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USGS2010

A comprehensive multilocus phylogeny for the wood-warblers and a revised classification of the Parulidae (Aves)

The birds in the family Parulidae-commonly termed the New World warblers or wood-warblers-are a classic model radiation for studies of ecological and behavioral differentiation. Although the monophyly of a 'core' wood-warbler clade is well established, no phylogenetic hypothesis for this group has included a full sampling of wood-warbler species diversity. We used parsimony, maximum likelihood, and Bayesian methods to reconstruct relationships among all genera and nearly all wood-warbler species, based on a matrix of mitochondrial DNA (5840 nucleotides) and nuclear DNA (6 loci, 4602 nucleotides) characters. The resulting phylogenetic hypotheses provide a highly congruent picture of wood-warbler relationships, and indicate that the traditional generic classification of these birds recognizes many non-monophyletic groups. We recommend a revised taxonomy in which each of 14 genera (Seiurus, Helmitheros, Mniotilta, Limnothlypis, Protonotaria, Parkesia, Vermivora, Oreothlypis, Geothlypis, Setophaga, Myioborus, Cardellina, Basileuterus, Myiothlypis) corresponds to a well-supported clade; these nomenclatural changes also involve subsuming a number of well-known, traditional wood-warbler genera (Catharopeza, Dendroica, Ergaticus, Euthlypis, Leucopeza, Oporornis, Parula, Phaeothlypis, Wilsonia). We provide a summary phylogenetic hypothesis that will be broadly applicable to investigations of the historical biogeography, processes of diversification, and evolution of trait variation in this well studied avian group. ?? 2010 Elsevier Inc.

Molecular Phylogenetics and Evolution↗

USGS2025

Development of PCR blocking primers enabling DNA metabarcoding analysis of dietary composition in hematophagous sea lamprey

Conventional dietary assessments are challenging in hematophagous species, particularly in sea lamprey ( Petromyzon marinus ). However, recent technological developments and molecular approaches have provided an attractive alternative through the use of DNA metabarcoding. While DNA metabarcoding has been used for dietary analyses in numerous species, including lampreys, applications of universal primers that detect a diverse set of prey items can be limited by the amplification of predator DNA. In this study, we designed and tested eight blocking primers designed to suppress the amplification of sea lamprey DNA with vertebrate-universal primers targeting the mitochondrial 12S rRNA gene. This approach allowed for the use of a single marker to amplify a taxonomically diverse suite of host species, in contrast to previous studies that used multiple taxon-specific primer pairs (e.g., Salmonidae, Cyprinidae, and Catostomidae). Candidate blocking primers evaluated in this study differed in base pair length, end sequence modification, and purification method. Samples with different sea lamprey-to-host DNA ratios were subjected to multiple detection methods including gel electrophoresis, quantitative PCR, and DNA metabarcoding to assess the ability of each blocking primer to selectively suppress amplification of the sea lamprey 12S gene region. All blocking primers tested performed well and demonstrated high effectiveness, suppressing sea lamprey reads by > 99.9% in mock communities and improving host DNA sequence recovery across various sample types, including wild-caught lamprey. Results show that the blocking primers evaluated can facilitate molecular diet analysis in sea lamprey, allowing the amplification of a taxonomically diverse range of host fish species with universal primers.

Great Lakes↗

USGS2015

Distance measures and optimization spaces in quantitative fatty acid signature analysis

Quantitative fatty acid signature analysis has become an important method of diet estimation in ecology, especially marine ecology. Controlled feeding trials to validate the method and estimate the calibration coefficients necessary to account for differential metabolism of individual fatty acids have been conducted with several species from diverse taxa. However, research into potential refinements of the estimation method has been limited. We compared the performance of the original method of estimating diet composition with that of five variants based on different combinations of distance measures and calibration-coefficient transformations between prey and predator fatty acid signature spaces. Fatty acid signatures of pseudopredators were constructed using known diet mixtures of two prey data sets previously used to estimate the diets of polar bears Ursus maritimus and gray seals Halichoerus grypus, and their diets were then estimated using all six variants. In addition, previously published diets of Chukchi Sea polar bears were re-estimated using all six methods. Our findings reveal that the selection of an estimation method can meaningfully influence estimates of diet composition. Among the pseudopredator results, which allowed evaluation of bias and precision, differences in estimator performance were rarely large, and no one estimator was universally preferred, although estimators based on the Aitchison distance measure tended to have modestly superior properties compared to estimators based on the Kullback-Leibler distance measure. However, greater differences were observed among estimated polar bear diets, most likely due to differential estimator sensitivity to assumption violations. Our results, particularly the polar bear example, suggest that additional research into estimator performance and model diagnostics is warranted.

