Seasonal habitat-use patterns of large mammals in a human-dominated landscape.

Large mammals in temperate climates typically display seasonal patterns of habitat use. However, these patterns are often overlooked because large mammals are usually surveyed at annual intervals. In addition, most studies focus on a single species and ignore other species with which the focal species could interact. Knowing seasonal patterns of habitat use in multiple species and understanding factors that cause these patterns can provide further detail on population dynamics and guide effective conservation planning. Here, using dynamic occupancy modeling, we analyze 11 years of camera-trap data collected in northwestern Anatolia, Turkey, to investigate seasonal habitat use of 8 large-mammal species: Brown Bear (Ursus arctos), Eurasian Lynx (Lynx lynx), Gray Wolf (Canis lupus), Red Fox (Vulpes vulpes), Wild Boar (Sus scrofa), Roe Deer (Capreolus capreolus), European Hare (Lepus europaeus), and Red Deer (Cervus elaphus). For each species, we study the strength of seasonality in habitat use and its dependence on human population density and elevation, which have been shown to affect distributions of species in the region. Although all species exhibited seasonality in habitat use, the strength of this seasonality varied among species; it was strongest in Wild Boar, Roe Deer, and Brown Bear. Moreover, except for Brown Bear, all species tended to avoid sites close to humans. The species responded differently to changing elevation; increasing elevation had both positive and negative effects on species-specific colonization and desertion probabilities, and these effects were likely related to either feeding habits or tendency to avoid humans. These results indicate that seasonality should be taken into consideration in population studies. However, because species differ, seasonality patterns should be identified separately for each species of interest, as differences in these patterns can explain the underlying dynamics of habitat-use patterns more accurately.

Camera trap surveys of Atlantic Forest mammals: A data set for analyses considering imperfect detection (2004-2020).

Camera traps became the main observational method of a myriad of species over large areas. Data sets from camera traps can be used to describe the patterns and monitor the occupancy, abundance, and richness of wildlife, essential information for conservation in times of rapid climate and land-cover changes. Habitat loss and poaching are responsible for historical population losses of mammals in the Atlantic Forest biodiversity hotspot, especially for medium to large-sized species. Here we present a data set from camera trap surveys of medium to large-sized native mammals (>1 kg) across the Atlantic Forest. We compiled data from 5380 ground-level camera trap deployments in 3046 locations, from 2004 to 2020, resulting in 43,068 records of 58 species. These data add to existing data sets of mammals in the Atlantic Forest by including dates of camera operation needed for analyses dealing with imperfect detection. We also included, when available, information on important predictors of detection, namely the camera brand and model, use of bait, and obstruction of camera viewshed that can be measured from example pictures at each camera location. Besides its application in studies on the patterns and mechanisms behind occupancy, relative abundance, richness, and detection, the data set presented here can be used to study species' daily activity patterns, activity levels, and spatiotemporal interactions between species. Moreover, data can be used combined with other data sources in the multiple and expanding uses of integrated population modeling. An R script is available to view summaries of the data set. We expect that this data set will be used to advance the knowledge of mammal assemblages and to inform evidence-based solutions for the conservation of the Atlantic Forest. The data are not copyright restricted; please cite this paper when using the data.

