Search

International audience; For sustainable management of exploited populations, it is required to have good knowledge on temporal trends in population density to adapt the harvest. In this regard, hunting statistics are often collected routinely by government agencies and associations. These data are used to assess demographic trends through the development of indices, which are in turn used to manage exploited populations in a sustainable way. However, these population indices depend on features of the hunting process (e.g. hunting effort, hunting conditions, probability of catch). In this study, we show how to use hunting logs to assess demographic trends in exploited populations while accounting for the components of the hunting process. In particular, we developed a catch-effort model to study how the hunting effort leads to mortality rate – hunting pressure – within a given habitat type and during a given period. We illustrated the usefulness of this approach using exploited wild boar (Sus scrofa) populations as a case study. We used a large hunting logs dataset to perform our study, with several hundreds of thousands hunting events for more than 10 years in two French departments in France, including information about the number of hunters, of wild boars culled and the date of the hunt. We showed that catchability is a key parameter to assess hunting pressure at a given time and place. This parameter varies both within the hunting season and between habitat types. Once this variation in catchability was accounted for, our catch-effort model allowed us to obtain estimates of relative densities of wild boar populations over the study period at the management unit scale. Thus, catch-effort models are powerful tools to assess population density and to understand the underlying hunting process. Our study offers straightforward and reproducible conceptual framework that can be applied routinely by wildlife managers on exploited populations and practitioners from hunting statistics logs.

We develop a new statistical procedure to monitor relative species abundances and their respective preferences for different habitat types, using opportunistic data. Following Giraud et al. (Biometrics 72(2):649–658, 2015), we combine the opportunistic data with some standardized data in order to correct the bias inherent to the opportunistic data collection. Species observations are modeled by Poisson distributions whose parameters quantify species abundances and habitat preferences, and are estimated using Bayesian computations. 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).

Accurate flyway delineation is a prerequisite for effective conservation and management of migratory bird populations, yet such limits have so far mostly been set subjectively. We present a statistical method to infer population boundaries from the analysis of ring recoveries, using a Bayesian framework. The approach was applied to Eurasian teal Anas crecca ringed in Camargue, southern France, and Abberton Reservoir, Essex, eastern England. The results presented show the boundaries of the two teal flyways in western Europe, with a zone of overlap, broadly matching those previously defined. The percentage of teal switching flyways (abmigration rate) was 2.4–2.6%, greater in birds ringed as juveniles than as adults. Abmigrants ended up at sites within the other flyway where the density of local birds was lower than expected by chance, suggesting abmigration resulted from exploratory or aberrant behaviour. The methodology presented here can be used to infer flyway boundaries of any bird with an adequate ring-re-encounter dataset, which has crucial consequences for the evaluation of their trends in abundance and hence conservation status, and the management of sustainable harvests.

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.

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 (Meles meles), 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 (, We used direct detections of 9,439 badgers Meles meles, to estimate badger relative abundances in 703 small agricultural regions of metropolitan France and a Bayesian modeling approach to identify the main environmental determinants influencing badger abundances. Despite a continuous distribution of badger in this country, we demonstrated that badger abundances in lowlands were mostly driven by biotic factors such as potential food resources and forest fragmentation, while in mountainous areas, abiotic factors drove the variations in badger relative abundance. 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 large spatial scale.

Hunting, including drive hunts, can be considered as a predation process. Although drive hunts are spatially and temporally well defined, the scale at which the hunting risk triggers anti-predator responses of prey remains poorly documented. The present study aims at 1) characterizing the delayed movement responses of female red deer Cervus elaphus to hunters and 2) understanding the main environmental determinants modulating these post-disturbance responses at the individual level. We hypothesized that red deer alter their movement behaviour for several days after a drive hunt by increasing their speed and exploring more space. Then, we predicted that individuals close to the hunting area would exhibit stronger responses (i.e. higher speed and larger home range size) than individuals located further away. We also expected topographic obstacles and vegetation cover to reduce the magnitude of the post-disturbance responses. We used GPS data from a large dataset collected on 91 female red deer in the National Hunting and Wildlife Reserve of La Petite Pierre (France), between 2004 and 2018. Each year, drive hunts are conducted by about 43 hunters and 6 dogs, once a week from October to February. Our results suggest that, over a 72-h period after a drive hunt, female red deer could show two contrasting strategies: 1) flight-type movements (i.e. higher maximum speed) associated with more space explored due to a temporary avoidance of the disturbed area and 2) lower movements to avoid detection and contacts with hunters. These two delayed responses are situation-specific, depending on risk proximity, landscape features, hunting conditions and individual characteristics. This study highlights that ungulate management using a ‘hunting for fear’ approach may be adjusted considering spatial and temporal scales of the effects of drive hunts on red deer movement patterns.

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.

