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At least 631 records · Page 35Linked to original sources
USGS2024

A "Region-Specific Model Adaptation (RSMA)" based training data method in large-scale land cover mapping

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.

Remote Sensing↗
USGS2006

A simulation model of land-use change in the Lake Tahoe Basin of California and Nevada, as used in a decision-support system

The Tahoe Land-Use Change model is a stochastic, spatially explicit simulation of future land-use change—in particular, development and retirement of individual parcels—in the Lake Tahoe Basin of California and Nevada. The Federal, State, and regional management agencies responsible for the basin are revising and integrating their 20-year plans to meet various goals, including maintaining or improving several environmental (e.g., lake clarity, forest health) and socioeconomic (e.g., affordable housing) characteristics. To assist this effort, the model projects the long-term outcomes of land-use-management decisions, including those relating to existing and potential government regulations, development activities, and conservation practices. The model results are probabilistic maps of parcel-specific changes in land use and the resulting changes in the amount and locations of developed parcels and land-use change. To capture the uncertainties and variation in the exact parcels of land selected for development or retirement by individuals acting in the basin, a single model run includes multiple iterations, from which cumulative statistics are taken to describe the results. The purpose of the model is to generate changes in the amounts and types of land use and land cover that form inputs to a basinwide model of pollutant loading to Lake Tahoe, which, in turn, generates inputs to a lake-clarity model. Together, these three models form a chain of tools that link land-use decisions to changes in a critical environmental quality—the clarity of Lake Tahoe—within a decision-support context. Eventually, these three models will become part of a larger, more complete decision-support system.

California, Nevada↗
USGS2019

Principles of translational science education

In a recent special issue in Frontiers in Ecology and the Environment, Enquist et al. (2017) present a welcome streamlining of modern applied ecology emphasizing a collaborative approach to applied ecological research involving resource-managers and scientists to produce actionable science: translational ecology (TE). The authors, including ecologists, social scientists, and conservation professionals, identified six principles defining the practice of translational ecology: collaboration, engagement, commitment, communication, process, and framing (Figure 1). In thinking about how science education can be enhanced by directly involving researchers in science education, we recognized remarkable overlap between the principles of translational ecology (Enquist et al. 2017) and a framework for developing mutually beneficial integrative partnerships between scientists and educators. Here, we describe scientist-educator analogies of the six principles of translational ecology: translational science education (TSE).

Frontiers in Ecology and Evolution↗
USGS2012

Chesapeake Bay Watershed - Protecting the Chesapeake Bay and its rivers through science, restoration, and partnership

The Chesapeake Bay, the Nation's largest estuary, has been degraded due to the impact of human-population increase, which has doubled since 1950, resulting in degraded water quality, loss of habitat, and declines in populations of biological communities. Since the mid-1980s, the Chesapeake Bay Program (CBP), a multi-agency partnership which includes the Department of Interior (DOI), has worked to restore the Bay ecosystem. The U.S. Geological Survey (USGS) has the critical role of providing unbiased scientific information that is utilized to document and understand ecosystem change to help assess the effectiveness of restoration strategies in the Bay and its watershed. The USGS revised its Chesapeake Bay science plan for 2006-2011 to address the collective needs of the CBP, DOI, and USGS with a mission to provide integrated science for improved understanding and management of the Bay ecosystem. The USGS science themes for this mission are: Causes and consequences of land-use change; Impact of climate change and associated hazards; Factors affecting water quality and quantity; Ability of habitat to support fish and bird populations; and Synthesis and forecasting to improve ecosystem assessment, conservation, and restoration.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS2024

Stopover population estimate and migration ecology of Red Knots C. c. rufa at Delaware Bay, USA, 2024

