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At least 217 records · Page 12Linked to original sources

USGS1996

Phylogenetic relationships within the Alcidae (Charadriiformes: Aves) inferred from total molecular evidence

The Alcidae is a unique assemblage of Northern Hemisphere seabirds that forage by "flying" underwater. Despite obvious affinities among the species, their evolutionary relationships are unclear. We analyzed nucleotide sequences of 1,045 base pairs of the mitochondrial cytochrome b gene and allelic profiles for 37 allozyme loci in all 22 extant species. Trees were constructed on independent and combined data sets using maximum parsimony and distance methods that correct for superimposed changes. Alternative methods of analysis produced only minor differences in relationships that were supported strongly by bootstrapping or standard error tests. Combining sequence and allozyme data into a single analysis provided the greatest number of relationships receiving strong support. Addition of published morphological and ecological data did not improve support for any additional relationship. All analyses grouped species into six distinct lineages: (1) the dovekie ( Alle alle ) and auks, (2) guillemots, (3) brachyramphine murrelets, (4) synthliboramphine murrelets, (5) true auklets, and (6) the rhinoceros auklet ( Cerorhinca monocerata ) and puffins. The two murres (genus Uria ) were sister taxa, and the black guillemot ( Cepphus grylle ) was basal to the other guillemots. The Asian subspecies of the marbled murrelet ( Brachyramphus marmoratus perdix ) was the most divergent brachyramphine murrelet, and two distinct lineages occurred within the synthliboramphine murrelets. Cassin's auklet ( Ptychoramphus aleuticus ) and the rhinoceros auklet were basal to the other auklets and puffins, respectively, and the Atlantic ( Fratercula arctica ) and horned ( Fratercula corniculata ) puffins were sister taxa. Several relationships among tribes, among the dovekie and auks, and among the auklets could not be resolved but resembled "star" phylogenies indicative of adaptive radiations at different depths within the trees.

Molecular Biology and Evolution↗

USGS2025

Bears avoid residential neighborhoods in response to the experimental reduction of anthropogenic attractants

Introduction: Urbanization is an extreme form of land use alteration, with human development driving changes in the distribution of resources available to wildlife. Some large carnivores have learned to exploit anthropogenic food resources in urban development, resulting in human-carnivore conflict that can have detrimental impacts to people and carnivores, as exemplified by American black bears. Management agencies commonly promote the use of bear-resistant garbage containers for reducing conflicts, but little is known about the actual behavioral responses of bears to this intervention. Methods: To understand whether black bears alter their behavior in response to changes in residential waste management, we investigated patterns of bear behavior in Durango, Colorado, where anthropogenic attractants were experimentally manipulated. Using location data from collared black bears, we modeled resource selection and movement in response to areas that had received bear-resistant garbage containers compared to those that did not. Results: Bears avoided residential areas where garbage availability had been reduced, and this avoidance response increased over subsequent years, potentially suggesting that bears were learning from the management intervention. Bear movement rates, however, were not notably affected by the garbage reduction. Discussion: Our findings highlight the importance of reducing the availability of anthropogenic attractants for changing bear behavior and reducing risk of urban human-bear conflict, and that these responses can strengthen over time as bears learn from the management intervention.

Colorado↗

USGS2016

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species-level Poisson processes and estimate patch-level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early-successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.

Ecology and Evolution↗

USGS2014

An integrated modeling approach to estimating Gunnison Sage-Grouse population dynamics: Combining index and demographic data

Evaluation of population dynamics for rare and declining species is often limited to data that are sparse and/or of poor quality. Frequently, the best data available for rare bird species are based on large‐scale, population count data. These data are commonly based on sampling methods that lack consistent sampling effort, do not account for detectability, and are complicated by observer bias. For some species, short‐term studies of demographic rates have been conducted as well, but the data from such studies are typically analyzed separately. To utilize the strengths and minimize the weaknesses of these two data types, we developed a novel Bayesian integrated model that links population count data and population demographic data through population growth rate ( λ ) for Gunnison sage‐grouse ( Centrocercus minimus ). The long‐term population index data available for Gunnison sage‐grouse are annual (years 1953–2012) male lek counts. An intensive demographic study was also conducted from years 2005 to 2010. We were able to reduce the variability in expected population growth rates across time, while correcting for potential small sample size bias in the demographic data. We found the population of Gunnison sage‐grouse to be variable and slightly declining over the past 16 years.

