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At least 1,297 records · Page 72Linked to original sources
USGS2025

Assessing the combined influence of biotic and anthropogenic stressors on polar bears to inform conservation planning

Developing conservation strategies for species vulnerable to the effects of climate change, like polar bears ( Ursus maritimus ), can be challenging given the uncertainty of future environmental conditions. Effective conservation planning requires identifying and ranking threats to the persistence of polar bears throughout their circumpolar range and then assessing the ability of mitigative actions to aid in meeting plan objectives. We used a Bayesian network model to (1) characterize the relative importance of multiple biotic and anthropogenic stressors on four ecoregional polar bear populations, at two future decadal time periods, and based on two Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios (Shared Socioeconomic Pathways [SSPs] 2.6 [low] and 8.5 [high]); and (2) identify achievable management actions that may enhance the prospects of long-term persistence. Normative model runs indicated that populations in all four ecoregions incurred increasing probabilities of being decreased or greatly decreased over time. The probabilities of polar bear populations being decreased or greatly decreased from mid- to end of the century ranged from ~55% to 87% for the SSP 2.6 emissions scenario, and 82% to 94% for the SSP 8.5 emissions scenario among ecoregions. Arctic sea ice conditions and marine prey availability had the greatest influences on future polar bear population outcomes and overrode any relative influence from all other stressors. Hunting mortality was the most influential individual anthropogenic stressor in the Archipelago and Seasonal Ice Ecoregions, whereas terrestrial refugia quality grouped with various anthropogenic activities or factors (e.g., resource extraction, oil spill) was most influential for the Polar Basin Divergent Ice and Polar Basin Convergent Ice ecoregions. Our findings indicate that near-term proactive management of multiple anthropogenic stressors could cumulatively reduce the decline in populations such that if future sea ice habitat loss is eventually curtailed, population abundance would be greater than it would have been otherwise. Additionally, our findings suggest that there is value in tailoring management actions to address ecoregion-specific threats, which may prove useful in informing the development of future circumpolar conservation plans.

Ecosphere↗
USGS2025

DNA metabarcoding and video camera collars yield different inferences about the summer diet of an arctic ungulate

The diets of wild ungulates are a foundational component of their ecology, influencing their behavior, body condition, and demography. With changing environmental conditions, there is a significant need to identify important forage items for ungulates, but this has often proved challenging. Declines in several barren-ground caribou herds across the North American Arctic have raised concerns about the influence of climate change on caribou forage conditions. Shifts in plant phenology, biomass, quality, and composition may be influencing caribou diets and subsequently affecting their body condition and demographic rates. Although forage is a primary driver of barren-ground caribou behavior and population dynamics, there is limited recent information about the specific foods they consume, and uncertainty about appropriate methods for identifying those foods. Investigators are increasingly using fecal DNA metabarcoding and video camera collars to assess ungulate diets, but comparative studies of these approaches are lacking. To examine the summer diets of barren-ground caribou, we used both fecal metabarcoding and video camera collars to identify forage used by the Porcupine caribou herd, which spans the Alaska–Yukon border. In 2021, we sampled the diets of adult females by collecting fecal samples and observing collar videos during 4 sampling occasions. We found that caribou consumed very specific forage items, and those items varied markedly across the growing season. Caribou predominantly consumed graminoids and lichens during early summer, and shrubs and forbs later in the season. Metabarcoding and video data provided significantly different estimates of diet for all taxonomic levels we evaluated, and inferences from the two approaches were often disparate. Metabarcoding failed to detect some items frequently consumed in videos, such as lichens, and indicated high use of other items rarely consumed, such as mosses. We found that video data provided greater taxonomic diversity and resolution for vascular plants and lichens, and more closely aligned with past research and biological expectations than data from fecal metabarcoding. Additional research is needed to be able to use these methods to identify the biomass of different forage items consumed.

