SEARCH · Geology Reports

Results for “Animals”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,081 records · Page 60Linked to original sources

USGS2019

Extreme site fidelity as an optimal strategy in an unpredictable and homogeneous environment

1.Animal site fidelity structures space use, population demography and ultimately gene flow. Understanding the adaptive selection for site fidelity patterns provides a mechanistic understanding to both spatial and population processes. This can be achieved by linking space use with environmental variability (spatial and temporal) and demographic parameters. However, rarely is the environmental context that drives the selection for site fidelity behaviour fully considered. 2. We use ecological theory to understand whether the spatial and temporal variability in breeding site quality can explain the site fidelity behaviour and demographic patterns of Gunnison sage-grouse (Centrocercus minimus). We examined female site fidelity patterns across multiple spatial scales: proximity of consecutive year nest locations, space-use overlap within and across the breeding and brooding seasons, and fidelity to a breeding patch. We also examined the spatial and temporal variability in nest, chick, juvenile and adult survival. 3. We found Gunnison sage-grouse to be site faithful to their breeding patch, area of use within the patch and generally where they nest, suggesting an “Always Stay” site fidelity strategy. This is an optimal evolutionary strategy when site quality is unpredictable. Further, we found limited spatial variability in survival within age groups, suggesting little demographic benefit to moving among patches. We suggest Gunnison sage-grouse site fidelity is driven by the unpredictability of predation in a relatively homogeneous environment, the lack of benefits and likely costs to moving across landscape patches and leaving known lek and breeding/brooding areas. 4. Space use and demography are commonly studied separately. More so, site fidelity patterns are rarely framed in the context of ecological theory, beyond questions related to the win-stay:lose-switch rule. To move beyond describing patterns and understand the adaptive selection driving species movements and their demographic consequences require integrating movement, demography and environmental variability in a synthetic framework. 5. Site fidelity theory provides a coherent framework to simultaneously investigate the spatial and population ecology of animal populations. Using it to frame ecological questions will lead to a more mechanistic understanding of animal movement, spatial population structuring and meta-population dynamics.

Colorado↗

USGS2013

Ecological feedbacks can reduce population-level efficacy of wildlife fertility control

1. Anthropogenic stress on natural systems, particularly the fragmentation of landscapes and the extirpation of predators from food webs, has intensified the need to regulate abundance of wildlife populations with management. Controlling population growth using fertility control has been considered for almost four decades, but nearly all research has focused on understanding effects of fertility control agents on individual animals. Questions about the efficacy of fertility control as a way to control populations remain largely unanswered. 2. Collateral consequences of contraception can produce unexpected changes in birth rates, survival, immigration and emigration that may reduce the effectiveness of regulating animal abundance. The magnitude and frequency of such effects vary with species-specific social and reproductive systems, as well as connectivity of populations. Developing models that incorporate static demographic parameters from populations not controlled by contraception may bias predictions of fertility control efficacy. 3. Many population-level studies demonstrate that changes in survival and immigration induced by fertility control can compensate for the reduction in births caused by contraception. The most successful cases of regulating populations using fertility control come from applications of contraceptives to small, closed populations of gregarious and easily accessed species. 4. Fertility control can result in artificial selection pressures on the population and may lead to long-term unintentional genetic consequences. The magnitude of such selection is dependent on individual heritability and behavioural traits, as well as environmental variation. 5. Synthesis and applications . Understanding species' life-history strategies, biology, behavioural ecology and ecological context is critical to developing realistic expectations of regulating populations using fertility control. Before time, effort and funding are invested in wildlife contraception, managers may need to consider the possibility that many species and populations can compensate for reduction in fecundity, and this could minimize any reduction in population growth rate.

