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At least 1,711 records · Page 95Linked to original sources
USGS1988

Assessment of the role of bottomland hardwoods in sediment and erosion control

Drainage and clearing of bottomland hardwoods have long been recognized by the U.S. Environmental Protection Agency (EPA) and the U.S. Fish and Wildlife Service (Service) as important impacts of Federal water projects in the lower Mississippi River Valley. More recently, the water quality impacts of such projects (e.g., increases in sediments, nutrients, and pesticides) have also become of concern. In 1984, in an effort to better define problems concerning wetland losses and water degradation, EPA initiated a cooperative project with the Western Energy and Land Use Team (now the National Ecology Research Center) of the Service. Three phases of the project were identified: 1. To collect existing literature and data; 2. To select, develop, and test the utility of methods to quantify the relationships between land use, cover types, soils, hydrology, and water quality (as represented by sediment); and 3. To apply selected methodologies to several sites within the Yazoo Basin of Mississippi to determine the, potential effectiveness of various management alternatives to reduce sediment yield, increase sediment deposition, and improve water quality. Methods development focused on linking a simulation of water and sediment movement to a computerized geographic information system. We had several objectives for the resulting model. We desired that it should: 1. Estimate the importance of bottomland and hardwoods as a cover type that performs the functions of erosion and sediment control, 2. Simulate effects of proportions of ' various cover types and their specific spatial configurations, 3. Be applicable to moderately large spatial areas with minimal site-specific calibration, 4. Simulate spatial patterns of sediment loss-gain over time, and 5. Represent both sediment detachment and transport. While it was recognized that impacts and management alternatives could be sorted roughly into landscape measures and channel measures, the decision was made to focus study efforts mainly on landscape measures. Landscape measures include altered drainage and flooding patterns, altered cover types (e.g., conversion of bottomland hardwoods to agricultural crops, reforestation of cropland to bottomland hardwoods, and creation of riparian buffer strips), altered cropping and tillage patterns, altered routing of water, and creation of buffer strips along wetlands and channel margins. Channel measures include vegetative bank stabilization, grade control structures, and regulation of channel water volume and velocity. During the first year of the study, EPA decided not to fund the third phase of the project. This required considerable rescoping of the project with the result that application of the sediment mobilization, routing, and deposition models to various management alternatives and portions of the Yazoo Basin was somewhat restricted. We believe, however, that this report will provide a good understanding of the various modes of sediment mobilization, transport, and deposition within the Yazoo Basin, as well as of the role of bottomland hardwoods. The model developed in this study could be applied to a variety of management or mitigation alternatives prior to implementation to determine their relative effectiveness. Policy, political, and socio-economic consequences of any proposed management/mitigation practice, however, must ultimately be taken into consideration by those charged with management of water resources within the Yazoo Basin before any practice is implemented. This study makes no effort to judge the feasibility of management alternatives in this regard.

Report↗
USGS2022

Quantifying the relationship between prey density, livestock and illegal killing of leopards

Many large mammalian carnivores are facing population declines due to illegal killing (e.g., shooting) and habitat modification (e.g., livestock farming). Illegal killing occurs cryptically and hence is difficult to detect. However, reducing illegal killing requires a solid understanding of its magnitude and underlying drivers, while accounting for the imperfect detection of illegal killing events. Despite the importance of illegal killing of large carnivores in comparison with other causes of mortality, its relationship with potential drivers such as livestock density and wild prey abundance is rarely described. Using ranger-collected data (2007-2019) of leopard killing events and data on covariates (livestock density, wild prey abundance, road length, protected area size, elevation) across Iran, we applied a single-visit N-mixture model to jointly model variation in detection probability and expected annualized number of leopard killing events. Over the study period, we estimated 428 leopard mortalities (95% CI 184–1014), which was 45% larger than the observed number. Expected intensity of leopard killing was positively related to protected area size, livestock density and wild prey abundance. Detection of leopard killing was higher in areas with more developed road networks. Synthesis and applications: Ranger based monitoring data on poaching of carnivores are cost effective, but traditional analysis does not take into account imperfect detection. We show that innovative statistics (single-visit N-mixture modeling) can reliably quantify poaching events and address their drivers, at large geographical scales. We used the example of the Persian leopard across Iran, but our approach is also applicable to understand killing dynamics of other species. Results suggest that a high frequency of leopard killing is likely to occur in areas with > 100 livestock per km 2 and > 450 individuals of wild prey per km 2 . This highlights the need for improved management of livestock grazing and effective measures around high-risk protected areas to mitigate human-leopard conflict and reduce killing of leopards.

