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At least 109 records · Page 6Linked to original sources
USGS2025

Density as a mechanism linking habitat disturbance to increased pathogen prevalence: Evidence from a natural experiment

Sudden habitat loss associated with environmental disturbance can trigger animals to move from affected to undisturbed areas, where increases in local density may occur. Although pathogen transmission is strongly related to local density, how crowding after habitat loss affects infection dynamics in wild populations remains unclear. Here we conceptualize the Disturbance-Density-Disease hypothesis, which posits that disturbance-induced habitat loss results in increased pathogen prevalence via increases in local density at adjacent, undisturbed patches. We then used data from before, during, and after flooding disturbance to test this hypothesis in boreal toads Anaxyrus boreas boreas co-occurring with the pathogenic fungus Batrachochytrium dendrobatidis ( Bd ). We collected Bd samples from captured individuals during a 5-year (2015–2019) mark-recapture study of boreal toads ( n = 1295) that breed in beaver ponds in western Wyoming, USA. During spring of 2017, an extreme flooding event destroyed several beaver dams, resulting in the loss of breeding habitat. We compared host density and pathogen prevalence pre- and post-disturbance at sites affected versus unaffected by flooding. At affected sites, population density and Bd prevalence increased at adjacent, undisturbed ponds following the sudden loss of habitat. Moreover, neither host density nor Bd prevalence increased at control sites in areas unaffected by flooding. Taken together, our results support hypothesized links between disturbance, adjacent increases in density, and subsequent increases in pathogen prevalence. Our study contributes to a growing body of ecological research leveraging natural experiments to extract insights from extreme disturbance events. By doing so, we demonstrate an important consequence of disturbance beyond proximate habitat loss and introduce a clear conceptual approach (the Disturbance-Density-Disease hypothesis) to understanding how pathogen transmission can be affected by disturbance via alterations to local density.

Wyoming↗
USGS2001

Biology and conservation of the common murre in California, Oregon, Washington, and British Columbia: Vol. 1, Natural history and population trends

Over the past 30 years, the common murre ( Uria aalge californica ) has been recognized as a prominent indicator of marine conservation issues in California, Oregon, Washington, and British Columbia, especially regarding oil pollution, certain fisheries, and human disturbance. To assist the effective management of the common murre and the marine environments in which they live, this summary of available information on the biology and regional status of the common murre has been sponsored by the U.S. Fish and Wildlife Service (Division of Migratory Bird Management). In Volume 1 (Chapter 1), the natural history of the common murre is summarized, drawing heavily on breeding studies from the South Farallon Islands, California, plus a host of detailed breeding studies from the North Atlantic Ocean. Population trends of the common murre are summarized in Volume 1 (Chapter 2), focusing on changes in whole-colony counts determined from aerial photographs between the late 1970s and 1995 in California, Oregon and Washington. Historical data and human impacts to murre colonies since the early nineteenth century are also summarized. Volume 2 will summarize population threats, conservation, and management. Information presented in Volume 1 has been obtained and recorded by a large number of researchers and natural historians over two centuries. From the 1960s to 1995, most work in California, Oregon, and Washington was sponsored by the U.S. Fish and Wildlife Service, Minerals Management Service, and California Department of Fish and Game. Important breeding biology studies were conducted at the South Farallon Islands (Farallon National Wildlife Refuge) by the Point Reyes Bird Observatory, in coordination with the U.S. Fish and Wildlife Service (San Francisco Bay National Wildlife Refuge). Colony surveys in California were conducted mainly by the U.S. Fish and Wildlife Service (San Francisco Bay National Wildlife Refuge), U.S. Geological Survey (Western Ecological Research Center, Dixon Field Station), Humboldt State University, and University of California (Santa Cruz). Colony surveys in Oregon and Washington were conducted mainly by the U.S. Fish and Wildlife Service (Oregon Coast National Wildlife Refuge and Washington Maritime National Wildlife Refuges). In British Columbia, most work from the 1960s to 1995 was sponsored and conducted by the Canadian Wildlife Service and Royal British Columbia Museum.

