Multistate Occupancy Models Research Articles

Integrating adult occurrence and reproduction data to identify conservation measures for amphibians.

Monitoring programs are pivotal to establishing sound management. Due to economic, logistic, and time limitations, monitoring programs often overlook differences among life-history stages. However, species occurrence does not necessarily mean population viability, and it is unclear to what extent monitoring programs that do not consider separately adult presence and reproduction provide effective management indications. Unfortunately, collecting data on certain life stages requires high sampling effort, leading to a trade-off between model reliability and resources needed for monitoring. We collected data on presence and reproduction of amphibians by monitoring 207 waterbodies in Lombardy (northern Italy) in 2017-2022. We then used multistate occupancy models to test whether certain environmental features, namely, pond area, hydroperiod, forest cover, shade, aquatic vegetation, and predators' presence, differentially affected adult occurrence and breeding probabilities of multiple amphibian species. To assess optimal sampling efforts, we modeled the detection probabilities of adults and reproduction across multiple species. Finally, we identified the optimal monitoring strategy under different scenarios of resource availability, comparing adult-only monitoring versus joint assessment of the occurrence of adults and reproduction. In many cases, the main drivers of adult occurrence and reproduction did not coincide because most investigated ecological variables affected one life stage or the other. Forest area, for instance, increased occurrence probabilities of adults of the endemic Rana latastei but showed no effect on their reproduction probabilities. Quantitative estimates of the sampling effort showed that occurrence of adults was easier to spot in 4 out of 7 species. Multicriteria decision analyses showed that when resources were scarce, monitoring adults was the optimal strategy for those 4 species. Conversely, with more resources, monitoring both adults and reproduction emerged as the best strategy for all the considered species. Integrated monitoring of adults and reproduction is essential to comprehensively identify effective conservation measures for amphibians.

Imperfect detection and misidentification affect inferences from data informing water operation decisions

AbstractObjectiveManagers can modify river flow regimes using fish monitoring data to minimize impacts from water management infrastructure. For example, operation of the gate‐controlled Delta Cross Channel (DCC) in California can negatively affect the endangered Sacramento River winter‐run Chinook Salmon Oncorhynchus tshawytscha. Although guidelines have been developed for DCC operations by using real‐time juvenile fish sampling count data, there is uncertainty about how environmental conditions influence fish occupancy and the extent to which those relationships are affected by sampling and identification error.MethodsWe evaluated the effect of environmental conditions, imperfect detection, and misidentification error on salmon occupancy by analyzing data using hierarchical multistate occupancy models. A total of 14,147 trawl tows and beach seine hauls were conducted on 1058 sampling days between October and December from 1996 to 2019. During these surveys, 2803 juvenile winter‐run Chinook Salmon were identified, and approximately 29% of the sampling days had at least one winter‐run juvenile detected.ResultThe probability of misidentifying an individual juvenile winter‐run Chinook Salmon in the field was estimated to be 0.056 based on fish identification examinations and genetic sampling. Occupancy varied considerably and was related to flow characteristics, water clarity, weather, time of year, and whether occupancy was detected during the previous sampling day. However, these relationships and their significance changed considerably when accounting for imperfect detection and the probability of misidentifying individual juvenile salmon. Detection was <0.3 under average sampling conditions during a single sample and was influenced by flow, water clarity, site, and volume sampled.ConclusionOur modeling results indicate that DCC gate closure decisions could occur on fewer days when imperfect detection and misidentification error are not accounted for. These findings demonstrate the need to account for identification and detection error while using monitoring data to assess factors influencing fish occupancy and inform future management decisions.

Spatial patterns of reproduction suggest marginal habitat limits continued range expansion of black bears at a forest-desert ecotone.