Colorado, Utah↗

USGS1999

Data set incongruence and correlated character evolution: An example of functional convergence in the hind-limbs of stifftail diving ducks

The unwitting inclusion of convergent characters in phylogenetic estimates poses a serious problem for efforts to recover phylogeny. Convergence is not inscrutable, however, particularly when one group of characters tracks phylogeny and another set tracks adaptive history. In such cases, convergent characters may be correlated with one or a few functional anatomical units and readily identifiable by using comparative methods. Stifftail ducks (Oxyurinae) offer one such opportunity to study correlated character evolution and function in the context of phylogenetic reconstruction. Morphological analyses place stifftail ducks as part of a large clade of diving ducks that includes the sea ducks (Mergini), Hymenolaimus, Merganetta , and Tachyeres , and possibly the pochards (Aythyini). Molecular analyses, on the other hand, place stifftails far from other diving ducks and suggest, moreover, that stifftails are polyphyletic. Mitochondrial cytochrome b gene sequences of eight stifftail species traditionally supposed to form a clade were compared with each other and with sequences from 50 other anseriform and galliform species. Stifftail ducks are not the sister group of sea ducks but lie outside the typical ducks (Anatinae). Of the four traditional stifftail genera, monophyly of Oxyura and its sister group relationship with Nomonyx are strongly supported. Heteronetta probably is the sister group of that clade, but support is weak. Biziura is not a true stifftail. Within Oxyura , Old World species ( O. australis, O. leucocephala, O. maccoa ) appear to form a clade, with New World species ( O. jamaicensis, O. vittata ) branching basally. Incongruence between molecules and morphology is interpreted to be the result of adaptive specialization and functional convergence in the hind limbs of Biziura and true stifftails. When morphological characters are divided into classes, only hind-limb characters are significantly in conflict with the molecular tree. Likewise, null models of synonymous and nonsynonymous substitution based on patterns of codon-degeneracy and chemical dissimilarity indicate that the nucleotide and amino acid changes postulated by the molecular tree are more plausible than those postulated by the morphological tree. These findings teach general lessons about the utility of highly adaptive characters (in particular those related to foraging ecology) and underscore the problems that convergence can pose for attempts to recover phylogeny. They also demonstrate how the concept of natural data partitions and simple models of evolution (e.g., parsimony, likelihood, neutrality) can be used to test the accuracy of independent phylogenetic estimates and provide arguments in favor of one tree topology over another.

Systematic Biology↗

USGS2009

Predicting bed shear stress and its role in sediment dynamics and restoration potential of the Everglades and other vegetated flow systems

Entrainment of sediment by flowing water affects topography, habitat suitability, and nutrient cycling in vegetated floodplains and wetlands, impacting ecosystem evolution and the success of restoration projects. Nonetheless, restoration managers lack simple decision-support tools for predicting shear stresses and sediment redistribution potential in different vegetation communities. Using a field-validated numerical model, we developed state-space diagrams that provide these predictions over a range of water-surface slopes, depths, and associated velocities in Everglades ridge and slough vegetation communities. Diminished bed shear stresses and a consequent decrease in bed sediment redistribution are hypothesized causes of a recent reduction in the topographic and vegetation heterogeneity of this ecosystem. Results confirmed the inability of present-day flows to entrain bed sediment. Further, our diagrams showed bed shear stresses to be highly sensitive to emergent vegetation density and water-surface slope but less sensitive to water depth and periphyton or floating vegetation abundance. These findings suggested that instituting a pulsing flow regime could be the most effective means to restore sediment redistribution to the Everglades. However, pulsing flows will not be sufficient to erode sediment from sloughs with abundant spikerush, unless spikerush density first decreases by natural or managed processes. Our methods provide a novel tool for identifying restoration parameters and performance measures in many types of vegetated aquatic environments where sediment erosion and deposition are involved.