Journal of Applied Ecology↗
USGS2013

Estimating true instead of apparent survival using spatial Cormack-Jolly-Seber models

Survival is often estimated from capture–recapture data using Cormack–Jolly–Seber (CJS) models, where mortality and emigration cannot be distinguished, and the estimated apparent survival probability is the product of the probabilities of true survival and of study area fidelity. Consequently, apparent survival is lower than true survival unless study area fidelity equals one. Underestimation of true survival from capture–recapture data is a main limitation of the method. We develop a spatial version of the CJS model that allows estimation of true survival. Besides the information about whether a specific individual was encountered at a given occasion, it is often recorded where the encounter occurred. Thus, information is available about the fraction of dispersal that occurs within the study area, and we use it to model dispersal and estimate true survival. Our model is formulated hierarchically and consists of survival, dispersal and observation submodels, assuming that encounters are possible anywhere within a study area. In a simulation study, our new spatial CJS model produced accurate estimates of true survival and dispersal behaviour for various sizes and shapes of the study area, even if emigration is substantial. However, when the information about dispersal is scarce due to low survival, low recapture probabilities and high emigration, the estimators are positively biased. Moreover, survival estimates are sensitive to the assumed dispersal kernel. We applied the spatial CJS model to a data set of adult red-backed shrikes ( Lanius collurio ). Apparent survival of males ( c . 0·5) estimated with the CJS model was larger than in females ( c . 0·4), but the application of the spatial CJS model revealed that both sexes had similar survival probabilities ( c . 0·6). The mean breeding dispersal distance in females was c . 700 m, while males dispersed only c . 250 m between years. Spatial CJS models enable study of dispersal and survival independent of study design constraints such as imperfect detection and size of the study area provided that some of the dispersing individuals remain in the study area. We discuss possible extensions of our model: alternative dispersal models and the inclusion of covariates and of a habitat suitability map.

Methods in Ecology and Evolution↗
USGS2012

Spring onset variations and trends in the continental United States: past and regional assessment using temperature-based indices

Phenological data are simple yet sensitive indicators of climate change impacts on ecosystems, but observations have not been made routinely or extensively enough to evaluate spatial and temporal patterns across most continents, including North America. As an alternative, many studies use weather-based algorithms to simulate specific phenological responses. Spring Indices (SI) are a set of complex phenological models that have been successfully applied to evaluate variations and trends in the onset of spring across the Northern Hemisphere’s temperate regions. To date, SI models have been limited by only producing output in locations where both the plants’ chilling and warmth requirements are met. Here, we develop an extended form of the SI (abbreviated SI-x) that expands their application into the subtropics by ignoring chilling requirements while still retaining the utility and accuracy of the original SI (now abbreviated SI-o). The validity of the new indices is tested, and regional SI anomalies are explored across the data-rich continental United States. SI-x variations from 1900 to 2010 show an abrupt and sustained delay in spring onset of about 4–8 d (around 1958) in parts of the Southeast and southern Great Plains, and a comparable advance of 4–8 d (around 1984) in parts of the northern Great Plains and the West. Atmospheric circulation anomalies, linked to large-scale modes of variability, exert modest but significant roles in the timing of spring onset across the United States on interannual and longer timescales. The SI-x are promising metrics for tracking spring onset variations and trends in mid-latitudes, relating them to relevant ecological, hydrological, and socioeconomic phenomena, and exploring connections between atmospheric drivers and seasonal timing.

