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At least 577 records · Page 32Linked to original sources
USGS2005

Human land use influences chronic wasting disease prevalence in mule deer

Human alteration of landscapes can affect the distribution, abundance, and behavior of wildlife. We explored the effects of human land use on the prevalence of chronic wasting disease (CWD) in mule deer (Odocoileus hemionus) populations residing in north-central Colorado. We chose best approximating models estimating CWD prevalence in relation to differences in human land use, sex, and geographic location. Prevalence was higher in developed areas and among male deer, suggesting anthropogenic influences on the occurrence of disease. We also found a relatively high degree of variation in prevalence across the three study sites, suggesting that spatial patterns in disease may be influenced by other factors operating at a broader, landscape scale. Our results suggest that multiple factors, including changes in land use, differences in exposure risk between sexes, and landscape-scaled heterogeneity, are associated with CWD prevalence in north-central Colorado.

Ecological Applications↗
USGS2006

Albatross species demonstrate regional differences in North Pacific marine contamination

Recent concern about negative effects on human health from elevated organochlorine and mercury concentrations in marine foods has highlighted the need to understand temporal and spatial patterns of marine pollution. Seabirds, long-lived pelagic predators with wide foraging ranges, can be used as indicators of regional contaminant patterns across large temporal and spatial scales. Here we evaluate contaminant levels, carbon and nitrogen stable isotope ratios, and satellite telemetry data from two sympatrically breeding North Pacific albatross species to demonstrate that (1) organochlorine and mercury contaminant levels are significantly higher in the California Current compared to levels in the high-latitude North Pacific and (2) levels of organochlorine contaminants in the North Paci.c are increasing over time. Black-footed Albatrosses (Phoebastria nigripes) had 370-460% higher organochlorine (polychlorinated biphenyls [PCBs], dichlorodiphenyltrichloroethanes [DDTs]) and mercury body burdens than a closely related species, the Laysan Albatross (P. immutabilis), primarily due to regional segregation of their North Pacific foraging areas. PCBs (the sum of the individual PCB congeners analyzed) and DDE concentrations in both albatross species were 130-360% higher than concentrations measured a decade ago. Our results demonstrate dramatically high and increasing contaminant concentrations in the eastern North Pacific Ocean, a finding relevant to other marine predators, including humans. ?? 2006 by the Ecological Society of America.

Ecological Applications↗
USGS2006

Additive effects of vertebrate predators on insects in a Puerto Rican coffee plantation

A variety of studies have established the value of shaded coffee plantations as habitat for birds. While the value of birds as biological controls in coffee has received some attention, the interactions between birds and other predators of insects have not been tested. We used exclosures to examine the effects of vertebrate predators on the arthropods associated with coffee, in particular the coffee leafminer (Leucoptera coffeella) and the flatid planthopper Petrusa epilepsis, in a shaded coffee plantation in Puerto Rico. We used a 2 x 2 factorial design with four treatments: exclusion of birds, lizards, birds and lizards, and control (no exclusion). Abundance of insects >5 mm increased when birds or both birds and lizards were removed. Birds and lizards had an additive effect for insects <5 mm and for all insects combined. Coffee leafminers showed a weak response to removal of predators while planthopper abundance increased significantly in the absence of avian predators. Arthropod predators and parasitoids did not differ significantly between treatments. Our findings suggest that vertebrate insectivores have an additive effect on insects in coffee and may help control abundances of some coffee pests. Equally important, we present evidence suggesting that they do not interfere with other known natural enemies of coffee pests. ?? 2006 by the Ecological Society of America.