As armadilhas fotográficas tornaram­se o principal método de observação de muitas espécies em grandes áreas. Os dados obtidos com armadilhas fotográficas podem ser usados para descrever os padrões e monitorar a ocupação, abundância e riqueza da vida selvagem, informação essencial para a conservação em tempos de rápidas mudanças climáticas e de cobertura do solo. A perda de habitat e a caça furtiva são responsáveis pelas perdas populacionais históricas de mamíferos no hotspot de biodiversidade da Mata Atlântica, especialmente para espécies de médio e grande porte. Aqui apresentamos um conjunto de dados de levantamentos com armadilhas fotográficas de mamíferos de médio e grande porte (>1 kg) em toda a Mata Atlântica. Compilamos dados de 5.380 armadilhas fotográficas instaladas no nível do chão em 3.046 locais, de 2004 a 2020, resultando em 43.068 registros de 58 espécies. Esses dados acrescentam aos conjuntos de dados existentes de mamíferos na Mata Atlântica por incluir as datas de operação das câmeras, que são necessárias para análises que lidam com detecção imperfeita. Também incluímos, quando disponíveis, informações sobre importantes preditores de detecção, como marca e modelo da câmera, uso de isca e obstrução do visor da câmera que pode ser medido a partir de imagens de exemplo em cada local da câmera. Além de estudos sobre os padrões e mecanismos por trás da ocupação, abundância relativa, riqueza e detecção, o conjunto de dados aqui apresentado pode ser usado para estudar os padrões de atividade diária das espécies, nível de atividade e interações espaço­temporais entre as espécies. Além disso, os dados podem ser usados em combinação com outras fontes de dados em diversas análises com modelagem populacional integrada. Um script R está disponível para visualizar um resumo do conjunto de dados. Esperamos que este conjunto de dados seja usado para aumentar o conhecimento sobre as assembleias de mamíferos e usado para informar soluções baseadas em evidências para a conservação da Mata Atlântica. Os dados não são restritos por direitos autorais e, por favor, cite este documento ao usar os dados.

Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies.

Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.

Population abundance estimates in conservation and biodiversity research.

Measuring and tracking biodiversity from local to global scales is challenging due to its multifaceted nature and the range of metrics used to describe spatial and temporal patterns. Abundance can be used to describe how a population changes across space and time, but it can be measured in different ways, with consequences for the interpretation and communication of spatiotemporal patterns. We differentiate between relative and absolute abundance, and discuss the advantages and disadvantages of each for biodiversity monitoring, conservation, and ecological research. We highlight when absolute abundance can be advantageous and should be prioritized in biodiversity monitoring and research, and conclude by providing avenues for future research directions to better assess the necessity of absolute abundance in biodiversity monitoring.

Bird species responses to rangeland management in relation to their traits: Rio de la Plata Grasslands as a case study.

Areas used for livestock production and dominated by native grasses represent a unique opportunity to reconcile biodiversity conservation and livestock production. However, limited knowledge of individual species' responses to rangeland management restricts our capacity to design grazing practices that favor endangered species and other priority birds. In this work, we applied Hierarchical Modelling of Species Communities (HMSC) to study individual species responses, as well as the influence of traits on such responses, to variables related to rangeland management using birds of the Rio de la Plata Grasslands as a case study. Based on presence-absence data collected in 454 paddocks across 46 ranches we inferred the response of 69 species considering imperfect detection. This degree of detail fills a major gap in rangeland management, as species-level responses can be used to achieve targeted conservation goals other than maximizing richness or abundance. We found that artificial pastures had an overall negative impact on many bird species, whereas the presence of tussocks had a positive effect, including all threatened species. Grassland specialists were in general sensitive to grass height and tended to respond positively to tussocks but negatively to tree cover. Controlling grass height via adjustments in stocking rate can be a useful tool to favor grassland specialists. To favor a wide range of bird species in ranches, a mosaic of short and tall native grasslands with patches of tussocks and trees is desirable. We also found that species-specific responses were modulated by their traits: small-sized birds responded positively to tussocks and tree cover while large species responded negatively to increasing grass height. Ground foragers preferred short grass while birds that scarcely use this stratum were not affected by grass height. Results on the influence of traits on bird responses are an important novelty in relation to previous work in rangelands and potentially increase our predicting capacity and model transferability across grassland regions.

A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change.

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

Decline in small mammal species richness in coastal-central California, 1997-2013.

