Utilisation des caméras de piégeage et des modèles de capture-recapture pour l'estimation des densités de chimpanzés d'Afrique occidentale (Pan troglodytes verus) en Côte d'Ivoire

Des estimations de densité exactes et précises sont indispensables pour évaluer les effets des menaces spécifiques sur une espèce, mesurer le succès de décisions de conservation, et pour comprendre l'écologie des populations animales. La méthode des caméras de piégeage, combinée aux modèles de capture-recapture (C-R), a récemment été mise au point pour surmonter les limitations des techniques conventionnelles d'inventaire des populations de grands singes. Cependant, aucune validation de la méthode n'a été réalisée à ce jour. Dans cette étude, je vise à valider l'utilisation de caméras de piégeage en combinaison avec les modèles de C-R pour estimer les densités de chimpanzés d'Afrique occidentale (Pan troglodytes verus). Plus précisément, je vise à identifier : 1) quelle est la meilleure méthode de C-R pour estimer les densités de chimpanzés par caméras de piégeage, 2) quel est l'effort de piégeage minimum requis pour des estimations de densités exactes et précises, et 3) si un placement aléatoire des caméras peut donner des mesures de densité fiables et robustes. Afin de répondre à ces trois objectifs, j'ai mené une étude de caméras de piégeage de 10 mois sur le territoire d'une communauté de chimpanzés habituée à la présence humaine, et donc où la densité totale de chimpanzés est déjà connue. Les caméras ont été placées selon deux placements différents : systématiquement, où les caméras ont été installées à chaque kilomètre, ou stratégiquement, à des endroits fréquemment visités par les chimpanzés. Les résultats montrent que tous les modèles de C-R ont donné des estimations de densité plus exactes et plus précises que les autres méthodes couramment utilisées pour le recensement des populations de grands singes. Les chimpanzés avaient deux fois plus de chances d'être filmés par les caméras placées de façon stratégique, mais les densités issues des caméras systématiques étaient aussi précises et robustes. Ainsi, cette étude met l'accent sur la pertinence des caméras de piégeage et des modèles de C-R comme outils de surveillance des populations des grands singes.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : caméras de piégeage, chimpanzés, pan troglodytes verus, suivi, densité, capture-marquage-recapture, modèles spatiaux de capture-marquage-recapture, Côte d'Ivoire

Model selection with overdispersed distance sampling data

We thank the Robert Bosch Foundation, the Max Planck Society and the University of St Andrews for funding.1. Distance sampling (DS) is a widely used framework for estimating animal abundance. DS models assume that observations of distances to animals are independent. Non‐independent observations introduce overdispersion, causing model selection criteria such as AIC or AICc to favour overly complex models, with adverse effects on accuracy and precision. 2. We describe, and evaluate via simulation and with real data, estimators of an overdispersion factor (ĉ), and associated adjusted model selection criteria (QAIC) for use with overdispersed DS data. In other contexts, a single value of ĉ is calculated from the “global” model, that is the most highly parameterised model in the candidate set, and used to calculate QAIC for all models in the set; the resulting QAIC values, and associated ΔQAIC values and QAIC weights, are comparable across the entire set. Candidate models of the DS detection function include models with different general forms (e.g. half‐normal, hazard rate, uniform), so it may not be possible to identify a single global model. We therefore propose a two‐step model selection procedure by which QAIC is used to select among models with the same general form, and then a goodness‐of‐fit statistic is used to select among models with different forms. A drawback of thi approach is that QAIC values are not comparable across all models in the candidate set. 3. Relative to AIC, QAIC and the two‐step model selection procedure avoided overfitting and improved the accuracy and precision of densities estimated from simulated data. When applied to six real datasets, adjusted criteria and procedures selected either the same model as AIC or a model that yielded a more accurate density estimate in five cases, and a model that yielded a less accurate estimate in one case. 4. Many DS surveys yield overdispersed data, including cue counting surveys of songbirds and cetaceans, surveys of social species including primates, and camera‐trapping surveys. Methods that adjust for overdispersion during the model selection stage of DS analyses therefore address a conspicuous gap in the DS analytical framework as applied to species of conservation concern.PostprintPeer reviewe