Ecological Applications↗
USGS2006

Carryover aquatic effects on survival of metamorphic frogs during pond emigration

In organisms with complex life cycles, physiological stressors during early life stages may have fitness-level impacts that are delayed into later stages or habitats. We tested the hypothesis that body size and date of metamorphosis, which are highly responsive to aquatic stressors, influence post-metamorphic survival and movement patterns in the terrestrial phase of an ephemeral pond-breeding frog by examining these traits in two populations of northern red-legged frogs (Rana aurora aurora). To increase variation of body size at metamorphosis, we manipulated food availability for 314 of 1045 uniquely marked tadpoles and estimated the probability that frogs survived and emigrated using concentric rings of drift fencing surrounding ponds and Bayesian capture-recapture modeling. The odds of surviving and emigrating from the ponds to the innermost drift fences, ???12 m, increased by factors of 2.20 (95% credibility intervals 1.39-4.23) and 2.54 (0.94-4.91) with each millimeter increase in snout-vent length and decreased by factors of 0.91 (0.85-0.96) and 0.89 (0.80-1.00) with each day's delay in metamorphosis for the two ponds. The odds of surviving and moving to the next ring of fencing, 12 m to ???40 m from the ponds, increased by a factor of 1.20 (0.45-4.06) with each millimeter increase in size. Our results demonstrated that body size and timing of metamorphosis relate strongly to the performance of newly metamorphosed frogs during their initial transition into terrestrial habitat. Carryover effects of aquatic stressors that reduce size and delay metamorphosis may have population-level impacts that are not expressed until terrestrial stages. Since changes in both aquatic and terrestrial systems are implicated in many amphibian declines, quantifying both immediate and delayed effects of stressors on demographic rates is critical to sound management. ?? 2006 by the Ecological Society of America.

Ecological Applications↗
USGS2012

Estimating landscape carrying capacity through maximum clique analysis

Habitat suitability (HS) maps are widely used tools in wildlife science and establish a link between wildlife populations and landscape pattern. Although HS maps spatially depict the distribution of optimal resources for a species, they do not reveal the population size a landscape is capable of supporting--information that is often crucial for decision makers and managers. We used a new approach, "maximum clique analysis," to demonstrate how HS maps for territorial species can be used to estimate the carrying capacity, N(k), of a given landscape. We estimated the N(k) of Ovenbirds (Seiurus aurocapillus) and bobcats (Lynx rufus) in an 1153-km2 study area in Vermont, USA. These two species were selected to highlight different approaches in building an HS map as well as computational challenges that can arise in a maximum clique analysis. We derived 30-m2 HS maps for each species via occupancy modeling (Ovenbird) and by resource utilization modeling (bobcats). For each species, we then identified all pixel locations on the map (points) that had sufficient resources in the surrounding area to maintain a home range (termed a "pseudo-home range"). These locations were converted to a mathematical graph, where any two points were linked if two pseudo-home ranges could exist on the landscape without violating territory boundaries. We used the program Cliquer to find the maximum clique of each graph. The resulting estimates of N(k) = 236 Ovenbirds and N(k) = 42 female bobcats were sensitive to different assumptions and model inputs. Estimates of N(k) via alternative, ad hoc methods were 1.4 to > 30 times greater than the maximum clique estimate, suggesting that the alternative results may be upwardly biased. The maximum clique analysis was computationally intensive but could handle problems with < 1500 total pseudo-home ranges (points). Given present computational constraints, it is best suited for species that occur in clustered distributions (where the problem can be broken into several, smaller problems), or for species with large home ranges relative to grid scale where resampling the points to a coarser resolution can reduce the problem to manageable proportions.

Ecological Applications↗
USGS2023

Assessing contaminants of emerging concern in the Great Lakes Ecosystem: A decade of method development and practical application

