PMC
July 2010

A general framework for the statistical exploration of the ecological niche.

J Theor Biol 2008 Jun 3;252(4):674-85. Epub 2008 Mar 3.

Université de Lyon, F-69000, Lyon, France; Université Lyon 1, CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France.

We propose a new statistical framework for the exploratory analysis of the ecological niche, the "General niche-environment system factor analysis" (GNESFA). The data required for this analysis are (i) a table giving the values of the environmental variables in each environment unit (EU, e.g., the patches of habitat on a vector map), (ii) a set of weights measuring the availability of the EUs to the species (e.g., the proportion of the study area covered by a given patch), and (iii) a set of utilization weights describing the use of the EUs by the focal species (e.g., the proportion of detections of the species in each patch). Each row of the table corresponds to a point in the multidimensional space defined by the environmental variables, and each point is associated with two weights. The GNESFA searches the directions in this space where the two weight distributions differ the most, choosing one distribution as the reference, and the other one as the focus. The choice of the utilization as the reference corresponds to the MADIFA (Mahalanobis distances factor analysis), which identifies the directions on which the available EUs are in average the furthest from the optimum of the niche, allowing habitat suitability modelling. The choice of the availability as the reference corresponds to the FANTER (Factor analysis of the niche, taking the environment as the reference), which identifies the directions on which the niche is the furthest from the average environment (marginality) and those on which the niche is the narrowest compared with the environment (specialization). The commonly used ENFA (Ecological niche factor analysis) is at the middle point between the MADIFA and the FANTER, considering both distributions as the reference and the focus simultaneously. When used concurrently, these three analyses allow an extensive exploration of the system.
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http://dx.doi.org/10.1016/j.jtbi.2008.02.036DOI Listing
June 2008