Efficient species-level monitoring at the landscape scale

Linking robust spatiotemporal datasets to assess and monitor habitat attributes of a threatened species

Accessibility of multispectral, multitemporal imagery combined with recent advances in cloud computing and machine learning approaches have enhanced our ability to model habitat characteristics across broad spatial and temporal scales. We integrated a large dataset of known nest and roost sites of a threatened species, the Mexican spotted owl (Strix occidentalis lucida), in the southwestern USA with Landsat imagery processed using the Continuous Change Detection and Classification (CCDC) time series algorithm on Google Earth Engine. We then used maximum entropy modeling (Maxent) to classify the landscape into four 'spectral similarity' classes that reflected the degree to which 30-m pixels contained a multispectral signature similar to that found at known owl nest/roost sites and mapped spectral similarity classes from 1986-2020. For map interpretation, we used nationally consistent forest inventory data to evaluate the structural and compositional characteristics of each spectral similarity class. We found a monotonic increase of structural characteristics typically associated with owl nesting and roosting over classes of increasing similarity, with the 'very similar' class meeting or exceeding published minimum desired management conditions for owl nesting and roosting. We also found an increased rate of loss of forest vegetation typical of owl nesting and roosting since the beginning of the 21st century that can be partly attributed to increased frequency and extent of large (≥400 ha) wildfires. This loss resulted in a 38% reduction over the 35-year study period in forest vegetation most similar to that used for owl nesting and roosting. Our modelling approach using cloud computing with time series of Landsat imagery provided a cost-effective tool for landscape-scale, multidecadal monitoring of vegetative components of a threatened species' habitat. Our approach could be used to monitor trends in the vegetation favored by any other species, provided that high-quality location data such as we presented here are available.

Detection Rate of Bird Species and What It Depends on: Tips for Field Surveys

Bird counting inevitably suffers from imperfect detection, which varies across species, habitats, period of the day, and seasons. Although various modeling techniques have recently been developed to account for this phenomenon, the biological basis of natural variation in detection remains insufficiently known. This study examined the bird species’ detection rate throughout the day, considering their body mass and diet type, concerning the environment and weather characteristics. Species detection rates were significantly affected by the number of individuals of that species but were unrelated to body mass. Overall, species with the highest detection rate were Corn bunting, Blackbird, European robin, House sparrow and Common chiffchaff. Granivores-insectivores and insectivores showed significant differences in detection rates throughout the day among habitats, with higher detection rates in grasslands during the afternoon. Insectivores showed higher detection rates in farmland during midday (warmest time of the day). Granivores, omnivores and scavengers did not show changes in detection rates in different day periods. Such patterns in daily detection rates were significant even when considering abundance and total species richness in each community. Finally, cloudiness was unrelated to the overall detection rate of birds, while temperature and wind affected detection rates in some guilds. Our findings provide some advice for choosing a suitable ornithological sampling method by considering the avian communities composition in combination with the type of environment, the diet of bird species, and the period of the day.

Detectability and impact of repetitive surveys on threatened West African crocodylians

West African crocodylians are among the most threatened and least studied crocodylian species globally. Assessing population status and establishing a basis for population monitoring is the highest priority action for this region. Monitoring of crocodiles is influenced by many factors that affect detectability, including environmental variables and individual- or population-level wariness. We investigated how these factors affect detectability and counts of the critically endangered Mecistops cataphractus and the newly recognized Crocodylus suchus. We implemented 195 repetitive surveys at 38 sites across Côte d'Ivoire between 2014 and 2019. We used an occupancy-based approach and a count-based GLMM analysis to determine the effect of environmental and anthropogenic variables on detection and modeled crocodile wariness over repetitive surveys. Despite their rarity and level of threat, detection probability of both species was relatively high (0.75 for M. cataphractus and 0.81 for C. suchus), but a minimum of two surveys were required to infer absence of either species with 90% confidence. We found that detection of M. cataphractus was significantly negatively influenced by fishing net encounter rate, while high temperature for the previous 48 h of the day of the survey increased C. suchus detection. Precipitation and aquatic vegetation had significant negative and positive influence, respectively, on M. cataphractus counts and showed the opposite effect for C. suchus counts. We also found that fishing encounter rate had a significant negative effect on C. suchus counts. Interestingly, survey repetition did not generally affect wariness for either species, though there was some indication that at least M. cataphractus was more wary by the fourth replicate. These results are informative for designing future survey and monitoring protocols for these threatened crocodylians in West Africa and for other endangered crocodylians globally.