Assessing the ecological risk of contaminants in the field typically involves consideration of a complex mixture of compounds which may or may not be detected via instrumental analyses. Further, there are insufficient data to predict the potential biological effects of many detected compounds, leading to their being characterized as contaminants of emerging concern (CECs). Over the past several years, advances in chemistry, toxicology, and bioinformatics have resulted in a variety of concepts and tools that can enhance the pragmatic assessment of the ecological risk of CECs. The present Focus article describes a 10+- year multiagency effort supported through the U.S. Great Lakes Restoration Initiative to assess the occurrence and implications of CECs in the North American Great Lakes. State-of-the-science methods and models were used to evaluate more than 700 sites in about approximately 200 tributaries across lakes Ontario, Erie, Huron, Michigan, and Superior, sometimes on multiple occasions. Studies featured measurement of up to 500 different target analytes in different environmental matrices, coupled with evaluation of biological effects in resident species, animals from in situ and laboratory exposures, and in vitro systems. Experimental taxa included birds, fish, and a variety of invertebrates, and measured endpoints ranged from molecular to apical responses. Data were integrated and evaluated using a diversity of curated knowledgebases and models with the goal of producing actionable insights for risk assessors and managers charged with evaluating and mitigating the effects of CECs in the Great Lakes. This overview is based on research and data captured in approximately about 90 peer-reviewed journal articles and reports, including approximately about 30 appearing in a virtual issue comprised of highlighted papers published in Environmental Toxicology and Chemistry or Integrated Environmental Assessment and Management . Environ Toxicol Chem 2023;42:2506–2518. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Environmental Toxicology and Chemistry↗
USGS2019

Genetic tagging in the Anthropocene: Scaling ecology from alleles to ecosystems

The Anthropocene is an era of marked human impact on the world. Quantifying these impacts 51 has become central to understanding the dynamics of coupled human-natural systems, resource52 dependent livelihoods, and biodiversity conservation. Ecologists are facing growing pressure to 53 quantify the size, distribution, and trajectory of wild populations in a cost-effective and socially54 acceptable manner. Genetic tagging, combined with modern computational and genetic analyses, 55 is an under-utilized tool to meet this demand, especially for wide-ranging, elusive, sensitive, and 56 low-density species. Genetic tagging studies are now revealing unprecedented insight into the 57 mechanisms that control the density, trajectory, connectivity and human-wildlife conflict for 58 populations over vast spatial scales. Here we outline the application of, and ecological inferences 59 from, new analytical techniques applied to genetically-tagged individuals, contrast this approach 60 with conventional methods, and describe how genetic tagging can be better applied to address 61 outstanding questions in ecology. We provide example analyses using a long-term genetic 62 tagging dataset of grizzly bears in the Canadian Rockies. The genetic tagging toolbox is a 63 powerful and overlooked ensemble that ecologists and conservation biologists can leverage to 64 generate evidence and meet the challenges of the Anthropocene.

Ecological Applications↗
USGS1993

Applications of bioenergetics models to fish ecology and management: where do we go from here?

Papers and panel discussions given during a 1992 symposium on bioenergetics models are summarized. Bioenergetics models have been applied to a variety of research and management questions related to fish stocks, populations, food webs, and ecosystems. Applications include estimates of the intensity and dynamics of predator-prey interactions, nutrient cycling within aquatic food webs of varying trophic structure, and food requirements of single animals, whole populations, and communities of fishes. As tools in food web and ecosystem applications, bioenergetics models have been used to compare forage consumption by salmonid predators across the Laurentian Great Lakes for single populations and whole communities, and to estimate the growth potential of pelagic predators in Chesapeake Bay and Lake Ontario. Some critics say that bioenergetics models lack sufficient detail to produce reliable results in such field applications, whereas others say that the models are too complex to be useful tools for fishery managers. Nevertheless, bioenergetics models have achieved notable predictive successes. Improved estimates are needed for model parameters such as metabolic costs of activity, and more complete studies are needed of the bioenergetics of larval and juvenile fishes. Future research on bioenergetics should include laboratory and field measurements of key model parameters such as weight-dependent maximum consumption, respiration and activity, and thermal habitats actually occupied by fish. Future applications of bioenergetics models to fish populations also depend on accurate estimates of population sizes and survival rates.