The richness and composition of a small mammal community inhabiting semiarid California oak woodland may be changing in response to climate change, but we know little about the causes or consequence of these changes. We applied a capture-mark-recapture model to 17 years (1997-2013) of live trapping data to estimate species-specific abundances. The big-eared woodrat was the most frequently captured species in the area, contributing 58% of total captures. All small mammal populations exhibited seasonal fluctuations, whereas those of the California mouse, brush mouse, and pinyon mouse declined during the study period. We also applied a multispecies dynamic occupancy model to our small mammal detection history data to estimate species richness, occupancy (ψ), detection (p), local extinction (ϵ), and colonization (γ) probabilities, and to discern factors affecting these parameters. We found that ψ decreased from 0.369 ± 0.088 in 1997 to 0.248 ± 0.054 in 2013; γ was lower during the dry season (May-September) than the wet season (October-April) and was positively influenced by total seasonal rainfall (slope parameter, ß = 0.859 ± 0.371; 95% CI = 0.132-1.587). Mean mammalian species richness decreased from 11.943 ± 0.461 in 1997 to 7.185 ± 0.425 in 2013. With highly variable climatic patterns expected in the future, especially increased frequency and intensity of droughts, it is important to monitor small mammal communities inhabiting threatened California oak woodlands.

Camera Trap Methods and Drone Thermal Surveillance Provide Reliable, Comparable Density Estimates of Large, Free-Ranging Ungulates.

Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) in a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark-resight ratios or (2) N-mixture modeling and (3) aerial thermal videography from a drone platform. All three methods yielded similar density estimates, suggesting that they converged on an accurate estimate. We also included environmental covariates in the N-mixture modeling to explore spatial habitat use, and we fit models for each season to understand temporal changes in population density. Deer occurred in greater densities on warmer, south-facing slopes in the autumn and winter and on cooler north-facing slopes and in areas with flatter terrain in the summer. Seasonal density estimates over two years suggested an annual cycle of higher densities in autumn and winter than in summer, indicating that the region may function as a refuge during the hunting season.

The method matters. A comparative study of biologging and camera traps as data sources with which to describe wildlife habitat selection.

Habitat use is a virtually universal activity among animals and is highly relevant as regards designing wildlife management and conservation actions. This has led to the development of a great variety of methods to study it, of which resource selection functions combined with biologging-derived data (RSF) is the most widely used for this purpose. However this approach has some constraints, such as its invasiveness and high costs. Analytical approaches taking into consideration imperfect detection coupled with camera trap data (IDM) have, therefore, emerged as a non-invasive cost-effective alternative. However, despite the fact that both approaches (RSF and IDM) have been used in habitat selection studies, they should also be comparatively assessed. The objective of this work is consequently to assess them from two perspectives: explanatory and predictive. This has been done by analyzing data obtained from camera traps (60 sampling sites) and biologging (17 animals monitored: 7 red deer Cervus elaphus, 6 fallow deer Dama dama and 4 wild boar Sus scrofa) in the same periods using IDM and RSF, respectively, in Doñana National Park (southern Spain) in order to explain and predict habitat use patterns for three studied species. Our results showed discrepancies between the two approaches, as they identified different predictors as being the most relevant to determine species intensity of use, and they predicted spatial patterns of habitat use with a contrasted level of concordance, depending on species and scale. Given these results and the characteristics of each approach, we suggested that although partly comparable interpretations can be obtained with both approaches, they are not equivalent but rather complementary. The combination of data from biologging and camera traps would, therefore, appear to be suitable for the development of an analytical framework with which to describe and characterise the habitat use processes of wildlife.

A multidisciplinary approach to estimating wolf population size for long-term conservation.

The wolf (Canis lupus) is among the most controversial of wildlife species. Abundance estimates are required to inform public debate and policy decisions, but obtaining them at biologically relevant scales is challenging. We developed a system for comprehensive population estimation across the Italian alpine region (100,000 km2 ), involving 1513 trained operators representing 160 institutions. This extensive network allowed for coordinated genetic sample collection and landscape-level spatial capture-recapture analyses that transcended administrative boundaries to produce the first estimates of key parameters for wolf population status assessment. Wolf abundance was estimated at 952 individuals (95% credible interval 816-1120) and 135 reproductive units (i.e., packs) (95% credible interval 112-165). We also estimated that mature individuals accounted for 33-45% of the entire population. The monitoring effort was spatially estimated thereby overcoming an important limitation of citizen science data. This is an important approach for promoting wolf-human coexistence based on wolf abundance monitoring and an endorsement of large-scale harmonized conservation practices.

