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At least 19 recordsLinked to original sources
USGS2018

Use of landscape simulation modeling to quantify resilience for ecological applications

Goals of fostering ecological resilience are increasingly used to guide U.S. public land management in the context of anthropogenic climate change and increasing landscape disturbances. There are, however, few operational means of assessing the resilience of a landscape or ecosystem. We present a method to evaluate resilience using simulation modeling. In this method, we use historical conditions (e.g., in North America, prior to European settlement), quantified using simulation modeling, to provide a comparative reference for contemporary conditions, where substantial departures indicate loss of resilience. Contemporary ecological conditions are compared statistically to the historical time series to create a resilience index, which can be used to prioritize landscapes for treatment and inform possible treatments. However, managing for resilience based on historical conditions is tenuous in the Anthropocene, which is characterized by rapid climate change, extensive human land use, altered disturbance regimes, and exotic species introductions. To account for the future variability of ecosystems resulting from climate and disturbance regime shifts, we augment historical simulations with simulations of ecosystem dynamics under projected climate and land use changes to assess the degree of departure from benchmark historical conditions. We use a mechanistic landscape model (FireBGCv2) applied to a large landscape in western Montana, USA, to illustrate the methods presented in this paper. Spatially explicit ecosystem modeling provides the vehicle to generate the historical and future time series needed to quantify potential resilience conditions associated with past and potential future conditions. Our methods show that given selection of a useful set of metrics, managers could use simulations like ours to evaluate potential future management directions.

Ecosphere↗
USGS2010

Bayesian Inference: with ecological applications

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

Book↗
USGS1988

Assessment of sampling stability in ecological applications of discriminant analysis

A simulation study was undertaken to assess the sampling stability of the variable loadings in linear discriminant function analysis. A factorial design was used for the factors of multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32 400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. Results from the simulations suggest that minimum sample sizes must exceed multivariate dimensionality by at least a factor of three to achieve reasonable levels of stability in discriminant function loadings. However, the requisite sample size would vary with respect to each of the design factors and, especially, with the overall amount of system variation. A review of 60 published studies and 142 individual analyses indicated that sample sizes in ecological studies often have met that requirement. However, individual group sample sizes frequently were very unequal, and checks of assumptions usually were not reported. We recommend that ecologists obtain group sample sizes that are at least three times as large as the number of variables measured.

Ecology↗
USGS2014

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several downscaled climate projections (or downscaled datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these downscaled datasets, known as downscaling, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for downscaling and the number of downscaled datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available downscaled datasets, how do these model outputs compare to each other? Which variables are available, and are certain downscaled datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these downscaled datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science-based climate change adaptation decisions.

Open-File Report↗
USGS1984

Chance-corrected classification for use in discriminant analysis: Ecological applications

A method for evaluating the classification table from a discriminant analysis is described. The statistic, kappa, is useful to ecologists in that it removes the effects of chance. It is useful even with equal group sample sizes although the need for a chance-corrected measure of prediction becomes greater with more dissimilar group sample sizes. Examples are presented.

American Midland Naturalist↗
USGS2012

Bioclimatic predictors for supporting ecological applications in the conterminous United States

The U.S. Geological Survey (USGS) has developed climate indices, referred to as bioclimatic predictors, which highlight climate conditions best related to species physiology. A set of 20 bioclimatic predictors were developed as Geographic Information Systems (GIS) continuous raster surfaces for each year between 1895 and 2009. The Parameter-elevation Regression on Independent Slopes Model (PRISM) and down-scaled PRISM data, which included both averaged multi-year and averaged monthly climate summaries, was used to develop these multi-scale bioclimatic predictors. Bioclimatic predictors capture information about annual conditions (annual mean temperature, annual precipitation, annual range in temperature and precipitation), as well as seasonal mean climate conditions and intra-year seasonality (temperature of the coldest and warmest months, precipitation of the wettest and driest quarters). Examining climate over time is useful when quantifying the effects of climate changes on species' distributions for past, current, and forecasted scenarios. These data, which have not been readily available to scientists, can provide biologists and ecologists with relevant and multi-scaled climate data to augment research on the responses of species to changing climate conditions. The relationships established between species demographics and distributions with bioclimatic predictors can inform land managers of climatic effects on species during decisionmaking processes.

