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Over the past few decades, the use of camera‐traps has revolutionized our ability to monitor populations of wild terrestrial mammals. While methods to estimate abundance from individually‐identifiable animals are well‐established, they are mostly restricted to species with clear natural markings or else necessitate invasive and often costly animal tagging campaigns. Estimating abundance or density from unmarked animals remains challenging. Several models recently developed to deal with this issue are promising, but are not widely used by field ecologists. Here, we developed a framework for applying the Space‐To‐Event (STE) model—originally designed to be used with time‐lapse images—on motion‐triggered camera‐trap data. Our approach involves performing bootstrap resampling on the photographic dataset to generate multiple datasets that are then used as input to the STE model. We tested our approach on 29 datasets, including 17 ungulate species from eight sites, in six different countries and various ecosystems. Then, we conducted a regression analysis to evaluate how variations in ecological and sampling conditions across studies affected the bias and precision of our STE density estimates. Our study shows that with a bootstrap resampling approach and information on animal activity and effective detection distances to animals, the STE model can be used to analyze motion‐trigger datasets and provide population density estimates that are similar to those from other methods. We found that measuring the camera viewshed was critical to prevent major negative biases in density estimates. Moreover, using a 1‐s sampling window was important to avoid the positive bias that results from violating the instantaneous‐sampling assumption. We found that precision increased with greater sampling effort and higher density populations. Based on these results, we highlight several issues from past studies that have applied the original timelapse‐based STE to motion‐trigger datasets, issues that our bootstrap resampling approach addresses. We caution that the STE model, whether applied to timelapse or motion‐triggered datasets, relies on strict assumptions. Any violations of these assumptions, such as non‐instantaneous sampling or the application of angle and distance of detection provided by the camera manufacturer, can cause biases in multiple directions that may be difficult to differentiate. [ABSTRACT FROM AUTHOR]

Interventions of the host–pathogen dynamics provide strong tests of relationships, yet they are still rarely applied across multiple populations. After American bullfrogs (Rana catesbeiana) invaded a wildlife refuge where federally threatened Chiricahua leopard frogs (R. chiricahuensis) were reintroduced 12 years prior, managers launched a landscape‐scale eradication effort to help ensure continued recovery of the native species. We used a before‐after‐control‐impact design and environmental DNA sampling of 19 eradication sites and 18 control sites between fall 2016 and winter 2020–2021 to measure community‐level responses to bullfrog eradication, including for two pathogens. Dynamic occupancy models revealed successful eradication from 94% of treatment sites. Native amphibians did not respond to bullfrog eradication, but the pathogens amphibian chytrid fungus (Batrachochytrium dendrobatidis) and ranaviruses were coextirpated with bullfrogs. Our spatially replicated experimental approach provides strong evidence that management of invasive species can simultaneously reduce predation and disease risk for imperiled species. [ABSTRACT FROM AUTHOR]

As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human‐carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False‐negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False‐positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data‐filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false‐positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification. Article impact statement: Use of ambiguous detections can improve understanding of large‐carnivore distribution dynamics. [ABSTRACT FROM AUTHOR]

Abstract: In occupancy studies, species misidentification can lead to false‐positive detections, which can cause severe estimator biases. Currently, all models that account for false‐positive errors only consider omnibus sources of false detections and are limited to single‐species occupancy. However, false detections for a given species often occur because of the misidentification with another, closely related species. To exploit this explicit source of false‐positive detection error, we develop a two‐species occupancy model that accounts for misidentifications between two species of interest. As with other false‐positive models, identifiability is greatly improved by the availability of unambiguous detections at a subset of site x occasions. Here, we consider the case where some of the field observations can be confirmed using laboratory or other independent identification methods (“confirmatory data”). We performed three simulation studies to (1) assess the model's performance under various realistic scenarios, (2) investigate the influence of the proportion of confirmatory data on estimator accuracy and (3) compare the performance of this two‐species model with that of the single‐species false‐positive model. The model shows good performance under all scenarios, even when only small proportions of detections are confirmed (e.g. 5%). It also clearly outperforms the single‐species model. We illustrate application of this model using a 4‐year dataset on two sympatric species of lungless salamanders: the US federally endangered Shenandoah salamander Plethodon shenandoah, and its presumed competitor, the red‐backed salamander Plethodon cinereus. Occupancy of red‐backed salamanders appeared very stable across the 4 years of study, whereas the Shenandoah salamander displayed substantial turnover in occupancy of forest habitats among years. Given the extent of species misidentification issues in occupancy studies, this modelling approach should help improve the reliability of estimates of species distribution, which is the goal of many studies and monitoring programmes. Further developments, to account for different forms of state uncertainty, can be readily undertaken under our general approach. [ABSTRACT FROM AUTHOR]

Abstract: The development and use of automated species detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide cost‐ and time‐effective means to process information‐rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors. We developed a hierarchical modelling framework for estimating species occupancy from automated species detection data. We explore design and optimization of data post‐processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false‐positive errors and utilizes information about both occurrence and abundance of detections to improve estimation. Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders. When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post‐validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post‐validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated. Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programmes. [ABSTRACT FROM AUTHOR]

The article reports on the geographical distribution of Tiger Salamanders, Ambystoma mavortium stebbinsi species in Sonora, Mexico. Topics discussed include loss of natural wetland habitats, inbreeding caused by small population sizes and limited gene flow and lack of information on the distribution of Tiger Salamanders in Sonora , Mexico that poses challenges to conservation planning.

Prospecting allows individuals to gather information on the local quality of potential future breeding sites. In a variable and heterogeneous environment, it plays a major role in breeding habitat selection and potentially helps individuals make optimal dispersal decisions. Although prospecting movements, involving visits to other breeding sites, have been observed in many species at relatively fine spatial scales, little is known about their occurrence at larger scales. Furthermore, the adaptive value of dispersal strategies in response to environmental changes remains poorly investigated., Here, our main objective is to highlight in what ways tracking devices could constitute powerful tools to study prospecting behaviour at various spatial scales. First, we stress the importance of considering prospecting movements involved in breeding habitat selection and we detail the type of data that can be collected. Then, we review the advantages and constraints associated with the use of tracking devices in this context, and we suggest new perspectives to investigate the behavioural strategies adopted by individuals during breeding habitat selection processes and dispersal decisions., The rapid development of new powerful electronic tools for tracking individual behaviour thus opens a wide range of opportunities. More specifically, it may allow a more thorough understanding of the role of scale-dependent dispersal behaviour in population responses to environmental changes. [ABSTRACT FROM AUTHOR]

As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human-carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False-negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False-positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data-filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false-positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification., (© 2018 Society for Conservation Biology.)