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At least 1,081 records · Page 60Linked to original sources
USGS2012

Evaluating the ability of regional models to predict local avian abundance

Spatial modeling over broad scales can potentially direct conservation efforts to areas with high species-specific abundances. We examined the performance of regional models for predicting bird abundance at spatial scales typically addressed in conservation planning. Specifically, we used point count data on wood thrush (Hylocichla mustelina) and blue-winged warbler (Vermivora cyanoptera) from 2 time periods (1995-1998 and 2006-2007) to evaluate the ability of regional models derived via Bayesian hierarchical techniques to predict bird abundance. We developed models for each species within Bird Conservation Region (BCR) 23 in the upper midwestern United States at 800-ha, 8,000-ha, and approximately 80,000-ha scales. We obtained count data from the Breeding Bird Survey and land cover data from the National Land Cover Dataset (1992). We evaluated predictions from the best models, as defined by an information-theoretic criterion, using point count data collected within an ecological subregion of BCR 23 at 131 count stations in the 1990s and again in 2006-2007. Competing (Deviance Information Criteria <5) blue-winged warbler models accounted for 67% of the variability and suggested positive associations with forest edge and proportion of forest at the 8,000-ha scale, and negative associations with forest patch area (800 ha) and wetness (800 ha and 80,000 ha). The regional model performed best for blue-winged warbler predicted abundances from point counts conducted in Iowa during 1995-1996 ( r s = 0.57; P = 0.14), the survey period that most closely aligned with the time period of data used for regional model construction. Wood thrush models exhibited positive correlations with point count data for all survey areas and years combined ( r s = 0.58, P &le; 0.001). In comparison, blue-winged warbler models performed worse as time increased between the point count surveys and vintage of the model building data ( r s = 0.03, P = 0.92 for Iowa and r s = 0.13, P = 0.51 for all areas, 2006-2007), likely related to the ephemeral nature of their preferred early successional habitat. Species abundance and sensitivity to changing habitat conditions seems to be an important factor in determining the predictive ability of regional models. Hierarchical models can be a useful tool for concentrating efforts at the scale of management units and should be one of many tools used by land managers, but we caution that the utility of such models may decrease over time for species preferring relatively ephemeral habitats if model inputs are not updated accordingly.

Journal of Wildlife Management↗
USGS2015

Assessment and Mmanagement of North American horseshoe crab populations, with emphasis on a multispecies framework for Delaware Bay, U.S.A. populations: Chapter 24

The horseshoe crab fishery on the US Atlantic coast represents a compelling fishery management story for many reasons, including ecological complexity, health and human safety ramifications, and socio-economic conflicts. Knowledge of stock status and assessment and monitoring capabilities for the species have increased greatly in the last 15 years and permitted managers to make more informed harvest recommendations. Incorporating the bioenergetics needs of migratory shorebirds, which feed on horseshoe crab eggs, into the management framework for horseshoe crabs was identified as a goal, particularly in the Delaware Bay region where the birds and horseshoe crabs exhibit an important ecological interaction. In response, significant effort was invested in studying the population dynamics, migration ecology, and the ecologic relationship of a key migratory shorebird, the Red Knot, to horseshoe crabs. A suite of models was developed that linked Red Knot populations to horseshoe crab populations through a mass gain function where female spawning crab abundance determined what proportion of the migrating Red Knot population reached a critical body mass threshold. These models were incorporated in an adaptive management framework wherein optimal harvest decisions for horseshoe crab are recommended based on several resource-based and value-based variables and thresholds. The current adaptive framework represents a true multispecies management effort where additional data over time are employed to improve the predictive models and reduce parametric uncertainty. The possibility of increasing phenologic asynchrony between the two taxa in response to climate change presents a potential challenge to their ecologic interaction in Delaware Bay.

