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At least 127 records · Page 7Linked to original sources

USGS2001

Assessing ecosystem effects of reservoir operations using food web-energy transfer and water quality models

We investigated the effects on the reservoir food web of a new temperature control device (TCD) on the dam at Shasta Lake, California. We followed a linked modeling approach that used a specialized reservoir water quality model to forecast operation-induced changes in phytoplankton production. A food web–energy transfer model was also applied to propagate predicted changes in phytoplankton up through the food web to the predators and sport fishes of interest. The food web–energy transfer model employed a 10% trophic transfer efficiency through a food web that was mapped using carbon and nitrogen stable isotope analysis. Stable isotope analysis provided an efficient and comprehensive means of estimating the structure of the reservoir's food web with minimal sampling and background data. We used an optimization procedure to estimate the diet proportions of all food web components simultaneously from their isotopic signatures. Some consumers were estimated to be much more sensitive than others to perturbations to phytoplankton supply. The linked modeling approach demonstrated that interdisciplinary efforts enhance the value of information obtained from studies of managed ecosystems. The approach exploited the strengths of engineering and ecological modeling methods to address concerns that neither of the models could have addressed alone: (a) the water quality model could not have addressed quantitatively the possible impacts to fish, and (b) the food web model could not have examined how phytoplankton availability might change due to reservoir operations.

Ecosystems↗

USGS2017

Assessing coastal wetland vulnerability to sea-level rise along the northern Gulf of Mexico coast: Gaps and opportunities for developing a coordinated regional sampling network

Coastal wetland responses to sea-level rise are greatly influenced by biogeomorphic processes that affect wetland surface elevation. Small changes in elevation relative to sea level can lead to comparatively large changes in ecosystem structure, function, and stability. The surface elevation table-marker horizon (SET-MH) approach is being used globally to quantify the relative contributions of processes affecting wetland elevation change. Historically, SET-MH measurements have been obtained at local scales to address site-specific research questions. However, in the face of accelerated sea-level rise, there is an increasing need for elevation change network data that can be incorporated into regional ecological models and vulnerability assessments. In particular, there is a need for long-term, high-temporal resolution data that are strategically distributed across ecologically-relevant abiotic gradients. Here, we quantify the distribution of SET-MH stations along the northern Gulf of Mexico coast (USA) across political boundaries (states), wetland habitats, and ecologically-relevant abiotic gradients (i.e., gradients in temperature, precipitation, elevation, and relative sea-level rise). Our analyses identify areas with high SET-MH station densities as well as areas with notable gaps. Salt marshes, intermediate elevations, and colder areas with high rainfall have a high number of stations, while salt flat ecosystems, certain elevation zones, the mangrove-marsh ecotone, and hypersaline coastal areas with low rainfall have fewer stations. Due to rapid rates of wetland loss and relative sea-level rise, the state of Louisiana has the most extensive SET-MH station network in the region, and we provide several recent examples where data from Louisiana’s network have been used to assess and compare wetland vulnerability to sea-level rise. Our findings represent the first attempt to examine spatial gaps in SET-MH coverage across abiotic gradients. Our analyses can be used to transform a broadly disseminated and unplanned collection of SET-MH stations into a coordinated and strategic regional network. This regional network would provide data for predicting and preparing for the responses of coastal wetlands to accelerated sea-level rise and other aspects of global change.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2001

Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling

Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is an extremely difficult and complex process requiring expertise in remotely sensed image classification, fire behavior, fuels modeling, ecology, and geographical information systems (GIS). This paper first presents the challenges of mapping fuels: canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and fuel model generalization. Then, four approaches to mapping fuels are discussed with examples provided from the literature: (1) field reconnaissance; (2) direct mapping methods; (3) indirect mapping methods; and (4) gradient modeling. A fuel mapping method is proposed that uses current remote sensing and image processing technology. Future fuel mapping needs are also discussed which include better field data and fuel models, accurate GIS reference layers, improved satellite imagery, and comprehensive ecosystem models.

