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.

A model-based hypothesis framework to define and estimate the diel niche via the 'Diel.Niche' R package.

How animals use the diel period (24-h light-dark cycle) is of fundamental importance to understand their niche. While ecological and evolutionary literature abound with discussion of diel phenotypes (e.g. diurnal, nocturnal, crepuscular, cathemeral), they lack clear and explicit quantitative definitions. As such, inference can be confounded when evaluating hypotheses of animal diel niche switching or plasticity across studies because researchers may be operating under different definitions of diel phenotypes. We propose quantitative definitions of diel phenotypes using four alternative hypothesis sets (maximizing, traditional, general and selection) aimed at achieving different objectives. Each hypothesis set is composed of mutually exclusive hypotheses defined based on the activity probabilities in the three fundamental periods of light availability (twilight, daytime and night-time). We develop a Bayesian modelling framework that compares diel phenotype hypotheses using Bayes factors and estimates model parameters using a multinomial model with linear inequality constraints. Model comparison, parameter estimation and visualizing results can be done in the Diel.Niche R package. A simplified R Shiny web application is also available. We provide extensive simulation results to guide researchers on the power to discriminate among hypotheses for a range of sample sizes (10-1280). We also work through several examples of using data to make inferences on diel activity, and include online vignettes on how to use the Diel.Niche package. We demonstrate how our modelling framework complements other analyses, such as circular kernel density estimators and animal movement modelling. Our aim is to encourage standardization of the language of diel activity and bridge conceptual frameworks and hypotheses in diel research with data and models. Lastly, we hope more research focuses on the ecological and conservation importance of understanding how animals use diel time.

Diel activity structures the occurrence of a mammal community in a human-dominated landscape.

Anthropogenic developments alter the environment and resources available to wildlife communities. In response to these real or perceived threats from this development, species may adjust their spatial occurrence. Additionally, wildlife species may adjust when in diel time (24-h light-dark cycle) they occupy sites on the landscape to adapt to changing conditions. However, many wildlife studies only focus on where a species does and does not occur, ignoring how species may shift their diel activity at sites to mitigate threats. We used a multi-state diel occupancy modeling framework to investigate how a community of mammals (mesocarnivores, urban-adapted omnivores, and herbivore/small mammals) respond to differing levels of anthropogenic development and forest cover across two climatic seasons. We collected camera trap data at 240 survey locations across the summer and winter of 2021-2022. We modeled multi-state diel occupancy for 14 mammal species with extent of development/forest and season hypothesized to influence diel occupancy and season hypothesized to influence the probability of detection. We found that all species displayed heterogeneity in both diel occupancy and detection either by extent of development/forest and or season. Within the mesocarnivore species group, coyote and red fox were less sensitive to development and had higher occupancy probability at these sites in general but used them more during the night, while more sensitive mesocarnivores including fisher and bobcat occupied the day state only when there was increasing forest cover. Our results highlight the importance of incorporating diel activity in habitat modeling to better understand the relationship between a species and its landscape, particularly in a region that is vulnerable to increased anthropogenic pressure.

Rethinking Habitat Occupancy Modeling and the Role of Diel Activity in an Anthropogenic World.

AbstractCurrent methods to model species habitat use through space and diel time are limited. Development of such models is critical when considering rapidly changing habitats where species are forced to adapt to anthropogenic change, often by shifting their diel activity across space. We use an occupancy modeling framework to specify the multistate diel occupancy model (MSDOM), which can evaluate species diel activity against continuous response variables that may impact diel activity within and across seasons or years. We used two case studies, fosas in Madagascar and coyotes in Chicago, Illinois, to conceptualize the application of this model and to quantify the impacts of human activity on species spatial use in diel time. We found support that both species varied their habitat use by diel states-in and across years and by human disturbance. Our results exemplify the importance of understanding animal diel activity patterns and how human disturbance can lead to temporal habitat loss. The MSDOM will allow more focused attention in ecology and evolution studies on the importance of the short temporal scale of diel time in animal-habitat relationships and lead to improved habitat conservation and management.