Transactions of the American Fisheries Society↗
USGS1981

Discriminant analysis in wildlife research: Theory and applications

Discriminant analysis, a method of analyzing grouped multivariate data, is often used in ecological investigations. It has both a predictive and an explanatory function, the former aiming at classification of individuals of unknown group membership. The goal of the latter function is to exhibit group separation by means of linear transforms, and the corresponding method is called canonical analysis. This discussion focuses on the application of canonical analysis in ecology. In order to clarify its meaning, a parametric approach is taken instead of the usual data-based formulation. For certain assumptions the data-based canonical variates are shown to result from maximum likelihood estimation, thus insuring consistency and asymptotic efficiency. The distorting effects of covariance heterogeneity are examined, as are certain difficulties which arise in interpreting the canonical functions. A 'distortion metric' is defined, by means of which distortions resulting from the canonical transformation can be assessed. Several sampling problems which arise in ecological applications are considered. It is concluded that the method may prove valuable for data exploration, but is of limited value as an inferential procedure.

Book chapter↗
USGS2007

Application of two tests of multivariate discordancy to fisheries data sets

The generalized (Mahalanobis) distance and multivariate kurtosis are two powerful tests of multivariate discordancies (outliers). Unlike the generalized distance test, the multivariate kurtosis test has not been applied as a test of discordancy to fisheries data heretofore. We applied both tests, along with published algorithms for identifying suspected causal variable(s) of discordant observations, to two fisheries data sets from Lake Erie: total length, mass, and age from 1,234 burbot, Lota lota; and 22 combinations of unique subsets of 10 morphometrics taken from 119 yellow perch, Perca flavescens. For the burbot data set, the generalized distance test identified six discordant observations and the multivariate kurtosis test identified 24 discordant observations. In contrast with the multivariate tests, the univariate generalized distance test identified no discordancies when applied separately to each variable. Removing discordancies had a substantial effect on length-versus-mass regression equations. For 500-mm burbot, the percent difference in estimated mass after removing discordancies in our study was greater than the percent difference in masses estimated for burbot of the same length in lakes that differed substantially in productivity. The number of discordant yellow perch detected ranged from 0 to 2 with the multivariate generalized distance test and from 6 to 11 with the multivariate kurtosis test. With the kurtosis test, 108 yellow perch (90.7%) were identified as discordant in zero to two combinations, and five (4.2%) were identified as discordant in either all or 21 of the 22 combinations. The relationship among the variables included in each combination determined which variables were identified as causal. The generalized distance test identified between zero and six discordancies when applied separately to each variable. Removing the discordancies found in at least one-half of the combinations (k=5) had a marked effect on a principal components analysis. In particular, the percent of the total variation explained by second and third principal components, which explain shape, increased by 52 and 44% respectively when the discordancies were removed. Multivariate applications of the tests have numerous ecological advantages over univariate applications, including improved management of fish stocks and interpretation of multivariate morphometric data. ?? 2007 Springer Science+Business Media B.V.

Environmental Biology of Fishes↗
USGS2026

Advances and applications of Unoccupied Aerial Systems (UAS) research in landscape ecology

Landscape ecologists have long depended on satellite and aerial remote sensing to address questions about landscape pattern and process, structure, and change (Foody 2023 ). Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) technology is becoming an increasingly popular research tool in environmental sciences allowing scientists to generate low-cost, high-quality, and high-resolution imagery on demand that can be tailored to specific research questions. While satellite data are of a fixed resolution and temporal interval, UAS offer researchers control and flexibility to design studies and collect data at resolutions and scales that provide ecologically relevant information at finer spatial resolutions (e.g., < 30 cm) than what is currently available from satellite platforms (typically > 3m), thus helping capture objects such as individual plant canopies, micro-topography, and individual animals. Unlike satellites with fixed orbits, UAS can be deployed at more optimal temporal frequencies for ecological monitoring. We organized the special collection “Advances and Applications of Unoccupied Aerial Systems (UAS) Research in Landscape Ecology” to showcase the many ways that UAS tools and technologies are currently applied to advance landscape ecological research. When we announced the collection in 2023, only 11 papers published in the journal Landscape Ecology used UAS data, which was a notably small number compared to many other general ecology, environmental science and remote sensing journals. In an attempt to understand why UAS were not more widely used in landscape ecology and provide possible solutions, we published a review article (Villarreal et al. 2025) that identified the challenges, knowledge gaps, and obstacles for the adoption of UAS technologies in landscape ecology research. The main issues we identified include: (1) an abundance of UAS methods papers in the existing literature, with comparatively few studies demonstrating how UAS can be applied to address ecological questions; (2) a perceived scale mismatch between the geographic extent of UAS data collection (local) compared to larger study areas (landscapes) and a need to design robust scaling approaches to connect fine-scale UAS data with broader ecological patterns; and (3) a need for improved integration of UAS data with other commonly used remote sensing datasets including historical high resolution aerial imagery. Additionally, researchers new to UAS remote sensing may be discouraged or overwhelmed by the general lack of scientific consensus and standardized protocols for typical tasks such as data collection, vegetation classification, and change detection, as well as restrictive and/or confusing policy, regulatory, and legal issues surrounding UAS operations (Villarreal et al. 2025).