Long-term trends in the occupancy of ants revealed through use of multi-sourced datasets

We combined participatory science data and museum records to understand long-term changes in occupancy for 29 ant species in Denmark over 119 years. Bayesian occupancy modelling indicated change in occupancy for 15 species: five increased, four declined and six showed fluctuating trends. We consider how trends may have been influenced by life-history and habitat changes. Our results build on an emerging picture that biodiversity change in insects is more complex than implied by the simple insect decline narrative.

COVID-19 disruption reveals mass-tourism pressure on nearshore sea turtle distributions and access to optimal breeding habitat

Quantifying the extent to which animals detect and respond to human presence allows us to identify pressure (disturbance) and inform conservation management objectively; however, obtaining baselines against which to compare human impact is hindered in areas where human activities are already well established. For example, Zakynthos Island (Greece, Mediterranean) receives around 850,000 visitors each summer, while supporting an important loggerhead sea turtle rookery (~300 individuals/season). The coronavirus (COVID-19)-driven absence of tourism in May-June 2020 provided an opportunity to evaluate the distribution dynamics of this population in the absence (2020) vs. presence (2018 and 2019) of visitors using programmed unmanned aerial system (UAS) surveys. Ambient sea temperature transitioned from suboptimal for breeding in May to optimal in late June, with turtle distribution appearing to shift from shallow (to benefit from waters 3-5°C above ambient) to deeper waters in 2018 and 2019, but not 2020. The 2020 data set demonstrated that increased tourism pressure, not temperature, drives turtles offshore. Specifically, >50% of turtles remained within 100 m of shore at densities of 25-50 visitors/km, even when sea temperature rose, with 2018 and 2019 data supporting this trend. Reduced access to warmer, nearshore waters by tourism could delay the onset of nesting and increase the length of the egg maturation period between nesting events (internesting interval) at this site. A coastal refuge zone could be delimited in May-June where touristic infrastructure is minimal, but also where turtles frequently aggregate. In conclusion, sea turtles appear capable of perceiving changes in the level of human pressure at fine spatial and temporal scales and adjusting their distribution accordingly.

Modelos de ocupación para datos de cámaras trampa

El uso de las cámaras trampa (CT) en la investigación de fauna silvestre puede generar conclusiones sesgadas cuando la detectabilidad imperfecta de especies no es considerada. Herramientas analíticas como los modelos de ocupación permiten estimar simultáneamente parámetros ecológicos corregidos por la probabilidad de detección. Sin embargo, es necesario implementar e interpretar de manera correcta los parámetros estimados por estos modelos para obtener inferencias con sentido biológico. Este trabajo presenta un marco conceptual base para diseñar de manera apropiada un análisis de ocupación por medio de datos de CT. Se discuten y se señalan recomendaciones generales para la definición de los elementos del modelo, el diseño del muestreo, así como estrategias de modelamiento estadísticos apropiadas dependiendo de los objetivos del estudio, las características de la especie y el tipo de datos obtenidos. Las decisiones tomadas por el investigador para definir cada uno de los componentes del modelo deben considerar la escala adecuada para que el fenómeno de estudio tenga sentido biológico. De esta manera, es posible generar inferencias y conclusiones robustas a partir de información de CT, lo que permite avanzar en el entendimiento de los mecanismos que subyacen a la ecología espacial de fauna silvestre y por lo tanto en su conservación.

Improving area of occupancy estimates for parapatric species using distribution models and support vector machines

As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN's extent of occurrence (EOO) is the general region including the species' range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data-poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data-poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice: Heteromys australis (Least Concern) and Heteromys teleus (Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species' congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low-data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations for H. teleus as its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. For H. teleus, the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species' true occupancy within the post-processed SDM prediction, we recommend that an updated listing for H. teleus include these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post-processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation.

Landscape variables affecting the Himalayan red panda Ailurus fulgens occupancy in wet season along the mountains in Nepal

The Himalayan red panda is an endangered mammal endemic to Eastern Himalayan and South Western China. Data deficiency often hinders understanding of their spatial distribution and habitat use, which is critical for species conservation planning. We used sign surveys covering the entire potential red panda habitat over 22,453 km² along the mid-hills and high mountains encompassing six conservation complexes in Nepal. To estimate red panda distribution using an occupancy framework, we walked 1,451 km along 446 sampled grid cells out of 4,631 grid cells in the wet season of 2016. We used single-species, single-season models to make inferences regarding covariates influencing detection and occupancy. We estimated the probability of detection and occupancy based on model-averaging techniques and drew predictive maps showing site-specific occupancy estimates. We observed red panda in 213 grid cells and found covariates such as elevation, distance to water sources, and bamboo cover influencing the occupancy. Red panda detection probability p^(SE) estimated at 0.70 (0.02). We estimated red panda site occupancy (sampled grid cells) and landscape occupancy (across the potential habitat) Ψ^(SE) at 0.48 (0.01) and 0.40 (0.02) respectively. The predictive map shows a site-specific variation in the spatial distribution of this arboreal species along the priority red panda conservation complexes. Data on their spatial distribution may serve as a baseline for future studies and are expected to aid in species conservation planning in priority conservation complexes.