Ecological Engineering↗

USGS2019

Southern California and range‐wide raccoon gastrointestinal helminth database

Local and global measurements of parasite prevalence and abundance are critical for understanding the dynamics that underlie the diversity, distribution, and evolution of infectious diseases. Here, we present a data set of gut helminths found in (1) raccoons throughout their range, based on primary literature from 1925–2017 and (2) raccoons in Santa Barbara County, California, USA surveyed from 2012 to 2015. The range‐wide data set has 1,256 parasite entries from 217 literature sources across three continents and 32 states in the USA. This data set includes a list of all recorded raccoon gut helminths (n = 100) and their presence and prevalence in surveyed raccoon populations. The Santa Barbara data set includes gut helminth data from 182 raccoons from one Southern California County. In addition to the presence and abundance data for 13 parasite species, this data set includes measurements of 7,465 individual raccoon roundworms (Baylisascaris procyonis). For both range‐wide and Santa Barbara data sets, we include information on parasite site of infection in host, sampling method, and sample size. We also provide geographic coordinates for infected raccoon populations (range‐wide database) and individuals (Santa Barbara). In the associated metadata, we include sampling methods and summary figures for both the range‐wide and Santa Barbara raccoon gut helminth records. There are no copyright or proprietary restrictions for research and/or teaching purposes. S. B. Weinstein and J. C. Van Wert contributed equally to this manuscript and are shared first authors.

California↗

USGS2025

The scientific benefits of a statewide, standardized, coastal wetland monitoring program in Hawaiʻi

In this viewpoint, we provide a scientific justification for a statewide, standardized, coastal wetland monitoring program for Hawaiʻi, USA. Hawaiian coastal wetlands provide important habitat for endangered waterbirds, invertebrates, plants, and the Hawaiian hoary bat (ʻōpeʻapeʻa; Lasiurus semotus ) as well as support Indigenous food systems. Currently, numerous agencies and groups in Hawaiʻi collect data on coastal wetlands, but information is not typically shared and methods are not standardized. A statewide, standardized, coastal wetland monitoring program with a centralized database would allow managers to keep better track of progress toward restoration goals, population changes of conservation-reliant species, outbreaks and impacts of avian botulism, rates of coastal salinization, and many other critical issues across space and time. Monitoring combined with targeted research could fill critical knowledge gaps about the types, functions, values, and biodiversity of Hawaiian coastal wetlands. Ultimately, the improved knowledge gained from long-term coastal wetland monitoring could inform landscape-scale restoration actions and adaptive management of coastal wetlands under sea-level rise and climate change.

Hawaii↗

USGS2023

Advances in wildlife abundance estimation using pedigree reconstruction

The conservation and management of wildlife populations, particularly for threatened and endangered species are greatly aided with abundance, growth rate, and density measures. Traditional methods of estimating abundance and related metrics represent trade-offs in effort and precision of estimates. Pedigree reconstruction is an emerging, attractive alternate approach because its use of one-time, noninvasive sampling of individuals to infer the existence of unsampled individuals. However, advances in pedigree reconstruction could improve its utility, including forming a measure of precision for the method, establishing required spatial sampling effort for accurate estimates, ascertaining the spatial extent of abundance estimates derived from pedigree reconstruction, and assessing how population density affects the estimator's performance. Using established relationships for a stochastic, spatially explicit simulated moose ( Alces americanus ) population, pedigree reconstruction provided accurate estimates of the adult moose population size and trend. Novel bootstrapped confidence intervals performed as expected with intensive sampling but underperformed with moderate sampling efforts that could produce abundance estimates with low bias. Adult population estimates more closely reflected the total number of adults in the extant population, rather than number of adults inhabiting the area where sampling occurred. Increasing sampling effort, measured as the proportion of individuals sampled and as the proportion of a hypothetical study area, yielded similar asymptotic patterns over time. Simulations indicated a positive relationship between animal density and sampling effort required for unbiased estimates. These results indicate that pedigree reconstruction can produce accurate abundance estimates and may be particularly valuable for surveying smaller areas and low-density populations.

