Using global remote camera data of a solitary species complex to evaluate the drivers of group formation.

The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of "social" and "solitary" inhibit our capacity to understand species and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family Mustelidae, Order Carnivora) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the solitary model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species' context-dependent proclivity toward aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognize complexities in the social behaviors of apparently solitary species limits our understanding not only of the individual species but also the causes and consequences of group formation.

Spatial proximity moderates genotype uncertainty in genetic tagging studies.

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower-quality samples cannot be used, and they assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (Pekania pennanti) in which 37% of hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63 and 42%, respectively, using three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.

Reserve design to optimize functional connectivity and animal density.

Ecological distance-based spatial capture-recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget-constrained optimization problem and conducted a simulation to explore 3 different SCR-informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density-weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space-use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home-range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long-term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density-weighted connectivity or preemptively imposing home-range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high-density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home-range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.

Diseño de Reservas para Optimizar la Conectividad Funcional y la Densidad Animal Resumen Los modelos de captura-recaptura espacial (CRE) basados en distancias ecológicas son un método prometedor para estimar la densidad animal y la conectividad, las cuales afectan los procesos poblacionales espaciales y, en última instancia, la persistencia de las especies. Exploramos cómo se puede integrar a los modelos CRE en los marcos de diseño de reserva que explícitamente reconocen tanto la distribución espacial de los individuos como su uso del espacio resultante de la estructura del paisaje. Formulamos el diseño de reservas de vida silvestre como un problema de optimización de presupuesto limitado y realizamos una simulación para explorar 3 diferentes objetivos de optimización informados por CRE que priorizaron diferentes metas de conservación mediante la maximización del número de individuos protegidos; la conectividad de la reserva y la conectividad ponderada por la densidad. También estudiamos el efecto sobre nuestros objetivos de hacer que los requerimientos individuales de uso de espacio fuesen satisfechos por la reserva de manera que se pudiese considerar que los individuos estaban protegidos (referidos como restricciones de rango de hogar). La maximización de la densidad de la población local resultó en reservas fragmentadas que probablemente no contribuyan a la persistencia de la población a largo plazo, mientras que la maximización de la conectividad produjo reservas que protegían al menor número de individuos. Sin embargo, la maximización de la conectividad ponderada por la densidad o la imposición preventiva de restricciones de rango de hogar en el diseño de reservas produjo reservas compuestas por conjuntos de parcelas mayormente compactas espacialmente que cubrían áreas de densidad alta en el paisaje con alta conectividad funcional entre ellas. Nuestros resultados cuantifican la extensión a la cual el diseño de reservas esta limitado por los requerimientos de rango de hogar individuales y resaltan que la consideración del uso de espacio individual en el objetivo y limitaciones puede ayudar al diseño de reservas que equilibren la abundancia y la conectividad de manera biológicamente relevante.

Integration of social and ecological sciences for natural resource decision making: challenges and opportunities.

The last 25 years have witnessed growing recognition that natural resource management decisions depend as much on understanding humans and their social interactions as on understanding the interactions between non-human organisms and their environment. Decision science provides a framework for integrating ecological and social factors into a decision, but challenges to integration remain. The decision-analytic framework elicits values and preferences to help articulate objectives, and then evaluates the outcomes of alternative management actions to achieve these objectives. Integrating social science into these steps can be hindered by failing to include social scientists as more than stakeholder-process facilitators, assuming that specific decision-analytic skills are commonplace for social scientists, misperceptions of social data as inherently qualitative, timescale mismatches for iterating through decision analysis and collecting relevant social data, difficulties in predicting human behavior, and failures of institutions to recognize the importance of this integration. We engage these challenges, and suggest solutions to them, helping move forward the integration of social and biological/ecological knowledge and considerations in decision-making.

Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition.