Journal of Applied Ecology↗

USGS2015

Managing more than the mean: Using quantile regression to identify factors related to large elk groups

Summary Animal group size distributions are often right-skewed, whereby most groups are small, but most individuals occur in larger groups that may also disproportionately affect ecology and policy. In this case, examining covariates associated with upper quantiles of the group size distribution could facilitate better understanding and management of large animal groups. We studied wintering elk groups in Wyoming, where group sizes span several orders of magnitude, and issues of disease, predation and property damage are affected by larger group sizes. We used quantile regression to evaluate relationships between the group size distribution and variables of land use, habitat, elk density and wolf abundance to identify conditions important to larger elk groups. We recorded 1263 groups ranging from 1 to 1952 elk and found that across all quantiles of group size, group sizes were larger in open habitat and on private land, but the largest effect occurred between irrigated and non-irrigated land [e.g. the 90th quantile group size increased by 135 elk (95% CI = 42, 227) on irrigation]. Only upper quantile group sizes were positively related to broad-scale measures of elk density and wolf abundance. For wolf abundance, this effect was greater on elk groups found in open habitats and private land than those in closed habitats or public land. If we had limited our analysis to mean or median group sizes, we would not have detected these effects. Synthesis and applications . Our analysis of elk group size distributions using quantile regression suggests that private land, irrigation, open habitat, elk density and wolf abundance can affect large elk group sizes. Thus, to manage larger groups by removal or dispersal of individuals, we recommend incentivizing hunting on private land (particularly if irrigated) during the regular and late hunting seasons, promoting tolerance of wolves on private land (if elk aggregate in these areas to avoid wolves) and creating more winter range and varied habitats. Relationships to the variables of interest also differed by quantile, highlighting the importance of using quantile regression to examine response variables more completely to uncover relationships important to conservation and management.

Wyoming↗

USGS2016

Habitat drives dispersal and survival of translocated juvenile desert tortoises

1.In spite of growing reliance on translocations in wildlife conservation, translocation efficacy remains inconsistent. One factor that can contribute to failed translocations is releasing animals into poor quality or otherwise inadequate habitat. 2.Here we used a targeted approach to test the relationship of habitat features to post-translocation dispersal and survival of juvenile Mojave desert tortoises Gopherus agassizii . 3.We selected three habitat characteristics—rodent burrows, substrate texture (prevalence and size of rocks), and washes (ephemeral river beds)–that are tied to desert tortoise ecology. At the point of release, we documented rodent burrow abundance, substrate texture, and wash presence and analysed their relationship to maximum dispersal. We also documented relative use by each individual for each habitat characteristic and analysed their relationships with survival and fatal encounters with a predator in the first year after release. 4.In general, the presence of refugia or other areas that enabled animals to avoid detection, such as burrows and substrate, decreased overall mortality as well as predator-mediated mortality. The presence of washes and substrate that enhanced the tortoises’ ability to avoid detection also associated with reduced dispersal away from the release site. These results indicate an important role for all three measured habitat characteristics in driving dispersal, survival, or fatal encounters with a predator in the first year after translocation. 5. Synthesis and applications . Resource managers using translocations as a conservation tool should prioritize acquiring data linking habitat to fitness. In particular, for species that depend on avoiding detection, refuges such as burrows and habitat that improved concealment had notable ability to improve survival and dispersal. Our study on juvenile Mojave desert tortoises showed that refuge availability or the distributions of habitat appropriate for concealment are important considerations for identifying translocation sites for species highly dependent on crypsis, camouflage, or other forms of habitat matching.

Journal of Applied Ecology↗

USGS2018

Applying a Bayesian weighted surveillance approach to detect chronic wasting disease in white‐tailed deer

