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Habitat destruction and over-hunting are increasingly threatening the arboreal primates of Central Africa. To establish effective conservation strategies, accurate assessments of primate density, abundance, and spatial distribution are required. To date, the method of choice for primate density estimation is line transect distance sampling. However, primates fleeing human observers violate methodological assumptions, biasing the accuracy of resulting estimates. In this study, we used line transect distance sampling to study five primate species along 378 km of transects in Salonga National Park, Democratic Republic of the Congo. We tested the effect of different levels of survey-inherent disturbance (i.e., cutting) on the number of observed (i) primate groups, and (ii) individuals within groups, by counting groups at three different time lags after disturbance of the transect, (i) a minimum of 3 h, (ii) 24 h, (iii) a minimum of 3 days. We found that survey-inherent disturbance led to underestimated densities, affecting both the number of encountered groups and of observed individuals. However, the response varied between species due to species-specific ecological and behavioral features. Piliocolobus tholloni and Colobus angolenis resumed an unaltered behavior only 24 h after disturbance, while Lophocebus aterrimus, Cercopithecus ascanius, and Cercopithecus wolfi required a minimum of 10 days. To minimize bias in density estimates, future surveys using line transect distance sampling should be designed considering survey-inherent disturbance. We recommend evaluating the factors driving primate response, including habitat type, niche occupation, and hunting pressure, peculiar to the survey-specific area and primate community under study., (© 2022. The Author(s).)

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., (© 2018 Wiley Periodicals, Inc.)

The role of spatial scale in ecological pattern formation such as the geographical distribution of species has been a major theme in research for decades. Much progress has been made on identifying spatial scales of habitat influence on species distribution. Generally, the effect of a predictor variable on a response is evaluated over multiple, discrete spatial scales to identify an optimal scale of influence. However, the idea to identify one optimal scale of predictor influence is misleading. Species-environment relationships across scales are usually sigmoid increasing or decreasing rather than humped-shaped, because environmental conditions are generally highly autocorrelated. Here, we use nest count data on bonobos (Pan paniscus) to build distribution models which simultaneously evaluate the influence of several predictors at multiple spatial scales. More specifically, we used forest structure, availability of fruit trees and terrestrial herbaceous vegetation (THV) to reflect environmental constraints on bonobo ranging, feeding and nesting behaviour, respectively. A large number of models fitted the data equally well and revealed sigmoidal shapes for bonobo-environment relationships across scales. The influence of forest structure increased with distance and became particularly important, when including a neighbourhood of at least 750 m around observation points; for fruit availability and THV, predictor influence decreased with increasing distance and was mainly influential below 600 and 300 m, respectively. There was almost no difference in model fit, when weighing predictor values within the extraction neighbourhood by distance compared to simply taking the arithmetic mean of predictor values. The spatial scale models provide information on bonobo nesting preferences and are useful for the understanding of bonobo ecology and conservation, such as in the context of mitigating the impact of logging. The proposed approach is flexible and easily applicable to a wide range of species, response and predictor variables and over diverse spatial scales and ecological settings., (© 2016 Wiley Periodicals, Inc.)

Animal biometrics is an emerging field that develops quantified approaches for representing and detecting the phenotypic appearance of species, individuals, behaviors, and morphological traits. It operates at the intersection between pattern recognition, ecology, and information sciences, producing computerized systems for phenotypic measurement and interpretation. Animal biometrics can benefit a wide range of disciplines, including biogeography, population ecology, and behavioral research. Currently, real-world applications are gaining momentum, augmenting the quantity and quality of ecological data collection and processing. However, to advance animal biometrics will require integration of methodologies among the scientific disciplines involved. Such efforts will be worthwhile because the great potential of this approach rests with the formal abstraction of phenomics, to create tractable interfaces between different organizational levels of life., (Copyright © 2013 Elsevier Ltd. All rights reserved.)