Ecology and Evolution↗

USGS2021

Transcriptome assembly and differential gene expression of the invasive avian malaria parasite Plasmodium relictum in Hawaiʻi

The malaria parasite Plasmodium relictum (lineage GRW4) was introduced less than a century ago to the native avifauna of Hawaiʻi, where it has since caused major declines of endemic bird populations. One of the native bird species that is frequently infected with GRW4 is the Hawaiʻi ʻamakihi ( Chlorodrepanis virens ). To achieve a better understanding of the transcriptional activities of this virulent parasite, we performed a controlled challenge experiment of 15 ʻamakihi that were infected with GRW4. Blood samples containing malaria parasites were collected at two time points (intermediate and peak infection stages) from host individuals that were either experimentally infected by mosquitoes or inoculated with infected blood. We then used RNA sequencing to assemble a high‐quality blood transcriptome of P. relictum GRW4, allowing us to quantify parasite expression levels inside individual birds. We found few significant differences (one to two transcripts) in GRW4 expression levels between host infection stages and between inoculation methods. However, 36 transcripts showed differential expression levels among all host individuals, indicating a potential presence of host‐specific gene regulation across hosts. To reduce the extinction risk of the remaining native bird species in Hawaiʻi, genetic resources of the local Plasmodium lineage are needed to enable further molecular characterization of this parasite. Our newly built Hawaiian GRW4 transcriptome assembly, together with analyses of the parasite's transcriptional activities inside the blood of Hawaiʻi ʻamakihi, can provide us with important knowledge on how to combat this deadly avian disease in the future.

Hawaii↗

USGS2022

Scat as a source of DNA for population monitoring

Sampling fecal droppings (scat) to genetically identify individual animals is an established method for monitoring mammal populations and could be highly useful for monitoring reptile populations. Whereas existing protocols for obtaining DNA from reptile scat focus on analyses of whole, fresh scat deposited during animal handling, the collection of scat naturally deposited by reptiles in situ, as required for non-invasive population monitoring, requires protocols to extract highly degraded DNA. Using surface swabs from such scats can reduce PCR inhibition, ecological impacts of removing scat, and zoonotic risks. We report on three related but independently designed studies of DNA analyses from scat swabs of herbivorous reptiles under natural desert conditions: two free-ranging desert tortoise species (Agassiz's desert tortoise, Gopherus agassizii , California, US, and Morafka's desert tortoise, G. morafkai , Arizona, US) and the common chuckwalla (Sauromalus atar ) (Arizona, US, and Sonora, MX). We analyzed samples from both tortoise species with the same set of 16 microsatellites and chuckwalla samples with four mtDNA markers; studies also varied in swab preservation medium and DNA extraction method. Microsatellite amplification success per sample, defined as ≥9 loci with amplification, varied between studies, with 15% for Agassiz's desert tortoise and 42% Morafka's desert tortoise. For chuckwallas, we successfully amplified and sequenced 50% of samples. We recovered fragments up to 400 bp for tortoises and 980 bp for chuckwallas from scat swab samples. This study demonstrates that genotypes can successfully be obtained from swabs of herbivorous reptile scat collected in the field under natural environmental conditions and emphasizes that repeat amplifications are necessary for genetic identification of individuals from non-invasive samples.

Arizona, California↗

USGS2024

Sero-epidemiology of Highly Pathogenic Avian Influenza viruses among wild birds in subarctic intercontinental transition zones