International Journal of Climatology↗
USGS2023

A recruitment niche framework for improving seed-based restoration

As larger tracts of land experience degradation, seed-based restoration (SBR) will be a primary tool to reestablish vegetation and ecosystem function. SBR has advanced in terms of technical and technological approaches, yet plant recruitment remains a major barrier in some systems, notably drylands. There is an unmet opportunity to test science-based approaches to seed mix design and application, based not only on diversity or local provenance, but on the unique recruitment strategies of species. We lay out a framework that uses a quantitative representation of species' recruitment niches to match them to targeted goals (e.g. drought or invasion resistance) and methods (e.g. precision tools and technologies) in SBR. We first describe how to quantify the recruitment niche with seed and seedling traits tied to observed recruitment responses to environmental factors. We then show how a quantified recruitment niche framework can serve as the foundation to address three major restoration challenges: (1) designing forward-looking seed mixes that increase resilience to future climate and disturbance, (2) accounting for natural recovery in SBR planning, and (3) applying precision seeding practices to maximize restoration success. Finally, we demonstrate these ideas with existing data and discuss key challenges to adoption in SBR practice. While the ideas in this framework are based in ecological theory, they will require substantial testing and refinement by scientists engaged in SBR efforts. If this framework is integrated into research agendas, we believe it has the potential to unify and advance diverse elements of seed-based restoration ecology and improve restoration outcomes.

Restoration Ecology↗
USGS2020

Monitoring nearshore ecosystem health using Pacific razor clams (Siliqua patula) as an indicator species

An emerging approach to ecosystem monitoring involves the use of physiological biomarker analyses in combination with gene transcription assays. For the first time, we employed these tools to evaluate the Pacific razor clam ( Siliqua patula ), which is important both economically and ecologically, as a bioindicator species in the northeast Pacific. Our objectives were to (1) develop biomarker and gene transcription assays with which to monitor the health of the Pacific razor clam, (2) acquire baseline biomarker and gene transcription reference ranges for razor clams, (3) assess the relationship between physiological and gene transcription assays and (4) determine if site-level differences were present. Pacific razor clams were collected in July 2015 and 2016 at three sites within each of two national parks in southcentral Alaska. In addition to determining reference ranges, we found differences in biomarker assay and gene transcription results between parks and sites which indicate variation in both large-scale and local environmental conditions. Our intent is to employ these methods to evaluate Pacific razor clams as a bioindicator of nearshore ecosystem health. Links between the results of the biomarker and gene transcription assays were observed that support the applicability of both assays in ecosystem monitoring. However, we recognize the need for controlled studies to examine the range of responses in physiology and gene transcripts to different stressors. sors.

Alaska↗
USGS2024

A framework for estimating economic impacts of ecological restoration

Ecological restoration projects are designed to improve natural and cultural resources. Spending on restoration also stimulates economic impacts to the restoration economy through the creation or support of jobs and business activity. This paper presents accessible methods for quantifying the economic impacts supported by restoration spending and is written to be a guide and toolbox for an interdisciplinary audience of restoration practitioners and economists. Measuring the economic impacts of restoration can be challenging due to lacking or limited data. The complex, collaborative, and heterogeneous nature of restoration projects can make it difficult to clearly track costs, contributing to limited availability and inconsistency in restoration cost data. And business classification systems, such as the North American Industrial Classification System (NAICS), do not include restoration-sectors that consistently describe the patterns of restoration spending. The aims of this paper are to (1) provide restoration practitioners and program managers with a clear understanding of the application of economic impact analyses to restoration, (2) provide a framework for collecting project cost data for economic impact analyses, and (3) provide modeling best practices and an example application of the framework.

Environmental Management↗
USGS2020

RestoreNet: An emerging restoration network reveals controls on seeding success across dryland ecosystems

Drylands are Earth's largest terrestrial biome and support one‐third of the global population. However, they are also highly vulnerable to land degradation. Despite widespread demand for dryland restoration and rehabilitation, little information is available to help land managers effectively re‐establish native perennial vegetation across drylands. RestoreNet is an emerging dryland restoration network that systematically tests revegetation techniques across environmental gradients. Using the RestoreNet experimental framework, we tested the effectiveness of restoration treatments (i.e. ConMod nurse plant structures, mulch, pits) that increase soil moisture and seed mixes with different climatic niches to achieve revegetation goals. Across sites, seedling recruitment was consistently influenced by treatment and seed mix type. Pit and mulch treatments increased total seedling density, with pits promoting the highest seeded species recruitment while limiting non‐native species establishment. Seeding increased total seedling density regardless of seed mix type, but cooler‐adapted seed mixes promoted greater seeded species density and resulted in lower density of unseeded (non‐native) species relative to warmer‐adapted mixes. Seedling recruitment was also controlled by the temporal and environmental context of restoration with the positive effect of high precipitation greatest in the weeks immediately following seeding. Above‐average precipitation during the study period across most of the sites may partially explain why the highest seeded species recruitment occurred in pit treatments and seed mixes with cooler, wetter niche requirements. Synthesis and applications . Results from the dryland restoration network, RestoreNet help to better understand variation in seeding and restoration treatment success across space and time in drylands. Relationships between restoration practices and environmental conditions in our study suggest the importance of anticipatory restoration strategies that forecast seasonal and sub‐seasonal weather conditions and select plant species with climate niche requirements appropriate for current and future climate conditions. This information is critical to land managers tasked with improving ecosystem conditions across degraded dryland regions.

