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Most extinctions worldwide have occurred on islands, primarily due to interactions with exotic species, but rarely due to interactions among endemic species. This potential exists on two of the California Channel Islands, Santa Cruz and Santa Rosa, as endemic island spotted skunks (Spilogale gracilis amphiala) appear to have rapidly declined to rarity, possibly due to interference competition with endemic island foxes (Urocyon littoralis). Niche partitioning is expected in response to interference competition; hence, it is surprising that skunks and foxes show extensive overlap in macrohabitat use and circadian activity. However, the role of microhabitat associations and fine-scale temporal activity in facilitating coexistence of the two species has not been explored. We evaluated microhabitat associations of island spotted skunks and island foxes across both islands using data from live traps and wildlife cameras collected during 2015–2017, and we analyzed fine-scale temporal activity using camera data from 2016 and 2017. On both islands, skunks had a positive response to ground-level cover provided by rugged topography or woody vegetation such as shrubs or trees, whereas foxes had a weak or negative response, suggesting partitioning of these microhabitat characteristics. Additionally, on both islands the peak in timing of skunk activity offset the peak for foxes, which implies that skunks use fine-scale adjustments in activity to avoid foxes. Past grazing by exotic herbivores likely reduced habitat refuges for skunks; however, as vegetation on both islands recovers, regrowth of shrubs and trees may provide cover that will improve prospects for coexistence of island spotted skunks and island foxes.

Large high-severity fires are increasing in frequency in many parts of the world, including the coniferous forests of the Sierra Nevada mountains. These “megafires” alter vegetation and environmental conditions in forests, yet their impacts on native wildlife remain poorly understood. Bats play an important role in forest ecosystems, but their responses to megafires likewise are understudied. We investigated bat responses to the King Fire, a megafire that burned nearly 40,000 ha within the Eldorado National Forest in 2014, half of it at high severity. From June to September 2017, we used remote acoustic recorders to survey bats at 26 sites with varying fire severity (unburned, mixed, and high severity). We analyzed data with Royle–Nichols occupancy models to investigate how bat space use was influenced by megafires, and whether this response was driven by prey availability, fire severity, or fire-altered habitat conditions. We calculated prey species richness, biomass, and abundance, from moths sampled with blacklight surveys. Vegetation covariates included tree density, canopy cover, and shrub density, measured along vegetation transects. To capture general effects of fire, we also included fire severity and the percentage of dead trees as potential covariates on space use. Prey variables were highest in unburned forests, were the most common predictors of, and generally had positive effects on bat space use. Responses to tree density and canopy cover varied by species; the most common vegetation covariate, shrub density, had weak positive effects on bat space use. In spite of the varying prey and vegetation conditions across fire severity categories, most bats showed weak to no response in space use to fire severity and tree mortality. We attribute this to the highly mobile nature of bats, which reduces the impact of potentially negative local conditions.

Wildfires in many western North American forests are becoming more frequent, larger, and severe, with changed seasonal patterns. In response, coniferous forest ecosystems will transition toward dominance by fire-adapted hardwoods, shrubs, meadows, and grasslands, which may benefit some faunal communities, but not others. We describe factors that limit and promote faunal resilience to shifting wildfire regimes for terrestrial and aquatic ecosystems. We highlight the potential value of interspersed nonforest patches to terrestrial wildlife. Similarly, we review watershed thresholds and factors that control the resilience of aquatic ecosystems to wildfire, mediated by thermal changes and chemical, debris, and sediment loadings. We present a 2-dimensional life history framework to describe temporal and spatial life history traits that species use to resist wildfire effects or to recover after wildfire disturbance at a metapopulation scale. The role of fire refuge is explored for metapopulations of species. In aquatic systems, recovery of assemblages postfire may be faster for smaller fires where unburned tributary basins or instream structures provide refuge from debris and sediment flows. We envision that more-frequent, lower-severity fires will favor opportunistic species and that less-frequent high-severity fires will favor better competitors. Along the spatial dimension, we hypothesize that fire regimes that are predictable and generate burned patches in close proximity to refuge will favor species that move to refuges and later recolonize, whereas fire regimes that tend to generate less-severely burned patches may favor species that shelter in place. Looking beyond the trees to forest fauna, we consider mitigation options to enhance resilience and buy time for species facing a no-analog future., Ecology and Evolution, 11 (18), ISSN:2045-7758