Landscape Ecology↗
USGS2009

Microbial disease and the coral holobiont

Tropical coral reefs harbour a reservoir of enormous biodiversity that is increasingly threatened by direct human activities and indirect global climate shifts. Emerging coral diseases are one serious threat implicated in extensive reef deterioration through disruption of the integrity of the coral holobiont – a complex symbiosis between the coral animal, endobiotic alga and an array of microorganisms. In this article, we review our current understanding of the role of microorganisms in coral health and disease, and highlight the pressing interdisciplinary research priorities required to elucidate the mechanisms of disease. We advocate an approach that applies knowledge gained from experiences in human and veterinary medicine, integrated into multidisciplinary studies that investigate the interactions between host, agent and environment of a given coral disease. These approaches include robust and precise disease diagnosis, standardised ecological methods and application of rapidly developing DNA, RNA and protein technologies, alongside established histological, microbial ecology and ecological expertise. Such approaches will allow a better understanding of the causes of coral mortality and coral reef declines and help assess potential management options to mitigate their effects in the longer term.

Trends in Microbiology↗
USGS2024

Integration of Indigenous Research Methodologies, Traditional Ecological Knowledge and molecular scatology in an assessment of mesocarnivore presence, diet and habitat use on Yurok Ancestral Lands.

Partnerships between Tribes and researchers in wildlife monitoring and application of Traditional Ecological Knowledge (TEK) have taken a variety of forms, and some scholars have noted a need for culturally sensitive approaches. Guided by Indigenous Research Methodologies, this research is coupled with Yurok TEK, or hlkelonah 'ue-megetohl ('to take care of the earth'), enabling an applied, culturally sensitive approach in partnership with the Yurok Tribe. We present results from a molecular scatology study of wildlife within the ancestral territory of the Yurok Tribe. Scats were collected opportunistically on road transects. All samples ( N = 132) were analysed via DNA barcoding and results matched to documented 'Oohl 'we-toh (Yurok language) names to determine the depositor species ( N = 8). Though there were four focal mesocarnivore species in our study, only bobcat ( Chmuuek; Lynx rufus ) and gray fox ( Wergers; Urocyon cinereoargenteus ) were detected as depositor species. Post hoc analyses were conducted to explore distribution, habitat use and selection in a use-availability context, and food habits of these two species. We found almost complete separation of bobcat and gray fox use of transects, as well as indication of partitioning of vegetation cover types and food. We demonstrate an integrated framework of Western and Indigenous sciences that allows the Indigenous researcher to transcend structured academic disciplinary boundaries. Our approach can be modified for partnerships between Tribes, agencies, academics and students for wildlife monitoring in broader geographic regions in various research applications.