Arizona↗
USGS2014

USGS ecosystem research for the next decade: advancing discovery and application in parks and protected areas through collaboration

Ecosystems within parks and protected areas in the United States and throughout the world are being transformed at an unprecedented rate. Changes associated with natural hazards, greenhouse gas emissions, and increasing demands for water, food, land, energy and mineral resources are placing urgency on sound decision making that will help sustain our Nation’s economic and environmental well-being (Millennium Ecosystem Assessment, 2005). In recognition of the importance of science in making these decisions, the U.S. Geological Survey (USGS) in 2007 identified ecosystem science as one of six science directions included in a comprehensive decadal strategy (USGS 2007). The Ecosystems Mission Area was identified as essential for integrating activity within the USGS and as a key to enhanced integration with other Federal and private sector research and management organizations (Myers at al., 2007). This paper focuses on benefits to parks and protected areas from the USGS Ecosystems Mission Area plan that expanded the scope of the original 2007 science strategy, to identify the Bureau’s work in ecosystem science over the next decade (Williams et al., 2013). The plan describes a framework that encompasses both basic and applied science and allows the USGS to continue to contribute meaningfully to conservation and management issues related to the Nation’s parks and ecological resources. This framework relies on maintaining long-standing, collaborative relationships with partners in both conducting science and applying scientific results. Here we summarize the major components of the USGS Ecosystems Science Strategy, articulating the vision, goals and strategic approaches, then outlining some of the proposed actions that will ultimately prove useful to those managing parks and protected areas. We end with a discussion on the future of ecosystem science for the USGS and how it can be used to evaluate ecosystem change and the associated consequences to management of our Nation’s natural resources.

The George Wright Forum↗
USGS2022

Comparison of microscopy and metabarcoding to identify pollen used by the critically endangered rusty patched bumble bee, Bombus affinis

Taxonomic analysis of pollen collected by bees can provide insights into their host plant use, providing information about the plant species selected for targeted conservation strategies. The two main identification approaches used are morphological analysis of pollen samples affixed to microscope slides (i.e., microscopic palynology) and molecular analysis of samples. Both methods are widely used for freshly collected materials and have been compared in multiple studies, yet their application to archived samples remains to be explored. Archived samples may be particularly useful for the study of rare or protected species, particularly when historical foraging patterns are uncertain. We used both methods to analyse pollen collected by the endangered rusty patched bumble bee species, Bombus affinis Cresson, applied to museum-archived specimens. Pollen samples were removed from the corbiculae of bumble bees originally collected in Michigan between 1914 and 1974. Samples included 24 rusty patched bumble bees each with large pollen loads on both corbiculae, allowing for pollen from the same bee to be analysed using both methods. DNA metabarcoding detected more plant taxa than light microscopy, and DNA barcoding also had higher taxonomic resolution when compared to taxa determined using light microscopy. In many instances, pollen could only be confidently identified to tribe or family with light microscopy. Discrepancy between methods decreased when taxa identified via DNA metabarcoding were binned into ecologically relevant groups corresponding to those identified using light microscopy. Although binning demonstrated smaller within-method variance, there was still minimal correspondence between the two methods. Results indicate there are benefits and biases unique to each method and highlight the utility of binning taxonomic results to morphological or ecological groupings.

Insect Conservation and Diversity↗
USGS2019

Spatial integration of biological and social objectives to identify priority landscapes for waterfowl habitat conservation