To offset the declining timber supply by shifting towards more sustainable forestry practices, industrial tree plantations are expanding in tropical production forests. The conversion of natural forests to tree plantation is generally associated with loss of biodiversity and shifts towards more generalist and disturbance tolerant communities, but effects of mixed-landuse landscapes integrating natural and plantation forests remain little understood. Using camera traps, we surveyed the medium-to-large bodied terrestrial wildlife community across two mixed-landuse forest management areas in Sarawak, Malaysia Borneo which include areas dedicated to logging of natural forests and adjacent planted

The associations of habitat area and fragmentation with species richness long have been major topics within community ecology. Recent discussion has focused on properly assessing fragmentation independent of habitat area (fragmentation per se), and on whether fragmentation has significant negative or positive associations with species richness. We created a novel, multiple-region, N-mixture community model (MNCM) to examine the relations of riparian area and fragmentation with species richness of breeding birds in mountain ranges within the Great Basin, Nevada, USA. Our MNCM accounts for imperfect detection in count data at the survey-point level while allowing comparisons of species richness among regions in which those points are embedded. We used individual canyons within mountain ranges as regions in our model and measured riparian area and the Normalized Landscape Shape Index, a metric of fragmentation that is independent of total riparian area. We found that riparian area, but not its fragmentation, was a primary predictor of canyon-level species richness of both riparian obligates and all species. The relationship between riparian area and riparian obligate species richness was nonlinear: canyons with ≥25 ha woody riparian vegetation had relatively high species richness, whereas species richness was considerably lower in canyons with25 ha. Our MNCM can be used to calculate other metrics of diversity that require abundance estimates. For example, Simpson's evenness of riparian obligate species had a weak negative association with riparian area and was not associated with fragmentation. Projections of future riparian contraction suggested that decreases in species richness are likely to be greatest in canyons that currently have moderate (~10-25 ha) amounts of riparian vegetation. Our results suggest that if a goal of management is to maximize the species richness of breeding birds in montane riparian areas in the Great Basin, it may be more effective to focus on total habitat area than on fragmentation of patches within canyons, and that canyons with at least moderate amounts of riparian vegetation should be prioritized.

Monitoring programs are key to determine bird population trends and to assess environmental policies, and therefore are central to conservation biology. The European approach commonly used to estimate bird population trends (TRends and Indices for Monitoring data, hereafter TRIM) has proved useful to fulfil this task, yet it fails to account for imperfect detection and assumes constant detectability across years. We tested the role of detectability for population trend estimation in an open Mediterranean farmland context, which is a dynamic landscape likely to undergo yearly changes in detectability, by using data of 30 bird species over a nine-year study period. We evaluated species-specific population trends under the TRIM approach and hierarchical distance sampling models (hereafter HDS) that estimate true abundance by accounting for imperfect detection. When comparing both methods, 13 species presented differences in population trend estimates between TRIM and HDS models. Moreover, detectability was not constant across the bird community: observer and year affected detection, and these effects varied among species. Our study highlights the importance of accounting for imperfect detection in bird monitoring programs to ensure reliable trend estimates, providing a first insight for an open farmland bird community. Aside from trend estimates, our HDS model may prove useful as a tool to obtain site-specific abundance estimates (for intance, within Special Protection Areas) and trend probabilities of bird populations.