A multi-species occupancy modeling approach to access the impacts of land use and land cover on terrestrial vertebrates in the Mumbai Metropolitan Region (MMR), Western Ghats, India

Urbanization is one of the main drivers in the conversion of natural habitats into different land use and land cover types (LULC) which threaten the local as well as global biodiversity. This impact is particularly alarming in tropical countries like India, where ~18% of the world's population live, and its ever-growing economy (i.e., industrial development) expanded urban areas by several folds. We undertook this study to examine the impacts of urbanization (i.e., LULC) on terrestrial vertebrates (mammals, birds, reptiles, and amphibians) in the Mumbai Metropolitan Region (MMR), Western Ghats, India. We sampled different habitats ranged from highly disturbed urban areas to less disturbed forested areas. Multiple sampling methods such as quadrat sampling, line transect, point count, and camera trapping were used to quantify the target taxa. We used multi-species occupancy modeling in the Bayesian framework to estimate detection probability and occupancy and to assess the effect of various LULC on different species. All four groups showed a significant negative impact of increasing anthropogenic habitat cover on occupancy. Out of 213 species detected in this study, 96% of mammals, 85% of birds, 93.75% of amphibians, and 69.43% of reptiles showed a negative effect of anthropogenic habitat cover. Evidence suggests that historical and recent human disturbances could have played an important role in transforming this area from semi-evergreen and moist deciduous forest to open, scrubby, dry deciduous, and fire-prone landscape. This might be the reason for the high occupancy of open and degraded forest habitat preferring species in our study area. We recommend species-rich areas in the MMR, e.g., Karnala Bird Sanctuary (KBS) and Prabalgad-Matheran-Malanggad Hill Range (PMMHR), must be conserved through habitat restoration, ecotourism, public awareness, and policymaking.

Habitat requirements of the Himalayan red panda (Ailurus fulgens) and threat analysis in Jigme Dorji National Park, Bhutan

Understanding the influence of anthropogenic disturbances on species' habitat use and distribution is critical to conservation managers in planning effective conservation strategies and mitigating the impact of development. Few studies have focused on the Himalayan red panda (Ailurus fulgens) in Bhutan. This study aimed to assess the habitat requirements and threats to this endangered species in the Khamaed subdistrict of the Jigme Dorji National Park, Bhutan. We employed a transect walk and plot-sampling survey design across two seasons, that is, winter and spring. In total, we surveyed 84 × 50 m radius circular plots along 51 km of existing trails within a 25.4 km2 study area. At 500 m intervals, we established plots at random distances and direction from the trail. We recorded direct sightings (n = 2) and indirect signs (n = 14), such as droppings and footprints as evidence of red panda presence within an altitudinal range of 2,414-3,618 m. We also noted 21 tree and 12 understory species within plots with red panda evidence; the dominant tree species was the Himalayan hemlock (Tsuga dumosa) and the Asian barberry (Berberis asiatica) as an understory species. Red panda presence showed a significant positive association with distance to water sources and fir forests. Plant disturbance and infrastructure, such as power transmission lines, were identified as prominent anthropogenic threats in the study area. Based on our findings, we recommend the development and implementation of local forest management plans, livestock intensification programs, and strict application of environmental impact assessment regulations to promote the conservation of the red panda in the region.

Evidence of region-wide bat population decline from long-term monitoring and Bayesian occupancy models with empirically informed priors

Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long-term encounter surveys with multi-season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model-based understanding about at-risk species with new data. This scenario is exemplified by a bat monitoring program in the Pacific Northwestern United States in which results from 8 years of surveys from 2003 to 2010 require updating with new data from 2016 to 2018. The new data were collected after the arrival of bat white-nose syndrome and expansion of wind power generation, stressors expected to cause population declines in at least two vulnerable species, little brown bat (Myotis lucifugus) and the hoary bat (Lasiurus cinereus). We used multi-season occupancy models with empirically informed prior distributions drawn from previous occupancy results (2003-2010) to assess evidence of contemporary decline in these two species. Empirically informed priors provided the bridge across the two monitoring periods and increased precision of parameter posterior distributions, but did not alter inferences relative to use of vague priors. We found evidence of region-wide summertime decline for the hoary bat ( λ ^  = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat ( λ ^  = 1.1 ± 0.10). White-nose syndrome was documented in the region in 2016 and may not yet have caused regional impact to the little brown bat. However, our discovery of hoary bat decline is consistent with the hypothesis that the longer duration and greater geographic extent of the wind energy stressor (collision and barotrauma) have impacted the species. These hypotheses can be evaluated and updated over time within our framework of pre-post impact monitoring and modeling. Our approach provides the foundation for a strategic evidence-based conservation system and contributes to a growing preponderance of evidence from multiple lines of inquiry that bat species are declining.

Delimiting a species' geographic range using posterior sampling and computational geometry

Accurate delimitation of the geographic range of a species is important for control of biological invasions, conservation of threatened species, and understanding species range dynamics under environmental change. However, estimating range boundaries is challenging because monitoring methods are imperfect, the area that might contain individuals is often incompletely surveyed, and species may have patchy distributions. In these circumstances, large areas can be surveyed without finding individuals despite occupancy extending beyond surveyed areas, resulting in underestimation of range limits. We developed a delimitation method that can be applied with imperfect survey data and patchy distributions. The approach is to construct polygons indicative of the geographic range of a species. Each polygon is associated with a specific probability such that each interior point of the polygon has at least that posterior probability of being interior to the true boundary according to a Bayesian model. The method uses the posterior distribution of latent quantities derived from an agent-based Bayesian model and calculates the posterior distribution of the range as a derived quantity from Markov chain Monte Carlo samples. An application of this method described here informed the Australian campaign to eradicate red imported fire ants (Solenopsis invicta).

Assessing arrays of multiple trail cameras to detect North American mammals

Motion triggered camera traps are an increasingly popular tool for wildlife research and can be used to survey for multiple species simultaneously. As with all survey techniques, it is crucial to conduct camera trapping research following study designs that include adequate spatial and temporal replication, and sufficient probability of detecting species presence. The use and configuration of multiple camera traps within a single survey site are understudied considerations that could have a substantial impact on detection probability. Our objective was to test the role that camera number (one, two or three units), and spacing along a linear transect (100 m or 150 m), have on the probability of detecting a species given it is present. From January to March, 2017 we collected data on six mammal species in Maine, USA: coyote (Canis latrans), fisher (Pekania pennanti), American marten (Martes americana), short-tailed weasel (Mustela erminea), snowshoe hare (Lepus americanus), and American red squirrel (Tamiasciurus hudsonicus). We used multi-scale occupancy modelling to compare pooled detection histories of different configuration of five cameras deployed at the same survey site (n = 32), and how the configuration would influence the probability of detecting a species given it was available at the site. Across all six species, we found substantial increases in probability of detection as the number of cameras increased from one to two (22 to 400 percent increase), regardless of the spacing between cameras. For most species the magnitude of the increase was less substantial when adding a third camera (4 to 85 percent increase), with coyote and snowshoe hare showing a pronounced effect. The influence of survey station features also varied by species. We suggest that using pooled data from two or three cameras at a survey site is a cost effective approach to increase detection success over a single camera.

A dynamic multi-scale occupancy model to estimate temporal dynamics and hierarchical habitat use for nomadic species

Distribution models are increasingly being used to understand how landscape and climatic changes are affecting the processes driving spatial and temporal distributions of plants and animals. However, many modeling efforts ignore the dynamic processes that drive distributional patterns at different scales, which may result in misleading inference about the factors influencing species distributions. Current occupancy models allow estimation of occupancy at different scales and, separately, estimation of immigration and emigration. However, joint estimation of local extinction, colonization, and occupancy within a multi-scale model is currently unpublished. We extended multi-scale models to account for the dynamic processes governing species distributions, while concurrently modeling local-scale availability. We fit the model to data for lark buntings and chestnut-collared longspurs in the Great Plains, USA, collected under the Integrated Monitoring in Bird Conservation Regions program. We investigate how the amount of grassland and shrubland and annual vegetation conditions affect bird occupancy dynamics and local vegetation structure affects fine-scale occupancy. Buntings were prevalent and longspurs rare in our study area, but both species were locally prevalent when present. Buntings colonized sites with preferred habitat configurations, longspurs colonized a wider range of landscape conditions, and site persistence of both was higher at sites with greener vegetation. Turnover rates were high for both species, quantifying the nomadic behavior of the species. Our model allows researchers to jointly investigate temporal dynamics of species distributions and hierarchical habitat use. Our results indicate that grassland birds respond to different covariates at landscape and local scales suggesting different conservation goals at each scale. High turnover rates of these species highlight the need to account for the dynamics of nomadic species, and our model can help inform how to coordinate management efforts to provide appropriate habitat configurations at the landscape scale and provide habitat targets for local managers.