Red Knots ( Calidris canutus rufa ) stop at Delaware Bay on the mid-Atlantic coast of North America during northward migration to feed on eggs of horseshoe crabs ( Limulus polyphemus ). Horseshoe crabs have been harvested for use as bait in eel ( Anguilla rostrata ) and whelk ( Busycotypus canaliculatus and Busycon carica ) fisheries since at least 1990. In the late 1990s and early 2000s, the number of Red Knots counted during aerial surveys at Delaware Bay declined, leading to conservation concern for Red Knots and shorebirds at Delaware Bay. In 2013, the Atlantic States Marine Fisheries Commission began using an Adaptive Resource Management (ARM) framework to manage the harvest of horseshoe crabs in the Delaware Bay region. The objective of the ARM framework is to manage sustainable harvest of Delaware Bay horseshoe crabs while maintaining ecosystem integrity and supporting Red Knot recovery with adequate stopover habitat. The ARM framework thus requires annual estimates of horseshoe crab population size and Red Knot stopover population size to recommend annual harvest quotas. We estimated the passage population of Red Knots at Delaware Bay in 2024 using a mark-recapture-resight investigation. We used a Bayesian analysis of a Jolly-Seber model, which accounts for turnover in the population and the probability of detection during surveys. The estimated passage population size in 2024 was 46,127 (95% credible interval: 39,286–57,799), an increase from 2023 (39,361 [33,724–47,556]). Since 2019, the stopover population has fluctuated between approximately 39,000 and 46,000, and appears stable given the broad overlap in the confidence intervals of the annual population estimates. The 2024 Red Knot stopover population estimate will inform decision making in the next horseshoe crab management cycle of the Atlantic States Marine Fisheries Commission.

Delaware, New Jersey↗
USGS2016

Predictive mapping of seabirds, pinnipeds and cetaceans off the Pacific Coast of Washington

About this report This report supports Washington-led marine spatial planning and responsible stewardship of natural and cultural resources by the Olympic Coast National Marine Sanctuary. Washington state agencies and the sanctuary continually seek the best available science to improve management of marine uses and stewardship of resources (Etheridge et al., 2010; Washington Department of Fish and Wildlife, 2015a). This report and associated data provide new, state- and sanctuary-requested information on seabird, pinniped, and cetacean distributions. Through spatial planning, information on species distributions can help to identify high-value conservation areas, minimize adverse effects of ocean uses and mitigate impacts of coastal hazards. Correspondingly, the Washington Department of Fish and Wildlife has already begun to use the maps of predicted relative density presented in this report to identify ecologically important areas off the Pacific Coast of Washington and apply this information to plan for offshore renewable energy development. This is the culmination of three years of work to compile information on seabirds, pinnipeds, and cetaceans, and advance a modeling framework that can integrate data sets and develop accurate predictions of relative density for important species off the Pacific Coast of Washington. Previous reports, which evaluated existing datasets of at-sea observations (Menza et al., 2014; Kracker and Menza, 2015) and presented superseded versions of seabird models (Menza et al., 2015), provided base information for this report. In addition to the maps in this published report, all new seabird, pinniped and cetacean predictions will be made publicly available as digital geospatial data through the National Centers for Environmental Information. This research supports the National Oceanic and Atmospheric Administration (NOAA) Coastal Zone Management Program, a voluntary partnership between the federal government and U.S. coastal and Great Lakes states and territories authorized by the Coastal Zone Management Act (CZMA) of 1972 to address national coastal issues. The act provides the basis for protecting, restoring, and responsibly developing our nation’s diverse coastal communities and resources. To meet the goals of the CZMA, the national program takes a comprehensive approach to coastal resource management – balancing the often competing and occasionally conflicting demands of coastal resource use, economic development, and conservation. A wide range of issues are addressed through the program, including coastal development, water quality, public access, habitat protection, energy facility siting, ocean governance and planning, coastal hazards, and climate change. Accurate maps of seabird and marine mammal distributions are an important tool for making informed management decisions that affect all of these issues.

Washington↗
USGS2014

Using ecological indicators and a decision support system for integrated ecological assessment at two national park units in the Mid-Atlantic region, U.S.A.