Investigating spatial patterns of animal occupancy and reproduction in peripheral populations can provide insight into factors that form species range boundaries. Following historical extirpation, American black bears (Ursus americanus) recolonized the western Great Basin in Nevada from the Sierra Nevada during the late 1900s. This range expansion, however, has not continued further into the Great Basin despite the presence of additional habitat. We aimed to quantify whether reduced reproduction toward the range edge contributes to this range boundary. We analyzed black bear detections from 100 camera traps deployed across black bear distribution in western Nevada using a multistate occupancy model that quantified the probability of occupancy and reproduction (i.e., female bears with cubs occupancy) in relation to changes in habitat type and habitat amount toward the range boundary. We detected a strong effect of habitat amount and habitat type on the probability of black bear occupancy and reproduction. At similar levels of landscape-scale habitat amount (e.g., 50%), estimated probability of occupancy for adult bears in piñon-juniper woodlands near the range boundary was 0.39, compared to ~1.0 in Sierra Nevada mixed-conifer forest (i.e., core habitat). Furthermore, estimated probability of cub occupancy, conditional on adult bear occupancy, in landscapes with 50% habitat was 0.32 in Great Basin piñon-juniper woodlands, compared to 0.92 in Sierra Nevada mixed-conifer forest. Black bear range in the western Great Basin conforms to the center-periphery hypothesis, with piñon-juniper woodland at the range edge supporting ecologically marginal habitat for the species compared to habitat in the Sierra Nevada. Further geographic expansion of black bears in the Great Basin may be limited by lower occupancy of reproducing females in piñon-juniper woodland. Center-periphery range dynamics may be common in large carnivore species, as their dispersal ability allows them to colonize low-quality habitat near range edges.

Integrating community science and agency‐collected monitoring data to expand monitoring capacity at large spatial scales

AbstractMonitoring species to better understand their status, ecology, and management needs is a major expense for agencies tasked with biodiversity conservation. Community science data have the potential to improve monitoring for minimal cost, given appropriate analytical frameworks. We describe a framework for integrating data from the eBird community science platform with agency‐collected monitoring data using a multistate occupancy model. Our model accounts for the structural differences across datasets and allows for estimation of both occupancy and breeding probabilities. The framework was applied to Common Loons (Gavia immer) in Washington State. A total of 766 sites had observation effort, of which 713 sites had only eBird effort, 26 sites had only Washington Department of Fish and Wildlife (WDFW) effort, and 27 sites had both. We predicted that the probability of occupancy was only 0.07 (95% Bayesian credible interval, BCI = 0.02–0.51) at the 2324 sites in our sampling frame, though the probability that Common Loons were breeding at occupied sites was 0.95 (95% BCI = 0.71–1.00). We found that probability of occupancy was positively related to waterbody size (probability of a positive effect = 0.88) and negatively related to an index of human influence (probability of a negative effect = 0.94). We found that probability of breeding at occupied sites was positively related to tree canopy cover (0.86), negatively related to elevation (0.99), and negatively related to barren, scrub/shrub, and herbaceous land cover (0.98). We found that state agency biologists were 16 times more likely to detect breeding Common Loons at a site than were eBird users (0.94, 95% BCI = 0.78–0.99 for agency biologists vs. 0.08, 95% BCI = 0.06–0.10 for eBird users). However, the amount of effort expended by eBird users meant that they confirmed Common Loons at 94 sites while agency biologists confirmed them at just 24 sites, although evidence of reproduction was only contributed by agency biologists. Our results provide a better understanding of the distribution of Common Loons in Washington, while further demonstrating that community science data can be a valuable complement to agency‐collected data, if appropriate frameworks are developed to integrate these data sources.

An evaluation of multistate occupancy models for estimating relative abundance and population trends

Detecting spatiotemporal changes in the abundances of organisms is key to effectively conserving species. While indices of abundance have long been used, there has been a shift toward model-based estimators that account for the detection process. Popular approaches including traditional occupancy models and N-mixture models entail tradeoffs. The traditional occupancy approach requires the researcher coarsen the characterization of abundance to the probability that a site is occupied or unoccupied. Conversely, N-mixture models make use of variation in counts, but perform poorly when individuals have low detectability or move into or out of sites between visits. Multistate occupancy models that differentiate relatively abundant from non-abundant states have the potential to fill this gap but have been underexplored. We conducted a simulation study to test whether multistate occupancy models could capture spatial abundance patterns and detect population declines in the face of low individual detection probability (p ≤ 0.3) and unmodeled heterogeneity (e.g., that arising from individual movement). We considered 10,773 scenarios to examine the effects of differing amounts of heterogeneity as well as alternative study designs, population parameters, and modeling choices. We tracked bias in the proportion of sites estimated to be in the abundant state for single-season models, and power to detect a declining trend across multiple years. We also evaluated data diagnostic metrics to provide guidance to users. Multistate occupancy models were able to differentiate sites with higher abundances from sites with lower abundances when there were at least medium levels of spatial heterogeneity in true abundances. If different sites were randomly selected each year, power to detect even large population declines (65%) was poor (power < 0.8). However, if the same sites were surveyed each year, and a dynamic multistate occupancy was used, multistate occupancy models could detect (power ≥ 0.8) relatively small declines (5-40%) in 20% of scenarios, and frequently detect large declines of 45-60% (mean power = 0.92). Conservation decisions rely on detecting change reliably, rarely needing absolute abundance information. Multistate occupancy models can improve our ability to detect changing abundance while accommodating low individual detection probability and heterogeneity in count monitoring data.