Book chapter↗
USGS2003

Predicting coastal retreat in the Florida Big Bend region of the Gulf Coast under climate change induced sea-level rise

Many wildlife preserves and refuges in coastal areas of our nation are slowly being inundated by rising sea-level. Land elevation and tidal flooding are key factors controlling the extent and zonation of coastal habitats. Warming of our global environment threatens to speed the rate of sea-level rise and perhaps further amplify the detrimental effects of tropical storms, droughts, and lightning fires. A field and modeling study was conducted to determine the current status of emergent vegetation and surficial hydrology and to predict marsh transgression under rising sea-level. Field surveys were conducted to relate vegetation cover and ecotones to surface elevation and tidal inundation. A regional site application of a GIS-based simulation model, WETLANDS, was developed to predict ecosystem response to changing sea-level conditions on a coastal reach of the Big Bend region in northwest Florida. The WETLANDS model contains functional probabilities of community tolerance to flooding conditions that dictate the rate and process of ecological succession and coastal retreat. Map information of hypsography and bathymetry of the study area were digitized and interpolated to construct a digital elevation model. Classified thematic mapper imagery of aquatic and terrestrial habitat at a community level was used to initialize model simulation by vegetative type. Model simulations were generated to predict a likelihood index of habitat change and conversion under different scenarios of sea-level rise. The WETLANDS model was applied to track the process and pattern of coastal inundation over space and time for low, mid, and high sea-level rise projections of 15, 50, and 95 cm over the next century. Model results indicated that major portions of this coastal zone will be permanently inundated by 2100, bringing about a combined migration of marsh habitat and displacement of forest habitat. Results show that lowland pine forests will undergo retreat on the order of thousands of hectares over the 21st century. Coastal marsh extent may actually increase slightly as a function of the low lying topography. Socioeconomic implications may be nominal for this area given its remote and fairly undeveloped and protected coast-202 line. The model offers a technological tool for research and policy purposes that allows for effective land and water management, risk assessment, and cumulative impact analysis of wetland systems and landscapes.

Florida↗
USGS2026

Spatially referenced watershed models for the binational Red–Assiniboine River Basin: Bayesian vs frequentist comparison

Excess nutrient loading remains a leading cause of declining water quality in lakes, estuaries, and coastal waters worldwide, with global economic costs of US$200 billion – US$2 trillion annually from impacts on fisheries, tourism, freshwater resources, and water treatment. Our study focuses on total phosphorus (TP) in Lake Winnipeg and its binational Red-Assiniboine River Basin, where nutrient inputs have degraded water quality and increased cyanobacterial blooms. These changes pose ecological, public health, and economic risks. We applied a spatially referenced watershed model with a hybrid statistical-mechanistic structure partitioning annual nutrient loads into land-use export, land-to-water delivery, and in-reservoir decay. Bayesian and traditional frequentist model calibrations were compared. In the frequentist model, coefficients for agricultural inputs, forests /wetlands, stream channels, precipitation, and reservoir losses were statistically significant, whereas coefficient for wastewater was not. In contrast, all variables were successfully calibrated using the Bayesian approach. Model results delineate TP-export hotspots across the basin, showing that 54–62% of TP originates from the U.S., with agricultural sources ranging 62–72%—highlighting the importance of agriculture-focused Best Management Practices. Given the global relevance of nutrient-driven water-quality challenges, our results highlight Bayesian calibration for robust risk assessment and adaptive nutrient management.

Red–Assiniboine River Basin↗
USGS2013

U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration

Executive Summary Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policymakers are faced with countless decisions each year at local, regional, and national levels on issues as diverse as renewable and nonrenewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decisionmaking is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and wellbeing of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decisionmakers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that U.S. Geological Survey (USGS) scientists and partners can play in building scientific understanding and providing timely information to decisionmakers. The strategy underscores the connection between scientific discoveries and the application of new knowledge, and it integrates ecosystem science and decisionmaking, producing new scientific outcomes to assist resource managers and providing public benefits. We envision the USGS as a leader in integrating scientific information into decisionmaking processes that affect the Nation’s natural resources and human well-being. The USGS is uniquely positioned to play a pivotal role in ecosystem science. With its wide range of expertise, the Bureau can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the Department of the Interior (DOI) and other partners in the natural sciences, in both conducting science and applying the results. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected components that reinforce our vision of the USGS providing science that is at the forefront of decisionmaking: Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. Develop tools, technologies, and capacities to inform decisionmaking about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. Support decisionmaking. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the four strategic approaches are a set of proposed actions that represent a sampling of specific USGS activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role in guiding sound decisionmaking during the next decade by advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Circular↗
USGS2012

The U.S. Geological Survey Ecosystem Science Strategy, 2012-2022 - Advancing discovery and application through collaboration

Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policy-makers are faced with countless decisions each year at local, state, tribal, territorial, and national levels on issues as diverse as renewable and non-renewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decision-making is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and well-being of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decision-makers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that USGS scientists and partners can play in building scientific understanding and providing timely information to decision-makers. The strategy underscores the connection between scientific discoveries and the application of new knowledge. The strategy integrates ecosystem science and decision-making, producing new scientific outcomes to assist resource managers and providing public benefits. The USGS is uniquely positioned to play an important role in ecosystem science. With its wide range of expertise, the agency can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the DOI and other partners in the natural sciences, in both conducting science and its application. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework and vision for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected and reinforcing components: • Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. • Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. • Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. • Develop tools, technologies, and capacities to inform decision-making about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. • Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: • Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. • Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. • Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. • Support decision-making. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the strategic approaches are a set of proposed actions that represent a sampling of specific activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct both discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role over the next decade in advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Open-File Report↗
USGS2012

Competition favors elk over beaver in a riparian willow ecosystem

Beaver (Castor spp.) conservation requires an understanding of their complex interactions with competing herbivores. Simulation modeling offers a controlled environment to examine long-term dynamics in ecosystems driven by uncontrollable variables. We used a new version of the SAVANNA ecosystem model to investigate beaver (C. canadensis) and elk (Cervus elaphus) competition for willow (Salix spp.). We initialized the model with field data from Rocky Mountain National Park, Colorado, USA, to simulate a 4-ha riparian ecosystem containing beaver, elk, and willow. We found beaver persisted indefinitely when elk density was <= 20 elk km (2). Beaver persistence decreased exponentially as elk density increased from 30 to 60 elk km(2), which suggests the presence of an ecological threshold. The interaction of beaver and elk herbivory shifted the size distribution of willow plants from tall to short when elk densities were >= 30 elk km (2). The loss of tall willow preceded rapid beaver declines, thus willow condition may predict beaver population trajectory in natural environments. Beaver were able to persist with slightly higher elk densities if beaver alternated their use of foraging sites in a rest-rotation pattern rather than maintained continuous use. Thus, we found asymmetrical competition for willow strongly favored elk over beaver in a simulated montane ecosystem. Finally, we discuss application of the SAVANNA model and mechanisms of competition relative to beaver persistence as metapopulations, ecological resistance and alternative state models, and ecosystem regulation.

Ecosphere↗
USGS2025

Introgression, phylogeography, and genomic species cohesion in the eastern North American white oak syngameon

Hybridization and interspecific gene flow play a substantial role in the evolution of plant taxa. The eastern North American white oak syngameon, a group of approximately 15 ecologically, morphologically and genomically distinguishable species, has long been recognised as a model system for studying introgressive hybridization in temperate trees. However, the prevalence, genomic context and environmental correlates of introgression in this system remain largely unknown. To assess introgression in the eastern North American white oak syngameon and population structure within the widespread Quercus macrocarpa , we conducted a rangewide survey of Q. macrocarpa and four sympatric eastern North American white oak species. Using a Hyb-Seq approach, we assembled a dataset of 3412 thinned single-nucleotide polymorphisms (SNPs) in 445 enriched target loci including 62 genes putatively associated with various ecological functions, as well as associated intronic regions and some off-target intergenic regions (not associated with the exons). Admixture analysis and hybrid class inference demonstrated species coherence despite hybridization and introgressive gene flow (due to backcrossing of F1s to one or both parents). Additionally, we recovered a genetic structure within Q. macrocarpa associated with latitude. Generalised linear mixed models (GLMMs) indicate that proximity to range edge predicts interspecific admixture, but rates of genetic differentiation do not appear to vary between putative functional gene classes. Our study suggests that gene flow between eastern North American white oak species may not be as rampant as previously assumed and that hybridization is most strongly predicted by proximity to a species' range margin.

Molecular Ecology↗
USGS2022

Woody plant encroachment of grassland and the reversibility of shrub dominance: Erosion, fire, and feedback processes

Many grass-dominated ecosystems in dryland regions have experienced increasing woody plant density and abundance during the past century. In many cases, this process has led to land degradation and declines in ecosystem functions. An example is the Chihuahuan Desert in the southwestern United States, which experienced different stages of shrub encroachment in the past 150 years. Among a wide variety of mechanisms to explain the grass–shrub transitions in this dryland system, soil erosion (both wind and water) and fire are particularly well studied. Here, we synthesize recent developments on the drivers and feedback in the process of shrub encroachment in the Chihuahuan Desert through the intercomparison of two Long Term Ecological Research (LTER) sites, namely Jornada and Sevilleta. Experimental and modeling studies support a conceptual framework, which underscores the important roles of erosion and fire in woody plant encroachment. Collectively, research at the Jornada LTER provided complementary, quantitative support to the well-known fertile-islands framework. Studies at the Sevilleta LTER expanded the framework, adding fire as a major disturbance to woody plants. Conceptual models derived from the synthesis represent the general understanding of shrub encroachment that emerged from research at these two sites, and can guide management interventions aimed at reducing or mitigating undesirable ecosystem state change in many other drylands worldwide.