Alaska, Yukon↗
USGS2025

Similar population dynamics before and after a chytridiomycosis outbreak in a tropical riparian amphibian species

Emerging infectious diseases can cause rapid, widespread host mortality, and the lack of demographic data before and after pathogen emergence complicates understanding mechanisms of host persistence. This challenge is further compounded by environmental conditions that influence host behavior, while driving pathogen growth and virulence. These interactions create complex disease outcomes that hinder predictions of when and how hosts endure pathogen outbreaks. Here, we analyzed 10 years of capture-mark-recapture data (2000–2014) spanning wet and dry seasons for male Espadarana prosoblepon in El Copé, Panama, encompassing a period before (2000–2004) and after (2010–2014) a Batrachochytrium dendrobatidis ( Bd ) outbreak using Jolly-Seber models. We found that post- Bd male E. prosoblepon population size (range in mean population size among primary periods = 136–225 individuals) was similar to pre- Bd population size (range in mean population size among primary periods = 201–242 individuals). Pre- Bd , average monthly survival probability in the wet season was 0.93 (95% credible interval [CI] = 0.90–0.96). Post- Bd , uninfected individuals had survival probability higher in the wet season (mean = 0.97; [95% CI = 0.95–0.98]) than the dry season (mean = 0.90 [95% CI = 0.84–0.94]), while survival probability for infected individuals decreased as a function of Bd infection intensity. Pre- Bd , mean monthly per-capita entry probability was 0.07 (95% CI = 0.05–0.10), and post- Bd , mean monthly per-capita entry probability was 0.06 (95% CI = 0.00–0.10). Lastly, infection probability during the wet season was lower (mean = 0.04 [95% CI = 0.03–0.05]) than the dry season (mean = 0.10 [95% CI = 0.05–0.15]), and recovery probability during the wet season was lower (mean = 0.19 [95% CI = 0.11–0.28]) than the dry season (mean = 0.54 [95% CI = 0.20–0.88]). Our findings suggest that survival probabilities of uninfected individuals, as well as per-capita entry probabilities, are similar pre- and post- Bd , leading to a stable and similar sized pre- Bd population. These results contribute to understanding disease dynamics and tropical amphibian ecology.

Ecosphere↗
USGS2025

Some of these are not like the others: Relative thermal sensitivity among anuran species of the Southeast United States

Estimating how close a species is to its upper thermal limits (i.e., warming tolerance, a thermal sensitivity index) and how that proximity changes across space enables spatially explicit identification of species with increased extinction risk as temperatures increase. Yet, thermal sensitivity is often difficult to calculate because it is the result of many traits. We aimed to synthesize multiple traits into a single estimate of relative terrestrial thermal sensitivity for 13 anuran species in the southeastern United States. We employed models that incorporate traits and microclimate variation to (1) estimate species warming tolerance (the difference between species critical thermal maximum and modeled operative temperature, an estimate of body temperature) and (2) investigate how warming tolerance varied with latitude (whereby latitude represents different temperature regimes and external drivers of thermal sensitivity). We ran mechanistic niche models across a 12° latitudinal gradient and 10 years to estimate individual operative temperature. We calculated the minimum, 25th percentile (hottest quarter), and median daily minimum warming tolerance. Estimates of minimum warming tolerance spanned −5 to 10°C ( Lithobates palustris and Gastrophryne carolinensis respectively) and differed among species. For most species, modeled operative temperatures exceeded species' critical thermal maximum during extreme warm temperatures (i.e., heat waves) in part of their range, and warming tolerance increased with latitude. During heat waves, five species had lower warming tolerance at higher latitudes, and three species' warming tolerance did not change with latitude. We identified species that are approaching their thermal limits in the Southeast and characterized spatial patterns of warming tolerance. Increased temperatures could increase anuran extinction risk, posing an additional challenge for threatened anuran species. Spatial patterns of warming tolerance were not consistent among species in our study, highlighting that patterns identified at higher taxonomic categories could be inconsistent at lower taxonomic categories.

southeastern United States↗
USGS2018

Inferring species interactions through joint mark–recapture analysis

Introduced species are frequently implicated in declines of native species. In many cases, however, evidence linking introduced species to native declines is weak. Failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. For many species, mark–recapture analysis is the more rigorous form of demographic analysis. However, to our knowledge, there are no mark–recapture models that allow for joint modeling of interacting species. Here, we introduce a two‐species mark–recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. We use a simulation study to explore bias and choose an approach to model selection. We then use the model to investigate species interactions between endangered humpback chub ( Gila cypha ) and introduced rainbow trout ( Oncorhynchus mykiss ) in the Colorado River between 2009 and 2016. In particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. We also project the long‐term effects of different rainbow trout abundances on adult humpback chub abundances. Our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. When the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. Effects on interspecific interactions on survival and capture probability were strongly supported, whereas support for the growth effect was weaker. Environmental factors were also identified to be important and in many cases stronger than interspecific interactions, and there was still substantial unexplained variation in growth and survival rates. The general approach presented here for combining mark–recapture data for two species is applicable in many other systems and could be modified to model abundance of the invader via other modeling approaches.