International Journal of Wildland Fire↗

USGS2015

Final project memorandum: sea-level rise modeling handbook: resource guide for resource managers, engineers, and scientists

Coastal wetlands of the Southeastern United States are undergoing retreat and migration from increasing tidal inundation and saltwater intrusion attributed to climate variability and sea-level rise. Much of the literature describing potential sea-level rise projections and modeling predictions are found in peer-reviewed academic journals or government technical reports largely suited to reading by other Ph.D. scientists who are more familiar or engaged in the climate change debate. Various sea-level rise and coastal wetland models have been developed and applied of different designs and scales of spatial and temporal complexity for predicting habitat and environmental change that have not heretofore been synthesized to aid natural resource managers of their utility and limitations. Training sessions were conducted with Federal land managers with U.S. Fish and Wildlife Service, National Park Service, and NOAA National Estuarine Research Reserves as well as state partners and nongovernmental organizations across the northern Gulf Coast from Florida to Texas to educate and to evaluate user needs and understanding of concepts, data, and modeling tools for projecting sea-level rise and its impact on coastal habitats and wildlife. As a result, this handbook was constructed from these training and feedback sessions with coastal managers and biologists of published decision-support tools and simulation models for sea-level rise and climate change assessments. A simplified tabular context was developed listing the various kinds of decision-support tools and ecological models along with criteria to distinguish the source, scale, and quality of information input and geographic data sets, physical and biological constraints and relationships, datum characteristics of water and land elevation components, utility options for setting sea-level rise and climate change scenarios, and ease or difficulty of storing, displaying, or interpreting model output. The handbook is designed to be a primer to understanding sea-level rise and a practical synthesis of the current state of knowledge and modeling tools as a resource guide for DOl land management needs and facilitating Landscape Conservation Cooperative (LCC) research and conservation initiatives.

Conference Paper↗

USGS2021

Joint species distribution models of Everglades wading birds to inform restoration planning

Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.

Florida↗

USGS2025

Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse

Autonomous recording units are increasingly being used to monitor wildlife on large geographic and temporal scales, paired with machine learning (ML) to automate detection of wildlife. However, false positive detections from ML classifiers can result in erroneous ecological models that can lead to misguided management and conservation actions. We used a two-stage general approach to understand and reduce false positive detections, a technique in which outputs of the primary classification model are passed to a secondary classification model to yield the probability that a detection from the primary model is a true positive detection. This approach is demonstrated on two open-source models that detect Ruffed Grouse ( Bonasa umbellus ). We analyzed over 9500 h of acoustic data collected in 2022–2023 from the Green Mountain National Forest in Vermont, USA, and found the two models detected different types of acoustic signals associated with differing life history traits. The first model yielded 4106 detections (71.5 % true positives) while the second model yielded 524 detections (17.0 % true positives). Secondary logistic regression models separated true positives and false positives with high accuracy (84.5 % and 89.8 % respectively). Our findings go beyond improving Ruffed Grouse monitoring and conservation efforts to, more broadly, illustrate how two-stage ML approaches can improve the use of model-derived detections in wildlife research.

Vermont↗

USGS2019

Wetland biomass and productivity in Coastal Louisiana: Base line data (1976-2015) and knowledge gaps for the development of spatially explicit models for ecosystem restoration and rehabilitation initiatives

Coastal Louisiana host 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Coastal Louisiana hosts 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Louisiana has lost approximately 4900 km2 of wetland area since the early 1930s. Despite the economic relevance of both wetland biomass and net primary productivity (NPP) as ecosystem services, there is a lack of vegetation simulation models to forecast the trends of those functional attributes at the landscape level as hydrological restoration projects are implemented. Here, we review the availability of peer-reviewed biomass and NPP wetland data (below and aboveground) published during the period 1976–2015 for use in the development, calibration and validation of high spatial resolution (<200 m × 200 m) vegetation process-based ecological models. We discuss and list the knowledge gaps for those species that represent vegetation community associations of ecological importance, including the long-term research issues associated to limited number of paired belowground biomass and productivity studies across hydrological basins currently undergoing different freshwater diversions management regimes and hydrological restoration priorities.