Using passive acoustic monitoring to estimate northern spotted owl landscape use and pair occupancy

AbstractManaging forests for biodiversity conservation while maintaining economic output is a major challenge globally and requires accurate and timely monitoring of imperiled species. In the Pacific Northwest, USA, forest management is heavily influenced by the status of northern spotted owls (Strix occidentalis caurina), which have been in continued population decline for the past four decades. The monitoring program for northern spotted owls is transitioning from mark–resight surveys to a passive acoustic framework, requiring development of alternative analysis approaches. To maintain relevance for conservation and management, these analyses must accurately track underlying population changes, identify responses to disturbance, and estimate occupancy of owl pairs. We randomly selected and surveyed 5‐km2 hexagons for 6 weeks using passive acoustic monitoring in the Olympic Peninsula of Washington and the Oregon Coast Range during the 2018 spotted owl breeding season. We used a convolutional neural network to identify spotted owl calls, followed by logistic regression to determine the sex of vocalizing owls to assign pair status. We implemented multistate occupancy models to estimate probabilities of detection, species‐level landscape use, and pair occupancy of spotted owls. We also quantified detections of barred owls (Strix varia), a congeneric competitor and important driver of spotted owl population declines. The overall rate of hexagon use by spotted owls was estimated at 0.21 (SD 0.04) after adjusting for imperfect detection, and pair occupancy was 0.07 (SD 0.02). The probability of detecting a pair (i.e., both female and male) during a weekly occasion was relatively low (0.03, SD 0.01), indicating that true pair occupancy was between 1.3 and 4.1 times greater than the proportion of hexagons with observed pair detections. Barred owls were ubiquitous, with a naïve occupancy rate of 0.97. The intensity of calling by barred owls had a weak, negative effect on the probability of spotted owls being paired when present but had little measurable effect on their detectability. This work establishes a framework that may be effective for spotted owl population monitoring and illustrates that pairs have very low detection probability, which—combined with increasingly low numbers of spotted owl pairs—is an important consideration for conservation and management.

MULTI-STATE OCCUPANCY MODEL ESTIMATES PROBABILITY OF DETECTION OF AN AQUATIC PARASITE USING ENVIRONMENTAL DNA: PSEUDOLOMA NEUROPHILIA IN ZEBRAFISH AQUARIA.

Detecting the presence of important parasites within a host and its environment is critical to understanding the dynamics that influence a pathogen's ability to persist, while accurate detection is also essential for the implementation of effective control strategies. Pseudoloma neurophilia is the most common pathogen reported in zebrafish (Danio rerio) research facilities. The only assays currently available for P. neurophilia are through lethal sampling, often requiring euthanasia of the entire population for accurate estimates of prevalence in small populations. We present a non-lethal screening method to detect P. neurophilia in tank water based on the detection of environmental DNA (eDNA) from this microsporidium, using a previously developed qPCR assay that was adapted to the digital PCR (dPCR) platform to complement current surveillance protocols. Using the generated dPCR data, a multi-state occupancy model was also implemented to predict the probability of detecting the microsporidium in tank water under different flow regimes and pathogen prevalence. The occupancy model revealed that samples collected in static conditions were more informative than samples collected from flow-through conditions, with a probability of detection at 80% and 47%, respectively. There was also a positive correlation between the frequency of detection in water and prevalence in fish based on qPCR.