Ecology↗
USGS2019

Evaluating consumptive and nonconsumptive predator effects on prey density using field times series data

Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor provide parameters for predictive models. State space models (SSMs) applied to time series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in a SSM framework to seasonal time series (1994-2012) using a recently developed, maximum likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g. nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B. longimanus strongly influences D. mendotae dynamics, with mean annual peak densities of B. longimanus observed in Lake Michigan estimated to cause a 61% reduction in D. mendotae population growth rate and a 59% reduction in peak biomass density. Further, the observed B. longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore provides evidence derived directly from survey data that the invasive zooplankter B. longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B. longimanus under different scenarios such as climate change.

Ecology↗
USGS2020

The seasonal energetic landscape of an apex marine carnivore, the polar bear

Divergent movement strategies have enabled wildlife populations to adapt to environmental change. In recent decades, the Southern Beaufort Sea subpopulation of polar bears (Ursus maritimus) has developed a divergent movement strategy in response to diminishing sea ice where the majority of the subpopulation (73–85%) stays on the sea ice in summer and the remaining bears move to land. Although declines in sea ice are generally considered a challenge to energy balance in polar bears residing in some regions of the Arctic, little quantitative data exists concerning the seasonal energy expenditures of this apex marine carnivore. We used GPS satellite collars with tri-axial accelerometers and conductivity sensors to measure the location, behavior, and energy expenditure of five adult female polar bears in the southern Beaufort Sea across seasons of sea ice breakup and minimum extent. Using a Bayesian mixed-effects model, we found that energy expenditure was influenced by month, ocean depth, and habitat type (sea ice or land). Total energy expenditure from May to October ranged from 37.7 – 47.2 mJ kg-1 for individual bears. Bears that moved to land expended 7% more energy on average from May to October than bears that remained on the receding sea ice. In August, when bears were moving from the sea ice to land or moving north with the receding pack ice, bears that moved to land spent 7% more time swimming and expended 22% more energy. Meaning the immediate cost of moving to land exceeded the cost of remaining on the receding summer pack ice. These findings suggest a physiological reason why the majority of the Southern Beaufort Sea subpopulation continues to inhabit a diminishing summer ice platform. However, bears that moved to land spent 29% more time in preferred hunting habitats over the continental shelf than bears that remained on the sea ice. Bears on land also had access to subsistence-harvested bowhead whale carcasses. Hence, our findings indicate there may be a greater overall energetic benefit to move to land in this region, which suggests that the use of the diminishing summer sea ice may be functioning as an ecological trap.

Alaska↗
USGS2020

Scientist’s guide to developing explanatory statistical models using causal analysis principles

Recent discussions of model selection and multimodel inference highlight a general challenge for researchers, which is how to clearly convey the explanatory content of a hypothesized model or set of competing models. The advice from statisticians for scientists employing multimodel inference is to develop a well‐thought‐out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create “well‐formed hypotheses”, as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well‐thought‐out coherent models, and scientists, who would like some practical advice for exactly how to do that.

Ecology↗
USGS2021

An integrated path for spatial capture–recapture and animal movement modeling

Ecologists and conservation biologists increasingly rely on spatial capture–recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.

Ecology↗
USGS2021

Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had a validation R2 of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogenous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time, yet the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.

Ecology↗
USGS2022

Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie

Diet analysis integrates a wide variety of visual, chemical, and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, for example, highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package mvtweedie that extends capabilities of widely used packages in R ( mgcv and ggplot2 ) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted Puffins ( Fratercula cirrhata ) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980–2000) and then Pacific herring and capelin (2000–2020), while wolves ( Canis lupus ligoni ) in southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines.