Waterfowl population management and habitat conservation compose one of the oldest and most successful adaptive management frameworks in the world. Since its inception, the North American Waterfowl Management Plan (NAWMP) has emphasized strategically targeted conservation investments in regions that most affect waterfowl population dynamics. By 2012, regional conservation had progressively become more science-based and strategic: many migratory bird partnerships had initiated or completed projects on mapping and modeling waterfowl distribution and abundances using geospatial techniques. However, when developing a map depicting and titled “Areas of Greatest Continental Significance to North American Ducks, Geese, and Swans” for the 2012 NAWMP Revision, waterfowl professionals articulated the need for improved decision frameworks and use of consistent datasets for refining large-scale spatial products depicting priority areas for waterfowl and people. This report describes a framework for developing a spatial value model to support the identification of North American geographies of importance to waterfowl during the breeding and non-breeding periods and to resource users who could potentially support (financially and (or) politically) waterfowl habitat conservation. Objectives used to identify priority geographies were determined through a collaborative process of the NAWMP Science Support Team, Priority Landscapes Committee (PLC), and other experts in the fields of waterfowl biology and ecology, environmental science, and human dimensions. ArcGIS Desktop was used as the platform for managing, analyzing, combining and displaying the spatial data as well as producing new data through spatial analysis functions. Thirty-eight spatial layers were developed, and several composite spatially explicit products (maps of North America) were produced based on PLC recommendations. The composite products have extensive similarities to the 2012 NAWMP map depicting areas of greatest continental significance to North American waterfowl. There are also some differences, especially in regions of the high Arctic and in Mexico. These differences between spatial value model maps and the 2012 NAWMP output likely arose from inclusion of social objectives, reduced dependence on expert opinion to generate abundance estimates, lack of population surveys in some regions and availability of expanded survey data in other regions, and use of model-based waterfowl population estimates for some unsurveyed areas. The structured decision-making framework application in this study is discussed, and the appropriate use of the products and their limitations are outlined. Additionally, options for future improvements are presented by identifying gaps in data collection, waterfowl-habitat association assumptions, and uncertainties related to social objectives. These spatial products are intended for use by national, regional, and province/state level wildlife professionals to aid their decisions in targeting waterfowl habitat conservation.

Open-File Report↗
USGS2025

Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities

The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.

Delaware, Maryland, Virginia↗
USGS2007

First steps in developing a multimetric macroinvertebrate index for the Ohio River

The causes of degradation of aquatic systems are often complex and stem from a variety of human influences. Comprehensive, multimetric biological indices have been developed to quantify this degradation and its effect on aquatic communities, and measure subsequent recovery from anthropogenic stressors. Traditionally, such indices have concentrated on small-to medium-sized streams. Recently, however, the Ohio River Fish Index (ORFIn) was created to assess biotic integrity in the Ohio River. The goal of the present project was to begin developing a companion Ohio River multimetric index using benthic macroinvertebrates. Hester-Dendy multiplate samplers were used to evaluate benthic macroinvertebrate assemblages in relation to a gradient of water quality disturbance, represented by varying distances downstream of industrial and municipal wastewater outfalls in the Ohio River. In August 1999 and 2000, samplers were set every 100 m downstream of outfalls (12 outfalls in 1999, 22 in 2000) for 300-1000 m, as well as at upstream reference sites. Candidate metrics (n = 55) were examined to determine which have potential to detect changes in water quality downstream of outfalls. These individual measures of community structure were plotted against distance downstream of each outfall to determine their response to water quality disturbance. Values at reference and outfall sites were also compared. Metrics that are ecologically relevant and showed a response to outfall disturbance were identified as potentially valuable in a multimetric index. Multiple box plots of index scores indicated greater response to outfall disturbance during periods of low-flow, and longitudinal river-wide trends. Evaluation of other types of anthropogenic disturbance, as well as continued analysis of the effects of chemical water quality on macroinvertebrate communities in future years will facilitate further development of a multimetric benthic macroinvertebrate index to evaluate biotic integrity in the Ohio River. Copyright ?? 2007 John Wiley & Sons, Ltd.

River Research and Applications↗
USGS2014

Accounting for imperfect detection in Hill numbers for biodiversity studies

Hill numbers unify biodiversity metrics by combining several into one expression. For example, species richness, Shannon's diversity index and the Gini–Simpson index are a few of the most used diversity measures, and they can be expressed as Hill numbers. Traditionally, Hill numbers have been calculated from relative abundance data, but the expression has been modified to use incidence data as well. We demonstrate an approach for estimating Hill numbers using an occupancy modelling framework that accounts for imperfect detection. We alter the Hill numbers formula to use occupancy probabilities as opposed to the incidence probabilities that have been used previously and to calculate its summations from the modelled species richness. After introducing the occupancy-based Hill numbers, we demonstrate the differences between them and the incidence-based Hill numbers previously used through a simulation study and two applications. In the simulation study and the two examples using real data, the occupancy-based Hill numbers were larger than the incidence-based Hill numbers, although species richness was estimated similarly using both methods. The occupancy-based Hill number estimators are always at their asymptotic values (i.e. as if an infinite number of samples have been taken for the study region), therefore making it easy to compare biodiversity between different assemblages. In addition, the Hill numbers are computed as derived quantities within a Bayesian hierarchical model, allowing for straightforward inference.