New Mexico↗
USGS2026

A simplified two-station approach for modeling metabolism in dam tailwaters subject to diel flow variation

Tailwaters are ubiquitous and highly managed ecosystems whose food webs often rely disproportionately on autochthonous energy. In situ continuous dissolved oxygen data are increasingly being used to estimate gross primary productivity and ecosystem respiration in rivers, but this approach is complicated in tailwaters, where upriver discontinuities (i.e., dams) violate commonly employed one-station approaches. In such cases, two-station metabolism models can be applied, although substantial diel variation in flow (a common outcome of hydropower production) requires more complex treatment of water parcel travel times. Here, we present a new two-station metabolism model that allows estimation of reach-scale gross primary productivity and ecosystem respiration in streams and rivers that experience within-day variation in flow. Our approach simplifies two-station variable flow model implementation compared to previous efforts. We apply our model to a 6-yr dissolved oxygen time series and use Bayesian inference to estimate daily gross primary productivity, ecosystem respiration, and gas exchange velocity ( k 600 ) for a ~12-km reach of the Colorado River downriver of Glen Canyon Dam. We compare our model's performance to a more mechanistically detailed and computationally intensive Eulerian dynamic flow model and also to a widely-used one-station model that uses assumptions of reach uniformity that are often strongly violated in tailwaters. These comparisons show that our metabolism estimates conform with output from the more detailed dynamic flow model and that the one-station approach deviates substantially from both two-station approaches. Our new stream metabolism model can help resolve a fundamental analytical impediment in tailwater ecology.

Arizona↗
USGS2018

Climate change and future wildfire in the western USA: An ecological approach to nonstationarity

We developed ecologically based climate‐fire projections for the western United States. Using a finer ecological classification and fire‐relevant climate predictors, we created statistical models linking climate and wildfire area burned for ecosections, which are geographic delineations based on biophysical variables. The results indicate a gradient from purely fuel‐limited (antecedent positive water balance anomalies or negative energy balance anomalies) to purely flammability‐limited (negative water balance anomalies or positive energy balance anomalies) fire regimes across ecosections. Although there are other influences (such as human ignitions and management) on fire occurrence and area burned, seasonal climate significantly explains interannual fire area burned. Differences in the role of climate across ecosections are not random, and the relative dominance of climate predictors allows objective classification of ecosection climate‐fire relationships. Expected future trends in area burned range from massive increases, primarily in flammability limited systems near the middle of the water balance deficit distribution, to substantial decreases, in fuel‐limited nonforested systems. We predict increasing area burned in most flammability‐limited systems but predict decreasing area burned in primarily fuel‐limited systems with a flammability‐limited (“hybrid”) component. Compared to 2030–2059 (2040s), projected area burned for 2070–2099 (2080s) increases much more in the flammability and flammability‐dominated hybrid systems than those with equal control and continues to decrease in fuel‐limited hybrid systems. Exceedance probabilities for historical 95th percentile fire years are larger in exclusively flammability‐limited ecosections than in those with fuel controls. Filtering the projected results using a fire‐rotation constraint minimizes overprojection due to static vegetation assumptions, making projections more conservative.

Earth's Future↗
USGS2017

Fish Bioenergetics 4.0: An R-based modeling application

Bioenergetics modeling is a widely used tool in fisheries management and research. Although popular, currently available software (i.e., Fish Bioenergetics 3.0) has not been updated in over 20 years and is incompatible with newer operating systems (i.e., 64‐bit). Moreover, since the release of Fish Bioenergetics 3.0 in 1997, the number of published bioenergetics models has increased appreciably from 56 to 105 models representing 73 species. In this article, we provide an overview of Fish Bioenergetics 4.0 (FB4), a newly developed modeling application that consists of a graphical user interface (Shiny by RStudio) combined with a modeling package used in the R computing environment. While including the same capabilities as previous versions, Fish Bioenergetics 4.0 allows for timely updates and bug fixes and can be continuously improved based on feedback from users. In addition, users can add new or modified parameter sets for additional species and formulate and incorporate modifications such as habitat‐dependent functions (e.g., dissolved oxygen, salinity) that are not part of the default package. We hope that advances in the new modeling platform will attract a broad range of users while facilitating continued application of bioenergetics modeling to a wide spectrum of questions in fish biology, ecology, and management.

