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At least 73 records · Page 4Linked to original sources
USGS2023

Compact or sprawling cities: Has the sparing-sharing framework yielded an ecological verdict?

Purpose of Review Global urban land area is growing faster than the urban population, raising concerns that sprawling, low-density development will reduce biodiversity and human wellbeing. The sparing-sharing framework, adapted from agroecology, provides one approach to assessing alternative urban growth patterns. It compares ecological outcomes in landscapes matched for total population and land area, but differing in configuration: land sparing (partitioned between densely urbanized and undeveloped areas) or land sharing (low-density development throughout). We reviewed the urban sparing-sharing literature since 2010 and recovered 15 studies conducted in 22 cities on four continents. Recent Findings Collectively, studies assessed effects of alternative development patterns on 296 species, 21 community metrics (such as species richness), and 26 indicators of ecosystem services or processes (such as carbon sequestration). Sparing was the best option for 51% of individual species; 43% of community metrics; and 27% of ecosystem service indicators. Summary Existing ecological research does not clearly favor one pattern or the other, and new approaches are needed to facilitate decision making and ecological insight. Specifically, future work could (1) explicitly evaluate optimized urban development patterns across multiple competing priorities (such as providing housing, delivering ecosystem services, and protecting priority species), (2) tackle issues of spatial scale and connectivity that are often ambiguous in sparing-sharing research, and (3) improve geographical representation. These advances can be made while preserving the key insight of the framework–that choices between alternative landscape configurations are only meaningful when those landscapes are matched for total area and the level of human needs met.

Current Landscape Ecology Reports↗
USGS2024

Abundance-mediated species interactions

Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes ( Canis latrans ), fishers ( Pekania pennanti ), and American marten ( Martes americana ) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

Ecology↗
USGS2002

Fragmentation: Is the message clear?

In this paper, we briefly discuss some of the fundamental problems arising from the inherent complexity of larger-scale ecological systems. We examine the tenuous assumption of a direct correspondence between ecological data and theory, we comment on a recent report that evaluated the efficacy of fragmentation experiments, and we briefly assess its implications for ecological research and conservation practice on the landscape scale.

Ecology and Society↗
USGS2024

Co-mast: Harmonized seed production data for woody plants across US long-term research sites

Plants display a range of temporal patterns of inter-annual reproduction, from relatively constant seed production to “mast seeding,” the synchronized and highly variable interannual seed production of plants within a population. Previous efforts have compiled global records of seed production in long-lived plants to gain insight into seed production, forest and animal population dynamics, and the effects of global change on masting. Existing datasets focus on seed production dynamics at the population scale but are limited in their ability to examine community-level mast seeding dynamics across different plant species at the continental scale. We harmonized decades of plant reproduction data for 141 woody plant species across nine Long-Term Ecological Research (LTER) or long-term ecological monitoring sites from a wide range of habitats across the United States. Plant reproduction data are reported annually between 1957 and 2021 and based on either seed traps or seed and/or cone counts on individual trees. A wide range of woody plant species including trees, shrubs, and lianas are represented within sites allowing for direct community-level comparisons among species. We share code for filtering of data that enables the comparison of plot and individual tree data across sites. For each species, we compiled relevant life history attributes (e.g., seed mass, dispersal syndrome, seed longevity, sexual system) that may serve as important predictors of mast seeding in future analyses. To aid in phylogenetically informed analyses, we also share a phylogeny and phylogenetic distance matrix for all species in the dataset. These data can be used to investigate continent-scale ecological properties of seed production, including individual and population variability, synchrony within and across species, and how these properties of seed production vary in relation to plant species traits and environmental conditions. In addition, these data can be used to assess how annual variability in seed production is associated with climate conditions and how that varies across populations, species, and regions. The dataset is released under a CC0 1.0 Universal public domain license.