Rapidly declining body size in an insectivorous bat is associated with increased precipitation and decreased survival.

Reduced food availability is implicated in declines in avian aerial insectivores, but the effect of nutritional stress on mammalian aerial insectivores is unclear. Unlike birds, insectivorous bats provision their young through lactation, which might protect nursing juveniles when prey availability is low but could increase the energetic burden on lactating females. We analyzed a 15-year capture-mark-recapture data set from 5312 individual little brown myotis (Myotis lucifugus) captured at 11 maternity colonies in northwestern Canada, to test the hypothesis that nutritional stress is impacting these mammalian aerial insectivores. We used long-bone (forearm [FA]) length as a proxy for relative access to nutrition during development, and body mass as a proxy for access to nutrition prior to capture. Average FA length and body mass both decreased significantly over the study period in adult females and juveniles, suggesting decreased access to nutrition. Effect sizes were very small, similar to those reported for declining body size in avian aerial insectivores. Declines in juvenile body mass were only observed in individuals captured in late summer when they were foraging independently, supporting our hypothesis that lactation provides some protection to nursing young during periods of nutritional stress. Potential drivers of the decline in bat size include one or both of (1) declining insect (prey) abundance, and (2) declining prey availability. Echolocating insectivorous bats cannot forage effectively during rainfall, which is increasing in our study area. The body mass of captured adult females and juveniles in our study was lower, on average, after periods of high rainfall, and higher after warmer-than-average periods. Finally, survival models revealed a positive association between FA length and survival, suggesting a fitness consequence to declines in body size. Our study area has not yet been impacted by bat white-nose syndrome (WNS), but research elsewhere has suggested that fatter bats are more likely to survive infection. We found evidence for WNS-independent shifts in the body size of little brown myotis, which can inform studies investigating population responses to WNS. More broadly, the cumulative effects of multiple stressors (e.g., disease, nutritional stress, climate change, and other pressures) on mammalian aerial insectivores require urgent attention.

Forest degradation drives widespread avian habitat and population declines.

In many regions of the world, forest management has reduced old forest and simplified forest structure and composition. We hypothesized that such forest degradation has resulted in long-term habitat loss for forest-associated bird species of eastern Canada (130,017 km2) which, in turn, has caused bird-population declines. Despite little change in overall forest cover, we found substantial reductions in old forest as a result of frequent clear-cutting and a broad-scale transformation to intensified forestry. Back-cast species distribution models revealed that breeding habitat loss occurred for 66% of the 54 most common species from 1985 to 2020 and was strongly associated with reduction in old age classes. Using a long-term, independent dataset, we found that habitat amount predicted population size for 94% of species, and habitat loss was associated with population declines for old-forest species. Forest degradation may therefore be a primary cause of biodiversity decline in managed forest landscapes.

Madagascar Terrestrial Camera Survey Database 2021: A collation of protected forest camera surveys from 2007-2021.