Know what you don't know: Embracing state uncertainty in disease‐structured multistate models

Abstract Hidden Markov models (HMMs) are broadly applicable hierarchical models that derive their utility from separating state processes from observation processes yielding the data. Multistate models such as mark–recapture and dynamic multistate occupancy models are HMMs frequently used in ecology. In their early formulations, states, such as pathogen infection status, were assumed to be perfectly observed without ambiguity. However, state uncertainty is a pervasive feature of many ecological studies, and multievent models were developed to explicitly account for it. We developed a novel extended multievent mark–recapture model that incorporates state uncertainty at multiple levels of detection. Using a disease‐structured example, both false negative and false positive state assignment errors are modelled at two levels of state assignment—the pathogen sampling process and the diagnostic process that samples are subjected to. We additionally describe methods to jointly model infection intensity to integrate heterogeneity in ecological parameters, such as mortality and infection dynamics, and the pathogen detection processes. We provide code to simulate and analyse datasets with various underlying ecological processes and fit our model to a mark–recapture dataset of Mixophyes fleayi (Fleay's barred frog) infected with the amphibian chytrid fungus (Batrachochytrium dendrobatidis, Bd). In our case study, we found evidence for various state assignment errors: the sampling protocol performed poorly in detecting Bd, pathogen detection was highly dependent on infection intensity and false positives were non‐negligible. Incorporating state uncertainty yielded significantly higher estimates of infection prevalence and 4–5 times lower rates of infection state transitions compared to those obtained from a traditional multistate model. Our results highlight that incorporating state assignment errors improves inference on the ecological process, especially when sensitivity and specificity of the state assignment processes are low. The general model structure can be applied to other HMMs, providing a foundation for modelling state uncertainty in related models. For disease‐structured multistate models, we recommend conducting robust design surveys and collecting samples during each capture event to facilitate incorporating pathogen detection errors.

Spatial Determinants of Livestock Depredation and Human Attitude Toward Wolves in Kailadevi Wildlife Sanctuary, Rajasthan, India

Gray wolves are capable of adapting to human-dominated landscapes by utilizing domestic prey as a source of food. Livestock depredation by wolves incurs a heavy economic loss to the villagers, resulting in negative attitudes toward the species and leading to increased conservation conflict. We used multi-state occupancy modeling on the interview data to assess the ecological factors governing livestock depredation by wolves. We also assessed the socio-demographic factors that may govern the attitude of villagers toward the wolf using ordinal regression. Over the past year, 64% of respondents reported a loss of livestock, in which goats (63%) comprised the major share, followed by sheep (22%) and cattle calves (15%). Wolves tend to hunt medium-sized domestic prey (sheep and goats) that commonly graze in open agricultural areas. The estimated livestock depredation probability of wolves was 0.84 (SD = ± 0.23). Depredation probability was influenced by habitat use by wolves, the extent of agricultural areas, scrubland area, and settlement size. Respondents with prior experience of livestock loss held more negative attitudes. Shepherds held more negative attitudes than other occupations. Increases in the respondent’s age and education level reflected a positive shift in attitudes toward the wolf. High economic loss caused by livestock depredation by wolves can lead to retaliatory persecution of wolves. Adequate compensation for livestock loss, along with better education and awareness can help lead to coexistence between wolves and humans in multi-use landscape of Kailadevi Wildlife Sanctuary, Rajasthan, India.

Predicting carnivore habitat use and livestock depredation risk with false-positive multi-state occupancy models

The cycle of livestock depredation and retaliatory killing constitutes a major threat to large carnivores worldwide and imposes considerable hardships on human communities. Mitigation efforts are often undertaken with little knowledge of ecological underpinnings and patterns of depredation, limiting conservationists' ability to develop, prioritize, and evaluate solutions. Carnivore detection and depredation data from interviews in affected communities may help address this gap, but such data are often prone to false-positive uncertainty. To address these challenges in the Pamir Mountains of Tajikistan we collected snow leopard, lynx, wolf, and bear detection and depredation reports from local communities via semi-structured interviews. We used a novel hierarchical multi-species multi-state occupancy model that accounted for potential false-positives to investigate carnivore site use and depredation concurrently with respondents' apparent vulnerability to that risk. Estimated false-positive probabilities were small, but failure to account for them overstated site use probabilities and depredation risk for all species. Although individual vulnerability was low, depredation was nonetheless commonplace. Carnivore site use was driven by clear habitat associations, but we did not identify any clearly important large-scale spatial correlates of depredation risk despite considerable spatial variation in that risk. Respondents who sheltered livestock in household corrals reinforced with wire mesh were less likely to report snow leopard depredations. Reducing depredation and retaliation at adequately large scales in the Pamirs will likely require a portfolio of species-specific strategies, including widespread proactive corral improvements. Our approach expanded inference on the often-cryptic processes surrounding human-carnivore conflict even though structured wildlife data were scarce.