AimUnsustainable hunting is leading to widespread defaunation across the tropics. To mitigate against this threat with limited conservation resources, stakeholders must make decisions on where to focus anti-poaching activities. Identifying priority areas in a robust way allows decision-makers to target areas of conservation importance, therefore maximizing the impact of conservation interventions.LocationAnnamite mountains, Vietnam and Laos.MethodsWe conducted systematic landscape-scale surveys across five study sites (four protected areas, one unprotected area) using camera-trapping and leech-derived environmental DNA. We analyzed detections within a Bayesian multi-species occupancy framework to evaluate species responses to environmental and anthropogenic influences. Species responses were then used to predict occurrence to unsampled regions. We used predicted species richness maps and occurrence of endemic species to identify areas of conservation importance for targeted conservation interventions.ResultsAnalyses showed that habitat-based covariates were uninformative. Our final model therefore incorporated three anthropogenic covariates as well as elevation, which reflects both ecological and anthropogenic factors. Conservation-priority species tended to found in areas that are more remote now or have been less accessible in the past, and at higher elevations. Predicted species richness was low and broadly similar across the sites, but slightly higher in the more remote site. Occupancy of the three endemic species showed a similar trend.Main conclusionIdentifying spatial patterns of biodiversity in heavily-defaunated landscapes may require novel methodological and analytical approaches. Our results indicate to build robust prediction maps it is beneficial to sample over large spatial scales, use multiple detection methods to increase detections for rare species, include anthropogenic covariates that capture different aspects of hunting pressure, and analyze data within a Bayesian multi-species framework. Our models further suggest that more remote areas should be prioritized for anti-poaching efforts to prevent the loss of rare and endemic species.

Understanding how and why animals use the environments where they occur is both foundational to behavioral ecology and essential to identify critical habitats for species conservation. However, some behaviors are more difficult to observe than others, which can bias analyses of raw observational data. To our knowledge, no method currently exists to model how animals use different environments while accounting for imperfect behavior-specific detection probability. We developed an extension of a binomial N-mixture model (hereafter the behavior N-mixture model) to estimate the probability of a given behavior occurring in a particular environment while accounting for imperfect detection. We then conducted a simulation to validate the model's ability to estimate the effects of environmental covariates on the probabilities of individuals performing different behaviors. We compared our model to a naïve model that does not account for imperfect detection, as well as a traditional N-mixture model. Finally, we applied the model to a bird observation data set in northwest Costa Rica to quantify how three species behave in forests and farms. Simulations and sensitivity analyses demonstrated that the behavior N-mixture model produced unbiased estimates of behaviors and their relationships with predictor variables (e.g., forest cover, habitat type). Importantly, the behavior N-mixture model accurately characterized uncertainty, unlike the naïve model, which often suggested erroneous effects of covariates on behaviors. When applied to field data, the behavior N-mixture model suggested that Hoffmann's woodpecker (Melanerpes hoffmanii) and Inca dove (Columbina inca) behaved differently in forested versus agricultural habitats, while turquoise-browed motmot (Eumomota superciliosa) did not. Thus, the behavior N-mixture model can help identify habitats that are essential to a species' life cycle (e.g., where individuals nest, forage) that nonbehavioral models would miss. Our model can greatly improve the appropriate use of behavioral survey data and conclusions drawn from them. In doing so, it provides a valuable path forward for assessing the conservation value of alternative habitat types.

To offset the declining timber supply by shifting towards more sustainable forestry practices, industrial tree plantations are expanding in tropical production forests. The conversion of natural forests to tree plantation is generally associated with loss of biodiversity and shifts towards more generalist and disturbance tolerant communities, but effects of mixed‐landuse landscapes integrating natural and plantation forests remain little understood. Using camera traps, we surveyed the medium‐to‐large bodied terrestrial wildlife community across two mixed‐landuse forest management areas in Sarawak, Malaysia Borneo which include areas dedicated to logging of natural forests and adjacent planted Acacia forests. We analyzed data from a 25‐wildlife species community using a Bayesian community occupancy model to assess species richness and species‐specific occurrence responses to Acacia plantations at a broad scale, and to remote‐sensed local habitat conditions within the different forest landuse types. All species were estimated to occur in both landuse types, but species‐level percent area occupied and predicted average local species richness were slightly higher in the natural forest management areas compared to licensed planted forest management areas. Similarly, occupancy‐based species diversity profiles and defaunation indices for both a full community and only threatened and endemic species suggested the diversity and occurrence were slightly higher in the natural forest management areas. At the local scale, forest quality was the most prominent predictor of species occurrence. These associations with forest quality varied among species but were predominantly positive. Our results highlight the ability of a mixed‐landuse landscape with small‐scale Acacia plantations embedded in natural forests to retain terrestrial wildlife communities while providing an alternate source of timber. Nonetheless, there was a tendency towards reduced biodiversity in planted forests, which would likely be more pronounced in plantations that are larger or embedded in a less natural matrix.