Methods in Ecology and Evolution↗
USGS2010

Potential spread of highly pathogenic avian influenza H5N1 by wildfowl: dispersal ranges and rates determined from large-scale satellite telemetry

1. Migratory birds are major candidates for long-distance dispersal of zoonotic pathogens. In recent years, wildfowl have been suspected of contributing to the rapid geographic spread of the highly pathogenic avian influenza (HPAI) H5N1 virus. Experimental infection studies reveal that some wild ducks, geese and swans shed this virus asymptomatically and hence have the potential to spread it as they move. 2. We evaluate the dispersive potential of HPAI H5N1 viruses by wildfowl through an analysis of the movement range and movement rate of birds monitored by satellite telemetry in relation to the apparent asymptomatic infection duration (AID) measured in experimental studies. We analysed the first large-scale data set of wildfowl movements, including 228 birds from 19 species monitored by satellite telemetry in 2006–2009, over HPAI H5N1 affected regions of Asia, Europe and Africa. 3. Our results indicate that individual migratory wildfowl have the potential to disperse HPAI H5N1 over extensive distances, being able to perform movements of up to 2900 km within timeframes compatible with the duration of asymptomatic infection. 4. However, the likelihood of such virus dispersal over long distances by individual wildfowl is low: we estimate that for an individual migratory bird there are, on average, only 5–15 days per year when infection could result in the dispersal of HPAI H5N1 virus over 500 km. 5. Staging at stopover sites during migration is typically longer than the period of infection and viral shedding, preventing birds from dispersing a virus over several consecutive but interrupted long-distance movements. Intercontinental virus dispersion would therefore probably require relay transmission between a series of successively infected migratory birds. 6. Synthesis and applications . Our results provide a detailed quantitative assessment of the dispersive potential of HPAI H5N1 virus by selected migratory birds. Such dispersive potential rests on the assumption that free-living wildfowl will respond analogously to captive, experimentally-infected birds, and that asymptomatic infection will not alter their movement abilities. Our approach of combining experimental exposure data and telemetry information provides an analytical framework for quantifying the risk of spread of avian-borne diseases.

Journal of Applied Ecology↗
USGS2019

Designing multi-scale hierarchical monitoring frameworks for wildlife to support management: A sage-grouse case study

Population monitoring is integral to the conservation and management of wildlife; yet, analyses of population demographic data rarely consider processes occurring across spatial scales, potentially limiting the effectiveness of adaptive management. Therefore, we developed a method to identify hierarchical levels of organization (i.e., populations) to define multiple spatial scales, specifically intended to help guide appropriate conservation and management actions. This approach can support mobile species with high site fidelity where surveys occur on birthing/breeding grounds or migratory stopovers. Our approach used a graphbased clustering algorithm (Spatial K’luster Analysis by Tree Edge Removal) that explicitly included habitat selection information at multiple scales and further refined with constraint-based rules. We applied these concepts to greater sage-grouse leks (breeding grounds), a species of conservation concern, in two different ecological contexts (Nevada and Wyoming, USA). The constraint-based rules accounted for inter-lek movement distances based on literature and field studies in Nevada from 2012 to 2016, included methods to support a spatially balanced monitoring design, and identified barriers to movements among leks based on resistance surfaces. We evaluated the performance of our hierarchical clusters in Nevada using independent data from radio-marked sage-grouse, and we found the finest-scaled cluster level captured ~90% of sagegrouse movements and mid-level scales captured ~97–99% of movements. We expected comparable performance for Wyoming, where we lacked radio-marked sage-grouse for an evaluation, because genetic studies estimate similar dispersal distances to our ~15 km inter-lek movement distance in Nevada. For sage-grouse and other mobile species with high site fidelity, our approach to defining these frameworks could prove valuable for conservation and management applications, such as improving estimation of scale-dependent population trends and guiding the prescription of management actions at spatial scales that align with identified threats. Specific to sage-grouse, our analysis sets the stage for designing a monitoring framework that relies on comparison of short- and long-term population trends across our defined spatial scales and identifies and disentangles factors driving local (e.g., habitat quality) and regional (e.g., climate) population changes, thereby supporting scale-dependent management and research needs for adaptive management practices.