Metapopulation dynamics of multiple species in a heterogeneous landscape

AbstractCharacterizing the diversity of demographic strategies among species can inform research in topics such as trait syndromes, community stability, coexistence, and ecological succession. However, this diversity can depend on the spatial scale considered: at the landscape scale, species often form metapopulations, that is sets of local, sometimes short‐lived, populations, inhabiting discrete habitat patches. Metapopulation dynamics are most frequently analyzed in individual species or pairs of interacting species because of the large amount of data required for multiple species, and because species vary in their perceptions of what constitutes a favorable or unfavorable habitat. Here we evaluate, using a case study, whether a metapopulation model can be used to generate accurate estimates of demographic parameters and to describe the diversity of dynamics, responses to environment, and prospects of long‐term persistence in a guild of species inhabiting a common fragmented landscape. We applied this approach to a guild of 22 mollusk species that inhabit freshwater habitats on two islands of Guadeloupe, to compare metapopulation dynamics among species. We analyzed a 15‐year time series of occupancy records for 278 sites using a multistate occupancy model that estimated colonization and extinction rates as a function of site‐specific and year‐specific environmental covariates, then used model results to simulate future island metapopulation dynamics. Despite the diverse array of metapopulation trajectories—a mix of species with either stable, increasing, declining, or fluctuating metapopulations—and the inherent challenges associated with such data (e.g., imperfect detection, spatial and temporal heterogeneity), our model accurately captured among‐patch variation in suitability for many mollusk taxa. The dynamics of rare species or species with habitat preferences not fully captured by the retained set of covariates were less well described. For several species, we detected a negative correlation between extinction and colonization. This variation in habitat suitability created species‐specific extinction‐resistant pockets in the landscape. Our comparative analysis also revealed that species had distinct strategies for metapopulation dynamics, such as “fast‐turnover” species with both a high proportion of occupied sites and a high rate of site extinction in the landscape.

Links in a sink: Interplay between habitat structure, ecological constraints and interactions with humans can influence connectivity conservation for tigers in forest corridors

Global land-use changes and rapid infrastructure development necessitate identification and conservation of wildlife corridors. Connectivity through corridors is shaped by species' structural, ecological and behavioral constraints. In multi-use landscapes, species' interactions with humans could additionally influence connectivity. Using the tiger Panthera tigris as a case study, we make simultaneous assessments of potential connectivity, habitat use and examine their links with the species' negative interactions with humans in central India. We assessed potential connectivity across 10, 000 sq. km of the Kanha–Pench forest corridor using graph-theoretic methods. Combining indirect sign surveys and occupancy models, we examined habitat use, and evaluated its congruence with potential connectivity. Next, we estimated spatial probabilities of livestock depredation through application of multi-state occupancy models to interview-based survey data from local residents. Habitat use by tigers was negatively associated with forest fragmentation and anthropogenic disturbance. Livestock depredation was positively associated with size of settlements and areas most frequented by tigers, and negatively with anthropogenic disturbance within forests. We found high congruence between connectivity and habitat use (r = 0.80); but the strong correlation did not hold in areas with very high levels of livestock depredation levels. Our results indicate that when areas of high use by tigers are constrained by limited connectivity, there are higher chances of human-tiger conflict, and these areas may be ecological traps for the species. Interactions with humans can be crucial in mediating connectivity for large carnivores in shared habitats. Our findings present an opportunity to consolidate areas where carnivore conservation and local livelihood needs can be balanced. Our framework also provides a foundation for spatial prioritization that incorporates a plurality of dimensions, with utility for connectivity conservation of other wide-ranging carnivores.