Surveillance is critical for early detection of emerging and re‐emerging infectious diseases. Weighted surveillance leverages heterogeneity in infection risk to increase sampling efficiency. Here, we apply a Bayesian approach to estimate weights for 16 surveillance classes of white‐tailed deer in Wisconsin, USA, relative to hunter‐harvested yearling males. We used these weights to conduct a surveillance programme for detecting chronic wasting disease (CWD) in white‐tailed deer at Shenandoah National Park (SHEN) in Virginia, USA. Generally, for surveillance, risk of infection increased with age and was greater in males. Clinical suspect deer had the highest risk, with weight estimates of 33.33 and 9.09 for community‐reported and hunter‐reported suspect deer, respectively. Fawns had the lowest risk with an estimated weight of 0.001. We used surveillance weights for Wisconsin deer to determine sampling effort required to detect a CWD‐positive case in SHEN if prevalence in yearling males ≥0.025. The sampling required to detect CWD was 37–91 adult deer, depending on the adult male:female ratio in the surveillance stream. We collected rectal biopsies from 49 female and 21 male adult deer, and 10 additional samples from vehicle‐killed deer. CWD was not detected and we concluded with 95% probability that prevalence in the reference population (yearling males) was between 0.0% and 3.6%. Synthesis and applications . Our approach allows managers to estimate relative surveillance weights for different host classes and quantify limits of disease detection in real time when only a sample of animals from a population can be tested, resulting in considerable cost savings for agencies performing wildlife disease detection surveillance. Additionally, it provides a rigorous means of estimating prevalence limits when a disease/pathogen is not detected in a sample set. It is therefore applicable to other wildlife, domestic animal and human disease systems, which can be characterized by surveillance classes with heterogeneous probability of infection. This methodology is also extendable to other disciplines such as invasive species, environmental toxicology, and generally, any ecological question seeking to efficiently use scarce financial and human resources to maximize the detection probability of a rare event.

Journal of Applied Ecology↗

USGS2013

Factors influencing detection of eDNA from a stream-dwelling amphibian

Environmental DNA (eDNA) methods for detecting and estimating abundance of aquatic species are emerging rapidly, but little is known about how processes such as secretion rate, environmental degradation, and time since colonization or extirpation from a given site affect eDNA measurements. Using stream-dwelling salamanders and quantitative PCR (qPCR) analysis, we conducted three experiments to assess eDNA: (i) production rate; (ii) persistence time under different temperature and light conditions; and (iii) detectability and concentration through time following experimental introduction and removal of salamanders into previously unoccupied streams. We found that 44–50 g individuals held in aquaria produced 77 ng eDNA/h for 2 h, after which production either slowed considerably or began to equilibrate with degradation. eDNA in both full-sun and shaded treatments degraded exponentially to <1% of the original concentration after 3 days. eDNA was no longer detectable in full-sun samples after 8 days, whereas eDNA was detected in 20% of shaded samples after 11 days and 100% of refrigerated control samples after 18 days. When translocated into unoccupied streams, salamanders were detectable after 6 h, but only when densities were relatively high (0.2481 individuals/m 2 ) and when samples were collected within 5 m of the animals. Concentrations of eDNA detected were very low and increased steadily from 6–24 h after introduction, reaching 0.0022 ng/L. Within 1 h of removing salamanders from the stream, eDNA was no longer detectable. These results suggest that eDNA detectability and concentration depend on production rates of individuals, environmental conditions, density of animals, and their residence time.

Molecular Ecology Resources↗

USGS2013

Integrating resource selection information with spatial capture--recapture

1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection. 2. In this paper we develop the first statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models. 3. Our method leads to increases in precision for estimating parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of RSFs and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. 4. Finally, we find that SCR models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. Consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture–recapture methods.

Methods in Ecology and Evolution↗

USGS2014

Using mark-recapture distance sampling methods on line transect surveys

Mark–recapture distance sampling (MRDS) methods are widely used for density and abundance estimation when the conventional DS assumption of certain detection at distance zero fails, as they allow detection at distance zero to be estimated and incorporated into the overall probability of detection to better estimate density and abundance. However, incorporating MR data in DS models raises survey and analysis issues not present in conventional DS. Conversely, incorporating DS assumptions in MR models raises issues not present in conventional MR. As a result, being familiar with either conventional DS methods or conventional MR methods does not on its own put practitioners in good a position to apply MRDS methods appropriately. This study explains the sometimes subtly different varieties of MRDS survey methods and the associated concepts underlying MRDS models. This is done as far as possible without giving mathematical details – in the hope that this will make the key concepts underlying the methods accessible to a wider audience than if we were to present the concepts via equations. We illustrate use of the two main types of MRDS model by using data collected on two different types of survey: a survey of ungulate faecal pellets where two observers searched independently of each other; and a cetacean survey that used a search protocol that could accommodate responsive movement, with only one observer searching independently and the other being aware of all detections. Synthesis and applications . Mark–recapture DS is a widely used method for estimating animal density and abundance when detection of animals at distance zero is not certain. Two observer configurations and three statistical models are described, and it is important to choose the most appropriate model for the observer configuration and target species in question. By way of making the methods more accessible to practicing ecologists, we describe the key ideas underlying MRDS methods, the sometimes subtle differences between them, and we illustrate these by applying different kinds of MRDS method to surveys of two different target species using different survey configurations.