Background: The geographic expansion and evolution of A/Goose/Guangdong/1/1996(H5N1) (Gs/GD) lineage H5Nx highly pathogenic avian influenza (HPAI) viruses since 1996 have raised awareness of enzootic circulation among migratory birds and the potential for intercontinental transport and spread. Recent Pacific- and Atlantic-route introductions of HPAI to North America were facilitated by avian migration through subarctic zones, specifically Alaska and Iceland. This study aimed to identify recent historical patterns of exposure to HPAI viruses among birds within and migrating through both regions and evaluate how geographic, demographic, and taxonomic differences contribute to exposure risk at two intercontinental staging locations. Methods: During 2010-2019, blood samples were obtained from captured wild migratory seabirds and waterfowl in Alaska and Iceland. All live birds were released following completion of sampling. Sampling date, species, sampling location, and age class was documented for each bird, and sex was documented when possible. Lentiviral pseudoviruses that express the influenza hemagglutinin surface glycoprotein for H5Nx HPAI and H5 low-pathogenicity avian influenza (LPAI) were constructed for use in serological assays to screen for and quantify titers of antibodies against the latter viruses. Data were analyzed to compare (a) categorical baseline ecological traits between Iceland and Alaska, and (b) ecological traits between birds identified to be seropositive and suggestive/seronegative/fully cross-reactive birds to H5Nx HPAI in Iceland and Alaska. Factors associated with seroreactivity to H5Nx HPAI and H5 LPAI were assessed. Results: The seroprevalence of HPAI among birds in both locations was 7.3% (112/1526). Findings reveal variability in seroprevalence by year, higher rates of exposure to H5 LPAI than H5Nx HPAI overall, and significantly more seropositive and suggestive exposure of birds to H5Nx HPAI in Alaska as compared to Iceland. Geographic, demographic, and taxonomic differences contribute to exposure risk between Alaska and Iceland. Most tested birds were immuno-naïve to HPAI in both locations, which indicates many migratory birds in the subarctic are susceptible to HPAI infection, demonstrating substantial risk for intercontinental transmission between Asia, Europe, and North America. Conclusions: Our findings provide further justification for increased viral and serosurveillance in Alaska and Iceland to monitor subarctic movements of migratory birds and intercontinental transmission dynamics of currently circulating and new strains of HPAI globally.

Research Square↗

USGS2025

Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids

Spatiotemporal causal inference methods are needed to detect the effect of interventions on indirectly measured epidemiological outcomes that go beyond studying spatiotemporal correlations. Chronic wasting disease (CWD) causes neurological degeneration and eventual death to white-tailed deer ( Odocoileus virginianus ) in Wisconsin. Targeted culling involves removing deer after traditional hunting seasons in areas with high CWD prevalence. The evaluation of the causal effects of targeted culling in the spread and growth of CWD is an important unresolved research and CWD management question that can guide surveillance efforts. Reaction–diffusion partial differential equations (PDEs) can be used to mechanistically model the underlying spatiotemporal dynamics of wildlife diseases, like CWD, allowing researchers to make inference about unobserved epidemiological quantities. These models indirectly regress spatiotemporal covariates on diffusion and growth rates parameterizing such PDEs, obtaining associational conclusions. In this work we develop an innovative method to obtain causal estimators for the effect of targeted culling interventions on CWD epidemiological processes using an inverse-probability-of-treatment-weighted technique by means of marginal structural models embedded in the PDE fitting process. Additionally we establish a novel scheme for sensitivity analysis under unmeasured confounder for testing the hypothesis of a significant causal effect in the indirectly measured epidemiological outcomes. Our methods can be broadly used to study the impact of spatiotemporal interventions and treatment exposures in the epidemiological evolution of infectious diseases that can help to inform future efforts to mitigate public health implications and wildlife disease burden.

Wisconsin↗

USGS2017

Accounting for imperfect detection of groups and individuals when estimating abundance

If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double-observer models, distance sampling models and combined double-observer and distance sampling models (known as mark-recapture-distance-sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under-counted, but not over-counted. The estimator combines an MRDS model with an N-mixture model to account for imperfect detection of individuals. The new MRDS-Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS-Nmix model to an MRDS model. Abundance estimates generated by the MRDS-Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re-allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.