High-resolution remote sensing and multistate occupancy estimation identify drivers of spawning site selection in fall chum salmon (Oncorhynchus keta) across a sub-Arctic riverscape

Groundwater upwellings provide warmer, stable overwinter temperatures for developing salmon embryos, which may be particularly important in cold, braided, gravel-bed sub-Arctic rivers. We used a three-year time series of aerial counts and remote sensing to estimate the distribution of low and high aggregations of spawning fall chum salmon (Oncorhynchus keta), classify approximately 0.5 km long river segments by geomorphic channel type, and map thermal variability along a 25.4 km stretch of the Teedriinjik River, Alaska. We used a dynamic multistate occupancy model to estimate detectability, occupancy, and the dynamics of spawning aggregations among river segments. Detectability was higher for large (&gt;150) relative to smaller aggregations. Unoccupied segments were likely to remain so from year to year; low abundance spawning segments were dynamic and rarely remained in that state for multiple years, while ∼20%–35% of high abundance segments remained stable, indicating the presence of high-quality spawning habitat. Spawning habitat use was associated with warmer water temperatures likely caused by groundwater upwellings. We identified spawning habitat characteristics and trends in usage by fall chum salmon, which will inform land management decisions and assist in evaluating impacts of shifting climate conditions and resource management on Arctic salmon populations.

Estimation of breeding probability can make monitoring data more revealing: a case study of amphibians.

Monitoring programs serve to detect trends in the distribution and abundance of species. To do so, monitoring programs often use static state variables. Dynamic state variables that describe population dynamics might be more valuable because they allow for a mechanistic understanding of the processes that lead to population trends. We fit multistate occupancy models to data from a country-wide multispecies amphibian occupancy monitoring program and estimated occupancy and breeding probabilities. If breeding probabilities are determinants of occupancy dynamics, then they may serve in monitoring programs as state variables that describe dynamic processes. The results showed that breeding probabilities were low and that a large proportion of the populations had to be considered to be non-breeding populations (i.e., populations where adults are present but no breeding occurs). For some species, the majority of populations were non-breeding populations. We found that non-breeding populations have lower persistence probabilities than populations where breeding occurs. Breeding probabilities may thus explain trends in occupancy but they might also explain other ecological phenomena, such as the success of invasive species, which had high breeding probabilities. Signs of breeding, i.e., the presence of eggs and larvae, were often hard to detect. Importantly, non-breeding populations also had low detection probabilities, perhaps because they had lower abundances. We suggest that monitoring programs should invest more in the detection of life history stages indicative of breeding, and also into the detection of non-breeding populations. We conclude that breeding probability should be used as a state variable in monitoring programs because it can lead to deeper insights into the processes driving occupancy dynamics.

A multi-state occupancy model to non-invasively monitor visible signs of wildlife health with camera traps that accounts for image quality.

Camera traps are an increasingly popular tool to monitor wildlife distributions. However, traditional analytical approaches to camera trap data are difficult to apply to visible wildlife characteristics in single images, such as infection status. Several parasites produce visible signs of infection that could be sampled via camera traps. Sarcoptic mange Sarcoptes scabiei is an ideal disease to study using cameras because it results in visible hair loss and affects a broad host range. Here, we developed a multi-state occupancy model to estimate the occurrence of mange in coyotes Canis latrans across an urban gradient. This model incorporates a secondary detection function for apparent by-image infection status to provide detection-corrected estimates of mange occurrence. We analysed a multi-year camera trap dataset in Chicago, Illinois, United States, to test whether the apparent occurrence of sarcoptic mange in coyotes Canis latrans increases with urbanization or varies through time. We documented visible signs consistent with current or recovering mange infection and variables we hypothesized would improve mange detection: The proportion of the coyote in frame, image blur and whether the image was in colour. We were more likely to detect coyotes with mange in images that were less blurry, in colour, and if a greater proportion of the coyote was visible. Mangy coyote occupancy was significantly higher in urban developed areas with low housing density and higher canopy cover whereas coyote occupancy, mangy or otherwise, decreased with urbanization. By incorporating image quality into our by-image detection function, we provide a robust method to non-invasively survey visible aspects of wildlife health with camera traps. Apparently mangy coyotes were associated with low-density forested neighbourhoods, which may offer vegetated areas while containing sources of anthropogenic resources. This association may contribute to human-wildlife conflict and reinforces posited relationships between infection risk and habitat use. More generally, our model could provide detection-corrected occupancy estimates of visible characteristics that vary by image such as body condition or injuries.