Una estrategia multidisciplinaria para la estimación del tamaño poblacional de los lobos para la conservación a largo plazo Resumen El lobo (Canis lupus) está entre las especies de fauna más controversiales. Se requieren estimaciones de abundancia para informar al debate público y las decisiones políticas, pero es un reto obtenerlos en escalas con relevancia biológica. Desarrollamos un sistema para la estimación completa de la población en la región alpina de Italia (100,000 km2 ), con la participación de 1,513 operadores entrenados que representan a 160 instituciones. Esta red extensa permitió una colecta coordinada de muestras genéticas y análisis de captura-recaptura espacial que trascendieron las fronteras administrativas para así producir las primeras estimaciones de los parámetros clave para la evaluación del estado de la población de los lobos. Se estimó la abundancia en 952 individuos (95% intervalo de confianza 816-1120) y 135 unidades reproductivas (es decir, manadas) (95% intervalo de confianza 112-165). También estimamos que los individuos maduros representaban el 33-45% de toda la población. El esfuerzo de monitoreo se estimó espacialmente, por lo que sobrepasó una limitación importante de la ciencia ciudadana. Esta estrategia es importante para promover la coexistencia entre lobos y humanos con base en el monitoreo de la abundancia y el apoyo a las prácticas armonizadas de conservación a gran escala.

N-mixture models provide informative crocodile (Crocodylus moreletii) abundance estimates in dynamic environments.

Estimates of animal abundance provide essential information for population ecological studies. However, the recording of individuals in the field can be challenging, and accurate estimates require analytical techniques which account for imperfect detection. Here, we quantify local abundances and overall population size of Morelet's crocodiles (Crocodylus moreletii) in the region of Calakmul (Campeche, Mexico), comparing traditional approaches for crocodylians (Minimum Population Size-MPS; King's Visible Fraction Method-VFM) with binomial N-mixture models based on Poisson, zero-inflated Poisson (ZIP) and negative binomial (NB) distributions. A total of 191 nocturnal spotlight surveys were conducted across 40 representative locations (hydrologically highly dynamic aquatic sites locally known as aguadas) over a period of 3 years (2017-2019). Local abundance estimates revealed a median of 1 both through MPS (min-max: 0-89; first and third quartiles, Q1-Q3: 0-7) and VFM (0-112; Q1-Q3: 0-9) non-hatchling C. moreletii for each aguada, respectively. The ZIP based N-mixture approach shown overall superior confidence over Poisson and NB, and revealed a median of 6 ± 3 individuals (min = 0; max = 120 ± 18; Q1 = 0; Q3 = 18 ± 4) jointly with higher detectabilities in drying aguadas with low and intermediate vegetation cover. Extrapolating these inferences across all waterbodies in the study area yielded an estimated ~10,000 (7,000-11,000) C. moreletii present, highlighting Calakmul as an important region for this species. Because covariates enable insights into population responses to local environmental conditions, N-mixture models applied to spotlight count data result in particularly insightful estimates of crocodylian detection and abundance.

Errors in aerial survey count data: Identifying pitfalls and solutions.

Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.

A review of spatial capture-recapture: Ecological insights, limitations, and prospects.

First described by Efford (2004), spatial capture-recapture (SCR) has become a popular tool in ecology. Like traditional capture-recapture, SCR methods account for imperfect detection when estimating ecological parameters. In addition, SCR methods use the information inherent in the spatial configuration of individual detections, thereby allowing spatially explicit estimation of population parameters, such as abundance, survival, and recruitment. Paired with advances in noninvasive survey methods, SCR has been applied to a wide range of species across different habitats, allowing for population- and landscape-level inferences with direct consequences for conservation and management. I conduct a literature review of SCR studies published since the first description of the method and provide an overview of their scope in terms of the ecological questions answered with this tool, taxonomic groups targeted, geography, spatio-temporal extent of analyses, and data collection methods. In addition, I review approaches for analytical implementation and provide an overview of parameters targeted by SCR studies and conclude with current limitations and future directions in SCR methods.