Ecology and Evolution↗

USGS2014

Cenozoic planktonic marine diatom diversity and correlation to climate change

Marine planktonic diatoms export carbon to the deep ocean, playing a key role in the global carbon cycle. Although commonly thought to have diversified over the Cenozoic as global oceans cooled, only two conflicting quantitative reconstructions exist, both from the Neptune deep-sea microfossil occurrences database. Total diversity shows Cenozoic increase but is sample size biased; conventional subsampling shows little net change. We calculate diversity from a separately compiled new diatom species range catalog, and recalculate Neptune subsampled-in-bin diversity using new methods to correct for increasing Cenozoic geographic endemism and decreasing Cenozoic evenness. We find coherent, substantial Cenozoic diversification in both datasets. Many living cold water species, including species important for export productivity, originate only in the latest Miocene or younger. We make a first quantitative comparison of diatom diversity to the global Cenozoic benthic ∂ 18 O (climate) and carbon cycle records (∂ 13 C, and 20-0 Ma pCO 2 ). Warmer climates are strongly correlated with lower diatom diversity (raw: rho = .92, p<.001; detrended, r = .6, p = .01). Diatoms were 20% less diverse in the early late Miocene, when temperatures and pCO 2 were only moderately higher than today. Diversity is strongly correlated to both ∂ 13 C and pCO 2 over the last 15 my (for both: r>.9, detrended r>.6, all p<.001), but only weakly over the earlier Cenozoic, suggesting increasingly strong linkage of diatom and climate evolution in the Neogene. Our results suggest that many living marine planktonic diatom species may be at risk of extinction in future warm oceans, with an unknown but potentially substantial negative impact on the ocean biologic pump and oceanic carbon sequestration. We cannot however extrapolate our my-scale correlations with generic climate proxies to anthropogenic time-scales of warming without additional species-specific information on proximate ecologic controls.

PLoS ONE↗

USGS2010

Sampling in ecology and evolution - bridging the gap between theory and practice

Sampling is a key issue for answering most ecological and evolutionary questions. The importance of developing a rigorous sampling design tailored to specific questions has already been discussed in the ecological and sampling literature and has provided useful tools and recommendations to sample and analyse ecological data. However, sampling issues are often difficult to overcome in ecological studies due to apparent inconsistencies between theory and practice, often leading to the implementation of simplified sampling designs that suffer from unknown biases. Moreover, we believe that classical sampling principles which are based on estimation of means and variances are insufficient to fully address many ecological questions that rely on estimating relationships between a response and a set of predictor variables over time and space. Our objective is thus to highlight the importance of selecting an appropriate sampling space and an appropriate sampling design. We also emphasize the importance of using prior knowledge of the study system to estimate models or complex parameters and thus better understand ecological patterns and processes generating these patterns. Using a semi-virtual simulation study as an illustration we reveal how the selection of the space (e.g. geographic, climatic), in which the sampling is designed, influences the patterns that can be ultimately detected. We also demonstrate the inefficiency of common sampling designs to reveal response curves between ecological variables and climatic gradients. Further, we show that response-surface methodology, which has rarely been used in ecology, is much more efficient than more traditional methods. Finally, we discuss the use of prior knowledge, simulation studies and model-based designs in defining appropriate sampling designs. We conclude by a call for development of methods to unbiasedly estimate nonlinear ecologically relevant parameters, in order to make inferences while fulfilling requirements of both sampling theory and field work logistics. ?? 2010 The Authors.

Ecography↗

USGS2023

Host vs. pathogen evolutionary arms race: Effects of exposure history on individual response to a genetically diverse pathogen

Introduction: Throughout their range, bighorn sheep ( Ovis canadensis ) populations have seen significant disease-associated declines. Unfortunately, understanding of the underlying epidemiological processes driving the disease dynamics in this species has hindered conservation efforts aimed at improving the health and long-term viability of these populations. Individual response to pathogen exposure emerges from dynamic interactions between competing evolutionary processes within the host and pathogen. The host’s adaptive immune system recognizes pathogens and mounts a defensive response. Pathogens have evolved strategies to overcome adaptive immune defenses including maintaining high genetic diversity through rapid evolution. The outcomes of this evolutionary warfare determine the success of pathogen invasion of the host and ultimately the success of conservation efforts. Methods: During an epizootic dominated by a single strain, we explore these host-pathogen dynamics by examining the variation in effects of pathogen invasion on captive bighorn sheep with differing histories of exposure to genetically diverse strains of Mycoplasma ovipneumoniae (Movi). We monitored clinical signs of disease and sampled animals and their environment to detect spread of Movi among 37 bighorn sheep separated into nine pens based on known exposure Results: We documented Movi transmission within and across pens and we detected Movi DNA in air, water, and invertebrate samples. Higher levels of antibody to Movi prior to the epizootic were associated with a lower likelihood of presenting clinical signs of pneumonia. Nonetheless, higher antibody levels in symptomatic individuals were associated with more severe progressive disease, increased probability and speed of pneumonia-induced mortality, and reduced likelihood of returning to a healthy state. Bighorn sheep with previous exposure to a strain other than the predominant epizootic strain were more likely to recover. Discussion: Our results indicate that Movi-strain variability was sufficient to overwhelm the adaptive host immunological defenses. This outcome indicates, in free-ranging herds, past exposure is likely insufficient to protect bighorn sheep from infection by new Movi strains, although it influences the progression of disease and recovery within the herd. Therefore, given Movi-strain variability and the lack of immunological protection from past exposure, focusing management efforts on minimizing the introduction of Movi into bighorn herds, through separation of domestic and bighorn sheep and avoidance of management activities that create commingling of bighorn sheep carrying differing Movi strains, will likely be the most effective approach for reducing the effects of disease and achieving bighorn sheep conservation goals.