British Columbia, California, Oregon, Washington↗
USGS2024

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High-resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double-counted plants) error in counts from high-resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post-wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high-resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting ( p ) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Methods in Ecology and Evolution↗
USGS2020

Impacts of periodic dredging on macroinvertebrate prey availability for benthic foraging fishes in central San Francisco Bay, California

Background Because of its importance for species covered under Federal Fishery Management Plans (FMPs), the San Francisco Bay (SFB) estuary has been designated as Essential Fish Habitat (EFH) under the Magnuson-Stevens Fishery Conservation and Management Act (MSA; 16 United States Code §18559b). Within this estuary, benthic macroinvertebrate communities provide important prey resources for many economically significant fish species that rely on EFH. Periodic maintenance dredging can impact benthic communities; however, there is a lack of scientific information specific to SFB regarding dredging effects on macroinvertebrates in fish foraging areas. In addition, rates of benthic community recolonization and recovery following dredging and subsequent effects on foraging fish are unknown. For this reason, it is difficult for regulatory and resource agencies to determine the impacts of maintenance dredging. Thus, the National Marine Fisheries Service (NMFS) and the consortium of agencies (U.S. Environmental Protection Agency [EPA], U.S. Army Corp of Engineers [USACE], San Francisco Regional Water Quality Control Board [SFRWQCB], and San Francisco Bay Conservation and Development Commission [BCDC]) that make up the San Francisco Bay Long Term Management Strategy for Dredging (LTMS) identified a study of dredging impacts on SFB fish foraging habitat as one of their highest priorities in their 2011 Programmatic EFH Agreement (U.S. Army Corp of Engineers and U.S. Environmental Protection Agency, 2011). The LTMS agencies identified the region of interest as shallow (<13 feet [<4 meters (m)] mean lower low water [MLLW]), soft-bottom (silt/clay soil texture) areas in the Central Bay of SFB that were periodically dredged (every 1–3 years). Fish species of interest were compiled by NMFS and included those managed by the Pacific Groundfish, Pacific Salmon, and Coastal Pelagic FMPs (pursuant to the MSA) as well as those listed under the California State or Federal Endangered Species Act (ESA; 16 U.S.C. §1531–1544) as threatened or endangered. Target species included leopard shark ( Triakis semifasciata ), big skate ( Raja binoculata ), English sole ( Parophrys vetulus ), starry flounder ( Platichthys stellatus) , brown rockfish ( Sebastes auriculatus ), green sturgeon ( Acipenser medirostris ; threatened species under Federal ESA), northern anchovy ( Engraulis mordax ), longfin smelt ( Spirinchus thaleichthys, threatened under California ESA), and Pacific sardine ( Sardinops sagax ). In addition, Dungeness crab ( Cancer magister ), California halibut ( Paralichthys californicus ), and white sturgeon ( Acipenser transmontanus ) also were included because they are substantial contributors to the California State fishery. To address LTMS priorities, U.S. Geological Survey, Western Ecological Research Center, San Francisco Bay Estuary Field Station (hereafter USGS) conducted a multi-phased project including an initial literature review, study design, pilot study, and implementation of a full study. The overarching goal was to assess the effects of periodic dredge operations (every 1–3 years) on benthic habitat for foraging fish in the Central Bay, with emphasis on the foraging requirements of target fish species and analyses of benthic macroinvertebrates in dredged areas compared to adjacent undredged reference areas. The USGS partnered with University of California, Davis, fisheries expert James Hobbs to synthesize existing knowledge of fish foraging ecology and review benthic infauna community composition in SFB with a focus on the Central Bay. The literature review (Phase I; De La Cruz and others, 2016) addressed key questions identified by the LTMS on benthic foraging fish in the study area, including the following: (1) What are target fish eating? (2) What are the seasonal differences in prey items and macroinvertebrate assemblages? (3) What are the annual differences in prey items and macroinvertebrate assemblages? (4) What are the predominant macroinvertebrate functional groups from the perspective of fish foraging? Phase II consisted of creating a framework for a functional assessment of maintenance dredging effects on foraging fish and drafting a full study design (De La Cruz and others, 2017), which was then tested in the Phase III pilot study. The Phase IV full study incorporated lessons learned from the pilot study. Here we focus on the results of the full study and implications for benthic foraging fishes.