Louisiana↗

USGS1997

USGS Wildland Fire Workshop, EROS Data Center, Sioux Falls, SD, July 9-10, 1997

Report on the first workshop on USGS wildland fire expertise and capabilities. The workshop brought together operational and research representatives ofthe Federal fire management agencies to initiate a more comprehensive assessment ofthe USGS's capabilities and the appropriate role(s) for USGS interaction and involvement with the fire community. The report discusses how the USGS can play a significant role in wildland fire research and offer the fire community several unique capabilities. Foremost capability is the role as data producer; fire detection, assessment, ecological modeling, and rehabilitation and restoration relying on digital elevation models, digital line-graphs, national technical means, and image data are also discussed. The bureau offers technical expertise using geographic information systems (GIS), image processing and analysis, data base management and clearinghouse experience, and access to national technical means data. The bureau must exploit the USGS's expertise and prominence in wildland fire research. The USGS is an experienced player in fire ecology, fire assessment, and fuels loading research. Finally, the USGS also offers strong interdisciplinary experience in watershed and geologic landslide modeling, which is key for assessing postfire effects.

Report↗

USGS2024

Climate change

Amphibian ecology and distribution are strongly correlated with climate. Regional patterns of amphibian biodiversity are intimately linked to temperature, evapotranspiration rate, and clines in humidity. While amphibians are and will continue to be adversely affected by recent and projected changes in climate, research suggests that adaptation may happen more slowly than the expected rate of environmental shifts. Here, we review conservation-relevant aspects of both realised and potential impacts of climate change, and outline options for amphibian conservation planning and management. Recent advances in our understanding of climate change impacts on amphibians have primarily stemmed from ecological modelling and direct assessment of climatic tolerances and dispersal capacities through physiological assays, landscape genetics, and dispersal tracking. Anthropogenic climate change has already altered amphibian assemblages and their impacts on ecosystem functioning and services. Because of known and hypothesised ecological tolerances, many amphibians might have reached or exceeded most limits in their ability to adapt to or tolerate further climate change, however the uncertainties are substantial. Implementation of conservation planning and action can help to forestall severe impacts of environmental shifts. Scientific research and science-based decision-making and policy development have already lagged; the current pace of conservation planning and action may not allow for effective identification of threats and mitigation. An increased response rate could help to avert further loss of amphibian biodiversity and decay of ecosystem services. The lack of basic field research in natural habitats continues to be an underlying challenge. We suggest priority areas of research to include the development of biologically realistic predictive models of amphibian response to climate change, field verification of model estimates and key parameters, population monitoring across multiple sites and taxa, and a combination of efforts within and across ecosystems to understand how impacts of climate change can be better mitigated.

Book chapter↗

USGS2025

A case for assemblage-level conservation to address the biodiversity crisis

Traditional conservation efforts have centred on safeguarding individual species, but these strategies have limitations in a world where entire ecosystems are rapidly changing. Ecosystem conservation can maintain critical ecological functions, but often lacks the detail necessary for the effective conservation of threatened or endangered species. The conservation of such species is mandated by policies and remains a dominant focus of natural resource management. In this Perspective, we propose that assemblage-level conservation targeting groups of taxonomically related or functionally similar species can bridge the gap between species and ecosystems and help to address global biodiversity loss. This approach has previously been limited by data and methodological constraints, but the ongoing growth of biodiversity data, advances in ecological modelling and breakthroughs in computational power have now made effective assemblage-level conservation feasible. Community models provide insights at both the species level and the assemblage level while appropriately accounting for species variability in detection during sampling and uncertainty in biological inferences. Assemblage-level conservation can link both species-specific needs and broader ecological dynamics, ultimately enabling effective strategies for conserving threatened species, ecological communities and ecosystem functions.