Madagascar is a threatened global biodiversity hotspot and conservation priority, yet we lack broad-scale surveys to assess biodiversity across space and time. To fill this gap, we collated camera trap surveys, capturing species occurrences within Madagascar into a single standardized database. This data set includes nine distinct protected areas of Madagascar and encompasses 13 subprojects, 38 camera arrays, and 1156 sampling units (independent camera site per survey) within two important biodiversity eco-regions: western dry deciduous forest and eastern humid rainforest. Camera surveys were conducted from June 2007 to January 2021. The final data set includes 17 unique families of mammals (Bovidae, Canidae, Cheirogaleidae, Daubentoniidae, Equidae, Eupleridae, Felidae, Hominidae, Indriidae, Lemuridae, Lepilemuridae, Muridae, Nesomyidae, Pteropodidae, Soricidae, Suidae, Tenrecidae) comprising 45 species and 27 unique families of birds (Accipitridae, Acrocephalidae, Alcedinidae, Bernieridae, Brachypteraciidae, Caprimulgidae, Cisticolidae, Columbidae, Coraciidae, Corvidae, Cuculidae, Dicruridae, Mesitornithidae, Monarchidae, Motacillidae, Muscicapidae, Numididae, Phasianidae, Rallidae, Sarothruridae, Strigidae, Sturnidae, Sulidae, Threskiornithidae, Upupidae, Vangidae, Zosteropidae) comprising 58 species. Images were processed and verified by individual project data set creators and camera operation and species tables were then collated. The final product represents the first broad-scale freely available standardized formal faunal database for Madagascar. Data are available through this publication and at DOI: 10.5281/zenodo.5801806. These data will be useful for examining species-level and community-level trends in occurrence across space or time within Madagascar and globally, evaluating native and invasive species dynamics, and will aid in determining species conservation status and planning for at-risk species. There are no copyright restrictions; please cite this paper when using the data for publication.

Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution.

Habitat selection is a fundamental animal behavior that shapes a wide range of ecological processes, including animal movement, nutrient transfer, trophic dynamics and population distribution. Although habitat selection has been a focus of ecological studies for decades, technological, conceptual and methodological advances over the last 20 yr have led to a surge in studies addressing this process. Despite the substantial literature focused on quantifying the habitat-selection patterns of animals, there is a marked lack of guidance on best analytical practices. The conceptual foundations of the most commonly applied modeling frameworks can be confusing even to those well versed in their application. Furthermore, there has yet to be a synthesis of the advances made over the last 20 yr. Therefore, there is a need for both synthesis of the current state of knowledge on habitat selection, and guidance for those seeking to study this process. Here, we provide an approachable overview and synthesis of the literature on habitat-selection analyses (HSAs) conducted using selection functions, which are by far the most applied modeling framework for understanding the habitat-selection process. This review is purposefully non-technical and focused on understanding without heavy mathematical and statistical notation, which can confuse many practitioners. We offer an overview and history of HSAs, describing the tortuous conceptual path to our current understanding. Through this overview, we also aim to address the areas of greatest confusion in the literature. We synthesize the literature outlining the most exciting conceptual advances in the field of habitat-selection modeling, discussing the substantial ecological and evolutionary inference that can be made using contemporary techniques. We aim for this paper to provide clarity for those navigating the complex literature on HSAs while acting as a reference and best practices guide for practitioners.

Sex-Segregated Range Use by Black-and-White Ruffed Lemurs (Varecia variegata) in Ranomafana National Park, Madagascar.

Ranging behavior is one important strategy by which nonhuman primates obtain access to resources critical to their biological maintenance and reproductive success. As most primates live in permanent social groups, their members must balance the benefits of group living with the costs of intragroup competition for resources. However, some taxa live in more spatiotemporally flexible social groups, whose members modify patterns of association and range use as a method to mitigate these costs. Here, we describe the range use of one such taxon, the black-and-white ruffed lemur (Varecia variegata), at an undisturbed primary rain forest site in Ranomafana National Park, Madagascar, and characterize sex differences in annual home range area, overlap, and daily distances traveled. Moreover, we characterize seasonal variability in range use and ask whether ranging behaviors can be explained by either climatic or reproductive seasonality. We found that females used significantly larger home ranges than males, though sexes shared equal and moderate levels of home range overlap. Overall, range use did not vary across seasons, although within sexes, male range use varied significantly with climate. Moreover, daily path length was best predicted by day length, female reproductive state, and sex, but was unrelated to climate variables. While the patterns of range use and spatial association presented here share some similarities with "bisexually bonded" community models described for chimpanzees, we argue that ruffed lemurs best conform to a "nuclear neighborhood" community model wherein nuclear (core) groups share the highest levels of home range overlap, and where these groups cluster spatially into adjacent "neighborhoods" within the larger, communally defended territory.