Due to climate change and past logging and fire suppression, the western US are experiencing increasingly large and frequent wildfires. Understanding how wildlife respond to these mega-fires is becoming increasingly relevant to protect and manage these populations. However, the lack of predictability inherent in such events makes studies difficult to plan. We took advantage of a large high-severity wildfire that burned adjacent to an ongoing study of mule deer (Odocoileus hemionus) on their summer range upslope of the fire to investigate their displacement onto our study area both immediately and upon their return to summer range the following year. We used spatial capture-recapture models in conjunction with noninvasive fecal DNA sampling to estimate density and non-spatial Pradel robust-design models to estimate apparent survival and recruitment rates. Compared to density before the fire, we observed an increase in deer density and an increase in per-capita recruitment rates one month after the fire. These findings suggest that the immediate response of at least some deer was to flee the fire upslope onto the study area rather than to downslope toward their winter range. These changes did not carry over into the following year, however, suggesting that deer formerly using the burned area as summer range may have returned there despite the high severity of the fire, or may have chosen new areas for their summer range. This suggests that, at least in the short term, the fire did not negatively affect the deer population.

The savannah enclaves (i.e. patches) in the southern Brazilian Amazonia are among the most threatened and poorly surveyed sites in Amazonia. As part of an extensive mammal survey, we set camera traps in three of these savannah enclaves. We obtained 23 independent records of pampas deer Ozotoceros bezoarticus, a medium sized Neotropical cervid that is strongly associated with open habitats and categorized as Vulnerable on the Brazilian Red List of threatened species. These savannah enclaves with confirmed populations of pampas deer lie outside the species’ previously presumed historical range and are at least 350 km from any known extant population. Together, these savannah enclaves add c. 4,000 km2 to the pampas deer's currently known range. The small pampas deer populations in these enclaves are probably isolated by a matrix of Amazon forest, raising questions about spatial genetic structure and meta-population dynamics, and making them vulnerable to local extinction. We highlight the need for further studies, particularly genetic, to assess the conservation status of these populations, the results of which could potentially inform management decisions in other areas of the heavily fragmented range of this species.

The study of mammals has promoted the development and testing of many ideas in contemporary ecology. Here we address recent developments in foraging and habitat selection, source–sink dynamics, competition (both within and between species), population cycles, predation (including apparent competition), mutualism, and biological invasions. Because mammals are appealing to the public, ecological insight gleaned from the study of mammals has disproportionate potential in educating the public about ecological principles and their application to wise management. Mammals have been central to many computational and statistical developments in recent years, including refinements to traditional approaches and metrics (e.g., capture-recapture) as well as advancements of novel and developing fields (e.g., spatial capture-recapture, occupancy modeling, integrated population models). The study of mammals also poses challenges in terms of fully characterizing dynamics in natural conditions. Ongoing climate change threatens to affect global ecosystems, and mammals provide visible and charismatic subjects for research on local and regional effects of such change as well as predictive modeling of the long-term effects on ecosystem function and stability. Although much remains to be done, the population ecology of mammals continues to be a vibrant and rapidly developing field. We anticipate that the next quarter century will prove as exciting and productive for the study of mammals as has the recent one.