Nevada, Wyoming↗
USGS2014

Sea Level Affecting Marshes Model (SLAMM) ‐ New functionality for predicting changes in distribution of submerged aquatic vegetation in response to sea level rise

Introduction Submerged aquatic vegetation (SAV) is an ecologically important habitat world‐wide. In Pacific Northwest (PNW) estuaries, SAV in the lower intertidal and shallow subtidal habitats are dominated by the native seagrass, Zostera marina Linnaeus, 1753. Within this report, SAV and seagrass refer to Z. marina seagrass beds in PNW estuaries. Z. marina provides important habitat for juvenile salmon, dungeness crabs, migratory shore birds, and benthic assemblages (e.g., Philips, 1984; Williamson, 2006; Ferraro and Cole, 2007; Shaughnessy et al., 2012). Z. marina typically occurs in a narrow depth range. For example, in Oregon estuaries Zostera marina primarily occurs within the depth range of ‐1 to +1 m relative to Mean Lower Low Water (MLLW) (Young et al. 2012). Because of their narrow depth range, the distribution of these seagrass beds are potentially vulnerable to sea level rise (SLR) through increased water depths and associated reductions in underwater light levels, alterations in tidal variations, altered water movement and wave action, and increased seawater intrusion (Short and Neckles, 1999). The “Sea‐Level Affecting Marshes Model” (SLAMM) is a moderate resolution model used to predict the effects of sea level rise on marsh habitats (Craft et al. 2009). SLAMM has been used extensively on both the west coast (e.g., Glick et al., 2007) and east coast (e.g., Geselbracht et al., 2011) of the United States to evaluate potential changes in the distribution and extent of tidal marsh habitats. However, a limitation of the current version of SLAMM, (Version 6.2) is that it lacks the ability to model distribution changes in seagrass habitat resulting from sea level rise. Because of the ecological importance of SAV habitats, U.S. EPA, USGS, and USDA partnered with Warren Pinnacle Consulting to enhance the SLAMM modeling software to include new functionality in order to predict changes in Zostera marina distribution within Pacific Northwest estuaries in response to sea level rise. Specifically, the objective was to develop a SAV model that used generally available GIS data and parameters that were predictive and that could be customized for other estuaries that have GIS layers of existing SAV distribution. This report describes the procedure used to develop the SAV model for the Yaquina Bay Estuary, Oregon, appends a statistical script based on the open source R software to generate a similar SAV model for other estuaries that have data layers of existing SAV, and describes how to incorporate the model coefficients from the site‐specific SAV model into SLAMM to predict the effects of sea level rise on Zostera marina distributions. To demonstrate the applicability of the R tools, we utilize them to develop model coefficients for Willapa Bay, Washington using site‐specific SAV data.

Oregon↗
USGS2013

Use of sediment amendments to rehabilitate sinking coastal swamp forests in Louisiana

Coastal wetlands are losing elevation worldwide, so that techniques to increase elevation such as sediment amendment might benefit these wetlands. This study examined the potential of sediment amendment to raise elevation and support the production and regeneration of vegetation in coastal forests in Louisiana. Before sediment amendment, the vegetation did not differ in these Taxodium distichum–Nyssa aquatica forests with respect to herbaceous and tree seedling composition, and sapling and tree characteristics. After the application of sediment in January 2007, sediment-amended swamps had higher elevations and salinity levels than natural swamps. The layer of sediment applied to Treasure Island in Jean Lafitte National Historic Park and Preserve was relatively deep (sediment depth at Site One and Site Two: 0.89 and 0.69 m, respectively, six months after application), and may have exceeded an optimal threshold. Sediment-amended swamp with the highest elevation had some tree mortality and little tree growth of T. distichum. Also, sediment-amended swamp had higher root biomasses of ruderal species, and lower species richness and cover of herbaceous species. Nevertheless, during controlled water releases during an oil spill emergency in 2010, both sediment-amended and reference forest had higher production levels than in other years. While sediment amendment is a compelling management alternative for sinking coastal wetlands, optimal thresholds were not determined for these T. distichum–N. aquatica swamps.

Louisiana↗