Data Series↗
USGS2021

EverForecast—A near-term forecasting application for ecological decision support

The Everglades Forecasting application (EverForecast) provides decision makers with a support tool to examine optimal allocations of water across the managed landscape while explicitly quantifying the conflicting needs of multiple species. Covering the Greater Everglades (a vast, subtropical wetland ecosystem in South Florida), EverForecast provides 6-month forecasts of daily projected water stage across the region. It then runs these forecasts through a suite of species models and illustrates potential tradeoffs. All output is summarized by subregion and hydrologic category. Decision makers can use these near-term forecasts to manage the transition from current conditions to future alternatives according to their management priorities.

Florida↗
USGS2003

Application of ecological criteria in selecting marine reserves and developing reserve networks

Marine reserves are being established worldwide in response to a growing recognition of the conservation crisis that is building in the oceans. However, designation of reserves has been largely opportunistic, or protective measures have been implemented (often overlapping and sometimes in conflict) by different entities seeking to achieve different ends. This has created confusion among both users and enforcers, and the proliferation of different measures provides a false sense of protection where little is offered. This paper sets out a procedure grounded in current understanding of ecological processes, that allows the evaluation and selection of reserve sites in order to develop functional, interconnected networks of fully protected reserves that will fulfill multiple objectives. By fully protected we mean permanently closed to fishing and other resource extraction. We provide a framework that unifies the central aims of conservation and fishery management, while also meeting other human needs such as the provision of ecosystem services (e.g., maintenance of coastal water quality, shoreline protection, and recreational opportunities). In our scheme, candidate sites for reserves are evaluated against 12 criteria focused toward sustaining the biological integrity and productivity of marine systems at both local and regional scales. While a limited number of sites will be indispensable in a network, many will be of similar value as reserves, allowing the design of numerous alternative, biologically adequate networks. Devising multiple network designs will help ensure that ecological functionality is preserved throughout the socioeconomic evaluation process. Too often, socioeconomic criteria have dominated the process of reserve selection, potentially undermining their efficacy. We argue that application of biological criteria must precede and inform socioeconomic evaluation, since maintenance of ecosystem functioning is essential for meeting all of the goals for reserves. It is critical that stakeholders are fully involved throughout this process. Application of the proposed criteria will lead to networks whose multifunctionality will help unite the objectives of different management entities, so accelerating progress toward improved stewardship of the oceans.

Ecological Applications↗
USGS2021

A more representative community of ecologists

Ecologists play a crucial role in providing solutions to the challenges facing the world. For most of the history of the field, however, the science of ecology has been pursued by white men, and increasingly, by white women. This lack of diversity is untenable today, not only because it is socially unjust, but also because solving environmental problems requires diversity. Ecology as a science is an extremely rewarding and fun career choice for many, but how can the field recruit a more diverse workforce to do this important and gratifying work? Attracting and retaining future ecologists of color is the focus of this Forum. How do young people choose a career in ecology and environmental sciences? For many ecologists, environmental scientists and managers, and future natural resource professionals, an early field experience provides a crucial introduction and gateway. A sojourn at a field station, an extended field trip, or field expedition has introduced many of today’s professionals to their fields, but at the same time, a growing literature documents problematic behavior, discrimination, and other forms of harassment that have been far too frequently reported, and little attention has been given to conscious or implicit exclusion of students from diverse backgrounds. In their lead article, Bowser and Cid, two leading researchers and long-time champions of diversity in ecology, build on experience in long-running programs as well as the literature to diagnose challenges facing diverse youth, and present approaches to encourage, rather than discourage, further engagement. They describe affirmative and creative measures that foster a sense of inclusion and community among young scholars; this sense of belonging and empowerment allows many to advance to studies and careers in ecology and environmental science. In the subsequent six papers, authors explore topics either responding to, or inspired by, the lead paper. They explore alternatives to a field experience as a gateway to a career in ecology or environmental sciences through, for example, data science or the social sciences. Other comment papers describe challenges faced in the next phases of an academic career, barriers faced by specific cultural groups and the approaches, challenges, and outcomes of programs aiming to increase the diversity of the environmental STEM workforce. All responses sound a clarion call for change to hear and value different voices and perspectives to be heard and valued. These include (1) structural and cultural change to our institutions and reward structures; (2) developing and nurturing personal relationships among students and their mentors, within teams and in internships; (3) making entry to ecology inviting and making advancement in ecology free from systemic barriers; and (4) broadening our vision of ecology, and the ways we learn about the world’s ecosystems. The Ecological Society of America, the home for Ecological Applications , is committed to the diversity, equity and inclusion needed to tackle environmental challenges in unity (https://www.esa.org/esablog/2020/09/24/time-for-action-esa-initiates-a-diversity-equity-inclusion-and-justice-deij-task-force/). The world needs all available talent and perspectives to meet environmental challenges today. Ecological Applications has long published occasional papers on the profession of ecology and never ones more important or timely than these. The editorial team is proud to provide an outlet for the voices of our field, in all its current, if inadequate, diversity and honored to host the passionate and committed views of our authors.