Ecology and Evolution↗

USGS2020

Microbiomes from biorepositories? 16S rRNA bacterial amplicon sequencing of archived and contemporary intestinal samples of wild mammals (Eulipotyphla: Soricidae)

Interest in gut microbial community composition has exploded recently as a result of the increasing ability to characterize these organisms and a growing understanding of their role in host fitness. New technologies, such as next generation amplicon (16S rRNA) sequencing, have enabled identification of bacterial communities from samples of diverse origin (e.g., fecal, skin, genital, environmental, etc.). Relatively little work, however, has explored the feasibility of utilizing historical samples (e.g., museum archived samples) of varying age, quality, and preservation type. Because natural history collections span multiple decades, these biorepositories have the potential to provide fundamental historical baselines to measure and better understand biodiversity on a changing planet. Utilizing even a small proportion of museum specimens could provide a means of sampling past microbial communities, allowing for direct comparison to contemporary communities and more complete understanding of dynamic shifts through time. We examined the feasibility of obtaining 16S rRNA amplicon microbiome data from whole gastrointestinal tracts (GIs) of shrews of varying age and preservation method, including 5 freshly collected shrew GIs immediately fixed in liquid nitrogen (LN2), 10 ten-year old shrew GIs frozen at −20°C (whole animal), and 10 shrews of varying ages (4 from 1968, 1 from 1980, 1 from 2001, 1 from 2004, 1 from 2007, 1 from 2011 and 2 from 2013) fixed and stored whole in 70% ethanol. Not surprisingly, results of 16S rDNA amplicon sequencing reveal significantly different bacterial communities between different preservation techniques and age of samples. Ten-year old frozen samples had bacterial communities most similar to freshly collected (LN2) samples, while the bacterial communities of both were significantly different from the 70% ethanol preserved samples of various ages. Amongst those preserved in 70% ethanol, age of samples also influenced bacterial community composition. Additionally, we compare results of OTU based and ASV based analyses. Looking ahead, field collectors and museums should develop and adopt best practices related to frozen preservation to ensure adequate material for future microbiome investigations.

Frontiers in Ecology and Evolution↗

USGS2024

A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data

Background Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is urgently needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. Methods We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ⇠ 85% of the individuals have been directly monitored. Results Through the protocol, we demonstrated how pre-network data permu tations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. Conclusions The protocol allows researchers to analyse GPS-based radio telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under di↵erent conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions

Article↗

USGS2014

The future of nearshore processes research

The nearshore is the transition region between land and the continental shelf including (from onshore to offshore) coastal plains, wetlands, estuaries, coastal cliffs, dunes, beaches, surf zones (regions of wave breaking), and the inner shelf (Figure ES-1). Nearshore regions are vital to the national economy, security, commerce, and recreation. The nearshore is dynamically evolving, is often densely populated, and is under increasing threat from sea level rise, long-term erosion, extreme storms, and anthropogenic influences. Worldwide, almost one billion people live at elevations within 10 m of present sea level. Long-term erosion threatens communities, infrastructure, ecosystems, and habitat. Extreme storms can cause billions of dollars of damage. Degraded water quality impacts ecosystem and human health. Nearshore processes, the complex interactions between water, sediment, biota, and humans, must be understood and predicted to manage this often highly developed yet vulnerable nearshore environment. Over the past three decades, the understanding of nearshore processes has improved. However, societal needs are growing with increased coastal urbanization and threats of future climate change, and significant scientific challenges remain. To address these challenges, members of academia, industry, and federal agencies (USGS, USACE, NPS, NOAA, FEMA, ONR) met at the “The Past and Future of Nearshore Processes Research: Reflections on the Sallenger Years and a New Vision for the Future” workshop to develop a nearshore processes research vision where societal needs and science challenges intersect. The resulting vision is comprised of three broad research themes: Long-term coastal evolution due to natural and anthropogenic processes: As global climate change alters the rates of sea level rise and potentially storm patterns and coastal urbanization increases over the coming decades, an understanding of coastal evolution is critical. Improved knowledge of long-term morphological, ecological, and societal processes and their interactions will result in an improved ability to simulate coastal change. This will enable proactive solutions for resilient coasts and better guidance for reducing coastal vulnerability. Extreme Events: Flooding, erosion, and the subsequent recovery: Hurricane Sandy caused flooding and erosion along hundreds of miles of shoreline, flooded New York City, and impacted communities and infrastructure. Overall U.S. coastal extreme event related economic losses have increased substantially. Furthermore, climate change may cause an increase in coastal extreme events and rising sea levels could increase the occurrence of extreme events. Addressing this research theme will result in an improved understanding of the physical processes during extreme events, leading to improved models of flooding, erosion, and recovery. The resulting societal benefit will be more resilient coastal communities. The physical, biological and chemical processes impacting human and ecosystem health: Nearshore regions are used for recreation, tourism, and human habitation, and provide habitat and valuable ecosystem services. These areas must be sustained for future generations, however overall coastal water quality is declining due to microbial pathogens, fertilizers, pesticides, and heavy metal contamination, threatening ecosystem and human health. To ensure sustainable nearshore regions, predictive real-time water- and sediment-based based pollutant modeling capabilities must be developed, which requires expanding our knowledge of the physics, chemistry, and biology of the nearshore. The resulting societal benefits will include better beach safety, healthier ecosystems, and improved mitigation and regulatory policies. The scientists and engineers of the U.S. nearshore community are poised to make significant progress on these research themes, which have significant societal impact. The U.S. nearshore community, including academic, government, and industry colleagues, recommends multi-agency investment into a coordinated development of observational and modeling research infrastructure to address these themes, as discussed in the whitepaper. The observational infrastructure should include development of new sensors and methods, focused observational programs, and expanded nearshore observing systems. The modeling infrastructure should include improved process representation, better model coupling, incorporation of data assimilation techniques, and testing of real-time models. The observations will provide test beds to compare and improve models.