Ecology↗
USGS2024

Intrinsic and environmental drivers of pairwise cohesion in wild Canis social groups

Animals within social groups respond to costs and benefits of sociality by adjusting the proportion of time they spend in close proximity to other individuals in the group (cohesion). Variation in cohesion between individuals, in turn, shapes important group-level processes such as subgroup formation and fission–fusion dynamics. Although critical to animal sociality, a comprehensive understanding of the factors influencing cohesion remains a gap in our knowledge of cooperative behavior in animals. We tracked 574 individuals from six species within the genus Canis in 15 countries on four continents with GPS telemetry to estimate the time that pairs of individuals within social groups spent in close proximity and test hypotheses regarding drivers of cohesion. Pairs of social canids ( Canis spp.) varied widely in the proportion of time they spent together (5%–100%) during seasonal monitoring periods relative to both intrinsic characteristics and environmental conditions. The majority of our data came from three species of wolves (gray wolves, eastern wolves, and red wolves) and coyotes. For these species, cohesion within social groups was greatest between breeding pairs and varied seasonally as the nature of cooperative activities changed relative to annual life history patterns. Across species, wolves were more cohesive than coyotes. For wolves, pairs were less cohesive in larger groups, and when suitable, small prey was present reflecting the constraints of food resources and intragroup competition on social associations. Pair cohesion in wolves declined with increased anthropogenic modification of the landscape and greater climatic variability, underscoring challenges for conserving social top predators in a changing world. We show that pairwise cohesion in social groups varies strongly both within and across Canis species, as individuals respond to changing ecological context defined by resources, competition, and anthropogenic disturbance. Our work highlights that cohesion is a highly plastic component of animal sociality that holds significant promise for elucidating ecological and evolutionary mechanisms underlying cooperative behavior.

Ecology↗
USGS2025

Intraspecific variation in masting across climate gradients is inconsistent with the environmental stress hypothesis

Year-to-year variation in seed crop size (i.e., masting) varies strongly among populations of the same species. Understanding what causes this variation is vital, as masting affects the ability of tree species to regenerate and determines the population dynamics of a wide variety of animals. It is commonly thought that environmental stress is a key driver of masting variability. The environmental stress hypothesis posits that more marginal conditions increase the strength of masting. Using 437 time series from 19 tree species, we find that this hypothesis fails to fully explain how masting varies across marginality gradients. We expected higher interannual variation and less frequent masting events at species margins but instead found that while mast years are indeed less frequent, the interannual variation was lower toward the margins. The observed patterns suggest that populations growing at the margins may invest more resources in low seed production years compared with their conspecifics, hedging their bets in these more challenging environments.

Ecology↗
USGS2021

What do you mean by false positive?

Misunderstandings regarding the term “false positive” present a significant hurdle to broad adoption of eDNA monitoring methods. Here, we identify three challenges to clear communication of false-positive error between scientists, managers, and the public. The first arises from a failure to distinguish between false-positive eDNA detection at the sample level and false-positive inference of taxa presence at the site level. The second is based on the large proportion of false positives that may occur when true-positive detections are likely to be rare, even when rates of contamination or other error are low. And the third misunderstanding occurs when conventional species detection approaches, often based on direct capture, are used to confirm eDNA approaches without acknowledging or quantifying the conventional approach's detection probability. The solutions to these issues include careful and consistent communication of error definitions, managing expectations of error rates, and providing a balanced discussion not only of alternative sources of species DNA, but also of the detection limitations of conventional methods. We argue that the benefit of addressing these misunderstandings will be increased confidence in the utility of eDNA methods and, ultimately, improved resource management using eDNA approaches.

Environmental DNA↗
USGS2021

eDNA metabarcoding outperforms traditional fisheries sampling and reveals fine-scale heterogeneity in a temperate freshwater lake