Contrasting effects of climate change on seasonal survival of a hibernating mammal.

Seasonal environmental conditions shape the behavior and life history of virtually all organisms. Climate change is modifying these seasonal environmental conditions, which threatens to disrupt population dynamics. It is conceivable that climatic changes may be beneficial in one season but result in detrimental conditions in another because life-history strategies vary between these time periods. We analyzed the temporal trends in seasonal survival of yellow-bellied marmots (Marmota flaviventer) and explored the environmental drivers using a 40-y dataset from the Colorado Rocky Mountains (USA). Trends in survival revealed divergent seasonal patterns, which were similar across age-classes. Marmot survival declined during winter but generally increased during summer. Interestingly, different environmental factors appeared to drive survival trends across age-classes. Winter survival was largely driven by conditions during the preceding summer and the effect of continued climate change was likely to be mainly negative, whereas the likely outcome of continued climate change on summer survival was generally positive. This study illustrates that seasonal demographic responses need disentangling to accurately forecast the impacts of climate change on animal population dynamics.

Improving spatial predictions of animal resource selection to guide conservation decision making.

Resource selection is often studied by ecologists interested in the environmental drivers of animal space use and movement. These studies commonly produce spatial predictions, which are of considerable utility to resource managers making habitat and population management decisions. It is thus paramount that predictions from resource selection studies are accurate. We evaluated model building and fitting strategies for optimizing resource selection function predictions in a use-availability framework. We did so by simulating low- and high-intensity spatial sampling data that respectively predicted study area and movement-based resource selection. We compared one of the most commonly used forms of statistical regularization, Akaike's Information Criterion (AIC), with the lesser used least absolute shrinkage and selection operator (LASSO). LASSO predictions were less variable and more accurate than AIC and were often best when considering additive and interacting variables. We explicitly demonstrate the predictive equivalence using the logistic and Poisson likelihoods and how it is lost when the available sample is too small. Regardless of modeling approach, interpreting the sign of coefficients as a measure of selection can be misleading when optimizing for prediction.

Correction: A comment on priors for Bayesian occupancy models.

[This corrects the article DOI: 10.1371/journal.pone.0192819.].

Accounting for location uncertainty in azimuthal telemetry data improves ecological inference.

BACKGROUND: Characterizing animal space use is critical for understanding ecological relationships. Animal telemetry technology has revolutionized the fields of ecology and conservation biology by providing high quality spatial data on animal movement. Radio-telemetry with very high frequency (VHF) radio signals continues to be a useful technology because of its low cost, miniaturization, and low battery requirements. Despite a number of statistical developments synthetically integrating animal location estimation and uncertainty with spatial process models using satellite telemetry data, we are unaware of similar developments for azimuthal telemetry data. As such, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models.We developed a hierarchical modeling framework to provide robust animal location estimates from one or more intersecting or non-intersecting azimuths. We used our azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into spatial ecological models. We evaluate the ATM with commonly used estimators (Lenth (1981) maximum likelihood and M-Estimators) using simulation. We also provide illustrative empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We further use simulation to better understand the relationship among location uncertainty, spatial covariate autocorrelation, and resource selection inference. RESULTS: We found the ATM to have good performance in estimating locations and the only model that has appropriate measures of coverage. Ignoring animal location uncertainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. Home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Furthermore, our simulation study clarified that incorporating location uncertainty helps reduce bias in resource selection coefficients across all levels of covariate spatial autocorrelation. CONCLUSION: The ATM can accommodate one or more azimuths when estimating animal locations, regardless of how they intersect; this ensures that all data collected are used for ecological inference. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies.

Adaptive management of animal populations with significant unknowns and uncertainties: a case study.