California↗
USGS2017

Quantifying site-specific physical heterogeneity within an estuarine seascape

Quantifying physical heterogeneity is essential for meaningful ecological research and effective resource management. Spatial patterns of multiple, co-occurring physical features are rarely quantified across a seascape because of methodological challenges. Here, we identified approaches that measured total site-specific heterogeneity, an often overlooked aspect of estuarine ecosystems. Specifically, we examined 23 metrics that quantified four types of common physical features: (1) river and creek confluences, (2) bathymetric variation including underwater drop-offs, (3) land features such as islands/sandbars, and (4) major underwater channel networks. Our research at 40 sites throughout Plum Island Estuary (PIE) provided solutions to two problems. The first problem was that individual metrics that measured heterogeneity of a single physical feature showed different regional patterns. We solved this first problem by combining multiple metrics for a single feature using a within-physical feature cluster analysis. With this approach, we identified sites with four different types of confluences and three different types of underwater drop-offs. The second problem was that when multiple physical features co-occurred, new patterns of total site-specific heterogeneity were created across the seascape. This pattern of total heterogeneity has potential ecological relevance to structure-oriented predators. To address this second problem, we identified sites with similar types of total physical heterogeneity using an across-physical feature cluster analysis. Then, we calculated an additive heterogeneity index, which integrated all physical features at a site. Finally, we tested if site-specific additive heterogeneity index values differed for across-physical feature clusters. In PIE, the sites with the highest additive heterogeneity index values were clustered together and corresponded to sites where a fish predator, adult striped bass ( Morone saxatilis ), aggregated in a related acoustic tracking study. In summary, we have shown general approaches to quantifying site-specific heterogeneity.

Massachusetts↗
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↗
USGS1980

The comparison of usage and availability measurements for evaluating resource preference

Modern ecological research often involves the comparison of the usage of habitat types or food items to the availability of those resources to the animal. Widely used methods of determining preference from measurements of usage and availability depend critically on the array of components that the researcher, often with a degree of arbitrariness, deems available to the animal. This paper proposes a new method, based on ranks of components by usage and by availability. A virtue of the rank procedure is that it provides comparable results whether a questionable component is included or excluded from consideration. Statistical tests of significance are given for the method. The paper also offers a hierarchical ordering of selection processes. This hierarchy resolves certain inconsistencies among studies of selection and is compatible with the analytic technique offered in this paper.

Ecology↗
USGS2015

A guide to Bayesian model selection for ecologists

The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.

Ecological Monographs↗
USGS2013

Beaver dams maintain fish biodiversity by increasing habitat heterogeneity throughout a low-gradient stream network

Understanding the relationship between heterogeneity and biodiversity is an active focus of ecological research. Although habitat heterogeneity is conceptually linked to biodiversity, the amount and configuration of heterogeneity that maintains biodiversity within ecosystems is not well understood, especially for an entire stream network. Here, we tested alternative outcomes about how habitat alterations caused by beaver dams affected native fish biodiversity. Specifically, we quantified in-stream habitat and fish assemblages above and below all beaver dams ( n = 15) and selected control sites ( n = 9), adjacent to beaver dams, within an entire, low-gradient stream network (Fish Brook, MA, U.S.A.). Beaver dams altered habitat within streams in four ways based on upstream versus downstream differences in stream width, depth, velocity and substratum. In general, habitat heterogeneity, measured using two indices, was greater at beaver dams than control sites. The diversity and abundance of fish around beaver dams were positively related to habitat heterogeneity. Faster water and the coarser substratum below beaver dams increased the amount of fluvial habitat available to native fish. This alteration can be critical for fish with life histories that depend on flowing water and hard substrata. In summary, within a stream network, beaver dams maintained fish biodiversity by altering in-stream habitat and increasing habitat heterogeneity. Understanding the relationship between habitat heterogeneity and biodiversity can advance basic freshwater ecology and provide science-based support for applied aquatic conservation