Report↗

USGS2022

Simple statistical models can be sufficient for testing hypotheses with population time series data

Time-series data offer wide-ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non-Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long-lived, lower-fecundity organisms (K life-history strategists), while model A, the simplest, tended to be supported for shorter-lived, high-fecundity organisms (r life-history strategists). Analysis of real-world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time-series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.

Ecology and Evolution↗

USGS2023

Gene expression and wildlife health: Varied interpretations based on perspective

We evaluated wildlife population health from the perspective of statistical means vs. variances. We outlined the choices necessary to provide the framework for our study. These consisted of spatial and temporal boundaries (e.g., choice of sentinel species, populations, time frame), measurement techniques (molecular to population level), and appropriate statistical analyses. We chose to assess the health of 19 sea otter populations, located in the north Pacific from the Aleutian Islands, AK, to Santa Barbara, CA, and varying in population growth rates and length of occupancy. Our focal metric was gene expression (i.e., mRNA transcripts) data that we had previously generated across sea otter populations as a measure of population health. We used statistical methods with different approaches (i.e., means vs. variances) and examined the subsequent interpretive outcomes and how these influence our assessment of “health.” Interpretations based on analyses using variances versus means overlapped to some degree. In general, sea otter populations with low variation in gene expression were limited by food resources and at or near carrying capacity. In populations where the variation in gene expression was moderate or high, four out of five populations were increasing in abundance, or had been recently increasing. Where we had additional information on sources of stressors at the level of the population, we were able to draw inferences from those stressors to specific gene expression results. For example, gene expression patterns of sea otters from Western Prince William Sound were consistent with long term exposure to petroleum hydrocarbons, whereas in Kachemak Bay, patterns were consistent with exposure to algal toxins. Ultimately, determination of population or ecosystem health will be most informative when multiple metrics are examined across disciplines in the context of specific scenarios and goals.