We conducted DNA capture-recapture monitoring of grizzly bears (Ursus arctos) from 5 to 17 years after hunting was stopped in two adjacent but genetically distinct populations in southwestern British Columbia, Canada. We used spatial capture-recapture and non-spatial Pradel robust design modelling to estimate population density, trends, and the demographic components of population change for each population. The larger population had 21.5 bears/1000 km2 and was growing (λPradel = 1.02 ± 0.02 SE; λsecr = 1.01 ± 4.6 × 10−5 SE) following the cessation of hunting. The adjacent smaller population had 6.3 bears/1000 km2 and was likely declining (λPradel = 0.95 ± 0.03 SE; λsecr = 0.98 ± 0.02 SE). Estimates of apparent survival and apparent recruitment indicated that lower recruitment was the dominant factor limiting population growth in the smaller population. Factors limiting reproductive rates and population density could include poor habitat quality, particularly the abundance of high-energy foods, genetic Allee effects due to a long period of population isolation, or demographic effects affecting infanticide rates. The cessation of hunting was insufficient to promote population recovery for the low density, isolated population. Our research highlights the importance of considering mortality thresholds in addition to small population effects and habitat quality when recovering large carnivore populations.

The Annamite mountains of Viet Nam and the Lao People's Democratic Republic (Lao) are an area of exceptional mammalian endemism but intensive poaching has defaunated much of the region, creating an extinction crisis for the endemic species. To make efficient use of limited conservation resources, it is imperative that conservation stakeholders obtain basic information about poorly known and threatened endemics. We present the first comprehensive information on the ecology, distribution and status of the little-known endemic Annamite striped rabbit Nesolagus timminsi. We used a systematic camera-trapping design to study the species in five areas in Viet Nam and Lao. In 29,180 camera-trap-nights we recorded 152 independent events at 36 of 266 stations. We obtained an additional 143 independent detections across 12 stations from a supplementary non-systematic survey. We analysed activity patterns and social behaviour. We also used single-species occupancy models to assess factors that influence occupancy at the landscape scale. We used N-mixture models to obtain local abundance estimates in one target area. The Annamite striped rabbit was found to be nocturnal and primarily solitary. Species occupancy was best explained by a proxy for past hunting pressure, with no significant relationships to current anthropogenic or environmental factors. Local abundance was 0.57 individuals per camera-trap station for one of our sites, and estimated to be zero at the other site where hunting appears to have been more intense. Our results provide information on priority areas for targeted anti-poaching efforts and give the first conservation baseline for the species.

Commercial timber extraction and agricultural expansion lead to substantial degradation and loss of tropical rainforests, posing a severe threat to biodiversity in South-east Asia. Although the impacts of these disturbances are well understood for some species, they remain largely unknown for most species, impeding any directed conservation efforts. One of these little known, presumably forest-dwelling species is the moonrat Echinosorex gymnura, an insectivorous mammal ranging from southern Myanmar and southern Thailand to Sumatra and Borneo. While the moonrat is currently listed as Least Concern on The IUCN Red List of Threatened Species, large parts of its range have been severely degraded due to unsustainable logging practices and the conversion of forest to oil palm plantations. We conducted a systematic large-scale camera trap survey in three forest reserves under different forest management strategies in Sabah, Malaysian Borneo. We recorded a total of 67 independent moonrat detections at 22 of 180 camera trap stations over 12,281 camera trap days. We analysed moonrat habitat associations using occupancy modelling and a set of 9 different environmental covariates. Canopy closure and forest quality were positively associated with moonrat occurrence and were the most important determinants of the species’ occurrence in our study area. Proximity to plantations (oil palm and industrial timber) and elevation negatively affected moonrat occurrence. Our results thus indicate that the moonrat strongly depends on high-quality lowland forest and is unlikely to occur close to plantations. Moonrat occupancy was highest in the sustainably managed forest reserve, suggesting that this species responds much better to reduced-impact logging compared to conventional logging practices. Our first ecological survey indicates that the moonrat may be more threatened by the continued conversion, degradation, and fragmentation of tropical lowland rainforests than previously assumed.