Disturbed Amazonian forests support diminished breeding bird communities

Abstract Twenty percent of the Brazilian Amazon has now been deforested, and deforestation rates are increasing. This compels us to evaluate the conservation potential of human-modified landscapes; yet the ecological value of regenerating and fragmented Amazonian forests remains poorly understood. To date, most faunal studies in disturbed forests have examined metrics derived from presence or abundance. Although valuable, these data cannot tell us how old-growth species are using disturbed forests. In this study, we complement presence data with breeding observations to assess more comprehensively the habitat quality of disturbed forests in central Amazonia. We synthesized breeding evidence from standardized passive mist-netting, mixed-species flock-following, and opportunistic nest-searching across a full annual cycle in 30–35-year-old secondary forest, 10-ha fragments, and undisturbed forest. We then used multistate occupancy models to estimate the number of undetected breeding species in each forest type, which illustrated that fewer species bred in secondary forest (–43%) and 10-ha fragments (–17%) than in undisturbed forest. Both these losses are larger than the associated decrease in species richness alone (–17% and –10%, respectively). Notably, we confirmed breeding by only three terrestrial and near-ground insectivores in fragments and secondary forest combined (of the 9 species found in undisturbed forest). Disturbed forests also supported fewer breeding individuals (–35% to –50%) and, in secondary forest, fewer successful breeding attempts (–24%). Encouragingly, however, some forest-dependent birds are breeding and producing fledglings in disturbed forests, including representatives from almost every guild. This was especially apparent for mixed-species flocks and army-ant followers, two guilds that have historically been considered vulnerable to anthropogenic disturbance. Therefore, despite a loss of breeding habitat in disturbed forests, these data suggest that landscapes composed of regenerating forest and small fragments have conservation potential for forest bird populations.

The peri-urban leopards of Kathmandu: assessing determinants of presence and predation on domestic animals

AbstractThe conservation of large carnivores in human-dominated landscapes needs to be reconciled with the safety of humans and domestic animals. This is especially true for the leopard Panthera pardus, which occurs extensively in agricultural landscapes and remnant forest tracts embedded within peri-urban areas such as Kathmandu district in Nepal. We carried out interviews in 321 households in this district to determine the extent of leopard habitat use and predation on domestic animals (dogs and goats) during October 2015–April 2016. We used multi-state occupancy models, and estimated probabilities of leopard habitat use (Ψ1) and predation on domestic animals (Ψ2) as a function of covariates, while accounting for imperfect detection. Our findings indicate that the rapidly urbanizing outskirts of Kathmandu city are used extensively by leopards. The estimated probability of fine-scale habitat use in 2 km2 sample units was 0.96 ± SE 0.05 and the probability of predation on domestic animals was 0.76 ± SE 0.15. Leopard attacks occurred in areas with high vegetation cover and abundant goats. Addressing the problem of leopard attacks on domestic animals will require developing a comprehensive mitigation plan that includes educational activities to raise awareness, measures to address grievances of affected local communities, interventions to prevent attacks on livestock, compensation programmes, and rapid response teams to ensure human and animal welfare in conflict-prone areas. Land-use planning in these peri-urban landscapes needs to facilitate the safe sharing of space between people and leopards.

