Anthropogenic effects on the feeding habits of wolves in an altered arid landscape of central Iran

We determined the diet of the poorly-studied Middle Eastern wolves (Canis lupus) in central Iran in 2009-2010. Food items consisted mainly of farmed chicken and domestic goat (i.e., anthropogenic resources) using both qualification and quantification methods. In contrast, we identified the remains of wild ungulates in negligible quantities. Our data simulations showed that poultry and goats are both primary food items of wolves in the study area. The relative importance of main prey items did not vary seasonally, and, although there were some minor differences in secondary food items, we did not reveal any seasonal effect in diet composition. The negligible consumption of wild prey strongly suggests that wolves are not, at present, a limiting factor for wild prey in our study area. Appropriate management of illegal dumping, in conjunction with excluding wolves and other carnivores from human refuse, would minimize the chance of human-carnivore encounters, wolf-livestock conflicts and, in turn, the persecution of carnivores. Our study contributed to our knowledge of the feeding ecology of the Middle Eastern wolves in areas with a relatively high abundance of anthropogenic foods and a moderately low abundance of wild prey

Counting bears in the Iranian Caucasus : Remarkable mismatch between scientifically-sound population estimates and perceptions

Lack of reliable information on the status of species often leads managers to exclusively rely on experiential knowledge, opinions or perceptions, usually derived from personnel associated with natural resource management agencies. Yet, the accuracy of these sources of information remains largely untested. We approached this challenge, which is particularly common for wildlife monitoring programs in developing countries, using a population of Asian brown bears (Ursus arctos) in the Iranian Caucasus as case study. We conducted a noninvasive, genetic, spatial capture-recapture (SCR) study to estimate bear density across a core protected area, Arasbaran Biosphere Reserve, and compared our estimate of bear abundance with rangers' perceptions as collated through interviews. The perceived abundance of bears by local rangers was between 3 and 5 times higher than our SCR estimate of 40 bears (2.5–97.5% Bayesian Credible Intervals = 27–70; density: 4.88 bears/100 km2). Our results suggest that basing management of the local bear population on perceptions of population status may result in overestimating the likelihood of population persistence. Our findings offer a scientific baseline for an evidence-based conservation policy for brown bears in Iran, and the broader Caucasus Ecoregion. The majority of threatened terrestrial megafauna occur in developing countries, where collecting and analyzing demographic data remain challenging. Delayed conservation responses due to the lack of, or erroneous knowledge of population status of such imperiled species may have serious consequences

Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects

Spatial capture-recapture (SCR) models are now widely used for estimating density from repeated individual spatial encounters. SCR accounts for the inherent spatial autocorrelation in individual detections by modelling detection probabilities as a function of distance between the detectors and individual activity centres. However, additional spatial heterogeneity in detection probability may still creep in due to environmental or sampling characteristics. if unaccounted for, such variation can lead to pronounced bias in population size estimates. Using simulations, we describe and test three Bayesian SCR models that use generalized linear mixed models (GLMM) to account for latent heterogeneity in baseline detection probability across detectors using: independent random effects (RE), spatially autocorrelated random effects (SARE), and a two-group finite mixture model (FM). Overall, SARE provided the least biased population size estimates (median RB: -9 -- 6%). When spatial autocorrelation was high, SARE also performed best at predicting the spatial pattern of heterogeneity in detection probability. At intermediate levels of autocorrelation, spatially-explicit estimates of detection probability obtained with FM where more accurate than those generated by SARE and RE. In cases where the number of detections per detector is realistically low (at most 1), all GLMMs considered here may require dimension reduction of the random effects by pooling baseline detection probability parameters across neighboring detectors ("aggregation") to avoid over-parameterization. The added complexity and computational overhead associated with SCR-GLMMs may only be justified in extreme cases of spatial heterogeneity. However, even in less extreme cases, detecting and estimating spatially heterogeneous detection probability may assist in planning or adjusting monitoring schemes