Massachusetts↗
USGS2004

Abundance estimation and conservation biology

Abundance is the state variable of interest in most population–level ecological research and in most programs involving management and conservation of animal populations. Abundance is the single parameter of interest in capture–recapture models for closed populations (e.g., Darroch, 1958; Otis et al., 1978; Chao, 2001). The initial capture–recapture models developed for partially (Darroch, 1959) and completely (Jolly, 1965; Seber, 1965) open populations represented efforts to relax the restrictive assumption of population closure for the purpose of estimating abundance. Subsequent emphases in capture–recapture work were on survival rate estimation in the 1970’s and 1980’s (e.g., Burnham et al., 1987; Lebreton et al.,1992), and on movement estimation in the 1990’s (Brownie et al., 1993; Schwarz et al., 1993). However, from the mid–1990’s until the present time, capture–recapture investigators have expressed a renewed interest in abundance and related parameters (Pradel, 1996; Schwarz & Arnason, 1996; Schwarz, 2001). The focus of this session was abundance, and presentations covered topics ranging from estimation of abundance and rate of change in abundance, to inferences about the demographic processes underlying changes in abundance, to occupancy as a surrogate of abundance. The plenary paper by Link & Barker (2004) is provocative and very interesting, and it contains a number of important messages and suggestions. Link & Barker (2004) emphasize that the increasing complexity of capture–recapture models has resulted in large numbers of parameters and that a challenge to ecologists is to extract ecological signals from this complexity. They offer hierarchical models as a natural approach to inference in which traditional parameters are viewed as realizations of stochastic processes. These processes are governed by hyperparameters, and the inferential approach focuses on these hyperparameters. Link & Barker (2004) also suggest that our attention should be focused on relationships between demographic processes such as survival and recruitment, the two quantities responsible for changes in abundance, rather than simply on the magnitudes of these quantities. They describe a type of Jolly–Seber capture–recapture model that permits inference about the underlying relationship between per capita recruitment rates and survival rates (Link & Barker, this volume). Implementation used Bayesian Markov Chain Monte Carlo methods and appeared to work well, yielding inferences about the relationship between recruitment and survival that were robust to selection of prior distribution. We believe that readers will find their arguments compelling, and we expect to see increased use of hierarchical modeling approaches in capture–recapture and related fields. Otto (presentation without paper) also recommended use of hierarchical models in analysis of multiple data sources dealing with population dynamics of North American mallards. He integrated survival inferences from ringing data, abundance information from aerial survey data, and recruitment information based on age ratios from a harvest survey. He used a Leslie matrix population projection model as an integrating framework and obtained estimates of breeding population size using all data.Otto’s approach also permitted inference about biases in estimated quantities. As with the work of Link & Barker (2004), we find Otto’s recommendation to use hierarchical models to integrate data from multiple sources to be very compelling. Alisauskas et al. (2004) report results of an analysis of capture–recapture data for a askatchewan population of white–winged scoters. They used the approach of Pradel (1996) to estimate population growth rate (See the PDF) directly. Estimates for 1975–1985 were quite low, but estimates for the recent period, 2000–2003,increased to values > 1. Parameter estimates for seniority, survival and per capita recruitment (Pradel, 1996) led to the inference that increased recruitment was largely responsible for the improvements in population status and growth. However, various data sources also indicated that this increase in recruitment was likely a result of increased immigration rather than improved reproduction on the area. This latter inference is important from a conservation perspective in indicating the importance of birds in other locations to growth and health of the study population. Lukacs and Burnham presented material to be published elsewhere that dealt with the use of genetic markers in capture–recapture studies. The data sources for such studies are samples of hair or feces, which are then analyzed using molecular genetic techniques in order to determine individual genotypes with respect to a usually small number of loci. Two types of classification error can arise in such analyses. First, if only a small number of loci is examined, then there may be nonnegligible probabilities that multiple individual animals will have the same genotypes. The second type of error arises during the polymerase chain reaction (PCR) process and can result from failure of alleles to amplify (allelic dropout) or from PCR inhibitors in hair and feces that produce the appearance of false alleles or misprinting (Creel et al., 2003). Lukacs and Burnham developed models that formally incorporate possible misclassification of samples resulting from these errors. These models permit estimation of parameters such as abundance and survival in a manner that properly incorporates this uncertainty of individual identity. We anticipate that noninvasive sampling based on molecular genetic analyses of hair or feces will become extremely important for some species, and that the models of Lukacs and Burnham will become very popular for such analyses. MacKenzie & Nichols (2004) discuss the use of occupancy (proportion of patches or habitat area that is occupied) as a surrogate for abundance. In cases of territorial species and where birds occur at low densities, the number of occupied patches may provide a reasonable estimate of abundance. In other cases, occupancy can be viewed as providing information about one tail of the abundance distribution, P (N = 0). The motivation for considering occupancy as a surrogate for abundance is that occupancy is based on so–called presence–absence surveys that are frequently less expensive of time and effort than methods that estimate abundance directly. We describe one set of models that can be used to estimate occupancy for a single season and another that can be used to estimate parameters such as local probabilities of extinction and colonization that are associated with occupancy dynamics. We outline a possible hybrid approach that combines occupancy data with data on marked individuals in order to betterexplore the mechanisms underlying occupancy dynamics. These five presentations made for an interesting session containing useful information and recommendations for future work. A number of themes connecting these presentations could be emphasized. For example, two of the presentations considered alternatives to standard capture–recapture sampling that can be used to draw inferences about abundance, or a portion of the abundance distribution, with field methods that should be less expensive than usual capture–recapture approaches of handling animals. We believe that the most important theme of the session was the emphasis on the processes responsible for changes in abundance. In particular, we are excited by the potential for using hierarchical models as a means of investigating relationships among vital rates and as a means of combining multiple sources of data relevant to system dynamics. Indeed, we expect the importance of this session theme to be reflected in the content and presentations of the next EURING meeting.