Nature Reviews Biodiversity↗

USGS2018

Quantification of the indirect use value of functional group diversity based on the ecological role of species in the ecosystem

An important issue in biodiversity valuation is gaining a better understanding of how biodiversity conservation affects economic activities and human welfare. Quantifying the economic benefits of biodiversity for human well-being is not straightforward. Here, we expand the ecosystem service cascade by (i) attributing a methodology to the different steps of the cascade to assess the effects of changes in functional group diversity on economic activities; (ii) including multiple attributes for defining functional diversity and (iii) integrating a dynamic ecological model simulating complex interactions and feedbacks between species with an economic model assessing the effects of changes in functional group diversity for gross revenues. The stepwise methodological framework integrates a production function approach with a market price-based approach in order to investigate the indirect use value of functional group diversity based on the ecological role of species in the ecosystem. The methodology is applied to estimate the relationship between the gross economic value of Chinook salmon (Pacific Northwest, United States) and the diversity of freshwater macroinvertebrates. The results of our analysis emphasize the importance of biological diversity for sustaining ecosystem goods and services. The analysis provides a tractable framework for quantitatively exploring the economic consequences of changes in functional group diversity.

Ecological Economics↗

USGS2025

Integrating sea level rise scenarios into Everglades restoration planning

One of the largest and most expensive restoration efforts in the world is occurring in the Everglades, a sub-tropical freshwater wetland system located in southern Florida. This unique ecosystem supports several endangered species, provides flood control for Florida’s large urban population, and provides water for both agriculture and drinking supply within the state. The Comprehensive Everglades Restoration Plan (CERP), authorized by Congress in 2000, guides federal, state, and local efforts to build the infrastructure necessary to bring more water into the Everglades and restore its ecological integrity, while balancing other water-related needs such as water supply and flood protection in the human environment. The Everglades encompasses the southern coast of Florida and restoration efforts are likely to be impacted by climate-induced sea level rise. However, currently, many project planning studies do not formally incorporate the potential impacts of sea level rise when evaluating restoration plan outcomes. Resource managers and project planners require methods and tools to confidently incorporate scenarios of sea level rise into their evaluations. The U.S. Geological Survey (USGS) and partners from the University of Florida worked with project planners from multiple agencies to identify restoration questions for consideration when addressing sea level rise. In addition, our project team sought to understand the types of sea level rise decision-support tools that would be of interest, and then worked with Everglades restoration managers and project planners to develop those tools. The tools developed by this project can be used by project planners to inform their decision-making abilities when considering multiple restoration plans across the Everglades landscape. Specifically, the novel ways to visualize output information from ecological models that came of this project can help project planners compare alternative restoration plans that include potential sea level rise impacts. This effort demonstrates how incorporating sea level rise scenarios into Everglades restoration project planning can help managers decide whether projects will maintain or improve ecological integrity and evaluate water availability for wildlife and humans.

Final Report↗

USGS2026

Basal area loss from fire using field-calibrated remote sensing refines western US fire severity measurements

The spatial patterns of fire effects and tree mortality have profound consequences for forest resilience. Cost-effective, medium-resolution, and spatiotemporally extensive fire severity measurements are essential for informing post-fire restoration and improving our understanding of wildfires—from forest stands to continents and from days to decades. Remote sensing advancements have improved burn severity mapping, but methods vary in interpretability, scalability, generalizability, and alignment with field measurements. One meaningful metric of fire effects on forests is proportion basal area loss, but existing methods are limited by a lack of region-specific field reference data and a scalable mapping framework. To address these issues, we compiled 3280 field reference plots from 123 fires in forests across the Western US to calculate the proportion of fire-induced basal area loss. We then used spatially cross-validated machine learning models with concurrent hyperparameter tuning to select a skillful, parsimonious model from a large candidate set of remotely-sensed, climatic, and topographic predictors. Spectral-only measures of severity over- or underestimated basal area loss in dry versus wet years and across aspects, demonstrating the value of incorporating climatic and topographic context. We also tested model performance on a separate holdout dataset in the Southwest US as a demonstration of reproducibility and transparency. We provide a Google Earth Engine tool for estimating proportional basal area loss for any fire perimeter in the Western US, enabling rapid map creation for land management and ecological modeling. All code, model parameters, and training data are released to support reproducibility, community adoption, regional refinement, and adaptation to new regions.