California↗
USGS2021

Creel surveys for social-ecological systems focused fisheries management

Recreational fisheries are social-ecological systems (SES), and knowledge of human dimensions coupled with ecology are critically needed to understand their system dynamics. Creel surveys, which typically occur in-person and on-site, serve as an important tool for informing fisheries management. Recreational fisheries creel data have the potential to inform large-scale understanding of social and ecological dynamics, but applications are currently limited by a disconnect between the questions posed by social-ecological researchers and the methods in which surveys are conducted. Although innovative use of existing data can increase understanding of recreational fisheries as SES, creel surveys should also adapt to changing information needs. These opportunities include using the specific temporal and spatial scope of creel survey data, integrating these data with alternative data sources, and increasing human dimensions understanding. This review provides recommendations for adapting survey design, implementation, and analysis for SES-focused fisheries management. These recommendations are: (1) increasing human dimensions knowledge; (2) standardization of surveys and data; (3) increasing tools and training available to fisheries scientists; and (4) increasing accessibility and availability of data. Incorporation of human dimensions information into creel surveys will increase the ability of fisheries management to regulate these important systems from an integrated SES standpoint.

Reviews in Fisheries Science & Aquaculture↗
USGS2018

Rivers are social–ecological systems: Time to integrate human dimensions into riverscape ecology and management

Incorporation of concepts from landscape ecology into understanding and managing riverine ecosystems has become widely known as riverscape ecology. Riverscape ecology emphasizes interactions among processes at different scales and their consequences for valued ecosystem components, such as riverine fishes. Past studies have focused strongly on understanding the ecological processes in riverscapes and how human actions modify those processes. It is increasingly clear, however, that an understanding of the drivers behind actions that lead to human modification also merit consideration, especially regarding how those drivers influence management efficacy. These indirect drivers of riverscape outcomes can be understood in the context of a diverse array of social processes, which we collectively refer to as human dimensions. Like ecological phenomena, social processes also exhibit complex interactions across spatiotemporal scales. Greater emphasis on feedbacks between social and ecological processes will lead scientists and managers to more completely understand riverscapes as complex, dynamic, interacting social–ecological systems. Emerging applications in riverscapes, as well as studies of other ecosystems, provide examples that can lead to stronger integration of social and ecological science. We argue that conservation successes within riverscapes may not come from better ecological science, improved ecosystem service analyses, or even economic incentives if the fundamental drivers of human behaviors are not understood and addressed in conservation planning and implementation.

WIREs Water↗
USGS2016

Study of biological communities subject to imperfect detection: Bias and precision of community N -mixture abundance models in small-sample situations

Community N -mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance ( &lambda; &macr; : 0.1, 0.5, 1, 5), detection probability ( p &macr; : 0.1, 0.2, 0.5), and number of sampling sites ( n site : 10, 20, 40) and visits ( n visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables ( &lambda; &macr; , p &macr; , n site , n visit ) increased. Detection probability p &macr; was most important for the estimates of mean abundance, while &lambda; &macr; was most influential for covariate effect and species richness estimates. For all parameters, increasing n site was more beneficial than increasing n visit . Minimal conditions for obtaining adequate performance of community abundance models were n site &ge; 20, p&macr; &ge; 0.2, and &lambda; &macr; &ge; 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate &beta; diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N -mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.

Ecological Research↗
USGS2025

Mapping predicted ecological states at landscape scales using remote sensing data and machine learning

Dryland ecosystems, covering 45% of the Earth's land and supporting over one-third of the global population, face significant threats from land degradation and ecological state change. Managing these ecosystems is complex, and science-based frameworks like Ecological Site Descriptions and state-and-transition models are essential tools for guiding decisions to support ecological health while maintaining stakeholder values such as grazing, wildlife, and recreation. However, alignment of these frameworks with smaller scale soil survey maps limits their applicability to broader ecological processes. Here, we extend these frameworks to larger landscapes with a machine learning approach that integrates large-scale, high-resolution vegetation data with identified ecological states from a data-driven state-and-transition model developed for a landscape-scale Ecological Site Group. A “global” model, which used combined inputs from multiple remotely sensed datasets, outperformed individual dataset models based on evaluation with independent data. Ecological state maps generated through this approach broaden the utility of state-and-transition models across Ecological Site Groups, providing a more spatially robust tool for land management at watershed and larger landscape scales. These methods, and the associated ecological state maps, can help meet critical needs for improved land condition assessments that support development of resource management plans and help identify priority areas for restoration and conservation.

Arizona, Colorado, New Mexico, Utah, Wyoming↗