The estimation of abundance of wildlife populations is an essential part of ecological research and monitoring. Spatially explicit capture–recapture (SCR) models are widely used for abundance and density estimation, frequently through individual identification of target species using camera‐trap sampling. Generalized spatial mark–resight (Gen‐SMR) is a recently developed SCR extension that allows for abundance estimation when only a subset of the population is recognizable by artificial or natural marks. However, in many cases, it is not possible to read the marks in camera‐trap pictures, even though individuals can be recognized as marked. We present a new extension of Gen‐SMR that allows for this type of incomplete identification. We used simulation to assess how the number of marked individuals and the individual identification rate influenced bias and precision. We demonstrate the model's performance in estimating red fox ( Vulpes vulpes ) density with two empirical datasets characterized by contrasting densities and rates of identification of marked individuals. According to the simulations, accuracy increases with the number of marked individuals ( m ), but is less sensitive to changes in individual identification rate (δ). In our case studies of red fox density estimation, we obtained a posterior mean of 1.60 (standard deviation SD: 0.32) and 0.28 ( SD : 0.06) individuals/km 2 , in high and low density, with an identification rate of 0.21 and 0.91, respectively. This extension of Gen‐SMR is broadly applicable as it addresses the common problem of incomplete identification of marked individuals during resighting surveys.
Predation is a key factor in prey population dynamics and could impact population recovery. One common means employed to recover prey populations is that of translocations, but most fail owing to high predation during the early stages. We tested whether conditioned odor aversion can reduce predation during animal translocations by using the predation of the European rabbit Oryctolagus cuniculus by the red fox Vulpes vulpes as a case study. Following a before‐after control‐impact design (BACI), we deployed bait stations monitored using camera‐traps in two zones to which rabbits were translocated. One week before the rabbits were released, microencapsulated levamisole was added to rabbit baits located in the treatment zones, along with vanilla essence as an odor cue. A total of 148 rabbits were distributed in artificial warrens with the odor cue and 68 of them were fitted with radio collars in order to determine their survival rates. The response to the treatment and translocation as regards subsequent rabbit abundance was evaluated using N‐mixture models, while rabbit establishment was evaluated using a warren use index (WUI). The treatment decreased the proportion of baits consumed by foxes, but this decrease was partially compensated by other predators. WUI and rabbit population growth increased significantly more after translocations in the treatment zones than in the control zones. The short‐term survival of translocated rabbits was also higher in the treatment zones than in the control zones. Our study showed that conditioned odor aversion reduced rabbit predation by foxes, and had a positive effect on rabbit population growth after translocation, since there was an increase in rabbit survival and warren establishment. This method could be used as a non‐lethal tool for the recovery of a key prey when carrying out programs concerning the reintroduction of endangered predators or for other vulnerable species requiring translocations.
Knowing the distribution of expanding carnivore species is paramount for identifying and addressing potential human–wildlife conflicts. Occupancy models are useful tools to estimate the distribution and the probability of detection of wildlife species. In this study, we used these models with an empirical dataset to compare different survey methods and their combinations in order to optimize the estimated distribution in central Iberian Peninsula of the Egyptian mongoose (Herpestes ichneumon), the only Herpestidae species occurring in Europe. In particular, we aimed to identify the most cost‐effective (most accurate, with the lowest bias and cost) method or combination of methods. Sign surveys along transects on foot, hair‐traps (baited and unbaited) and camera‐traps were used as detection methods. We replicated these methods in 10 sampling units within four study zones in which the species was known to occur. We employed occupancy models to estimate the detection probability for each method in each zone, using covariates exclusively for detection probability, and made combinations of all methods. Camera‐trapping was the most precise and least biased single method, followed by transects on foot. In contrast, both baited and unbaited hair‐traps produced biased estimates of occupancy. However, camera‐traps was the most costly method, whereas single unbaited hair‐traps had the lowest cost. Our results demonstrate that the combination of several methods provides more precise and unbiased estimates of occupancy than those obtained from single methods. Even so, a biased method could contribute to improve the estimates if combined with other unbiased and precise methods. We recommend considering not only the precision and bias, but also the cost and effort required by each method to achieve the most cost‐effective results in distribution studies of carnivore species.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.