Conservation and management decision making in natural resources is challenging due to numerous uncertainties and unknowns, especially relating to understanding system dynamics. Adaptive resource management (ARM) is a formal process to making logical and transparent recurrent decisions when there are uncertainties about system dynamics. Despite wide recognition and calls for implementing adaptive natural resource management, applications remain limited. More common is a reactive approach to decision making, which ignores future system dynamics. This contrasts with ARM, which anticipates future dynamics of ecological process and management actions using a model-based framework. Practitioners may be reluctant to adopt ARM because of the dearth of comparative evaluations between ARM and more common approaches to making decisions. We compared the probability of meeting management objectives when managing a population under both types of decision frameworks, specifically in relation to typical uncertainties and unknowns. We use a population of Sandhill Cranes as our case study. We evaluate each decision process under varying levels of monitoring and ecological uncertainty, where the true underlying population dynamics followed a stochastic age-structured population model with environmentally driven vital rate density dependence. We found that the ARM framework outperformed the currently employed reactive decision framework to manage Sandhill Cranes in meeting the population objective across an array of scenarios. This was even the case when the candidate set of population models contained only naïve representations of the true population process. Under the reactive decision framework, we found little improvement in meeting the population objective even if monitoring uncertainty was eliminated. In contrast, if the population was monitored without error within the ARM framework, the population objective was always maintained, regardless of the population models considered. Contrary to expectation, we found that age-specific optimal harvest decisions are not always necessary to meet a population objective when population dynamics are age structured. Population managers can decrease risks and gain transparency and flexibility in management by adopting an ARM framework. If population monitoring data has high sampling variation and/or limited empirical knowledge is available for constructing mechanistic population models, ARM model sets should consider a range of mechanistic, descriptive, and predictive model types.

A comment on priors for Bayesian occupancy models.

Understanding patterns of species occurrence and the processes underlying these patterns is fundamental to the study of ecology. One of the more commonly used approaches to investigate species occurrence patterns is occupancy modeling, which can account for imperfect detection of a species during surveys. In recent years, there has been a proliferation of Bayesian modeling in ecology, which includes fitting Bayesian occupancy models. The Bayesian framework is appealing to ecologists for many reasons, including the ability to incorporate prior information through the specification of prior distributions on parameters. While ecologists almost exclusively intend to choose priors so that they are "uninformative" or "vague", such priors can easily be unintentionally highly informative. Here we report on how the specification of a "vague" normally distributed (i.e., Gaussian) prior on coefficients in Bayesian occupancy models can unintentionally influence parameter estimation. Using both simulated data and empirical examples, we illustrate how this issue likely compromises inference about species-habitat relationships. While the extent to which these informative priors influence inference depends on the data set, researchers fitting Bayesian occupancy models should conduct sensitivity analyses to ensure intended inference, or employ less commonly used priors that are less informative (e.g., logistic or t prior distributions). We provide suggestions for addressing this issue in occupancy studies, and an online tool for exploring this issue under different contexts.

A Model to Inform Management Actions as a Response to Chytridiomycosis-Associated Decline.

Decision-analytic models provide forecasts of how systems of interest will respond to management. These models can be parameterized using empirical data, but sometimes require information elicited from experts. When evaluating the effects of disease in species translocation programs, expert judgment is likely to play a role because complete empirical information will rarely be available. We illustrate development of a decision-analytic model built to inform decision-making regarding translocations and other management actions for the boreal toad (Anaxyrus boreas boreas), a species with declines linked to chytridiomycosis caused by Batrachochytrium dendrobatidis (Bd). Using the model, we explored the management implications of major uncertainties in this system, including whether there is a genetic basis for resistance to pathogenic infection by Bd, how translocation can best be implemented, and the effectiveness of efforts to reduce the spread of Bd. Our modeling exercise suggested that while selection for resistance to pathogenic infection by Bd could increase numbers of sites occupied by toads, and translocations could increase the rate of toad recovery, efforts to reduce the spread of Bd may have little effect. We emphasize the need to continue developing and parameterizing models necessary to assess management actions for combating chytridiomycosis-associated declines.