Methods in Ecology and Evolution↗

USGS2015

A functional model for characterizing long-distance movement behaviour

Advancements in wildlife telemetry techniques have made it possible to collect large data sets of highly accurate animal locations at a fine temporal resolution. These data sets have prompted the development of a number of statistical methodologies for modelling animal movement. Telemetry data sets are often collected for purposes other than fine-scale movement analysis. These data sets may differ substantially from those that are collected with technologies suitable for fine-scale movement modelling and may consist of locations that are irregular in time, are temporally coarse or have large measurement error. These data sets are time-consuming and costly to collect but may still provide valuable information about movement behaviour. We developed a Bayesian movement model that accounts for error from multiple data sources as well as movement behaviour at different temporal scales. The Bayesian framework allows us to calculate derived quantities that describe temporally varying movement behaviour, such as residence time, speed and persistence in direction. The model is flexible, easy to implement and computationally efficient. We apply this model to data from Colorado Canada lynx ( Lynx canadensis ) and use derived quantities to identify changes in movement behaviour.

Methods in Ecology and Evolution↗

USGS2020

Hierarchical computing for hierarchical models in ecology

Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade-offs between capturing ecological complexity in statistical models and implementing these models. We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation-assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual- and population-level migratory characteristics. Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm. For complex ecological statistical models, like those for animal movement, multi-species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation-assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena.

Article↗

USGS2022

Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity

1. Autonomous Recording Units (ARUs) are now widely used to survey communities of species. These surveys generate spatially and temporally replicated counts of unmarked animals, but such data typically include false negatives and misclassified detections, both of which may vary across sites in proportion to abundance. These data challenges can bias estimates of occupancy, and the typical approach of verifying individual detections is expensive. 2. We developed a Bayesian implementation of a two-species, false-positive N-mixture model for estimating occupancy from ARU data or other counts of unmarked animals that does not require manual verification. The model accounts for species misclassification and abundance-induced detection heterogeneity, as well as false negatives. To evaluate this model, we simulated 200 data sets for each of 29 scenarios, including scenarios in which misclassifications outnumbered correct classifications for rare species. We also applied the model to acoustic surveys of bats conducted on Fort Carson Army Post and Piñon Canyon Maneuver Site, Colorado, USA. 3. In the simulation study, bias, coverage, and root mean square error for occupancy estimates obtained from the two-species false-positive N-mixture model were superior to metrics obtained from two competing two-species false-positive occupancy models. Across 29 scenarios, absolute bias was consistently low (range: -0.03–0.07), while coverage averaged 93% (range: 74%–98%). For alternative occupancy models, absolute bias was often high (range: -0.36–0.39), and coverage averaged from 47%–65%. Although our model included an abundance parameter, abundance estimates were not reliable. For two species of Myotis bats, we estimated that 1%–5% of field-recorded detections were misclassified. Estimated occupancy (0.91 and 0.76) was lower than naïve estimates (1.00 and 0.94). Competing occupancy models implausibly estimated local occupancy of 0.00 at sites with numerous detections. 4. Our two-species, false-positive N-mixture model is significant because it accounts for detection heterogeneity and improves occupancy estimates without expensive manual verification of detections. Our field application indicated that misclassifications were not common, yet affected occupancy inferences. Given that ARUs are increasingly used to survey a broad range of taxa, such an occupancy model could be widely useful.

Methods in Ecology and Evolution↗

USGS2022

A statistical framework for modelling migration corridors

1. Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. GPS tracking data allows for mapping of corridors from directly observed movements, providing important insights, but tracking data is absent for many populations. 2. We developed a novel statistical corridor modeling approach that predicts movement corridors from cost-distance movement models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad-hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer (Odocoileus hemionus) herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green-up, snow-off date, and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds. 3. The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude, and confidence levels of the effects of environmental variables on corridors. The effects environmental variables had on corridors differed depending on the herd. Within-herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross-herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds. 4. In addition to the practical benefit of mapping corridors for management, our statistical corridor modeling framework sets the stage for evaluating fundamental questions about the fitness tradeoffs, navigation, learning, fidelity, and movement constraints that influence migratory and other corridor-generating behavior. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modeling approach is also readily applied to non-migratory animal movements.

