A half a century of measuring ungulate body condition using indices: is it time for a change?

From a literature review of five wildlife ecology journals since 1937, we document how using indices to monitor ungulate body condition is common practice, with the kidney fat index (KFI = weight of fat around the kidneys/weight of kidneys without fat × 100) as the favoured tool (82% of studies). In this context, we highlight the problems of using indices when underlying statistical assumptions are not met (isometry, parallel slopes between treatments). We show, with real and simulated data for two cervids with contrasting fat storage strategies, how results from analysis of variance of KFI values differ from analysis of covariance (ANCOVA) of raw data. We conclude that the KFI is affected by the restrictions typically associated with derived index values, and as a consequence, statistical analysis of the KFI could generate spurious results leading to erroneous interpretations concerning variation in body condition of ungulate populations. Thus, we recommend analysing fat weight as an untransformed variable in ANCOVA (kidney weight as covariate) to describe body condition variation in ungulates

Les ongulés et leur gestion en France

International audienceThis chapter explores the management objectives and the different approaches to wildlife management of large ungulates in France. A detailed analysis is done about the species present, their numerical status and the distribution of the different ungulates that occur. Management systems are described (legislative and administrative structures) with an evaluation of the current management practices

Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data

In our paper "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data" (Global Ecology and Biogeography) we use GPS tracking data from 1,498 from 49 different species to evaluate the expert-based habitat suitability data from the International Union for Conservation of Nature (IUCN). Therefore, we used the GPS tracking data to estimate two measures of habitat suitability for each individual animal and habitat type: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN’s classification into suitable, marginal and unsuitable habitat types. Our results showed that IUCN habitat suitability data were in accordance with the GPS data (>95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a >50% probability of agreement based on proportional habitat use and selection ratios, respectively. These findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, our study shows that GPS tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data. In this dataset we provide the measures of habitat suitability for each individual and each habitat type, calculated using different methods. In addition, we provide data on the body mass and IUCN Red List category of the species, as well as whether the species can be considered a habitat specialist or habitat generalist

Data of "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data"

In our paper "Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data" (Global Ecology and Biogeography) we use GPS tracking data from 1,498 from 49 different species to evaluate the expert-based habitat suitability data from the International Union for Conservation of Nature (IUCN). Therefore, we used the GPS tracking data to estimate two measures of habitat suitability for each individual animal and habitat type: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN’s classification into suitable, marginal and unsuitable habitat types. Our results showed that IUCN habitat suitability data were in accordance with the GPS data (>95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a >50% probability of agreement based on proportional habitat use and selection ratios, respectively. These findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, our study shows that GPS tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data. In this dataset we provide the measures of habitat suitability for each individual and each habitat type, calculated using different methods. In addition, we provide data on the body mass and IUCN Red List category of the species, as well as whether the species can be considered a habitat specialist or habitat generalist