Fisheries Magazine↗
USGS2021

Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags

Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. We show that integrating geolocator recovery data and mark–resight data enables unbiased estimates of both migratory connectivity between breeding and nonbreeding populations and region-specific survival probabilities for wintering locations. Using simulations, we first demonstrate that an integrated Bayesian model returns unbiased estimates of transition probabilities between seasonal ranges. We also used simulations to determine how different sampling designs influence the estimability of transition probabilities. We then parameterized the model with tracking data and mark–resight data from declining Painted Bunting ( Passerina ciris ) populations breeding in the eastern United States, hypothesized to be threatened by the illegal pet trade in parts of their Caribbean, nonbreeding range. Consistent with this hypothesis, we found that male buntings wintering in Cuba were 20% less likely to return to the breeding grounds than birds wintering elsewhere in their range. Improving inferences from archival tags through proper data collection and further development of integrated models will advance our understanding of the full annual cycle ecology of migratory species.

Ornithological Applications↗
USGS2020

Low stand density moderates growth declines during hot droughts in semi-arid forests

Increasing heat and aridity in coming decades is expected to negatively impact tree growth and threaten forest sustainability in dry areas. Maintaining low stand density has the potential to mitigate the negative effects of increasingly severe droughts by minimizing competitive intensity. However, the direct impact of stand density on the growing environment (i.e. soil moisture), and the specific drought metrics that best quantify that environment, are not well explored for any forest ecosystem. We examined the relationship of varying stand density (i.e. basal area) on soil moisture and stand‐level growth in a long‐term (multi‐decadal), ponderosa pine Pinus ponderosa , forest management experiment. We accounted for the influence of stand‐level density on moisture availability by measuring and modelling soil moisture using an ecosystem water balance model. To quantify the growing environment, we developed metrics of ecological drought that integrate the influence of moisture availability in the soil with moisture demand by the atmosphere. We paired these results with stand‐level dendrochronological data, avoiding the potential bias introduced from individual tree‐based assessments, and used critical climate period analysis to identify the timing and duration of these drought metrics that most relate to forest growth. We found that stand‐level growth is highly responsive to the combination of high temperature and low soil moisture. Growth in all stands was negatively related to temperature and positively related to moisture availability, although the sensitivity of growth to those conditions varied among stand density treatments. Growth enhancement during cool years is greatest in low density stands. In addition, low density stands displayed substantially higher long‐term average growth than higher density stands and maintained higher growth even when temperatures were high. Growth in low density stands also increased more than higher density stands in response to greater long‐term moisture availability. Synthesis and applications . We quantified the influence of stand‐level density on the environmental conditions that determine tree growth and related forest growth to patterns of moisture supply and demand. Our drought metrics, and analytical approach for quantifying drought impacts on forest growth, are a novel approach for assessing forest vulnerability to drought under climate change. These results provide new perspective on the potential for density management to mitigate drought stress and maintain forest stand growth during and after drought events in water‐limited forests.

Arizona↗
USGS2018

Differing modes of biotic connectivity within freshwater ecosystem mosaics

We describe a collection of aquatic and wetland habitats in an inland landscape, and their occurrence within a terrestrial matrix, as a “freshwater ecosystem mosaic” (FEM). Aquatic and wetland habitats in any FEM can vary widely, from permanently ponded lakes, to ephemerally ponded wetlands, to groundwater‐fed springs, to flowing rivers and streams. The terrestrial matrix can also vary, including in its influence on flows of energy, materials, and organisms among ecosystems. Biota occurring in a specific region are adapted to the unique opportunities and challenges presented by spatial and temporal patterns of habitat types inherent to each FEM. To persist in any given landscape, most species move to recolonize habitats and maintain mixtures of genetic materials. Species also connect habitats through time if they possess needed morphological, physiological, or behavioral traits to persist in a habitat through periods of unfavorable environmental conditions. By examining key spatial and temporal patterns underlying FEMs, and species‐specific adaptations to these patterns, a better understanding of the structural and functional connectivity of a landscape can be obtained. Fully including aquatic, wetland, and terrestrial habitats in FEMs facilitates adoption of the next generation of individual‐based models that integrate the principles of population, community, and ecosystem ecology.