Ecological Applications↗
USGS2025

Applications of unoccupied aerial systems (UAS) in landscape ecology: A review of recent research, challenges and emerging opportunities

Context Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. Objectives The review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. Methods We conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. Results We summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. Conclusion We observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics.

Landscape Ecology↗
USGS2019

Successful molecular detection studies require clear communication among diverse research partners

Molecular detection techniques are powerful tools used in ecological applications ranging from diet analyses to pathogen surveillance. Research partnerships that use these tools often involve collaboration among professionals with expertise in field biology, laboratory techniques, quantitative modeling, wildlife disease, and natural resource management. However, in many cases, each of these collaborators lacks specific knowledge about the approaches, decisions, methods, and terminology used by their research partners, which can impede effective communication and act as a barrier to the efficient use of molecular data for ecological inferences and subsequent conservation decision making. We outline a collaborative framework to assist colleagues with diverse types of expertise to effectively translate their scientific and management needs to research partners from other specialties. The molecular techniques used to detect organisms will continue to advance both in sophistication and in the breadth of ecological applications. Our objective is to enable ecologists to harness the full utility of these methods by developing effective collaborative partnerships.

Frontiers in Ecology and the Environment↗
USGS2022

A framework for prioritizing contaminants in retrospective ecological assessments: Application in the Milwaukee Estuary (Milwaukee, WI)

Watersheds are subjected to diverse anthropogenic inputs, exposing aquatic biota to a wide range of chemicals. Detection of multiple, different chemicals can challenge natural resource managers who often have to determine where to allocate potentially limited resources. Here, we describe a weight-of-evidence framework for retrospectively prioritizing aquatic contaminants. To demonstrate framework utility, we used data from 96-h caged fish studies to prioritize chemicals detected in the Milwaukee Estuary (WI, USA; 2017–2018). Across study years, 77/178 targeted chemicals were detected. Chemicals were assigned prioritization scores based on spatial and temporal detection frequency, environmental distribution, environmental fate, ecotoxicological potential, and effect prediction. Chemicals were sorted into priority bins based on the intersection of prioritization score and data availability. Data-limited chemicals represented those that did not have sufficient data to adequately evaluate ecotoxicological potential or environmental fate. Seven compounds (fluoranthene, benzo[ a ]pyrene, pyrene, atrazine, metolachlor, phenanthrene, and DEET) were identified as high or medium priority and data sufficient and flagged as candidates for further effects-based monitoring studies. Twenty-one compounds were identified as high or medium priority and data limited and flagged as candidates for further ecotoxicological research. Fifteen chemicals were flagged as the lowest priority in the watershed. One of these chemicals (2-methylnaphthalene) displayed no data limitations and was flagged as a definitively low-priority chemical. The remaining chemicals displayed some data limitations and were considered lower-priority compounds (contingent on further ecotoxicological and environmental fate assessments). The remaining 34 compounds were flagged as low or medium priority. Altogether, this prioritization provided a screening-level (non-definitive) assessment that could be used to focus further resource management and risk assessment activities in the Milwaukee Estuary. Furthermore, by providing detailed methodology and a practical example with real experimental data, we demonstrated that the proposed framework represents a transparent and adaptable approach for prioritizing contaminants in freshwater environments. Integr Environ Assess Manag 2023;00:1–21. © 2022 SETAC

Wisconsin↗
USGS2003

Structural Equation Modeling: Applications in ecological and evolutionary biology

This book presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems. Supplementary information can be found at the authors website, http://www.jamesbgrace.com/ . • Details why multivariate analyses should be used to study ecological systems • Exposes unappreciated weakness in many current popular analyses • Emphasizes the future methodological developments needed to advance our understanding of ecological systems.