A model for analyzing clustered occurrence data.

Spatial or temporal clustering commonly arises in various biological and ecological applications, for example, species or communities may cluster in groups. In this paper, we develop a new clustered occurrence data model where presence-absence data are modeled under a multivariate negative binomial framework. We account for spatial or temporal clustering by introducing a community parameter in the model that controls the strength of dependence between observations thereby enhancing the estimation of the mean and dispersion parameters. We provide conditions to show the existence of maximum likelihood estimates when cluster sizes are homogeneous and equal to 2 or 3 and consider a composite likelihood approach that allows for additional robustness and flexibility in fitting for clustered occurrence data. The proposed method is evaluated in a simulation study and demonstrated using forest plot data from the Center for Tropical Forest Science. Finally, we present several examples using multiple visit occupancy data to illustrate the difference between the proposed model and those of N-mixture models.

Boat-electrofishing transect location and flow levels: influence on riverine fish monitoring in non-wadeable habitats.

Fisheries monitoring can be improved by studying the influence of gear selectivity, sampling design, and habitat conditions. We used boat-electrofishing data to investigate how sample unit placement (shoreline and channel transects) and sampling conditions (low and high flow years) affect detection of fishes in a highly regulated Ontario (Canada) river system. Species detection histories associated with a spatially replicated sampling design was fit to a Bayesian hierarchical site occupancy model for 14 fishes. Habitat (transect location) had a significant effect on detection probabilities (p) for all species, with shoreline sampling more likely to detect species than channel. Sampling year had a significant effect on detectability of six species. The relative influence of habitat and sampling year varied among species. Detection probabilities based on combined shoreline and channel transect data across both years ranged from 0.09 to 0.48 and were positively correlated to species abundance. High detection probabilities and precise occupancy estimates were associated with sunfish and bass abundant in shoreline habitats. Small-bodied species closely associated with the riverbed or exhibiting schooling behavior tended to be poorly detected. Power to detect future changes in species distribution is expected to differ based on habitat sampled, with greater power associated with shoreline boat-electrofishing. Detecting small to moderate changes is not likely for most species. The addition of channel transects did not improve the effectiveness of shoreline sampling. Improvements to the current design could be realized by reallocating sampling effort from channel to shoreline and testing different gear in deep (> 3 m) habitats.

A note on investigating co-occurrence patterns and dynamics for many species, with imperfect detection and a log-linear modeling parameterization.

Patterns in, and the underlying dynamics of, species co-occurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterizations have been published that could be used with the same fundamental modeling framework that accounts for imperfect detection, although each parameterization has different advantages and disadvantages.We propose a parameterization based on log-linear modeling that does not require a species hierarchy to be defined (in terms of dominance) and enables a numerically robust approach for estimating covariate effects.Conceptually, the parameterization is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, "symmetric," interpretations of parameter estimates.The parameterization can be applied to many species, in either a maximum likelihood or Bayesian estimation framework. We illustrate the method using camera-trapping data collected on three mesocarnivore species in South Texas.

Trends in bird abundance differ among protected forests but not bird guilds.

Improved monitoring and associated inferential tools to efficiently identify declining bird populations, particularly of rare or sparsely distributed species, is key to informed conservation and management across large spatiotemporal regions. We assess abundance trends for 106 bird species in a network of eight forested national parks located within the northeast United States from 2006 to 2019 using a novel hierarchical model. We develop a multispecies, multiregion, removal-sampling model that shares information across species and parks to enable inference on rare species and sparsely sampled parks and to evaluate the effects of local forest structure. Trends in bird abundance over time varied widely across parks, but species showed similar trends within parks. Three parks (Acadia National Park and Marsh-Billings-Rockefeller and Morristown National Historical Parks [NHP]) decreased in bird abundance across all species, while three parks (Saratoga NHP and Roosevelt-Vanderbilt and Weir-Farm National Historic Sites) increased in abundance. Bird abundance peaked at medium levels of basal area and high levels of percent forest and forest regeneration, with percent forest having the largest effect. Variation in these effects across parks could be a result of differences in forest structural stage and diversity. By sharing information across both communities and parks, our novel hierarchical model enables uncertainty-quantified estimates of abundance across multiple geographical (i.e., network, park) and taxonomic (i.e., community, guild, species) levels over a large spatiotemporal region. We found large variation in abundance trends across parks but not across bird guilds, suggesting that local forest condition might have a broad and consistent effect on the entire bird community within a given park. Research should target the three parks with overall decreasing trends in bird abundance to further identify what specific factors are driving observed declines across the bird community. Understanding how bird communities respond to local forest structure and other stressors (e.g., pest outbreaks, climate change) is crucial for informed and lasting management.