Idaho, Wyoming↗

USGS2024

Detecting stochasticity in population time series using a non-parametric test of intrinsic predictability

1. Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise. 2. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie ( Pygoscelis adeliae ) and emperor penguin ( Aptenodytes forsteri ) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species. 3. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%). 4. rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance.

Methods in Ecology and Evolution↗

USGS2024

Thinking beyond the closure assumption: Designing surveys for estimating biological truth with occupancy models

Occupancy models estimate distributions of imperfectly detected species, but violations of the closure assumption can bias results. However, researchers working with mobile animals may find it impossible to eliminate such violations. Here, we tested the hypothesis that occupancy models fit to realistic sampling data can generate unbiased occupancy estimates for an itinerant Wood Thrush ( Hylocichla mustelina ) population. In 2013 and 2014, we tracked movements of 41 breeding Wood Thrush males. We modelled territory shift probabilities using logistic exposure models and within-territory movements using continuous-time stochastic process models. We then constructed an individual-based model, simulated (1000 iterations) spatiotemporal locations for individuals and simulated sampling these populations using 162 different point count protocols with variable spatial (sampling radius and point placement method), and temporal (survey length, between-survey intervals and number of surveys) characteristics. We compared occupancy estimates with true values of instantaneous, daily and seasonal occupancy from the simulations. We parameterized continuous time stochastic process models based on movements within 34 unique territories and estimated a daily territory shift probability of 0.0099 (95% CI: 0.0060, 0.0152). Simulated data indicated that estimates of occupancy ranged from 0.18 (0.06, 1.00) to 0.80 (0.71, 0.89) depending on protocol characteristics. Occupancy estimates increased with increasing survey radius, survey length and between-survey interval. Protocols using shorter surveys and between-survey intervals were good estimators for instantaneous occupancy (low bias and mean-squared error) but poor estimators for daily and seasonal occupancy; longer surveys and intervals generated unbiased estimators of daily occupancy but underestimated seasonal occupancy. Logistic regression models that ignored imperfect detection outperformed occupancy models for estimating instantaneous occupancy but not daily or seasonal occupancy. For mobile animals, occupancy of sampling sites changes in space and time. Consequently, the spatial and temporal aspects of a sampling protocol have strong, but predictable, effects on occupancy model parameter estimates. Our results demonstrate that how these factors interact is critical for designing surveys that produce occupancy estimates representative of the biological process of interest to a researcher.

Methods in Ecology and Evolution↗

USGS2025

Challenges and opportunities for data integration to improve estimation of migratory connectivity

Understanding migratory connectivity, or the linkage of populations between seasons, is critical for effective conservation and management of migratory wildlife. A growing number of tools are available for understanding where migratory individuals and populations occur throughout the annual cycle. Integration of the diverse measures of migratory movements can help elucidate migratory connectivity patterns with methodology that accounts for differences in sampling design, directionality, effort, precision and bias inherent to each data type. The R package MigConnectivity was developed to estimate population-specific connectivity and the range-wide strength of those connections. New functions allow users to integrate intrinsic markers, tracking and long-distance reencounter data, collected from the same or different individuals, to estimate population-specific transition probabilities (estTransition) and the range-wide strength of those transition probabilities (estStrength). We used simulation and real-world case studies to explore the challenges and limitations of data integration based on data from three migratory bird species, Painted Bunting ( Passerina ciris ), Yellow Warbler ( Setophaga petechia ) and Bald Eagle ( Haliaeetus leucocephalus ), two of which had bidirectional data. We found data integration is useful for quantifying migratory connectivity, as single data sources are less likely to be available across the species range. Furthermore, accurate strength estimates can be obtained from either breeding-to-nonbreeding or nonbreeding-to-breeding data. For bidirectional data, integration can lead to more accurate estimates when data are available from all regions in at least one season. The ability to conduct combined analyses that account for the unique limitations and biases of each data type is a promising possibility for overcoming the challenge of range-wide coverage that has been hard to achieve using single data types. The best-case scenario for data integration is to have data from all regions, especially if the question is range-wide or data are bidirectional. Multiple data types on animal movements are becoming increasingly available and integration of these growing datasets will lead to a better understanding of the full annual cycle of migratory animals.