Dynamic multistate occupancy modeling to evaluate population dynamics under a scenario of preferential sampling

AbstractEffective conservation of animal populations depends on the availability of reliable data derived from rigorous monitoring protocols, which allows us to assess trends and understand the processes they are governed by. Nevertheless, population monitoring schemes are hampered by multiple sources of errors resulting from specific logistical and survey constraints. Two common complications are the non‐visitation of some sites in certain years and preferential sampling (PS), that is, the tendency to survey “better” sites disproportionately more often. Both factors can lead to serious biases unless accommodated into models. We used 22 yr of nest‐monitoring data to develop a dynamic multistate occupancy model, including a PS component to investigate occupancy and reproduction dynamics in a peripheral Black Stork (Ciconia nigra) population in Spain. We analyzed the effects of climate and nesting substrate (tree vs. cliff) on population dynamics and accounted for PS and non‐visitation biases using a model that distinguished three territorial states: unoccupied, occupied without, or occupied with successful reproduction. We found strong evidence for positive PS, and when accounting for this bias, lower population size estimates were generated. Black stork nests had a high probability of remaining in the same state from one year to the next, with successful nests more likely to be occupied again and to be successful the following year than occupied but unsuccessful or unoccupied nests. Nesting substrate and spring precipitation did not influence state transition probabilities or the probability of reproductive success; nevertheless, cliff nest occupancy was overall higher than tree nest occupancy. Our results highlight the importance of correcting for non‐visitation and PS in habitat occupancy models. If these potential biasing effects are not accounted for, inferences of population size may be overestimate. Multistate occupancy models with correction for PS offer a powerful analytical framework for data collected as part of population studies of unmarked animals. These models compensate for common methodological biases in biological surveys and can help implement efficient conservation strategies based on robust population dynamics estimates.

Urbanization’s influence on the distribution of mange in a carnivore revealed with multistate occupancy models

Increasing urbanization and use of urban areas by synanthropic wildlife has increased human and domestic animal exposure to zoonotic diseases and exacerbated epizootics within wildlife populations. Consequently, there is a need to improve wildlife disease surveillance programs to rapidly detect outbreaks and refine inferences regarding spatiotemporal disease dynamics. Multistate occupancy models can address potential shortcomings in surveillance programs by accounting for imperfect detection and the misclassification of disease states. We used these models to explore the relationship between urbanization, slope, and the spatial distribution of sarcoptic mange in coyotes (Canis latrans) inhabiting Fort Irwin, California, USA. We deployed remote cameras across 180 sites within the desert surrounding the populated garrison and classified sites by mange presence or absence depending on whether a symptomatic or asymptomatic coyote was photographed. Coyotes selected flatter sites closer to the urban area with a high probability of use (0.845, 95% credible interval (CRI): 0.728, 0.944); site use decreased as the distance to urban areas increased (standardized [Formula: see text] = -1.354, 95% CRI -2.423, -0.619). The probability of correctly classifying mange presence at a site also decreased further from the urban area and was probably related to the severity of mange infection. Severely infected coyotes, which were more readily identified as symptomatic, resided closer to the urban area and were most likely dependent on urban resources for survival; urban resources probably contributed to sustaining the disease. Multistate occupancy models represent a flexible framework for estimating the occurrence and spatial extent of observable infectious diseases, which can improve wildlife disease surveillance programs.

Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes.

Landscape change is a key driver of biodiversity declines due to habitat loss and fragmentation, but spatially shifting resources can also facilitate range expansion and invasion. Invasive populations are reproductively successful, and landscape change may buoy this success.We show how modeling the spatial structure of reproductive success can elucidate the mechanisms of range shifts and sustained invasions for mammalian species with attendant young. We use an example of white‐tailed deer (deer; Odocoileus virginianus) expansion in the Nearctic boreal forest, a North American phenomenon implicated in severe declines of threatened woodland caribou (Rangifer tarandus).We hypothesized that deer reproductive success is linked to forage subsidies provided by extensive landscape change via resource extraction. We measured deer occurrence using data from 62 camera traps in northern Alberta, Canada, over three years. We weighed support for multiple competing hypotheses about deer reproductive success using multistate occupancy models and generalized linear models in an AIC‐based model selection framework.Spatial patterns of reproductive success were best explained by features associated with petroleum exploration and extraction, which offer early‐seral vegetation resource subsidies. Effect sizes of anthropogenic features eclipsed natural heterogeneity by two orders of magnitude. We conclude that anthropogenic early‐seral forage subsidies support high springtime reproductive success, mitigating or exceeding winter losses, maintaining populations. Synthesis and Applications. Modeling spatial structuring in reproductive success can become a key goal of remote camera‐based global networks, yielding ecological insights into mechanisms of invasion and range shifts to inform effective decision‐making for global biodiversity conservation.