We implemented an integrated ecological assessment using a GIS-based decision support system model for Upper Delaware Scenic and Recreational River (UPDE) and Delaware Water Gap National Recreation Area (DEWA)—national park units with the mid-Atlantic region of the United States. Our assessment examined a variety of aquatic and terrestrial indicators of ecosystem components that reflect the parks’ conservation purpose and reference condition. Our assessment compared these indicators to ecological thresholds to determine the condition of park watersheds. Selected indicators included chemical and physical measures of water quality, biologic indicators of water quality, and landscape condition measures. For the chemical and physical measures of water quality, we used a water quality index and each of its nine components to assess the condition of water quality in each watershed. For biologic measures of water quality, we used the Ephemeroptera, Plecoptera, Trichoptera aquatic macroinvertebrate index and, secondarily, the Hilsenhoff aquatic macroinvertebrate index. Finally, for the landscape condition measures of our model, we used percent forest and percent impervious surface. Based on our overall assessment, UPDE and DEWA watersheds had an ecological assessment score of 0.433 on a −1 to 1 fuzzy logic scale. This score indicates that, in general, the natural resource condition within watersheds at these parks is healthy or ecologically unimpaired; however, we had only partial data for many of our indicators. Our model is iterative and new data may be incorporated as they become available. These natural parks are located within a rapidly urbanizing landscape—we recommend that natural resource managers remain vigilant to surrounding land uses that may adversely affect natural resources within the parks.

New Jersey, New York, Pennsylvania↗
USGS2003

Global patterns of the isotopic composition of soil and plant nitrogen

We compiled new and published data on the natural abundance N isotope composition (δ 15 N values) of soil and plant organic matter from around the world. Across a broad range of climate and ecosystem types, we found that soil and plant δ 15 N values systematically decreased with increasing mean annual precipitation (MAP) and decreasing mean annual temperature (MAT). Because most undisturbed soils are near N steady state, the observations suggest that an increasing fraction of ecosystem N losses are 15 N-depleted forms (NO 3 , N 2 O, etc.) with decreasing MAP and increasing MAT. Wetter and colder ecosystems appear to be more efficient in conserving and recycling mineral N. Globally, plant δ 15 N values are more negative than soils, but the difference (δ 15 N plant -δ 15 N soil ) increases with decreasing MAT (and secondarily increasing MAP), suggesting a systematic change in the source of plant-available N (organic/NH 4 + versus NO 3 − ) with climate. Nitrogen isotopes reflect time integrated measures of the controls on N storage that are critical for predictions of how these ecosystems will respond to human-mediated disturbances of the global N cycle.

Global Biogeochemical Cycles↗
USGS2023

Scenario-Based Decision Analysis: Integrated scenario planning and structured decision making for resource management under climate change

Managing resources under climate change is a high-stakes and daunting task, especially because climate change and associated complex biophysical responses engender sustained directional changes as well as abrupt transformations. This environmental non-stationarity challenges assumptions and expectations among scientists, managers, rights holders, and stakeholders. These challenges are anything but straightforward – a high degree of uncertainty impedes our ability to predict the environmental trajectory with confidence, and affected resources often span multiple governance jurisdictions or are subject to competing management objectives. Fortunately, tools exist to help grapple with such challenges. Two commonly used tools are scenario planning (SP) and structured decision making (SDM). SP is a well-established approach for assessing system response and facilitating decision making under a wide range of conditions that are uncertain and uncontrollable, such as those associated with adapting to climate change. However, SP lacks a defined structure for establishing objectives, quantifying tradeoffs, and evaluating the performance of candidate decisions to meet those objectives. SDM, on the other hand, is rooted in decision theory and focuses on explicit (often quantitative) assessment of the expected outcomes of choosing among a set of decision alternatives. SDM has been criticized for an inability to account for surprises and for imposing an overly narrow framing of problems to increase tractability. We discuss the strengths and limitations of SDM and SP as experienced through their application in various resource-management contexts, and then propose a new generalized framework – Scenario-Based Decision Analysis (SBDA) – that integrates these complementary approaches. SBDA structures resource management problems and solutions while considering uncertainties and surprises to inform resource management decision making.