Measuring the multidimensional diversity properties of a community is of great importance for ecologists, conservationists and stakeholders. Diversity profiles, a plotted series of Hill numbers, simultaneously capture the common diversity indices. However, diversity metrics require information on species abundance, often relying on raw count data without accounting for imperfect and varying detection. Hierarchical occupancy models account for variation in detectability, and Hill numbers have been expanded to allow estimation based on occupancy probability. But the ability of occupancy-based diversity profiles to reproduce patterns in abundance-based diversity has not been investigated. Here, we fit community occupancy models to simulated animal communities to explore how well occupancy-based diversity profiles reflect patterns in true abundance-based diversity. Because we expect occupancy-based diversity to be overestimated, we further tested a occupancy thresholding approach to reduce potential biases in the estimated diversity profiles. Finally, we use empirical bird community data to present how the framework can be extended to consider species similarity. The simulation study showed that occupancy-based diversity profiles produced among-community patterns in diversity similar to true abundance diversity profiles, although within-community diversity was generally overestimated. Applying an occupancy threshold reduced positive bias, but resulted in negative bias in richness estimates and slightly reduced the ability to reproduce true differences among the simulated communities; thus, we do not recommend application of this threshold. Application of our approach to a large bird dataset indicated differential species diversity patterns in communities of different habitat types. Accounting for phylogenetic and ecological similarities between species reduced variability in diversity among habitats. Our framework allows investigating the complexity of diversity from species detection data, while accounting for imperfect and varying detection probabilities, as well as species similarities. Visualizing results in the form of diversity profiles facilitates comparison of diversity between sites or across time. The approach offers opportunities for further development, for example by using local abundances estimated using the Royle-Nichols or N-mixture models and further exploration of thresholding methods. In spite of some challenges, occupancy-based diversity profiles are useful for studying and monitoring patterns in biodiversity.

Reintroductions are an important recovery tool for endangered species but have had varying success. We used demographic data to evaluate the use of reintroductions for the recovery of the federally endangered Sierra Nevada Yellow-legged Frog (Rana sierrae) in stream populations in its northern range where the species is particularly at risk. We conducted a capture–mark–recapture study from 2016–2018 in four diverse streams. One of the streams contained a high abundance of frogs that allowed us to investigate demography in a relatively healthy population. In another of the streams, we tested a reintroduction using captive-reared frogs. We used robust design Huggins and Pradel models to estimate abundance, apparent overwinter survival, recruitment, longevity, and sex ratios. Annual abundance estimates were small, with ≤52 wild adult frogs at three streams and few tadpoles or subadults seen in any of the streams. The oldest frog in our streams was at least 13 yr. Estimates of apparent survival rates of wild adults ranged from 0.55 ± 0.05 standard error (SE) to 0.90 ± 0.05 SE, and estimates of annual recruitment ranged from 0.02 ± 0.11 SE to 0.26 ± 0.03 SE per site. High survival rates leading to long-lived adults may facilitate persistence of these stream populations, whereas low recruitment may limit population growth and recovery. The two largest populations were in intermittent streams, which may reflect their roles as a refuge from fishes. At least 52% of captive-reared frogs survived their first summer after release and at least 36% survived their first winter. Apparent overwinter survival of captive-reared frogs was lower than for wild frogs, ranging from 0.29 ± 0.13 SE to 0.56 ± 0.14 SE. Because of the apparent low survival of wild eggs, tadpoles, and subadults, releasing adults as was done in this study, rather than younger life stages, may be more successful. Our results offer promise for the use of reintroductions to augment depleted populations and suggest that further research on factors affecting recruitment and survival of younger life stages is needed.

As we sit in the vortex of the Covid‐19 outbreak, individual energies are focused on staying safe and juggling the personal, social and financial impacts of the pandemic and political responses to it. These impacts are profoundly re‐shaping our lives, with many commentators suggesting that ‘normality’ will be permanently redefined for all sectors of society. The future is not clear because the maelstrom is so intense that it is unlikely that the dust will settle any time soon. This pandemic will be one of the major game changers for humanity in the 21st century. The conservation impacts are set to be huge, and this is an understatement. It is remarkable how little past attention has been given to identifying the conservation impacts of human responses to pandemics and preparing for these, especially given considerable investment in global biodiversity and conservation‐focused horizon scanning exercises over the last decade (e.g. Sutherland et al., 2020). Conservation scientists, practitioners and policy‐makers must urgently address this lack of preparation and innovate solutions to confront the challenges arising from the radically altered economics, attitudes and behaviours imposed by Covid‐19. Our job is to think creatively and collaboratively with other sectors of society to ensure that recent progress in implementing effective conservation and protection of nature is not lost. We must also insist that conservationists contribute to re‐shaping the future post‐Covid‐19 world, to ensure that potential benefits to nature conservation and protection are realized. We identify three broad challenges and a diverse set of potential positive developments that require urgent attention and strategy development. We cannot afford to sit back and wait to see what happens as the new world emerges, or to be unprepared when the next pandemic hits.