Animal Biodiversity and Conservation↗
USGS2016

Evaluation of downscaled, gridded climate data for the conterminous United States

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

Ecological Applications↗
USGS2006

Relationships between reef fish communities and remotely sensed rugosity measurements in Biscayne National Park, Florida, USA

The realization that coral reef ecosystem management must occur across multiple spatial scales and habitat types has led scientists and resource managers to seek variables that are easily measured over large areas and correlate well with reef resources. Here we investigate the utility of new technology in airborne laser surveying (NASA Experimental Advanced Airborne Research Lidar (EAARL)) in assessing topographical complexity (rugosity) to predict reef fish community structure on shallow (<10 m deep) patch reefs. Marine portions of Biscayne National Park, Florida, USA, were surveyed remotely using the EAARL, and reef fish populations were visually surveyed on 10 patch reefs at independent, randomly selected stations ( n = 10–13 per reef). Rugosity at each station was assessed in situ by divers using the traditional chain-transect method (10-m scale), and remotely using the EAARL submarine topography data at multiple spatial scales (2, 5, and 10 m). The rugosity and biological datasets were analyzed together to elucidate the predictive power of EAARL rugosity in describing the variance in reef fish community variables and to assess the correlation between chain-transect and EAARL rugosity. EAARL rugosity was not well correlated with chain-transect rugosity, or with species richness of fishes (although statistically significant, the amount of variance explained by the model was very low). Variance in reef fish community attributes was better explained in reef-by-reef variability than by physical variables. However, once the reef-by-reef variability was taken into account in a two-way analysis of variance, the importance of rugosity could be seen on individual reefs. Fish species richness and abundance were statistically higher at high rugosity stations compared to medium and low rugosity stations, as predicted by prior ecological research. The EAARL shows promise as an important mapping tool for reef resource managers as they strive to inventory and protect coral reef resources.