Evidence of significantly higher island feral cat abundance compared with the adjacent mainland

Context Feral cats (Felis catus) impact the health and welfare of wildlife, livestock and humans worldwide. They are particularly damaging where they have been introduced into island countries such as Australia and New Zealand, where native prey species evolved without feline predators. Kangaroo Island, in South Australia, is Australia’s third largest island and supports several threatened and endemic species. Cat densities on Kangaroo Island are thought to be greater than those on the adjacent South Australian mainland, based on one cat density estimate on the island that is higher than most estimates from the mainland. The prevalence of cat-borne disease in cats and sheep is also higher on Kangaroo Island than the mainland, suggesting higher cat densities. A recent continental-scale spatial model of cat density predicted that cat density on Kangaroo Island should be about double that of the adjacent mainland. However, although cats are believed to have severe impacts on some native species on the island, other species that are generally considered vulnerable to cat predation have relatively secure populations on the island compared with the mainland. Aims The present study aimed to compare feral cat abundance between Kangaroo Island and the adjacent South Australian mainland using simultaneous standardised methods. Based on previous findings, we predicted that the relative abundance of feral cats on Kangaroo Island would be approximately double that on the South Australian mainland. Methods Standardised camera trap surveys were used to simultaneously estimate the relative abundance of feral cats on Kangaroo Island and the adjacent South Australian mainland. Survey data were analysed using the Royle–Nichols abundance-induced heterogeneity model to estimate feral cat relative abundance at each site. Key results Cat abundance on the island was estimated to be over 10 times greater than that on the adjacent mainland. Conclusions Consistent with predictions, cat abundance on the island was greater than on the adjacent mainland. However, the magnitude of this difference was much greater than expected. Implications The findings show that the actual densities of cats at local sites can vary substantially from predictions generated by continental-scale models. The study also demonstrates the value of estimating abundance or density simultaneously across sites using standardised methods.

Simulations inform design of regional occupancy-based monitoring for a sparsely distributed, territorial species

Sparsely distributed species attract conservation concern, but insufficient information on population trends challenges conservation and funding prioritization. Occupancy-based monitoring is attractive for these species, but appropriate sampling design and inference depend on particulars of the study system. We employed spatially explicit simulations to identify minimum levels of sampling effort for a regional occupancy monitoring study design, using white-headed woodpeckers (Picoides albolvartus), a sparsely distributed, territorial species threatened by habitat decline and degradation, as a case study. We compared the original design with commonly proposed alternatives with varying targets of inference (i.e., species range, space use, or abundance) and spatial extent of sampling. Sampling effort needed to achieve adequate power to observe a long-term population trend (≥80% chance to observe a 2% yearly decline over 20 years) with the previously used study design consisted of annually monitoring ≥120 transects using a single-survey approach or ≥90 transects surveyed twice per year using a repeat-survey approach. Designs that shifted inference toward finer-resolution trends in abundance and extended the spatial extent of sampling by shortening transects, employing a single-survey approach to monitoring, and incorporating a panel design (33% of units surveyed per year) improved power and reduced error in estimating abundance trends. In contrast, efforts to monitor coarse-scale trends in species range or space use with repeat surveys provided extremely limited statistical power. Synthesis and applications. Sampling resolutions that approximate home range size, spatially extensive sampling, and designs that target inference of abundance trends rather than range dynamics are probably best suited and most feasible for broad-scale occupancy-based monitoring of sparsely distributed territorial animal species.

Sampling scales define occupancy and underlying occupancy-abundance relationships in animals