Ecology↗
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↗
USGS1988

Results of a survey of U.S. Fish and Wildlife Service Endangered Species and Ecological Services Field Offices, Refuges, Hatcheries, and Research Centers

The National Ecology Research Center (Center), as part of an ongoing research study on the effects of low altitude aircraft operations on fish and wildlife, conducted a survey in January 1987 of all U.S. Fish and Wildlife Service (Service) regional directors, research center directors, Ecological Services and Endangered Species field offices supervisors, refuge manager, and hatchery manager. The objective of the survey was to determine the nature and extent of aircraft-induced impacts on fish and wildlife species, populations, and habitat utilization. The field installation managers and biologists were asked to provide background information or data on fish and wildlife reactions to low-altitude aircraft disturbances, including physiological, behavioral, and reproductive/population effects. Specifically, the survey asked for information such as: (1) observations of animal reaction(s) to aircraft operations, e.g., desert bighorn sheep scare behavior in response to aircraft overflights and hatchery fish seizures and death following intense sonic booms; and instances of areas where aircraft noise is known or believed to be responsible for reduced population size, e.g. areas along heavily used aircraft flight corridors where breeding waterfowl densities are lower than in similar habitat away from the noise area.

Report↗
USGS1998

Nitrogen and carbon soil dynamics in response to climate change in a high-elevation ecosystem in the Rocky Mountains, U.S.A.

We have implemented a long-term snow-fence experiment at the Niwot Ridge Long-Term Ecological Research (NWT) site in the Colorado Front Range of the Rocky Mountains, U.S.A., to assess the effects of climate change on alpine ecology and biogeochemical cycles. The responses of carbon (C) and nitrogen (N) dynamics in high-elevation mountains to changes in climate are investigated by manipulating the length and duration of snow cover with the 2.6 × 60 m snow fence, providing a proxy for climate change. Results from the first year of operation in 1994 showed that the period of continuous snow cover was increased by 90 d. The deeper and earlier snowpack behind the fence insulated soils from winter air temperatures, resulting in a 9°C increase in annual minimum temperature at the soil surface. The extended period of snow cover resulted in subnivial microbial activity playing a major role in annual C and N cycling. The amount of C mineralized under the snow as measured by CO 2 production was 22 g m -2 in 1993 and 35 g m -2 in 1994, accounting for 20% of annual net primary above-ground production before construction of the snow fence in 1993 and 31% after to snow fence was constructed in 1994. In a similar fashion, maximum subnivial N 2 O flux increased 3-fold behind the snow fence, from 75 μg N m -2 d -1 in 1993 to 250 μg N m -2 d -1 in 1994. The amount of N lost from denitrification was greater than the annual atmospheric input of N in snowfall. Surface litter decomposition studies show that there was a significant increase in the litter mass loss under deep and early snow, with no significant change under medium and little snow conditions. Changes in climate that result in differences in snow duration, depth, and extent may therefore produce large changes in the C and N soil dynamics of alpine ecosystems.

Colorado↗
USGS2019

U.S. Geological Survey—Northern Prairie Wildlife Research Center 2017 research activity report

The mission of Northern Prairie Wildlife Research Center is to provide scientific information needed to conserve and manage the Nation’s natural capital for current and future generations, with an emphasis on migratory birds, Department of the Interior trust resources, and ecosystems of the Nation’s interior. This report provides an overview of the studies conducted at Northern Prairie during fiscal year 2017 in pursuit of this mission. Studies are organized under a framework developed by the U.S. Geological Survey Ecosystems Mission Area, identifying primary and secondary alignment with focal areas of research, and summarizing recent scientific products resulting from these studies. Partnerships with Federal, State, and non-Governmental organizations are essential to a robust program of applied ecological research, and we thank our many collaborators and colleagues whose contributions made this work possible.

Circular↗
USGS2020

U.S. Geological Survey—Northern Prairie Wildlife Research Center 2018 research activity report

The mission of Northern Prairie Wildlife Research Center is to provide scientific information needed to conserve and manage the Nation’s natural capital for current and future generations, with an emphasis on migratory birds, Department of the Interior trust resources, and ecosystems of the Nation’s interior. This report provides an overview of the studies conducted at Northern Prairie during fiscal year 2018 in pursuit of this mission. Studies are organized under a framework developed by the U.S. Geological Survey Ecosystems Mission Area, identifying primary and secondary alignment with focal areas of research, and summarizing recent scientific products resulting from these studies. Partnerships with Federal, State, and non-Governmental organizations are essential to a robust program of applied ecological research, and we thank our many collaborators and colleagues whose contributions made this work possible.