Florida↗
USGS2024

Community synchrony in seed production is associated with trait similarity and climate across North America

Mast seeding, the synchronous and highly variable production of seed crops in perennial plants, is a population level phenomenon and has cascading effects in ecosystems. Mast seeding studies are typically conducted at the population/species level. Much less is known about synchrony in mast seeding between species because the necessary long-term data are rarely available. To investigate synchrony between species within communities, we used long-term data from seven forest communities in the US Long-Term Ecological Research (LTER) network, ranging from tropical rainforest to taiga. We focus on cross-species synchrony and i) quantify synchrony in reproduction overall and within LTER sites, ii) test for relationships between synchrony with trait and phylogenetic similarity, and iii) investigate how climate conditions are related to levels of synchrony. Overall, synchrony in reproduction between woody plant species was greater than expected by chance, but also spanned a wide range of values between species. Based on 11 traits for 103 species (plus phylogenetic relatedness), cross-species synchrony in reproduction was driven primarily by trait similarity with phylogeny being largely unimportant, and synchrony was higher in sites with greater climatic water deficit. Synchronous masting within a community has consequences for understanding forest regeneration dynamics and consumer-resource interactions.

North America↗
USGS2012

Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions

1. Understanding the factors affecting species occurrence is a pre-eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. MAXENT is a widely used software program designed to model and map species distribution using presence-only data. 2. We provide a critical review of MAXENT as applied to species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that MAXENT produces a number of poorly defined indices that are not directly related to the actual parameter of interest &ndash; the probability of occurrence ( &psi; ). This focus on an index was motivated by the belief that it is not possible to estimate &psi; from presence-only data; however, we demonstrate that &psi; is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of species detection. 3. The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that MAXENT produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that MAXENT predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the MAXENT method. 4. As with MAXENT, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.

Methods in Ecology and Evolution↗
USGS2010

Geographic variation in the plumage coloration of willow flycatchers Empidonax traillii

The ability to identify distinct taxonomic groups of birds (species, subspecies, geographic races) can advance ecological research efforts by determining connectivity between the non-breeding and breeding grounds for migrant species, identifying the origin of migrants, and helping to refine boundaries between subspecies or geographic races. Multiple methods are available to identify taxonomic groups (e.g., morphology, genetics), and one that has played an important role for avian taxonomists over the years is plumage coloration. With the advent of electronic devices that can quickly and accurately quantify plumage coloration, the potential of using coloration as an identifier for distinct taxonomic groups, even when differences are subtle, becomes possible. In this study, we evaluated the degree to which plumage coloration differs among the four subspecies of the willow flycatcher Empidonax traillii, evaluated sources of variation, and considered the utility of plumage coloration to assign subspecies membership for individuals of unknown origin. We used a colorimeter to measure plumage coloration of 374 adult willow flycatchers from 29 locations across their breeding range in 2004 and 2005. We found strong statistical differences among the mean plumage coloration values of the four subspecies; however, while individuals tended to group around their respective subspecies' mean color value, the dispersion of individuals around such means overlapped. Mean color values for each breeding site of the three western subspecies clustered together, but the eastern subspecies' color values were dispersed among the other subspecies, rather than distinctly clustered. Additionally, sites along boundaries showed evidence of intergradation and intermediate coloration patterns. We evaluated the predictive power of colorimeter measurements on flycatchers by constructing a canonical discriminant model to predict subspecies origin of migrants passing through the southwestern U.S. Considering only western subspecies, we found that individuals can be assigned with reasonable certainty. Applying the model to migrants sampled along the Colorado River in Mexico and the U.S. suggests different migration patterns for the three western subspecies. We believe that the use of plumage coloration, as measured by electronic devices, can provide a powerful tool to look at ecological questions in a wide range of avian species.

Journal of Avian Biology↗
USGS2022

How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?