Spatial capture-recapture model performance with known small-mammal densities.

Abundance and density of wild animals are important ecological metrics. However, estimating either is fraught with challenges; spatial capture-recapture (SCR) models are a relatively new class of models that attempt to ameliorate common challenges, providing a statistically coherent framework to estimate abundance and density. SCR models are increasingly being used in ecological and conservation studies of mammals worldwide, but have received little testing with empirical field data. We use data collected via a web and grid sampling design to evaluate the basic SCR model where small-mammal abundance (N) and density (D) are known (via exhaustive sampling). We fit the basic SCR model with and without a behavioral effect to 11 small-mammal populations for each sampling design using a Bayesian and likelihood SCR modeling approach. We compare SCR and ad hoc density estimators using frequentist performance measures. We found Bayesian and likelihood SCR estimates of density (D) and abundance (N) to be similar. We also found SCR models to have moderately poor frequentist coverage of D and N (45-73%), high deviation from truth (i.e., accuracy; D, 17-29%; N, 16-29%), and consistent negative bias across inferential paradigms, sampling designs, and models. With the trapping grid data, the basic SCR model generally performed more poorly than the best ad hoc estimator (behavior CR super-population estimate divided by the full mean maximum distance moved estimate of the effective trapping area), whereas with the trapping web data, the best-performing SCR model (null) was comparable to the best distance model. Relatively poor frequentist SCR coverage resulted from higher precision (SCR coefficients of variation [CVs] < ad hoc CVs); however D and D were fairly well correlated (r2 range of 0.77-0.96). SCR's negative relative bias (i.e., average underestimation of the true density) suggests additional heterogeneity in detection and/or that small mammals maintained asymmetric home ranges. We suggest caution in the use of the basic SCR model when trapping animals in a sampling grid and more generally when small sample sizes necessitate the spatial scale parameter (σ) apply to all individuals. When possible, researchers should consider variation in detection and incorporate individual biological and/or ecological variation at the trap level when modeling σ.

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations.

1. Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. 2. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. 3. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring-summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. 4. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond. 5. Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Teeth, sex, and testosterone: aging in the world's smallest primate.

Mouse lemurs (Microcebus spp.) are an exciting new primate model for understanding human aging and disease. In captivity, Microcebus murinus develops human-like ailments of old age after five years (e.g., neurodegeneration analogous to Alzheimer's disease) but can live beyond 12 years. It is believed that wild Microcebus follow a similar pattern of senescence observed in captive animals, but that predation limits their lifespan to four years, thus preventing observance of these diseases in the wild. Testing whether this assumption is true is informative about both Microcebus natural history and environmental influences on senescence, leading to interpretation of findings for models of human aging. Additionally, the study of Microcebus longevity provides an opportunity to better understand mechanisms of sex-biased longevity. Longevity is often shorter in males of species with high male-male competition, such as Microcebus, but mouse lemurs are sexually monomorphic, suggesting similar lifespans. We collected individual-based observations of wild brown mouse lemurs (Microcebus rufus) from 2003-2010 to investigate sex-differences in survival and longevity. Fecal testosterone was measured as a potential mechanism of sex-based differences in survival. We used a combination of high-resolution tooth wear techniques, mark-recapture, and hormone enzyme immunoassays. We found no dental or physical signs of senescence in M. rufus as old as eight years (N = 189, ages 1-8, mean = 2.59 ± 1.63 SE), three years older than captive, senescent congeners (M. murinus). Unlike other polygynandrous vertebrates, we found no sex difference in age-dependent survival, nor sex or age differences in testosterone levels. While elevated male testosterone levels have been implicated in shorter lifespans in several species, this is one of the first studies to show equivalent testosterone levels accompanying equivalent lifespans. Future research on captive aged individuals can determine if senescence is partially a condition of their captive environment, and studies controlling for various environmental factors will further our understanding of senescence.

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.