Journal of the American Water Resources Associatio↗
USGS2019

A dam passage performance standard model for American shad

Objectives for recovery of alosines commonly involve improving fish passage at dams during migration. However, a quantitative basis for dam passage performance standards is largely absent. We describe development of a stochastic life-history-based simulation model for American shad, Alosa sapidissima , to estimate effects of dam passage and migratory delay on abundance, spatial distribution of spawning adults, and demographic structuring in space and time. We used the Penobscot River, Maine, USA, as a case study to examine sensitivity of modeled population metrics and probability of achieving specific management goals to inputs. Spawner abundance and percentage of repeat spawners were most sensitive to survival and migration delay at dams, marine survival, and temperature cues for migratory events. Recovery objectives related to abundance and spatial distribution of spawners were achievable under multiple scenarios, but high rates of upstream and downstream passage were necessary. The simulation indicated trade-offs between upstream and downstream passage efficacy whereby increased downstream passage was required to maintain or increase population abundance in conjunction with increased upstream passage. This model provides a quantitative support tool for managers to inform ecologically based decisions about a suite of management scenarios to facilitate recovery and sustainability of diadromous fish populations.

Maine↗
USGS2004

Simulating the dynamics of linear forests in Great Plains agroecosystems under changing climates

Most forest growth models are not suitable for the highly fragmented, linear (or linearly shaped) forests in the Great Plains agroecosystems (e.g., windbreaks, riparian forest buffers), where such forests are a minor but ecologically important component of the land mosaics. This study used SEEDSCAPE, a recently modified gap model designed for cultivated land mosaics in the Great Plains, to simulate the effects of climate change on the dynamics of such linear forests. We simulated the dynamics of windbreaks with different initial planting species richness and widths (light changes as the selected resulting factor) using current climate data and nested regional circulation models (RegCMs). Results indicated that (i) it took 70-80 simulation years for the linear forests to reach a steady state under both normal (present-day) and warming climates; (ii) warming climates would reduce total aboveground tree biomass and the spatial variation in biomass, but increase dominance in the linear forests, especially in the upland forests; (iii) linear forests with higher planting species richness and smaller width produced higher aboveground tree biomass per unit area; and (iv) the same species performed very differently with different climate scenarios, initial planting diversity, and forest widths. Although the model still needs further improvements (e.g., the effects of understory species should be included), the model can serve as a useful tool in modeling the succession of linear forests in human-dominated land mosaics under changing climates and may also have significant practical implications in other systems. ?? 2004 NRC Canada.

Canadian Journal of Forest Research↗
USGS2009

A New Map of Standardized Terrestrial Ecosystems of the Conterminous United States

A new map of standardized, mesoscale (tens to thousands of hectares) terrestrial ecosystems for the conterminous United States was developed by using a biophysical stratification approach. The ecosystems delineated in this top-down, deductive modeling effort are described in NatureServe's classification of terrestrial ecological systems of the United States. The ecosystems were mapped as physically distinct areas and were associated with known distributions of vegetation assemblages by using a standardized methodology first developed for South America. This approach follows the geoecosystems concept of R.J. Huggett and the ecosystem geography approach of R.G. Bailey. Unique physical environments were delineated through a geospatial combination of national data layers for biogeography, bioclimate, surficial materials lithology, land surface forms, and topographic moisture potential. Combining these layers resulted in a comprehensive biophysical stratification of the conterminous United States, which produced 13,482 unique biophysical areas. These were considered as fundamental units of ecosystem structure and were aggregated into 419 potential terrestrial ecosystems. The ecosystems classification effort preceded the mapping effort and involved the independent development of diagnostic criteria, descriptions, and nomenclature for describing expert-derived ecological systems. The aggregation and labeling of the mapped ecosystem structure units into the ecological systems classification was accomplished in an iterative, expert-knowledge-based process using automated rulesets for identifying ecosystems on the basis of their biophysical and biogeographic attributes. The mapped ecosystems, at a 30-meter base resolution, represent an improvement in spatial and thematic (class) resolution over existing ecoregionalizations and are useful for a variety of applications, including ecosystem services assessments, climate change impact studies, biodiversity conservation, and resource management.

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