Occupancy-abundance (OA) relationships are a foundational ecological phenomenon and field of study, and occupancy models are increasingly used to track population trends and understand ecological interactions. However, these two fields of ecological inquiry remain largely isolated, despite growing appreciation of the importance of integration. For example, using occupancy models to infer trends in abundance is predicated on positive OA relationships. Many occupancy studies collect data that violate geographical closure assumptions due to the choice of sampling scales and application to mobile organisms, which may change how occupancy and abundance are related. Little research, however, has explored how different occupancy sampling designs affect OA relationships. We develop a conceptual framework for understanding how sampling scales affect the definition of occupancy for mobile organisms, which drives OA relationships. We explore how spatial and temporal sampling scales, and the choice of sampling unit (areal vs. point sampling), affect OA relationships. We develop predictions using simulations, and test them using empirical occupancy data from remote cameras on 11 medium-large mammals. Surprisingly, our simulations demonstrate that when using point sampling, OA relationships are unaffected by spatial sampling grain (i.e., cell size). In contrast, when using areal sampling (e.g., species atlas data), OA relationships are affected by spatial grain. Furthermore, OA relationships are also affected by temporal sampling scales, where the curvature of the OA relationship increases with temporal sampling duration. Our empirical results support these predictions, showing that at any given abundance, the spatial grain of point sampling does not affect occupancy estimates, but longer surveys do increase occupancy estimates. For rare species (low occupancy), estimates of occupancy will quickly increase with longer surveys, even while abundance remains constant. Our results also clearly demonstrate that occupancy for mobile species without geographical closure is not true occupancy. The independence of occupancy estimates from spatial sampling grain depends on the sampling unit. Point-sampling surveys can, however, provide unbiased estimates of occupancy for multiple species simultaneously, irrespective of home-range size. The use of occupancy for trend monitoring needs to explicitly articulate how the chosen sampling scales define occupancy and affect the occupancy-abundance relationship.

A statistically rigorous sampling design to integrate avian monitoring and management within Bird Conservation Regions

Monitoring is an essential component of wildlife management and conservation. However, the usefulness of monitoring data is often undermined by the lack of 1) coordination across organizations and regions, 2) meaningful management and conservation objectives, and 3) rigorous sampling designs. Although many improvements to avian monitoring have been discussed, the recommendations have been slow to emerge in large-scale programs. We introduce the Integrated Monitoring in Bird Conservation Regions (IMBCR) program designed to overcome the above limitations. Our objectives are to outline the development of a statistically defensible sampling design to increase the value of large-scale monitoring data and provide example applications to demonstrate the ability of the design to meet multiple conservation and management objectives. We outline the sampling process for the IMBCR program with a focus on the Badlands and Prairies Bird Conservation Region (BCR 17). We provide two examples for the Brewer's sparrow (Spizella breweri) in BCR 17 demonstrating the ability of the design to 1) determine hierarchical population responses to landscape change and 2) estimate hierarchical habitat relationships to predict the response of the Brewer's sparrow to conservation efforts at multiple spatial scales. The collaboration across organizations and regions provided economy of scale by leveraging a common data platform over large spatial scales to promote the efficient use of monitoring resources. We designed the IMBCR program to address the information needs and core conservation and management objectives of the participating partner organizations. Although it has been argued that probabilistic sampling designs are not practical for large-scale monitoring, the IMBCR program provides a precedent for implementing a statistically defensible sampling design from local to bioregional scales. We demonstrate that integrating conservation and management objectives with rigorous statistical design and analyses ensures reliable knowledge about bird populations that is relevant and integral to bird conservation at multiple scales.

Large anthropogenic impacts on a charismatic small carnivore: Insights from distribution surveys of red panda Ailurus fulgens in Nepal

Protected areas are key to preserving biodiversity and maintaining ecosystem services. However, their ability to ensure long-term survival of threatened andendangered species varies across countries, regions and landscapes. Distribution surveys can beparticularly important for assessing the value of protected areas, and gauging their efficacy incatering to species-specific requirements. We assessed the conservation value of one such reserve for a charismatic yet globally endangered species, the red panda Ailurus fulgens,in the light of on-going land-use transformation in Nepal. We conducted field surveys forindirect signs of red pandas along forest trails in 25-km² sampling grid cells (n = 54) of Dhorpatan Hunting Reserve, and confronted a set of ecological hypotheses to the data using hierarchical occupancy models. We estimated overall occupancy at Ψ(SE) = 0.41 (0.007), with relatively high site-level detectability [p = 0.93 (SE = 0.001)]. Our results show that despitebeing a subsistence form of small-scale resource use, extraction of bamboo and livestock grazing negatively affected panda occurrence, albeit at different intensities. The amount of bamboo cover,rather than the overall proportion of forest cover, had greater influence on the panda occurrence. Despite availability of bamboo cover, areas with bamboo extraction and anthropogenic disturbances were less likely to be occupied by pandas. Together, these results suggest that long-term persistence of red pandas in this reserve and elsewhere across the species’ range will require preventing commercial extractionof bamboo, coupled with case-specific regulation of anthropogenic exploitation of red panda habitats.