Mute Swan Cygnus olor numbers have recently increased in a dramatic fashion in Western Europe and in North America, suggesting there could be potential consequences for the rest of the waterbird community. Breeding Mute Swan pairs may behave territorially towards other waterbirds, taking advantage of their larger size, and hence cause concern regarding their potential effects on waterbird communities. We studied how the within-site distributions of breeding Mute Swans and other waterbirds were related to each other, in order to assess if there is support to the assertion that breeding Mute Swans may affect the distribution of the other waterfowl within waterbodies. We mapped waterbird and swan distribution within fishponds during the Mute Swan breeding period. Relying on spatial point pattern analysis, our first finding is that breeding Mute Swans were located in the vicinity of the other waterbirds, using the same area within fishpond. Waterbirds do not completely desert the area used by breedi...

Sympatric populations of species with similar ecology are limited by competition for available resources. While quantifying niche overlap between species in interaction offers a useful description of coexistence patterns, the lack of correspondence between niche overlap and competition prevents any functional interpretation. Using an innovative approach for analysing spatial distributions of individuals from two sympatric species, we aim to fill the gap. We applied our models to data collected on sympatric females of roe deer and red deer. Using the null model approach commonly applied in community ecology, we tested in a first model for deviation from a random distribution of female roe deer in relation to female red deer. We took into account constraints generated by both the marked sedentary habits and habitat use (avoidance of mature forest) of roe deer in this null model. In a second null model, we removed the habitat constraints to avoid any lack of power of our tests. We then compared the overlap index calculated from roe deer and red deer locations with the distribution expected under each of these null models. As we failed to reject the null model in both cases, we tested a third null model simulating an identical distribution of roe deer and red deer home ranges and we rejected it. Our results show that the distribution of female roe deer does not depend on the distribution of female red deer, indicating an absence of competitive interactions between the deer species. This conclusion relies on the application of the null model approach, which provides a suitable way of performing a formal test of interspecific competition rooted in explicitly defined hypotheses, and could not have been reached using simple overlap indices as generally performed when assessing competitive interactions. We thus encourage scientists to apply this null model analysis to population ecology.

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.

International audience; In most previous studies of habitat selection, the use of a given habitat type is assumed to be directly proportional to its availability. However, the use and (or) the selection of a given habitat may be conditional on the availability of that habitat. We aim here to (i) identify the environmental variables involved in habitat selection, (ii) identify classes of individuals with similar patterns of habitat selection, and (iii) assess whether habitat use changes with changing availability of habitat types, within monthly home ranges of female western roe deer (Capreolus capreolus (L., 1758)). We found that some females adjust their habitat use according to the distribution of resources within habitats. Females with similar home ranges in terms of resource quantity and quality showed similar patterns of habitat selection. Differences in habitat use between 2 years with contrasting resource availability showed that temporal changes of environmental conditions influenced the pattern of habitat selection by female roe deer. Habitat selection also differed between periods of the life cycle likely because of contrasted energy requirements. This study shows that the relationship between habitat use by herbivores and habitat availability is nonproportional, and that the availability of resources influences use mostly at intermediate values.

Studies of animal movements have been popularized for many large and shy species by the increasing use of radio telemetry methods (VHF and GPS technologies). Data are collected with high sampling frequency, and consist of successive observations of the position of an individual animal. The statistical analysis of such data poses several problems due to the lack of independence of successive observations. However, the statistical description of the temporal autocorrelation between successive steps is rarely performed by ecologists studying the patterns of animals movements. The aim of this paper is to warn ecologists against the consequences of failing to consider this aspect. We discuss the various issues related to analyzing autocorrelated data, and show how the exploratory analysis of autocorrelation can both reveal important biological insights and help to improve the accuracy of movement models. We suggest some tools that can be used to measure, test, and adjust for temporal autocorrelation. A short ecological illustration is presented.

The Mahalanobis distances have been introduced in habitat selection studies for the estimation of environmental suitability maps (ESMs). The pixels of raster maps of a given area correspond to points in the multidimensional space defined by the mapped environmental variables (ecological space). The Mahalanobis distances measure the distances in this space between these points and the mean of the ecological niche (i.e., the hypothesized optimum for the species) regarding the structure of the niche. The map of these distances over the area of interest is an estimated ESM. Several authors recently noted that the use of a single optimum for the niche of a species may lead to biased predictions of animal occurrence. They proposed to use instead a minimum set of basic habitat requirements, found by partitioning the Mahalanobis distances into a restricted set of biologically meaningful axes. However, the statistical approach they proposed does not take into account the environmental conditions on the area where the niche was sampled (i.e., the environmental availability), and we show that including this availability is necessary. We used their approach as a basis to develop a new exploratory tool, the Mahalanobis distance factor analysis (MADIFA), which performs an additive partitioning of the Mahalanobis distances taking into account this availability. The basic habitat requirements of a species can be derived from the axes of the MADIFA. This method can also be used to compute ESMs using only this small number of basic requirements, therefore including only the biologically relevant information. We also prove that the MADIFA is complementary to the commonly used ecological-niche factor analysis (ENFA). We used the MADIFA method to analyze the niche of the chamois Rupicapra rupicapra in a mountainous area. This method adds to the existing set of tools for the description of the niche.