Alaska, British Columbia, California, Oregon, Wash↗

USGS2017

Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data

Knowledge of animal diets provides essential insights into their life history and ecology, although diet estimation is challenging and remains an active area of research. Quantitative fatty acid signature analysis (QFASA) has become a popular method of estimating diet composition, especially for marine species. A primary assumption of QFASA is that constants called calibration coefficients, which account for the differential metabolism of individual fatty acids, are known. In practice, however, calibration coefficients are not known, but rather have been estimated in feeding trials with captive animals of a limited number of model species. The impossibility of verifying the accuracy of feeding trial derived calibration coefficients to estimate the diets of wild animals is a foundational problem with QFASA that has generated considerable criticism. We present a new model that allows simultaneous estimation of diet composition and calibration coefficients based only on fatty acid signature samples from wild predators and potential prey. Our model performed almost flawlessly in four tests with constructed examples, estimating both diet proportions and calibration coefficients with essentially no error. We also applied the model to data from Chukchi Sea polar bears, obtaining diet estimates that were more diverse than estimates conditioned on feeding trial calibration coefficients. Our model avoids bias in diet estimates caused by conditioning on inaccurate calibration coefficients, invalidates the primary criticism of QFASA, eliminates the need to conduct feeding trials solely for diet estimation, and consequently expands the utility of fatty acid data to investigate aspects of ecology linked to animal diets.

Ecology and Evolution↗

USGS2025

U.S. Geological Survey science strategy to address highly pathogenic avian influenza and its effects on wildlife health 2025–29

Executive Summary Highly pathogenic avian influenza (HPAI) is an ecologically and economically important animal disease that can also directly affect humans (a “zoonotic” disease). HPAI was once limited almost exclusively to domestic poultry but has rapidly adapted to diverse animal hosts. Viruses causing HPAI now appear to be maintained and dispersed by wild birds largely independent of poultry, though HPAI continues to cause considerable economic losses and supply chain disruptions in the domestic poultry trade. Coincident with the adaptation of HPAI viruses to wild birds, particularly waterfowl and gulls, increasingly diverse wild bird hosts are becoming exposed to HPAI, often resulting in disease and death. More sporadically, HPAI has caused mass mortality events, particularly among seabirds. Furthermore, viral spillover to wild and domestic mammals has become more common. Spillover to wild mammals has resulted in mortality among diverse terrestrial and marine taxa, including episodic losses of such scale as to represent potential conservation challenges. Since approximately March 2024, HPAI has also affected dairy cows, which represents a new threat to the agricultural economy. Lastly, HPAI has increasingly affected humans through domestic animal exposures, exemplifying the considerable implications of this disease beyond animal health. Rapid changes in the ecology of HPAI are currently outpacing research efforts. For example, it is not entirely clear which newly established hosts may become reservoirs for HPAI viruses (in other words, capable of maintaining HPAI viruses within a broad population indefinitely) and how this may influence viral evolution and dissemination. As a result, there are considerable information gaps regarding HPAI in wildlife that, if filled, would improve the ability of scientists, managers, agricultural industry representatives, and healthcare professionals to understand and to anticipate the effects of HPAI on wild animal, domestic animal, environmental, and human health (“One Health”). The U.S. Geological Survey (USGS) is the lead Federal agency providing scientific research on avian influenza viruses (AIVs), including HPAI viruses, that affect wildlife for which the Department of the Interior (DOI) has management authority. States have jurisdiction over wildlife on Federal lands within their borders (43 CFR § 24.3), so the USGS Ecosystems Mission Area (EMA) coordinates with State natural resource management agencies. The EMA focuses its research on HPAI through priorities identified by the USGS Avian Influenza Science Team ( app. 1 ). Priorities identified by the USGS Avian Influenza Science Team are based on Administration priorities, Congressional direction, and discussions with State, Federal, and Tribal natural resource management agencies that identify specific scientific gaps that need to be filled to inform sound wildlife management decisions. Notable non-DOI Federal partners include the U.S. Department of Agriculture, the lead for the HPAI regulatory response in poultry and livestock, and the Centers for Disease Control and Prevention (CDC), the lead agency for the HPAI response pertaining to human health. The USGS offers unique expertise and capacity pertaining to research on diseases affecting free-ranging wildlife populations. This expertise has been critical to interjurisdictional surveillance and capacity-building efforts, including programs administered by the U.S. Department of Agriculture and the CDC. The USGS also provides resources, guidance, and tools to inform surveillance and interventions conducted by natural resource management agencies. More specifically, the USGS EMA provides objective and rigorous scientific data for inferring (1) the utility of new methods to detect and characterize AIVs, including those maintained in wildlife and the environment; (2) effects of HPAI on wildlife; (3) spatiotemporal patterns of wildlife host and AIV dispersal; (4) the presence and persistence of AIVs in the environment; (5) how HPAI in wildlife influences consumptive and nonconsumptive utilization of wildlife; (6) how new tools and scientific methods may promote sound management decisions for HPAI-affected wildlife, particularly species of conservation concern; and (7) the combined effects of HPAI and other stressors on ecosystem health and resiliency. This science strategy builds upon research outlined in a previous USGS science strategy for HPAI (2016–20) by Harris and others (2016) . This strategy also details research priorities identified by the Administration (for example, U.S. Department of Agriculture, 2025 ) and others based on USGS Avian Influenza Science Team discussions with natural resource management agencies to address HPAI and wildlife health over the next 5 years (2025–29). This strategy presents 7 goals and 26 objectives that focus USGS and partner efforts on priorities that will fill data gaps regarding the effects of HPAI on wildlife managed by or co-managed with the U.S. Department of the Interior such that agencies and partners might anticipate or limit adverse effects on public resources. This strategy also identifies research priorities intended to address HPAI in wildlife and wildlife habitat that are anticipated to support interjurisdictional One Health efforts.