Idaho, Oregon, South Dakota, Washington↗

USGS2025

Simple bagged movement models for telemetry data

Determining which statistical methods are appropriate for data is both user and data dependent and prone to change as new methodology becomes available. This process encompasses model ideation, model selection, and determining appropriate use of statistical methods. Literature on models for animal movement emerging in the past two decades has yielded a rich collection of statistical methods garnering much deserved positive attention. Among such efforts, there is limited investigation of the broader place for simple machine learning methodology in animal movement modeling. We propose a bagged (i.e., bootstrap aggregated) animal movement model using simple, off-the-shelf machine learning algorithms. The model is intuitive, retains statistical inference about characteristics of animal movement (i.e., estimated from model-based summary statistics), and only requires knowledge of elementary statistical and machine learning analysis to understand. We show by simulation that our model can provide unbiased estimates of pertinent characteristics of animal movement (e.g., daily displacement) in the presence of large and realistic location error. We believe that increasing accessible literature on simple machine learning animal movement models provides valuable pedagogical and practical support for researchers using statistical models to study animal movement.

Ecology and Evolution↗

USGS2015

Barrier Island Shorelines Extracted from Landsat Imagery

Introduction Changes to barrier islands occur at time scales that vary from the few hours it takes an individual storm to pass (Morton, 2008) to the millennia it takes for coastal systems to undergo geologic evolution. Developing an understanding of how barrier islands will respond to climate change, sea level rise, and major storms over a range of time scales is relevant to studies of physical, geological, ecological, and societal processes and will help to guide and improve management of our coastal resources (Sallenger and others, 1987). Observations of coastal processes made over a range of spatial and temporal scales and from a variety of instrument platforms (for example, in situ and remote remote sensing) are required to understand and eventually predict the evolution of coastal systems. The deployment of Landsat and other earth-observing satellites within the last few decades has provided an opportunity to observe barrier islands at frequent intervals, often many times a year. This sample frequency is much higher and the spatial coverage much greater than most routine high-resolution topographic surveys (Guy and others, 2014). In addition, the historical record of these datasets have become long enough to document shorter- (that is, annual) and longer-term (that is, decadal) changes from a single data source. Certain aspects of barrier island morphology, such as island size, shape, and position, can be determined from these images and can indicate erosion, land loss, and island breakup (McBride and others, 1989; Plant and Guy, 2013). The shoreline is a common variable used as a metric for coastal erosion or change (Himmelstoss and others, 2010). Although shorelines are often extracted from topographic data (for example, ground-based surveys and light detection and ranging [lidar]), image-based shorelines, corrected for their inherent uncertainties (Moore and others, 2006), have provided much of our understanding of long-term shoreline change because they pre-date routine lidar elevation survey methods. Image-based shorelines continue to be valuable because of their higher temporal resolution compared to costly airborne lidar surveys. A method for extracting sandy shorelines from 30-meter (m) resolution Landsat imagery is presented here.