Methods in Ecology and Evolution↗

USGS2018

Optimizing release strategies: A stepping-stone approach to reintroduction

Evaluation of alternative management strategies enables informed decisions to accelerate species recovery. For reintroductions, post-release survival to reproductive age is a key parameter influencing population growth. Here we trial a ‘stepping-stone’ method to maximize the success of captive-bred animals when the availability of more suitable wild-born release candidates is limited. Our approach makes use of relatively safe and established wild populations to prepare captive-bred individuals for eventual translocation to a final release destination, thus building resilience through establishment of multiple populations over time. We developed a novel multievent model integrating encounter history and biotelemetry data to evaluate reintroduction strategies for the critically endangered Vancouver Island marmot (Marmota vancouverensis). We compared post-release survival of 176 individuals (52 wild-born, 47 captive-bred marmots released directly to destinations, and 77 captive-bred marmots released with a stepping-stone approach). Survival estimates to prime breeding-age (PBA), were then used to quantify expected success of potential release strategies. Our analysis indicates that post-release survival varies by source population and release method, as well as age, season, year, and years since release. Conditional on an objective of maximizing survival to PBA, our results suggest that using wild-born marmots for translocations as often as possible, and stepping-stone captive-bred marmots prior to final release, will result in the best outcomes. There was a 0.86 probability that survival to PBA was greater for captive-bred marmots released as yearlings using a stepping-stone approach (survival to PBA mode = 0.13, 95% CRI = 0.05-0.30) than for captive-bred animals that were directly released to destination sites as one-year-olds (survival to PBA mode = 0.04, 95% CRI = 0.01-0.24). Consequently, the stepping-stone approach yields much higher population establishment or growth potential than previous release strategies that used captive-bred marmots. Optimizing the combination of release candidates, sites, and timing can thereby increase the effectiveness of reintroductions.

British Columbia↗

USGS2017

On the reliability of N‐mixture models for count data

N‐mixture models describe count data replicated in time and across sites in terms of abundance N and detectability p . They are popular because they allow inference about N while controlling for factors that influence p without the need for marking animals. Using a capture–recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of N and p problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in p problematic. By counter example, we show that even if p is constant after adjusting for covariate effects (the “constant p ” assumption) scientifically plausible alternative models in which N (or its expectation) is non‐identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N‐mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p .

Biometrics↗

USGS2016

Logging impacts on avian species richness and composition differ across latitudes relative to foraging and breeding habitat preferences

Understanding the causes underlying changes in species diversity is a fundamental pursuit of ecology. Animal species richness and composition often change with decreased forest structural complexity associated with logging. Yet differences in latitude and forest type may strongly influence how species diversity responds to logging. We performed a meta-analysis of logging effects on local species richness and composition of birds across the world and assessed responses by different guilds (nesting strata, foraging strata, diet, and body size). This approach allowed identification of species attributes that might underlie responses to this anthropogenic disturbance. We only examined studies that allowed forests to regrow naturally following logging, and accounted for logging intensity, spatial extent, successional regrowth after logging, and the change in species composition expected due to random assembly from regional species pools. Selective logging in the tropics and clearcut logging in temperate latitudes caused loss of species from nearly all forest strata (ground to canopy), leading to substantial declines in species richness (up to 27% of species). Few species were lost or gained following any intensity of logging in lower-latitude temperate forests, but the relative abundances of these species changed substantially. Selective logging at higher-temperate latitudes generally replaced late-successional specialists with early-successional specialists, leading to no net changes in species richness but large changes in species composition. Removing less basal area during logging mitigated the loss of avian species from all forests and, in some cases, increased diversity in temperate forests. This meta-analysis provides insights into the important role of habitat specialization in determining differential responses of animal communities to logging across tropical and temperate latitudes.

Biological Reviews↗