Accommodating the role of site memory in dynamic species distribution models.

First-order dynamic occupancy models (FODOMs) are a class of state-space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatiotemporally correlated. Consequently, the non-independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second-order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 yr of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long-term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations.

Positive early-late life-history trait correlations in elephant seals.

Correlations between early- and late-life performance are a major prediction of life-history theory. Negative early-late correlations can emerge because biological processes are optimized for early but not late life (e.g., rapid development may accelerate the onset of senescence; "developmental theory of aging") or because allocation to early-life performance comes at a cost in terms of late-life performance (as in the disposable soma theory). But variation in genetic and environmental challenges that each individual has to cope with during early life may also lead to positive early-late life-history trait correlations (the "fixed heterogeneity" or "individual quality" hypothesis). We analyzed individual life-history trajectories of 7,420 known-age female southern elephant seals (Mirounga leonina) monitored over 36 yr to determine how actuarial senescence (a proxy for late-life performance) correlate with age at first reproduction (a proxy for early-life performance). As some breeding events may not be detected in this field study, we used a custom "multievent" hierarchical model to estimate the age at first reproduction and correlate it to other life-history traits. The probability of first reproduction was 0.34 at age 3, with most females breeding for the first time at age 4, and comparatively few at older ages. Females with an early age of first reproduction outperformed delayed breeders in all aspects we considered (survival, rate of senescence, net reproductive output) but one: early breeders appeared to have an onset of actuarial senescence 1 yr earlier compared to late breeders. Genetics and environmental conditions during early life likely explain the positive correlation between early- and late-life performance. Our results provide the first evidence of actuarial senescence in female southern elephant seals.

Occupancy of wild southern pig-tailed macaques in intact and degraded forests in Peninsular Malaysia.

Deforestation is a major threat to terrestrial tropical ecosystems, particularly in Southeast Asia where human activities have dramatic consequences for the survival of many species. However, responses of species to anthropogenic impact are highly variable. In order to establish effective conservation strategies, it is critical to determine a species' ability to persist in degraded habitats. Here, we used camera trapping data to provide the first insights into the temporal and spatial distribution of southern pig-tailed macaques (Macaca nemestrina, listed as 'Vulnerable' by the IUCN) across intact and degraded forest habitats in Peninsular Malaysia, with a particular focus on the effects of clear-cutting and selective logging on macaque occupancy. Specifically, we found a 10% decline in macaque site occupancy in the highly degraded Pasoh Forest Reserve from 2013 to 2017. This may be strongly linked to the macaques' sensitivity to intensive disturbance through clear-cutting, which significantly increased the probability that M. nemestrina became locally extinct at a previously occupied site. However, we found no clear relationship between moderate disturbance, i.e., selective logging, and the macaques' local extinction probability or site occupancy in the Pasoh Forest Reserve and Belum-Temengor Forest Complex. Further, an identical age and sex structure of macaques in selectively logged and completely undisturbed habitat types within the Belum-Temengor Forest Complex indicated that the macaques did not show increased mortality or declining birth rates when exposed to selective logging. Overall, this suggests that low to moderately disturbed forests may still constitute valuable habitats that support viable populations of M. nemestrina, and thus need to be protected against further degradation. Our results emphasize the significance of population monitoring through camera trapping for understanding the ability of threatened species to cope with anthropogenic disturbance. This can inform species management plans and facilitate the development of effective conservation measures to protect biodiversity.