Open-File Report↗

USGS2026

Incorporating data sets with multiple sources of uncertainty in integrated species distribution models

Data integration methods aim to improve species distribution estimates by incorporating multiple sources of uncertainty across datasets. Two major sources of uncertainty are: (1) variation in sampling effort across space and within datasets, and (2) variation in reliability associated with data collection protocols or timing among datasets. Our goal was to evaluate how different approaches to address these uncertainties influence predictive performance of integrated models. We modeled distributions of four bird species using three datasets that differed in sampling design. We examined three strategies to reduce uncertainty: (1) filtering data, (2) incorporating functions that account for uncertainty in observation models, and (3) varying how datasets are integrated into a single estimate. We first examine methods to account for variable effort in observations, focusing on both spatial differences in sampling intensity and effort given to a single observation record. We then examine approaches to account for data sets with differing reliability. Sampling effort was best addressed through conservative filtering, including spatial thinning and excluding observations with highly variable effort. Next, we considered how to account for potential false positive detections—due to either misidentification or changes in distributions. We found that treating less reliable data as a covariate, an approach previously suggested for data integration that can greatly speed up model fitting, performed well. Other effective approaches included directly modeling false positive rates and complete exclusion of less reliable data sets. Our results provide insights into best practices in integrated modeling for handling uncertainty in integrated models. We demonstrate the flexible options available when using integrated models to address uncertainty.

Pennsylvania↗

USGS2006

Predicting woodrat ( Neotoma ) responses to anthropogenic warming from studies of the palaeomidden record

Aim The influence of anthropogenic climate change on organisms is an area of great scientific concern. Increasingly there is recognition that abrupt climate transitions have occurred over the late Quaternary; studies of these shifts may yield insights into likely biotic responses to contemporary warming. Here, we review research undertaken over the past decade investigating the response of Neotoma (woodrats) body size and distribution to climate change over the late Quaternary (the last 40,000 years). By integrating information from woodrat palaeomiddens, historical museum specimens and field studies of modern populations, we identify potential evolutionary responses to climate change occurring over a variety of temporal and spatial scales. Specifically, we characterize climatic thresholds in the past that led to local species extirpation and/or range alterations rather than in situ adaptation, and apply them to anticipate potential biotic responses to anthropogenic climate change. Location Middens were collected at about 55 sites scattered across the western United States, ranging from about 34 to 46° N and about 104 to 116° W, respectively. Data for modern populations were drawn from studies conducted in Death Valley, California, Missoula, Montana and the Sevilleta LTER site in central New Mexico. Methods We analysed faecal pellets from midden series collected at numerous cave sites across the western United States. From these we estimated body mass using techniques validated in earlier studies. We compared body size fluctuations at different elevations in different regions and integrated these results with studies investigating temperature–body size tradeoffs in modern animals. We also quantify the rapidity of the size changes over the late Quaternary to estimate the evolutionary capacity of woodrats to deal with predicted rates of anthropogenic climate change over the next century. Results We find remarkable similarities across the geographical range to late Quaternary climate change. In the middle of the geographical range woodrats respond in accordance to Bergmann's rule: colder climatic conditions select for larger body size and warmer conditions select for smaller body size. Patterns are more complicated at range boundaries, and local environmental conditions influence the observed response. In general, woodrat body size fluctuates with approximately the same amplitude and frequency as climate; there is a significant and positive correlation between woodrat body size and generalized climate proxies (such as ice core records). Woodrats have achieved evolutionary rates of change equal to or greater than those needed to adapt in situ to anthropogenic climate change. Main conclusions In situ body size evolution is a likely outcome of climate change, and such shifts are part of a normal spectrum of adaptation. Woodrats appear to be subject to ongoing body size selection in response to fluctuating environmental conditions. Allometric considerations suggest that these shifts in body size lead to substantial changes in the physiology, life history and ecology of woodrats, and on their direct and indirect interactions with other organisms in the ecosystem. Our work highlights the importance of a finely resolved and long-term record in understanding biotic responses to climatic shifts.

Journal of Biogeography↗

USGS2020

Spatial capture–recapture with random thinning for unidentified encounters

Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of “marked” and “unmarked” individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites. Here we describe a “random thinning” SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE. We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low‐density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear ( Ursus arctos ) density in Oriental Cantabrian Mountains (North Spain). Our model can improve density estimation for noninvasive sampling studies for low‐density populations with low rates of individual identification, by making use of available data that might otherwise be discarded.