western United States↗

USGS2021

Responding to ecological transformation: Mental models, external constraints, and manager decision-making

Ecological transformation creates many challenges for public natural resource management and requires managers to grapple with new relationships to change and new ways to manage it. In the context of unfamiliar trajectories of ecological change, a manager can resist, accept, or direct change, choices that make up the resist-accept-direct (RAD) framework. In this article, we provide a conceptual framework for how to think about this new decision space that managers must navigate. We identify internal factors (mental models) and external factors (social feasibility, institutional context, and scientific uncertainty) that shape management decisions. We then apply this conceptual framework to the RAD strategies (resist, accept, direct) to illuminate how internal and external factors shape those decisions. Finally, we conclude with a discussion of how this conceptual framework shapes our understanding of management decisions, especially how these decisions are not just ecological but also social, and the implications for research and management.

BioScience↗

USGS2016

Fluctuating water depths affect American alligator ( Alligator mississippiensis ) body condition in the Everglades, Florida, USA

Successful restoration of wetland ecosystems requires knowledge of wetland hydrologic patterns and an understanding of how those patterns affect wetland plant and animal populations.Within the Everglades, Florida, USA restoration, an applied science strategy including conceptual ecological models linking drivers to indicators is being used to organize current scientific understanding to support restoration efforts. A key driver of the ecosystem affecting the distribution and abundance of organisms is the timing, distribution, and volume of water flows that result in water depth patterns across the landscape. American alligators (Alligator mississippiensis) are one of the ecological indicators being used to assess Everglades restoration because they are a keystone species and integrate biological impacts of hydrological operations through all life stages. Alligator body condition (the relative fatness of an animal) is one of the metrics being used and targets have been set to allow us to track progress. We examined trends in alligator body condition using Fulton’s K over a 15 year period (2000–2014) at seven different wetland areas within the Everglades ecosystem, assessed patterns and trends relative to restoration targets, and related those trends to hydrologic variables. We developed a series of 17 a priori hypotheses that we tested with an information theoretic approach to identify which hydrologic factors affect alligator body condition. Alligator body condition was highest throughout the Everglades during the early 2000s and is approximately 5–10% lower now (2014). Values have varied by year, area, and hydrology. Body condition was positively correlated with range in water depth and fall water depth. Our top model was the “Current” model and included variables that describe current year hydrology (spring depth, fall depth, hydroperiod, range, interaction of range and fall depth, interaction of range and hydroperiod). Across all models, interaction between range and fall water depth was the most important variable (relative weight of 1.0) followed by spring and fall water depths (0.99), range (0.96), hydroperiod (0.95) and interaction between range and hydroperiod (0.95). Our work provides additional evidence that restoring a greater range in annual water depths is important for improvement of alligator body condition and ecosystem function. This information can be incorporated into both planning and operations to assist in reaching Everglades restoration goals.