Biological Conservation↗
USGS2018

Integrating physiological stress into the movement ecology of migratory ungulates: A spatial analysis with mule deer

Rapid climate and human land-use change may limit the ability of long-distance migratory herbivores to optimally track or “surf” high-quality forage during spring green-up. Understanding how anthropogenic and environmental stressors influence migratory movements is of critical importance because of their potential to cause a mismatch between the timing of animal movements and the emergence of high-quality forage. We measured stress hormones (fecal glucocorticoid metabolites; FGMs) to test hypotheses about the effects of high-quality forage tracking, human land use, and use of stopover sites on the physiological state of individuals along a migratory route. We collected and analyzed FGM concentrations from 399 mule deer (Odocoileus hemionus) samples obtained along a 241 km migratory route in western Wyoming, USA, during spring 2015 and 2016. In support of a fitness benefit hypothesis, individuals occupying areas closer to peak forage quality had decreased FGM levels. Specifically, for every 10-day interval closer to peak forage quality, we observed a 7% decrease in FGMs. Additionally, we observed support for both an additive anthropogenic stress hypothesis and a hypothesis that stopovers act as physiological refugia, wherein individuals sampled far from stopover sites exhibited 341% higher FGM levels if in areas of low landscape integrity compared to areas of high landscape integrity. Overall, our findings indicate that the physiological state of mule deer during migration is influenced by both anthropogenic disturbances and their ability to track high-quality forage. The availability of stopovers, however, modulates physiological responses to those stressors. Thus, our results support a recent call for the prioritization of stopover locations and connectivity between those locations in conservation planning for migratory large herbivores.

Colorado, Wyoming↗
USGS2016

Resource waves: phenological diversity enhances foraging opportunities for mobile consumers

Time can be a limiting constraint for consumers, particularly when resource phenology mediates foraging opportunity. Though a large body of research has explored how resource phenology influences trophic interactions, this work has focused on the topics of trophic mismatch or predator swamping, which typically occur over short periods, at small spatial extents or coarse resolutions. In contrast many consumers integrate across landscape heterogeneity in resource phenology, moving to track ephemeral food sources that propagate across space as resource waves. Here we provide a conceptual framework to advance the study of phenological diversity and resource waves. We define resource waves, review evidence of their importance in recent case studies, and demonstrate their broader ecological significance with a simulation model. We found that consumers ranging from fig wasps ( Chalcidoidea) to grizzly bears ( Ursus arctos ) exploit resource waves, integrating across phenological diversity to make resource aggregates available for much longer than their component parts. In model simulations, phenological diversity was often more important to consumer energy gain than resource abundance per se. Current ecosystem-based management assumes that species abundance mediates the strength of trophic interactions. Our results challenge this assumption and highlight new opportunities for conservation and management. Resource waves are an emergent property of consumer–resource interactions and are broadly significant in ecology and conservation.

Ecology↗
USGS2021

Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species

Anas acuta (Northern pintail; hereafter pintail) was selected as a model species on which to base a decision-support framework linking regional actions to continental-scale population and harvest objectives. This framework was then used to engage stakeholders, such as Landscape Conservation Cooperatives’ (LCCs’) habitat management partners within areas of importance to pintails, while maximizing cross-taxa effects from the framework. The mathematical framework for the model had been previously developed for pintails. A key assumption incorporated into the model is that density dependence in survival occurs during the post-hunting (winter) period, where resources are hypothesized to be limiting. Because few data are available to directly inform this process, the approach used was to build a hierarchical Bayesian integrated population model (IPM) that simultaneously uses data from bird-band recoveries, breeding population counts, and harvest surveys to estimate values of parameters of an annual population projection model, including population size, survival rate, reproductive rate, and process and observation error variances, that are logically consistent with each other, given the mathematical structure imposed through the IPM. The main accomplishments of this study are (1) development of an IPM for pintail to guide harvest and habitat management, (2) development of a Prairie Parkland Region breeding submodel to predict pintail productivity, (3) development of statistical methodology to estimate pintail productivity (as measured by the ratio of juvenile to adults in hunter-collected wing samples) and winter survival and to relate these estimates to covariates, and (4) illustration of how to use a model and estimated parameter values to predict pintail population size and sustainable harvest as a function of habitat. Estimation of pintail survival from bird-banding data shows that there has been relatively little variation in survival over the period 1960–2013. A productivity model showed strong effects of breeding ground conditions, wintering-ground precipitation, and density dependence on pintail productivity. Thus, most temporal variation in pintail demographic rates has been due to effects on reproduction and not survival, including effects of breeding or wintering-ground habitat. These results indicate that habitat conservation efforts may be most effective if they focus on maintaining or increasing breeding and wintering-ground habitat to increase pintail productivity rather than pintail survival. Environmental perturbations in excess of historical experience, such as what could occur under climate change, might have meaningful effects on survival but cannot be estimated with current data. Direct effects of climate, land use, or management are likely to be greater on productivity than survival, but substantial uncertainty remains about predictions of equilibrium population size and sustainable yield.