We propose here some refinements of the ecological-niche factor analysis (ENFA) to describe precisely one organism’s habitat selection. The ENFA is based on the concept of the ecological niche, and provides a measure of the realised niche within the available space from the computation of two parameters, the marginality and the specialization. By measuring the departure of the ecological niche from the average available habitat, the marginality identifies the preference of the individual, population, or species for specific conditions of the environment among the whole set of possibilities. The specialization appears as a consequence of the narrowness of the niche on some environmental variables. The ENFA is a factorial analysis that extracts one axis of marginality and several axes of specialization. We present here the use of biplots (i.e., the projection of both the pixels of the map and the environmental variables in the subspace extracted by the ENFA) as a way to identify the key-variables for management, assessing which habitat features are of prime importance and should be preserved or reinforced. With the help of this tool, we are now able to describe much more precisely the habitat selection of the organism under focus. In our application to the lynx in the Vosges mountains, based on sightings as well as other indices of lynx presence, we thus underlined a strong avoidance of agricultural areas by the lynx. We also highlighted the relative indifference of the lynx to the proximity of artificial areas and at the opposite, the sensitivity to the proximity of highways. The ENFA provides a suitable way to measure habitat use/selection under a large range of ecological contexts and should be used to define precisely the ecological niche and therefore identify the characteristics searched for by the organism under study.

The practical analysis of space use and habitat selection by animals is often a problem due to the lack of well-designed programs. I present here the “adehabitat” package for the R software, which offers basic GIS (Geographic Information System) functions, methods to analyze radio-tracking data and habitat selection by wildlife, and interfaces with other R packages. These tools can be downloaded freely on the internet. Because the functions of this package can be combined with other functions of R, “adehabitat” provides a powerful environment for the analysis of the space and habitat use.

Two kinds of wildlife habitat studies can be distinguished in the literature: hindcasting and forecasting studies. Hindcasting studies aim to emphasize among a large set of habitat variables those that are of interest for the focus species, whereas forecasting studies are intended to predict habitat selection according to a small number of habitat variables for a given area. We provide here a new analytical tool which relies on the concept of ecological niche, the K-select analysis, for hindcasting studies of habitat selection by animals using radio-tracking data. Each habitat variable defines one dimension in the ecological space. For each animal, the difference between the vector of average available habitat conditions and the vector of average used conditions defines the marginality vector. Its size is proportional to the importance of habitat selection, and its direction indicates which variables are selected. By performing a non-centered principal component analysis of the table containing the coordinates of the marginality vectors of each animal (row) on the habitat variables (column), the K-select analysis returns a linear combination of habitat variables for which the average marginality is greatest. It is a synthesis of variables which contributes the most to the habitat selection. As with principal component analysis, the biological significance of the factorial axes is deduced from the loading of variables. An example is provided: habitat selection by wild boar is studied in a Mediterranean habitat using the K-select analysis. The numerous advantages of the analysis (a large number of variables that can be included, individual variability in habitat selection taken into account, a lack of too strict underlying hypotheses) make it a powerful approach in radio-tracking studies designed to identify habitat variables that are selected by animals.

Heterogeneity of infectious disease transmission can be generated by individual differences in the frequency of contacts with susceptible individuals, in the ability to transmit the infectious agent or in the duration of infection, and by spatial variation in the distribution, density or movements of hosts. Identifying spatial and individual heterogeneity can help improving management strategies to eradicate or mitigate infectious diseases, by targeting the individuals or areas that are responsible for most transmissions. Individual-based models allow quantifying the respective role of these sources of heterogeneity by integrating potential mechanisms that generate heterogeneity and then by tracking transmissions caused by each infected individual. In this study, we provide an individual-based model of endemic brucellosis Brucella melitensis transmission in the population of Alpine ibex (Capra ibex) of the Bargy massif (France) by taking advantage of detailed information available on ibex population dynamics, behaviour, and habitat use, and on epidemiological surveys. This host-pathogen system is expected to be subject of both individual and spatial heterogeneity. We first estimated the transmission probabilities, hitherto unknown, of the two main transmission routes of the infection (i.e., exposure to infectious births/abortions and venereal transmission). Then, we quantified heterogeneity at both individual and spatial levels. We found that both transmission routes are not negligible to explain the data, and that there is a high amount of heterogeneity of the host-pathogen system at the individual level, with females generating around 90% of the new cases of brucellosis infection. Males transmit infection at a lesser extent but still play a non-negligible role because they move between subpopulations and thereby create opportunities for spreading the infection spatially by venereal transmission. Two particular socio-spatial units are hotspots of transmission, and act as sources of transmission for the other units. These results may have important implications for disease management strategies.