Observed distances from camera traps to Maxwell's duikers

Filmed Maxwell's duikers were assigned to distance intervals; recorded distances are the midpoints of the intervals. "Sample.Label" references the trap location. "Effort" is temporal effort, i.e. the number of 2-second time-steps over which the camera operated. "multiplier" describes spatial effort, as the proportion of a circle covered by the angle of view of the camera. The data is formatted as a "flatfile" suitable for use with distance sampling software including Distance and associated R packages

Observed distances from camera traps to Maxwell's duikers at times of peak activity

Filmed Maxwell's duikers were assigned to distance intervals; recorded distances are the midpoints of the intervals. "Sample.Label" references the trap location. "Effort" is temporal effort, i.e. the number of 2-second time-steps over which the camera operated. "multiplier" describes spatial effort, as the proportion of a circle covered by the angle of view of the camera. The data is formatted as a "flatfile" suitable for use with distance sampling software including Distance and associated R packages. This file includes only observations recorded at times of peak activity

Montrave_cuecount

Raw cue count data from songbirds at Montrave Estate, Scotland, originally presented in Buckland ST (2006). Point transect surveys for songbirds: robust methodologies. The Auk 123:345–357, and reanalyzed by Howe, Buckland, Despres-Einspenner, and Kuehl (2018). Model selection with overdispersed distance sampling data. Data were copied from program Distance and uploaded as a .csv file. A Distance project file is available at distancesampling.org

Data from: Model selection with overdispersed distance sampling data

1. Distance sampling (DS) is a widely-used framework for estimating animal abundance. DS models assume that observations of distances to animals are independent. Non-independent observations introduce overdispersion, causing model selection criteria such as AIC or AICc to favour overly complex models, with adverse effects on accuracy and precision. 2. We describe, and evaluate via simulation and with real data, estimators of an overdispersion factor (c ̂), and associated adjusted model selection criteria (QAIC) for use with overdispersed DS data. In other contexts, a single value of c ̂ is calculated from the “global” model, i.e., the most highly-parameterized model in the candidate set, and used to calculate QAIC for all models in the set; the resulting QAIC values, and associated ΔQAIC values and QAIC weights, are comparable across the entire set. Candidate models of the DS detection function include models with different general forms (e.g., half-normal, hazard rate, uniform), so it may not be possible to identify a single global model. We therefore propose a two-step model selection procedure by which QAIC is used to select among models with the same general form, and then a goodness-of-fit statistic is used to select among models with different forms. A drawback of this approach is that QAIC values are not comparable across all models in the candidate set. 3. Relative to AIC, QAIC and the two-step model selection procedure avoided overfitting and improved the accuracy and precision of densities estimated from simulated data. When applied to six real data sets, adjusted criteria and procedures selected either the same model as AIC or a model that yielded a more accurate density estimate in 5 cases, and a model that yielded a less accurate estimate in 1 case. 4. Many DS surveys yield overdispersed data, including cue counting surveys of songbirds and cetaceans, surveys of social species including primates, and camera-trapping surveys. Methods that adjust for overdispersion during the model selection stage of DS analyses therefore address a conspicuous gap in the DS analytical framework as applied to species of conservation concern

An assessment of the efficacy of camera traps for studying demographic composition and variation in chimpanzees (Pan troglodytes).

Demographic factors can strongly influence patterns of behavioral variation in animal societies. Traditionally, these factors are measured using longitudinal observation of habituated social groups, particularly in social animals like primates. Alternatively, noninvasive biomonitoring methods such as camera trapping can allow researchers to assess species occupancy, estimate population abundance, and study rare behaviors. However, measures of fine-scale demographic variation, such as those related to age and sex structure or subgrouping patterns, pose a greater challenge. Here, we compare demographic data collected from a community of habituated chimpanzees (Pan troglodytes verus) in the Taï Forest using two methods: camera trap videos and observational data from long-term records. By matching data on party size, seasonal variation in party size, measures of demographic composition, and changes over the study period from both sources, we compared the accuracy of camera trap records and long-term data to assess whether camera trap data could be used to assess such variables in populations of unhabituated chimpanzees. When compared to observational data, camera trap data tended to underestimate measures of party size, but revealed similar patterns of seasonal variation as well as similar community demographic composition (age/sex proportions) and dynamics (particularly emigration and deaths) during the study period. Our findings highlight the potential and limitations of camera trap surveys for estimating fine-scale demographic composition and variation in primates. Continuing development of field and statistical methods will further improve the usability of camera traps for demographic studies