Understanding biodiversity in aquatic systems is critical to ecological research and conservation efforts, but accurately measuring species richness using traditional methods can be challenging. Environmental DNA (eDNA) metabarcoding, which uses high-throughput sequencing and universal primers to amplify DNA from multiple species present in an environmental sample, has shown great promise for augmenting results from traditional sampling to characterize fish communities in aquatic systems. Few studies, however, have compared exhaustive traditional sampling with eDNA metabarcoding of corresponding water samples at a small spatial scale. We intensively sampled Boardman Lake (1.4 km 2 ) in Michigan, USA, from May to June in 2019 using gill and fyke nets and paired each net set with lake water samples collected in triplicate. We analyzed water samples using eDNA metabarcoding with 12S and 16S fish-specific primers and compared estimates of fish diversity among methods. In total, we set 60 nets and analyzed 180 1 L lake water samples. We captured a total of 12 fish species in our traditional gear and detected 40 taxa in the eDNA water samples, which included all the species observed in nets. The 12S and 16S assays detected a comparable number of taxa, but taxonomic resolution varied between the two genes. In our traditional gear, there was a clear difference in the species selectivity between the two net types, and there were several species commonly detected in the eDNA samples that were not captured in nets. Finally, we detected spatial heterogeneity in fish community composition across relatively small scales in Boardman Lake with eDNA metabarcoding, but not with traditional sampling. Our results demonstrated that eDNA metabarcoding was substantially more efficient than traditional gear for estimating community composition, highlighting the utility of eDNA metabarcoding for assessing species diversity and informing management and conservation.

Michigan↗
USGS2025

A metadata checklist and data formatting guidelines to make eDNA FAIR (Findable, Accessible, Interoperable and Reusable)

The success of environmental DNA (eDNA) approaches for species detection has revolutionized biodiversity monitoring and distribution mapping. Targeted eDNA amplification approaches, such as quantitative PCR, have improved our understanding of species distribution, and metabarcoding-based approaches have enabled biodiversity assessment at unprecedented scales and taxonomic resolution. eDNA datasets, however, are often scattered across repositories with inconsistent formats, varying access restrictions, and inadequate metadata; this limits their interoperation, reuse, and overall impact. Adopting FAIR (Findable, Accessible, Interoperable, and Reusable) data practices with eDNA data can transform the monitoring of biodiversity and individual species and support data-driven biodiversity management across broad scales. FAIR practices remain underdeveloped in the eDNA community, partly due to gaps in adapting existing vocabularies, such as Darwin Core (DwC) and Minimum Information about any (x) Sequence (MIxS), to eDNA-specific needs and workflows. To address these challenges, we propose a comprehensive FAIR eDNA (FAIRe) Metadata Checklist, which integrates existing data standards and introduces new terms tailored to eDNA workflows. Metadata are systematically linked to both raw data (e.g., metabarcoding sequences, Ct/Cq values of targeted qPCR assays) and derived biological observations (e.g., Amplicon Sequence Variant (ASV)/Operational Taxonomic Unit (OTU) tables, species presence/absence). Along with formatting guidelines, tools, templates, and example datasets, we introduce a standardized, ready-to-use approach for FAIR eDNA practices. Through broad collaboration, we seek to integrate these guidelines into established biodiversity and molecular data standards, promote journal data policies, and foster user-driven improvements and uptake of FAIR practices among eDNA data producers. In proposing this standardized approach and developing a long-term plan with key databases and data standard organizations, the goal is to enhance accessibility, maximize reuse, and elevate the scientific impact of these valuable biodiversity data resources.

Environmental DNA↗
USGS2015

Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.

Environmetrics↗
USGS2016

Hierarchical animal movement models for population-level inference

New methods for modeling animal movement based on telemetry data are developed regularly. With advances in telemetry capabilities, animal movement models are becoming increasingly sophisticated. Despite a need for population-level inference, animal movement models are still predominantly developed for individual-level inference. Most efforts to upscale the inference to the population level are either post hoc or complicated enough that only the developer can implement the model. Hierarchical Bayesian models provide an ideal platform for the development of population-level animal movement models but can be challenging to fit due to computational limitations or extensive tuning required. We propose a two-stage procedure for fitting hierarchical animal movement models to telemetry data. The two-stage approach is statistically rigorous and allows one to fit individual-level movement models separately, then resample them using a secondary MCMC algorithm. The primary advantages of the two-stage approach are that the first stage is easily parallelizable and the second stage is completely unsupervised, allowing for an automated fitting procedure in many cases. We demonstrate the two-stage procedure with two applications of animal movement models. The first application involves a spatial point process approach to modeling telemetry data, and the second involves a more complicated continuous-time discrete-space animal movement model. We fit these models to simulated data and real telemetry data arising from a population of monitored Canada lynx in Colorado, USA.

Environmetrics↗