Book↗
USGS2018

DNA mixtures for ecology

Mixtures of DNA from multiple contributors present a novel opportunity to count individuals to inform fish and wildlife ecology. We apply a likelihood-based framework to estimate the number of contributors to a DNA mixture for ecological applications. We then assess the performance of DNA mixture estimation through a combination of simulation analyses, laboratory testing, and a field trial to estimate fish predation rates from stomach content analysis. Simulations indicated reasonably sized genetic marker panels could estimate the number of contributors to mixtures comprised of up to 10 individuals, with potential to resolve larger mixtures with additional markers. Mixture estimates demonstrated robustness to common genotyping errors associated with fish and wildlife genetics applications. Laboratory trials demonstrated that DNA combined from multiple yellow perch ( Perca flavescens ) could be successfully genotyped with a 14-loci microsatellite panel and led to successful estimation for up to 5-contributor mixtures. Stomach content analysis with DNA mixtures indicated a 5-fold increase in estimated predation rates of yellow perch by largemouth bass ( Micropterus salmoides ) relative to conventional visual assessment of diet contents which can miss partially digested prey items. DNA mixtures have potential to expand applications of count-based ecological analyses. Technical challenges in generating genotypes from DNA mixtures may initially limit their use, however, advances in next generation genotyping platforms are anticipated to surmount these obstacles. Chiefly, we envision opportunity for DNA mixtures to advance eDNA analysis beyond presence/absence based inference to enumeration of specimens.

Methods in Ecology and Evolution↗
USGS2012

Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

Introduction Evaluating the environmental impacts of climate change on water resources and biological components of the landscape is an integral part of hydrologic and ecological investigations, and the resultant land and resource management in the twenty-first century. Impacts of both climate and simulated hydrologic parameters on ecological processes are relevant at scales that reflect the heterogeneity and complexity of landscapes. At present, simulations of climate change available from global climate models [GCMs] require downscaling for hydrologic or ecological applications. Methods Using statistically downscaled future climate projections developed using constructed analogues, a methodology was developed to further downscale the projections spatially using a gradient-inverse-distance-squared approach for application to hydrologic modeling at 270-m spatial resolution. Results This paper illustrates a methodology to downscale and bias-correct national GCMs to subkilometer scales that are applicable to fine-scale environmental processes. Four scenarios were chosen to bracket the range of future emissions put forth by the Intergovernmental Panel on Climate Change. Fine-scale applications of downscaled datasets of ecological and hydrologic correlations to variation in climate are illustrated. Conclusions The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

Ecological Processes↗
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↗
USGS2017

Acoustic telemetry and fisheries management

This paper reviews the use of acoustic telemetry as a tool for addressing issues in fisheries management, and serves as the lead to the special Feature Issue of Ecological Applications titled “Acoustic Telemetry and Fisheries Management” . Specifically, we provide an overview of the ways in which acoustic telemetry can be used to inform issues central to the ecology, conservation, and management of exploited and/or imperiled fish species. Despite great strides in this area in recent years, there are comparatively few examples where data have been applied directly to influence fisheries management and policy. We review the literature on this issue, identify the strengths and weaknesses of work done to date, and highlight knowledge gaps and difficulties in applying empirical fish telemetry studies to fisheries policy and practice. We then highlight the key areas of management and policy addressed, as well as the challenges that needed to be overcome to do this. We conclude with a set of recommendations about how researchers can, in consultation with stock assessment scientists and managers, formulate testable scientific questions to address and design future studies to generate data that can be used in a meaningful way by fisheries management and conservation practitioners. We also urge the involvement of relevant stakeholders (managers, fishers, conservation societies, etc.) early on in the process (i.e. in the co-creation of research projects), so that all priority questions and issues can be addressed effectively.

Ecological Applications↗