Discrepancies in occupancy and abundance approaches to identifying and protecting habitat for an at-risk species

Predicting how environmental factors affect the distribution of species is a fundamental goal of conservation biology. Conservation biologists rely on species distribution and abundance models to identify key habitat characteristics for species. Occupancy modeling is frequently promoted as a practical alternative to use of abundance in identifying habitat quality. While occupancy and abundance are potentially governed by different limiting factors operating at different scales, few studies have directly compared predictive models for these approaches in the same system. We evaluated how much occupancy and abundance are driven by the same environmental factors for a species of conservation concern, the greater short-horned lizard (Phrynosoma hernandesi). Occupancy was most strongly dictated by precipitation, temperature, and density of ant mounds. While these factors were also in the best-supported predictive models for lizard abundance, the magnitude of the effects varied, with the sign of the effect changing for temperature and precipitation. These discrepancies show that while occupancy modeling can be an efficient approach for conservation planning, predictors of occupancy probability should not automatically be equated with predictors of population abundance. Understanding the differences in factors that control occupancy versus abundance can help us to identify habitat requirements and mitigate the loss of threatened species.

Conserving tigers in working landscapes

Tiger (Panthera tigris) conservation efforts in Asia are focused on protected areas embedded in human-dominated landscapes. A system of protected areas is an effective conservation strategy for many endangered species if the network is large enough to support stable metapopulations. The long-term conservation of tigers requires that the species be able to meet some of its life-history needs beyond the boundaries of small protected areas and within the working landscape, including multiple-use forests with logging and high human use. However, understanding of factors that promote or limit the occurrence of tigers in working landscapes is incomplete. We assessed the relative influence of protection status, prey occurrence, extent of grasslands, intensity of human use, and patch connectivity on tiger occurrence in the 5400 km(2) Central Terai Landscape of India, adjacent to Nepal. Two observer teams independently surveyed 1009 km of forest trails and water courses distributed across 60 166-km(2) cells. In each cell, the teams recorded detection of tiger signs along evenly spaced trail segments. We used occupancy models that permitted multiscale analysis of spatially correlated data to estimate cell-scale occupancy and segment-scale habitat use by tigers as a function of management and environmental covariates. Prey availability and habitat quality, rather than protected-area designation, influenced tiger occupancy. Tiger occupancy was low in some protected areas in India that were connected to extensive areas of tiger habitat in Nepal, which brings into question the efficacy of current protection and management strategies in both India and Nepal. At a finer spatial scale, tiger habitat use was high in trail segments associated with abundant prey and large grasslands, but it declined as human and livestock use increased. We speculate that riparian grasslands may provide tigers with critical refugia from human activity in the daytime and thereby promote tiger occurrence in some multiple-use forests. Restrictions on human-use in high-quality tiger habitat in multiple-use forests may complement existing protected areas and collectively promote the persistence of tiger populations in working landscapes.

The effects of urbanization on population density, occupancy, and detection probability of wild felids

Urbanization is a primary driver of landscape conversion, with far-reaching effects on landscape pattern and process, particularly related to the population characteristics of animals. Urbanization can alter animal movement and habitat quality, both of which can influence population abundance and persistence. We evaluated three important population characteristics (population density, site occupancy, and species detection probability) of a medium-sized and a large carnivore across varying levels of urbanization. Specifically, we studied bobcat and puma populations across wildland, exurban development, and wildland-urban interface (WUI) sampling grids to test hypotheses evaluating how urbanization affects wild felid populations and their prey. Exurban development appeared to have a greater impact on felid populations than did habitat adjacent to a major urban area (i.e., WUI); estimates of population density for both bobcats and pumas were lower in areas of exurban development compared to wildland areas, whereas population density was similar between WUI and wildland habitat. Bobcats and pumas were less likely to be detected in habitat as the amount of human disturbance associated with residential development increased at a site, which was potentially related to reduced habitat quality resulting from urbanization. However, occupancy of both felids was similar between grids in both study areas, indicating that this population metric was less sensitive than density. At the scale of the sampling grid, detection probability for bobcats in urbanized habitat was greater than in wildland areas, potentially due to restrictive movement corridors and funneling of animal movements in landscapes influenced by urbanization. Occupancy of important felid prey (cottontail rabbits and mule deer) was similar across levels of urbanization, although elk occupancy was lower in urbanized areas. Our study indicates that the conservation of medium- and large-sized felids associated with urbanization likely will be most successful if large areas of wildland habitat are maintained, even in close proximity to urban areas, and wildland habitat is not converted to low-density residential development.