Maps of the distribution and abundance of dominant plants derived from satellite data are essential for ecological research and management, particularly in the vast semiarid shrub-steppe. Appropriate application of these maps requires an understanding of model accuracy and precision, and how it might vary across space, time, and different vegetation types. For a 113 k Ha burn area, we compared modeled maps of different vegetation cover types created from satellite data to ‘benchmark” models based on intensive field sampling (~1500-2000 plots resampled annually for 5 years) for three new satellite-derived models: USDA Rangeland Analysis Platform (RAP), the USGS Rangeland Condition Monitoring Assessment and Projection (RCMAP), and USGS fractional estimate of exotic annual grass cover (USGS-fractional-EAG). We assessed out-of-sample point accuracy and asked if and how accuracy changed each year due to vegetation shifts, new images, and model improvements (i.e. model versions). We also assessed how map agreement between satellite-based and field-based models changed with scale of application, topography, and time since fire. Accuracy and map agreement varied considerably among the vegetation types and across time and space (r 2 ranging from 0 to 0.53), and some of the variability was predictable. All models tended to over or underestimate cover when field-measured cover was relatively low or high, respectively, i.e. a “false moderating effect”. Accuracy was greater and improved with newer versions of RAP (+0.05 to 0.29 r 2 ) compared to RCMAP and USGS fractional model estimates, and in some cases was greater than field-based models. Variability in map agreement tended to decrease with larger areas sampled (particularly in areas >12 km), and this scale dependency was more evident in RAP and USGS-fractional-EAG models. Creating a “fair” basis for comparison of spatial models of low-statured semiarid vegetation derived from satellite compared to field data is not trivial because scaling the field data to the scale of large satellite pixels (or downscaling satellite-based models to field scale) requires modeling and associated model uncertainty. Accuracy can vary considerably and understanding the variation can help guide application of the models to the appropriate time, place, and variables.

Idaho, Oregon↗
USGS2019

An analysis of autocorrelation and bias in home range estimation

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function AKDE, Silvermans rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area ) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area . To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animals movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area . While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area , AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

Ecological Monographs↗
USGS1999

Long-term experimental manipulation of winter snow regime and summer temperature in arctic and alpine tundra

Three 60 m long, 2·8 m high snowfences have been erected to study long-term effects of changing winter snow conditions on arctic and alpine tundra. This paper describes the experimental design and short-term effects. Open-top fiberglass warming chambers are placed along the experimental snow gradients and in controls areas outside the fences; each warming plot is paired with an unwarmed plot. The purpose of the experiment is to examine short- and long-term changes to the integrated physical-biological systems under simultaneous changes of winter snow regime and summer temperature, as part of the Long-Term Ecological Research network and the International Tundra Experiment. The sites were at Niwot Ridge, Colorado, a temperate high altitude site in the Colorado Rockies, and Toolik Lake, Alaska, a high-latitude site. Initial results indicate that although experimental designs are essentially identical at the arctic and alpine sites, experimental effects are different. The drift at Niwot Ridge lasts much longer than do the Toolik Lake drifts, so that the Niwot Ridge fence affects both summer and winter conditions, whereas the Toolik Lake fence affects primarily winter conditions. The temperature experiment also differs in effect between the sites. Although the average temperature increase at the two sites is similar (daily increase 1·5 °C at Toolik and 1·9 °C at Niwot Ridge), at Toolik Lake there is only minor diurnal variation, whereas at Niwot Ridge the daytime increases are extreme on sunny days (as much as 7–10 °C), and minimum nighttime temperatures in the chambers are often slightly cooler than ambient (by about 1 °C). The experimental drifts resulted in wintertime increases in temperature and CO 2 flux. Temperatures under the deep drifts were much more consistent and warmer than in control areas, and at Niwot Ridge remained very close to 0 °C all winter. These increased temperatures were likely responsible for observed increases in system carbon loss. Initial changes to the aboveground biotic system included an increase in growth in response to both snow and warming, despite a reduced growing season. This is expected to be a transient response that will eventually be replaced by reduced growth. At least one species, Kobresia myosuroides , had almost completely died at Niwot Ridge three years after fence construction, whereas other species were increasing. We expect in both the short- and long-term to see the strongest effects of snow at the Niwot Ridge site, and stronger effects of temperature at Toolik Lake.

Hydrological Processes↗