Circular↗

USGS2026

Seasonal drivers of density in a subarctic population of northern red-backed voles

Northern red-backed voles ( Clethrionomys rutilus ) are an important species in the boreal forest ecosystem, both as herbivores and as a key food source for many mammalian and avian predators. They exhibit dramatic inter- and intra-annual population fluctuations, for which causes are not entirely known. We monitored northern red-backed vole densities in Denali National Park and Preserve through time with the goal of examining how environmental factors influenced density over time. Using a 30-year record of mark-recapture data, we used spatially explicit capture-recapture methods to estimate autumn and early summer densities each year. We assessed cyclic patterns in density, variation in amplitude, and any periodicity of population fluctuations using post hoc linear modeling. We found that the vole population appeared to be cyclic with a 2–4 year period, although the pattern varied somewhat among sampling sites. Our results indicated an association between white spruce ( Picea glauca ) seed production and vole density, implying white spruce seeds were either an important source of food during winter seasons, or that the environmental triggers that promote high seed fall were also associated with increased vole density. We also found a negative effect of an autumn harshness index, indicating winter conditions play a role in vole density in the following season. Finally, we found evidence of a negative density-dependent relationship between autumn and early summer. Together, these findings suggest a system in which density dependence and cyclic relationships are irregular but highly influential, with environmental effects capable of enhancing or moderating their impact. Continued monitoring of voles, alongside more thorough assessments of environmental conditions, may provide additional insight into the complex population dynamics of this species.

Alaska↗

USGS2023

Effects of environment and metacommunity delineation on multiple dimensions of stream fish beta diversity

Introduction: Beta diversity represents changes in community composition among locations across a landscape. While the effects of human activities on beta diversity are becoming clearer, few studies have considered human effects on the three dimensions of beta diversity: taxonomic, functional, and phylogenetic. Including anthropogenic factors and multiple dimensions of biodiversity may explain additional variation in stream fish beta diversity, providing new insight into how metacommunities are structured within different spatial delineations. Methods: In this study, we used a 350 site stream fish abundance dataset from South Carolina, United States to quantify beta diversity explainable by spatial, natural environmental, and anthropogenic variables. We investigated three spatial delineations: (1) a single whole-state metacommunity delineated by political boundaries, (2) two metacommunities delineated by a natural geomorphic break separating uplands from lowlands, and (3) four metacommunities delineated by natural watershed boundaries. Within each metacommunity we calculated taxonomic, functional, and phylogenetic beta diversity and used variation partitioning to quantify spatial, natural environmental, and anthropogenic contributions to variations in beta diversity. Results: We explained 25–81% of the variation in stream fish beta diversity. The importance of these three factors in structuring metacommunities differed among the diversity dimensions, providing complementary perspectives on the processes shaping beta diversity in fish communities. The effect of spatial, natural environmental, and anthropogenic factors varied among the spatial delineations, which indicate conclusions drawn from variation partitioning may depend on the spatial delineation chosen by researchers. Discussion: Our study highlights the importance of considering human effects on metacommunity structure, quantifying multiple dimensions of beta diversity, and careful consideration of user-defined metacommunity boundaries in beta diversity analyses.

South Carolina↗