Florida↗

USGS2012

Patterns and controlling factors of species diversity in the Arctic Ocean

Aim The Arctic Ocean is one of the last near-pristine regions on Earth, and, although human activities are expected to impact on Arctic ecosystems, we know very little about baseline patterns of Arctic Ocean biodiversity. This paper aims to describe Arctic Ocean-wide patterns of benthic biodiversity and to explore factors related to the large-scale species diversity patterns. Location Arctic Ocean. Methods We used large ostracode and foraminiferal datasets to describe the biodiversity patterns and applied comprehensive ecological modelling to test the degree to which these patterns are potentially governed by environmental factors, such as temperature, productivity, seasonality, ice cover and others. To test environmental control of the observed diversity patterns, subsets of samples for which all environmental parameters were available were analysed with multiple regression and model averaging. Results Well-known negative latitudinal species diversity gradients (LSDGs) were found in metazoan Ostracoda, but the LSDGs were unimodal with an intermediate maximum with respect to latitude in protozoan foraminifera. Depth species diversity gradients were unimodal, with peaks in diversity shallower than those in other oceans. Our modelling results showed that several factors are significant predictors of diversity, but the significant predictors were different among shallow marine ostracodes, deep-sea ostracodes and deep-sea foraminifera. Main conclusions On the basis of these Arctic Ocean-wide comprehensive datasets, we document large-scale diversity patterns with respect to latitude and depth. Our modelling results suggest that the underlying mechanisms causing these species diversity patterns are unexpectedly complex. The environmental parameters of temperature, surface productivity, seasonality of productivity, salinity and ice cover can all play a role in shaping large-scale diversity patterns, but their relative importance may depend on the ecological preferences of taxa and the oceanographic context of regions. These results suggest that a multiplicity of variables appear to be related to community structure in this system.

Journal of Biogeography↗

USGS2021

Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA

Wildfires of uncharacteristic severity, a consequence of climate changes and accumulated fuels, can cause amplified or novel impacts to archaeological resources. The archaeological record includes physical features associated with human activity; these exist within ecological landscapes and provide a unique long-term perspective on human–environment interactions. The potential for fire-caused damage to archaeological materials is of major concern because these resources are irreplaceable and non-renewable, have social or religious significance for living peoples, and are protected by an extensive body of legislation. Although previous studies have modeled ecological burn severity as a function of environmental setting and climate, the fidelity of these variables as predictors of archaeological fire effects has not been evaluated. This study, focused on prehistoric archaeological sites in a fire-prone and archaeologically rich landscape in the Jemez Mountains of New Mexico, USA, identified the environmental and climate variables that best predict observed fire severity and fire effects to archaeological features and artifacts.

New Mexico↗

USGS2012

Spatial ecology of refuge selection by an herbivore under risk of predation

Prey species use structures such as burrows to minimize predation risk. The spatial arrangement of these resources can have important implications for individual and population fitness. For example, there is evidence that clustered resources can benefit individuals by reducing predation risk and increasing foraging opportunity concurrently, which leads to higher population density. However, the scale of clustering that is important in these processes has been ignored during theoretical and empirical development of resource models. Ecological understanding of refuge exploitation by prey can be improved by spatial analysis of refuge use and availability that incorporates the effect of scale. We measured the spatial distribution of pygmy rabbit (Brachylagus idahoensis) refugia (burrows) through censuses in four 6-ha sites. Point pattern analyses were used to evaluate burrow selection by comparing the spatial distribution of used and available burrows. The presence of food resources and additional overstory cover resources was further examined using logistic regression. Burrows were spatially clustered at scales up to approximately 25 m, and then regularly spaced at distances beyond ~40 m. Pygmy rabbit exploitation of burrows did not match availability. Burrows used by pygmy rabbits were likely to be located in areas with high overall burrow density (resource clusters) and high overstory cover, which together minimized predation risk. However, in some cases we observed an interaction between either overstory cover (safety) or understory cover (forage) and burrow density. The interactions show that pygmy rabbits will use burrows in areas with low relative burrow density (high relative predation risk) if understory food resources are high. This points to a potential trade-off whereby rabbits must sacrifice some safety afforded by additional nearby burrows to obtain ample forage resources. Observed patterns of clustered burrows and non-random burrow use improve understanding of the importance of spatial distribution of refugia for burrowing herbivores. The analyses used allowed for the estimation of the spatial scale where subtle trade-offs between predation avoidance and foraging opportunity are likely to occur in a natural system.

Ecosphere↗