Although interspecific competition plays a principal role in shaping species behaviour and demography, little is known about the population-level outcomes of competition between large carnivores, and the mechanisms that facilitate coexistence. We conducted a multilandscape analysis of two widely distributed, threatened large carnivore competitors to offer insight into coexistence strategies and assist with species-level conservation. We evaluated how interference competition affects occupancy, temporal activity and population density of a dominant competitor, the lion (Panthera leo), and its subordinate competitor, the leopard (Panthera pardus). We collected camera-trap data over 3 years in 10 study sites covering 5,070 km2 . We used multispecies occupancy modelling to assess spatial responses in varying environmental and prey conditions and competitor presence, and examined temporal overlap and the relationship between lion and leopard densities across sites and years. Results showed that both lion and leopard occupancy was independent of-rather than conditional on-their competitor's presence across all environmental covariates. Marginal occupancy probability for leopard was higher in areas with more bushy, "hideable" habitat, human (tourist) activity and topographic ruggedness, whereas lion occupancy decreased with increasing hideable habitat and increased with higher abundance of very large prey. Temporal overlap was high between carnivores, and there was no detectable relationship between species densities. Lions pose a threat to the survival of individual leopards, but they exerted no tractable influence on leopard spatial or temporal dynamics. Furthermore, lions did not appear to suppress leopard populations, suggesting that intraguild competitors can coexist in the same areas without population decline. Aligned conservation strategies that promote functioning ecosystems, rather than target individual species, are therefore advised to achieve cost- and space-effective conservation.

Estimating population density and connectivity of American mink using spatial capture-recapture.

Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture-recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture-recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km² area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture-recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.

Density-habitat relationships of white-tailed deer (Odocoileus virginianus) in Finland.

In heterogeneous landscapes, resource selection constitutes a crucial link between landscape and population-level processes such as density. We conducted a non-invasive genetic study of white-tailed deer in southern Finland in 2016 and 2017 using fecal DNA samples to understand factors influencing white-tailed deer density and space use in late summer prior to the hunting season. We estimated deer density as a function of landcover types using a spatial capture-recapture (SCR) model with individual identities established using microsatellite markers. The study revealed second-order habitat selection with highest deer densities in fields and mixed forest, and third-order habitat selection (detection probability) for transitional woodlands (clear-cuts) and closeness to fields. Including landscape heterogeneity improved model fit and increased inferred total density compared with models assuming a homogenous landscape. Our findings underline the importance of including habitat covariates when estimating density and exemplifies that resource selection can be studied using non-invasive methods.

Incorporating citizen science data in spatially explicit integrated population models.

Information about population abundance, distribution, and demographic rates is critical for understanding a species' ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence-absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture-recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi-season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population-level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost-efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

Author Correction: Large-scale variation in density of an aquatic ecosystem indicator species.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Large-scale variation in density of an aquatic ecosystem indicator species.

Monitoring indicator species is a pragmatic approach to natural resource assessments, especially when the link between the indicator species and ecosystem state is well justified. However, conducting ecosystem assessments over representative spatial scales that are insensitive to local heterogeneity is challenging. We examine the link between polychlorinated biphenyl (PCB) contamination and population density of an aquatic habitat specialist over a large spatial scale using non-invasive genetic spatial capture-recapture. Using American mink (Neovison vison), a predatory mammal and an indicator of aquatic ecosystems, we compared estimates of density in two major river systems, one with extremely high levels of PCB contamination (Hudson River), and a hydrologically independent river with lower PCB levels (Mohawk River). Our work supports the hypothesis that mink densities are substantially (1.64-1.67 times) lower in the contaminated river system. We demonstrate the value of coupling the indicator species concept with well-conceived and spatially representative monitoring protocols. PCBs have demonstrable detrimental effects on aquatic ecosystems, including mink, and these effects are likely to be profound and long-lasting, manifesting as population-level impacts. Through integrating non-invasive data collection, genetic analysis, and spatial capture-recapture methods, we present a monitoring framework for generating robust density estimates across large spatial scales.

Trap configuration and spacing influences parameter estimates in spatial capture-recapture models.

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.