Community occupancy models estimate species-specific parameters while sharing information across species by treating parameters as sampled from a common distribution. When communities consist of discrete groups, shrinkage of estimates toward the community mean can mask differences among groups. Infinite-mixture models using a Dirichlet process (DP) distribution, in which the number of latent groups is estimated from the data, have been proposed as a solution. In addition to community structure, these models estimate species similarity, which allows testing hypotheses about whether traits drive species response to environmental conditions. We develop a community occupancy model (COM) using a DP distribution to model species-level parameters. Because clustering algorithms are sensitive to dimensionality and distinctiveness of clusters, we conducted a simulation study to explore performance of the DP-COM with different dimensions (i.e., different numbers of model parameters with species-level DP random effects) and under varying cluster differences. Because the DP-COM is computationally expensive, we compared its estimates to a COM with a normal random species effect. We further applied the DP-COM model to a bird data set from Uganda. Estimates of the number of clusters and species cluster identity improved with increasing difference among clusters and increasing dimensions of the DP; but the number of clusters was always overestimated. Estimates of number of sites occupied and species and community-level covariate coefficients on occupancy probability were generally unbiased with (near-) nominal 95% Bayesian Credible Interval coverage. Accuracy of estimates from the normal and the DP-COM was similar. The DP-COM clustered 166 bird species into 27 clusters regarding their affiliation with open or woodland habitat and distance to oil wells. Estimates of covariate coefficients were similar between a normal and the DP-COM. Except sunbirds, species within a family were not more similar in their response to these covariates than the overall community. Given that estimates were consistent between the normal and the DP-COM, and considering the computational burden for the DP models, we recommend using the DP-COM only when the analysis focuses on community structure and species similarity, as these quantities can only be obtained under the DP-COM.

The persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog Atelocynus microtis is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species.

Monitoring population size and growth over time is vital for the conservation of endangered species. Mountain gorillas Gorilla beringei beringei remain in two small populations that span the borders of the Democratic Republic of Congo, Rwanda and Uganda. Each population contains two subpopulations that receive differing levels of protection: the monitored groups are visited daily by park staff and researchers and can be counted by sight, whereas the number and growth rate of unmonitored gorillas must be estimated indirectly. Here, we re-analyze published data from a survey in 2010 combined with new results from a survey conducted during two sampling occasions in 2015 and 2016 to estimate mountain gorilla abundance and growth in the Virunga Massif between 2010 and 2016. Using genetic analysis of non-invasively collected samples and two capture?mark?recapture estimates, we estimated that the 186 detected genotypes represented 221 (95% credible interval: 204?243) to 251 (205?340) unmonitored gorillas in 2016. Together with the 418 monitored gorillas, the overall population of the Virunga Massif thus reached 639 (622?661) to 669 (623?758) individuals. We estimated the growth of the entire Virunga Massif population at about 3% per year, but determined that the growth of the monitored gorillas (4.4%) mainly drove that increase. In contrast, the trend of the unmonitored subpopulation could not be determined with confidence because both models provided 95% CI that encompassed zero: 0.5% per year (?0.7% to +1.7%) and 1.1% (?2.7% to +4.4%). While the overall growth rate represents a rare success story for primate conservation, our results highlight the need for greater protection of unmonitored gorillas.