Summer Precipitation Predicts Spatial Distributions of Semiaquatic Mammals

Climate change is predicted to increase the frequency of droughts and intensity of seasonal precipitation in many regions. Semiaquatic mammals should be vulnerable to this increased variability in precipitation, especially in human-modified landscapes where dispersal to suitable habitat or temporary refugia may be limited. Using six years of presence-absence data (2007–2012) spanning years of record-breaking drought and flood conditions, we evaluated regional occupancy dynamics of American mink (Neovison vison) and muskrats (Ondatra zibethicus) in a highly altered agroecosystem in Illinois, USA. We used noninvasive sign surveys and a multiseason occupancy modeling approach to estimate annual occupancy rates for both species and related these rates to summer precipitation. We also tracked radiomarked individuals to assess mortality risk for both species when moving in terrestrial areas. Annual model-averaged estimates of occupancy for mink and muskrat were correlated positively to summer precipitation. Mink and muskrats were widespread during a year (2008) with above-average precipitation. However, estimates of site occupancy declined substantially for mink (0.56) and especially muskrats (0.09) during the severe drought of 2012. Mink are generalist predators that probably use terrestrial habitat during droughts. However, mink had substantially greater risk of mortality away from streams. In comparison, muskrats are more restricted to aquatic habitats and likely suffered high mortality during the drought. Our patterns are striking, but a more mechanistic understanding is needed of how semiaquatic species in human-modified ecosystems will respond ecologically in situ to extreme weather events predicted by climate-change models.

Recommended survey designs for occupancy modelling using motion-activated cameras: insights from empirical wildlife data

Motion-activated cameras are a versatile tool that wildlife biologists can use for sampling wild animal populations to estimate species occurrence. Occupancy modelling provides a flexible framework for the analysis of these data; explicitly recognizing that given a species occupies an area the probability of detecting it is often less than one. Despite the number of studies using camera data in an occupancy framework, there is only limited guidance from the scientific literature about survey design trade-offs when using motion-activated cameras. A fuller understanding of these trade-offs will allow researchers to maximise available resources and determine whether the objectives of a monitoring program or research study are achievable. We use an empirical dataset collected from 40 cameras deployed across 160 km(2) of the Western Slope of Colorado, USA to explore how survey effort (number of cameras deployed and the length of sampling period) affects the accuracy and precision (i.e., error) of the occupancy estimate for ten mammal and three virtual species. We do this using a simulation approach where species occupancy and detection parameters were informed by empirical data from motion-activated cameras. A total of 54 survey designs were considered by varying combinations of sites (10-120 cameras) and occasions (20-120 survey days). Our findings demonstrate that increasing total sampling effort generally decreases error associated with the occupancy estimate, but changing the number of sites or sampling duration can have very different results, depending on whether a species is spatially common or rare (occupancy = ψ) and easy or hard to detect when available (detection probability = p). For rare species with a low probability of detection (i.e., raccoon and spotted skunk) the required survey effort includes maximizing the number of sites and the number of survey days, often to a level that may be logistically unrealistic for many studies. For common species with low detection (i.e., bobcat and coyote) the most efficient sampling approach was to increase the number of occasions (survey days). However, for common species that are moderately detectable (i.e., cottontail rabbit and mule deer), occupancy could reliably be estimated with comparatively low numbers of cameras over a short sampling period. We provide general guidelines for reliably estimating occupancy across a range of terrestrial species (rare to common: ψ = 0.175-0.970, and low to moderate detectability: p = 0.003-0.200) using motion-activated cameras. Wildlife researchers/managers with limited knowledge of the relative abundance and likelihood of detection of a particular species can apply these guidelines regardless of location. We emphasize the importance of prior biological knowledge, defined objectives and detailed planning (e.g., simulating different study-design scenarios) for designing effective monitoring programs and research studies.

Unifying elemental stoichiometry and metabolic theory in predicting species abundances

While metabolic theory predicts variance in population density within communities depending on population average body masses, the ecological stoichiometry concept relates density variation across communities to varying resource stoichiometry. Using a data set including biomass densities of 4959 populations of soil invertebrates across 48 forest sites we combined these two frameworks. We analyzed how the scaling of biomass densities with population-averaged body masses systematically interacts with stoichiometric variables. Simplified analyses employing either only body masses or only resource stoichiometry are highly context sensitive and yield variable and often misleading results. Our findings provide strong evidence that analyses of ecological state variables should integrate allometric and stoichiometric variables to explain deviations from predicted allometric scaling and avoid erroneous conclusions. In consequence, our study provides an important step towards unifying two prominent ecological theories, metabolic theory and ecological stoichiometry.