Circular↗
USGS2021

U.S. Geological Survey—Northern Prairie Wildlife Research Center 2019–20 research activity report

The mission of Northern Prairie Wildlife Research Center is to provide scientific information needed to conserve and manage the Nation’s natural capital for current and future generations, with an emphasis on migratory birds, Department of the Interior trust resources, and ecosystems of the Nation’s interior. This report provides an overview of the studies conducted at Northern Prairie during fiscal years 2019–20 in pursuit of this mission. Studies are organized under a framework developed by the U.S. Geological Survey Ecosystems Mission Area, identifying primary and secondary alignment with focal areas of research, and summarizing recent scientific products resulting from these studies. Partnerships with Federal, State, and non-Governmental organizations are essential to a robust program of applied ecological research, and we thank our many collaborators and colleagues whose contributions made this work possible.

North Dakota↗
USGS2024

U.S. Geological Survey—Northern Prairie Wildlife Research Center 2021–23 research activity report

The mission of Northern Prairie Wildlife Research Center is to provide scientific information needed to conserve and manage the Nation’s natural capital for current and future generations, with an emphasis on migratory birds, Department of the Interior trust resources, and ecosystems of the Nation’s interior. This report provides an overview of the studies conducted at Northern Prairie during fiscal years 2021–23 in pursuit of this mission. Studies are organized under a framework developed by the U.S. Geological Survey Ecosystems Mission Area, identifying primary and secondary alignment with focal areas of research, and summarizing recent scientific products resulting from these studies. Partnerships with Federal, State, and non-Governmental organizations are essential to a robust program of applied ecological research, and we thank our many collaborators and colleagues whose contributions made this work possible.

Circular↗
USGS2015

From bacteria to elephants: Effects of land-use legacies on biodiversity and ecosystem structure in the Serengeti-Mara ecosystem: Chapter 8

Generally, ecological research has considered the aboveground and belowground components of ecosystems separately. Consequently, frameworks for integrating the two components are not well developed. Integrating the microbial components into ecosystem ecology requires different approaches from those offered by plant ecology, partly because of the scales at which microbial processes operate and partly because of measurement constraints. Studies have begun to relate microbial community structure to ecosystem function. results suggest that excluding people and livestock from the MMNR, or preventing heavier livestock from grazing around settlements, may not change the general structure of the ecosystem (soils, plant structure), but can change the numbers and diversity of wildlife, nematodes and microbes in this ecosystem in subtle ways.

Book chapter↗
USGS2014

Wildlife friendly roads: the impacts of roads on wildlife in urban areas and potential remedies

Roads are one of the most important factors affecting the ability of wildlife to live and move within an urban area. Roads physically replace wildlife habitat and often reduce habitat quality nearby, fragment the remaining habitat, and cause increased mortality through vehicle collisions. Much ecological research on roads has focused on whether animals are successfully crossing roads, or if the road is a barrier to wildlife movement, gene flow, or functional connectivity. Roads can alter survival and reproduction for wildlife, even among species such as birds that cross roads easily. Here we examine the suite of potential impacts of roads on wildlife, but we focus particularly on urban settings. We report on studies, both in the literature and from our own experience, that have addressed wildlife and roads in urban landscapes. Although road ecology is a growing field of study, relatively little of this research, and relatively few mitigation projects, have been done in urban landscapes. We also draw from the available science on road impacts in rural areas when urban case studies have not fully addressed key topics.

Book chapter↗
USGS2017

Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question—How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km 2 ); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation—approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.

Ecology and Evolution↗
USGS2018

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Proceedings of the National Academy of Sciences of↗
USGS2017

Book review: Serendipity: An ecologist’s quest to understand nature

A common thought among graduate students is: “how do established scientists get where they are today?” In Serendipity: An Ecologist’s Quest to Understand Nature , James Estes offers a personal reflection on research experiences spanning his 50-year career, beginning as a Ph.D. student in 1970 and concluding with recognition as a member of the National Academy of Sciences in 2014. Estes chronologically outlines the foundational trophic cascade ecology research that he and colleagues conducted in the Aleutian Islands, examining key relationships among kelp forests, sea otters, sea urchins, and killer whales through anecdotal stories of achievement and challenge. Estes’ 3 main goals in writing this book are to: (1) recount what he had learned from 50 years of research;...

Journal of Mammalogy↗
USGS2010

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

Journal of Applied Ecology↗