Ecology & Evolution↗

USGS2020

Determinants and consequences of dispersal in vertebrates with complex life cycles: a review of pond-breeding amphibians

Dispersal is a central process in ecology and evolution. It strongly influences the dynamics of spatially structured populations, by affecting population growth rate and local colonization-extinction processes. Dispersal can also influence evolutionary processes because it determines rates and patterns of gene flow in spatially structured populations and is closely linked to local adaptation. For these reasons, dispersal has received considerable attention from ecologists and evolutionary biologists. However, although it has been studied extensively in taxa such as birds and mammals, much less is known about dispersal in vertebrates with complex life cycles such as pond-breeding amphibians. Over the past two decades, researchers have taken an interest in amphibian dispersal and initiated both fundamental and applied studies, using a broad range of experimental and observational approaches. This body of research reveals complex dispersal patterns, causations and syndromes, with dramatic consequences for the demography and genetics of amphibian populations. In this review, our goals are to (1) redefine and clarify the concept of amphibian dispersal, (2) review current knowledge about the effects of individual (i.e., condition-dependent dispersal) and environmental (i.e., context-dependent dispersal) factors during the three stages of dispersal (i.e., emigration, immigration, transience), (3) identify the demographic and genetic consequences of dispersal in spatially structured amphibian populations, and (4) propose new research avenues to extend our understanding of amphibian dispersal. In particular, we emphasize the need to (1) quantify dispersal rate and distance rigorously using suitable model systems, (2) investigate the genetic basis and dispersal evolution patterns, and (3) examine dispersal-related eco-evolutionary dynamics. These proposed research avenues tap from the recent advances in quantitative and molecular methods and have the potential to improve our understanding of dispersal in organisms with complex life cycles.

The Quarterly Review of Biology↗

USGS1996

Developing a temporal database of urban development for the Baltimore/Washington region

The U.S. Geological Survey (USGS), the University of Maryland Baltimore County (UMBC), and the U.S. Bureau of the Census are working together as a multiagency, multidisciplinary team in developing a temporal database that documents the growth of the Baltimore-Washington metropolitan region. This database consists of urban development, principal transportation, shoreline, and population density change. The urban development theme, considered a primary data layer in the study of urban land transformation resulting from human impact on the land, is the focus of this paper. The Baltimore-Washington Spatial Dynamics and Human Impacts Study builds on earlier research efforts that mapped urban land use change for the San Francisco Bay area (Acevedo and Bell, 1994; Bell and others, 1995; Kirtland and others, 1994). In developing a temporal database (Acevedo and others, in press), the team participants hope to provide data that can be used to study patterns of urban growth; assess ecological, environmental, and climatic impacts of urban change; and model and predict future urbanization patterns and impacts (Clarke and others, 1996). Both the San Francisco and Baltimore-Washington regions were selected because of the rapid urban growth and resulting impacts on their ecosystems. The Chesapeake Bay region in particular has undergone extensive environmental agitation due to the hydrologic problems that have arisen from the increase in impermeable surfaces and structures, that is buildings and pavement that physically cover the soil. Because of the inability of water to percolate into the ground, little purification occurs by filtration. Water runs over paved surfaces and quickly washes high levels of toxins directly into the water system. Toxins like gasoline, oil, and fertilizer have dramatically affected the local streams, rivers, and the bay. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. This paper describes the techniques used to map the extent of urban areas for Phase I and does not discuss Phase II in detail because the work is still in progress. In this study, urban development is defined as areas of intensive use, with much of the land covered by structures. The built-up areas are characterized by the existence of a systematic street pattern, and the relative concentration of buildings and associated intensive use areas, such as parking lots. Using this definition, urban development does not refer to political boundaries and may include incorporated or unincorporated areas as well as military reservations. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. To build the urban component of the temporal database, a multidisciplinary team was assembled and a phased approach initiated. Expanding on procedures developed for the San Francisco Regional Study (Bell and others, 1995), the team developed data definitions, a classification scheme, compilation criteria, mapping specifications, guidelines for source materials, and metadata specifications to support development of a logically consistent dataset. Extensive documentation procedures were established to ensure consistency in data collection, and for subsequent application to other regions. Phase II was the implementation of the regional mapping effort. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. The study area for Phase I consisted of an approximate area of 15- by 15-minute segment centered around the city of Baltimore (fig. 1). Phase I was used as a prototype for the technique development and integration that the multiagency collaborative effort would require. The regional study, Phase II, encompassed a 2-degree square centered on Washington, D.C. With more than 7 million people spread across 39 counties, the Baltimore-Washington region is one the Nation's fastest growing metropolitan areas. The two cities are rapidly merging into one.

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