With continued global changes, such as climate change, biodiversity loss, and habitat fragmentation, the need for assessment of long‐term population dynamics and population monitoring of threatened species is growing. One powerful way to estimate population size and dynamics is through capture–recapture methods. Spatial capture (SCR) models for open populations make efficient use of capture–recapture data, while being robust to design changes. Relatively few studies have implemented open SCR models, and to date, very few have explored potential issues in defining these models. We develop a series of simulation studies to examine the effects of the state‐space definition and between‐primary‐period movement models on demographic parameter estimation. We demonstrate the implications on a 10‐year camera‐trap study of tigers in India. The results of our simulation study show that movement biases survival estimates in open SCR models when little is known about between‐primary‐period movements of animals. The size of the state‐space delineation can also bias the estimates of survival in certain cases.We found that both the state‐space definition and the between‐primary‐period movement specification affected survival estimates in the analysis of the tiger dataset (posterior mean estimates of survival ranged from 0.71 to 0.89). In general, we suggest that open SCR models can provide an efficient and flexible framework for long‐term monitoring of populations; however, in many cases, realistic modeling of between‐primary‐period movements is crucial for unbiased estimates of survival and density.

The Sunda stink-badger Mydaus javanensis is one of the most frequently recorded carnivore species in portions of Sabah, Malaysian Borneo. However, its current distribution on Borneo is patchy, with recent records lacking from areas where the species was previously considered common. We assessed the hypothesis that occurrence of Sunda stink-badgers is restricted to areas of high earthworm density. We also assessed the influence of forest disturbance on occurrence, as the species is thought to be disturbance-tolerant. We compiled camera-trap data from three commercial forest reserves in central Sabah collected during 2008–2010 and 2014–2015. We used single season occupancy modeling to estimate probability of occupancy and detection. We obtained 323 detections of Sunda stink-badger over 19,875 trap-nights. We found no fine-scale association between occurrence and earthworm abundance, suggesting that Sunda stink-badgers have broader diets than currently assumed, or that earthworm density potentially influences their occurrence at larger scales. The influence of forest disturbance on occurrence was mixed; although our results suggested that Sunda stink-badgers might have a higher probability of occupancy in more disturbed forests, it is possible that a disturbance threshold exists where extreme forest conversion (e.g., oil palm plantations, human settlements) results in lower occupancy. We did not find strong associations with proximity to water, oil palm plantation, bare earth, or shrub landcovers, suggesting that Sunda stink-badgers are not affected by edge effects or the proximity of disturbed areas. Despite our large dataset across three forest reserves, this study is only a first step in understanding the current irregular distribution of Sunda stink-badgers on Borneo. Further studies across a larger gradient of habitat disturbance are needed to determine potential habitat disturbance thresholds, which will aid in management of this species as the landscape on Borneo continues to undergo anthropogenic change.

Restoration of western dry forests in the USA often focuses on reducing fuel loads. In the range of the spotted owl, these treatments may reduce canopy cover and tree density, which could reduce preferred habitat conditions for the owl and other sensitive species. In particular, high canopy cover (≥70%) has been widely reported to be an important feature of spotted owl habitat, but averages of stand-level forest cover do not provide important information on foliage height and gap structure. To provide better quantification of canopy structure, we used airborne LiDAR imagery to identify canopy cover in different height strata and the size and frequency of gaps that were associated with owl nest sites, protected activity centers (PACs), and territories within four study areas and 316 owl territories. Although total canopy cover was high in nest stands and PAC areas, the cover in tall (>48 m) trees was the canopy structure most highly selected for, while cover in lower strata (2–16 m) was avoided compared to availability in the surrounding landscape. Tall tree cover gradually decreased and lower strata cover increased as distance increased from the nest. Large (>1000 m2) gaps were not found near nests, but otherwise there was no difference in gap frequencies and sizes between PACs and territories and the surrounding landscape. Using cluster analysis we classified canopy conditions into 5 structural classes and 4 levels of canopy cover to assess the relationship between total canopy cover and tree size within nest sites, PACs, and territories. High canopy cover (≥70%) mostly occurs when large tree cover is high, indicating the two variables are often confounded. Our results suggest that the cover of tall trees may be a better predictor of owl habitat than total canopy cover because the latter can include cover in the 2–16 m strata – conditions that owls actually avoid. Management strategies designed to preserve and facilitate the growth of tall trees while reducing the cover and density of understory trees may improve forest resilience to drought and wildfire while also maintaining or promoting the characteristics of owl habitat.