Open-File Report↗
USGS2017

An integrated population model for bird monitoring in North America

Integrated population models (IPMs) provide a unified framework for simultaneously analyzing data sets of different types to estimate vital rates, population size, and dynamics; assess contributions of demographic parameters to population changes; and assess population viability. Strengths of an IPM include the ability to estimate latent parameters and improve the precision of parameter estimates. We present a hierarchical IPM that combines two broad‐scale avian monitoring data sets: count data from the North American Breeding Bird Survey (BBS) and capture–recapture data from the Monitoring Avian Productivity and Survivorship (MAPS) program. These data sets are characterized by large numbers of sample sites and observers, factors capable of inducing error in the sampling and observation processes. The IPM integrates the data sets by modeling the population abundance as a first‐order autoregressive function of the previous year's population abundance and vital rates. BBS counts were modeled as a log‐linear function of the annual index of population abundance, observation effects (observer identity and first survey year), and overdispersion. Vital rates modeled included adult apparent survival, estimated from a transient Cormack‐Jolly‐Seber model using MAPS data, and recruitment (surviving hatched birds from the previous season + dispersing adults) estimated as a latent parameter. An assessment of the IPM demonstrated it could recover true parameter values from 200 simulated data sets. The IPM was applied to data sets (1992–2008) of two bird species, Gray Catbird ( Dumetella carolinensis ) and Wood Thrush ( Hylocichla mustelina ) in the New England/Mid‐Atlantic coastal Bird Conservation Region of the United States. The Gray Catbird population was relatively stable (trend +0.4% per yr), while the Wood Thrush population nearly halved (trend −4.5% per yr) over the 17‐yr study period. IPM estimates of population growth rates, adult survival, and detection and residency probabilities were similar and as precise as estimates from the stand‐alone BBS and CJS models. A benefit of using the IPM was its ability to estimate the latent recruitment parameter. Annual growth rates for both species correlated more with recruitment than survival, and the relationship for Wood Thrush was stronger than for Gray Catbird. The IPM's unified modeling framework facilitates integration of these important data sets.

Ecological Applications↗
USGS2012

Directional connectivity in hydrology and ecology

Quantifying hydrologic and ecological connectivity has contributed to understanding transport and dispersal processes and assessing ecosystem degradation or restoration potential. However, there has been little synthesis across disciplines. The growing field of ecohydrology and recent recognition that loss of hydrologic connectivity is leading to a global decline in biodiversity underscore the need for a unified connectivity concept. One outstanding need is a way to quantify directional connectivity that is consistent, robust to variations in sampling, and transferable across scales or environmental settings. Understanding connectivity in a particular direction (e.g., streamwise, along or across gradient, between sources and sinks, along cardinal directions) provides critical information for predicting contaminant transport, planning conservation corridor design, and understanding how landscapes or hydroscapes respond to directional forces like wind or water flow. Here we synthesize progress on quantifying connectivity and develop a new strategy for evaluating directional connectivity that benefits from use of graph theory in ecology and percolation theory in hydrology. The directional connectivity index (DCI) is a graph-theory based, multiscale metric that is generalizable to a range of different structural and functional connectivity applications. It exhibits minimal sensitivity to image rotation or resolution within a given range and responds intuitively to progressive, unidirectional change. Further, it is linearly related to the integral connectivity scale length—a metric common in hydrology that correlates well with actual fluxes—but is less computationally challenging and more readily comparable across different landscapes. Connectivity-orientation curves (i.e., directional connectivity computed over a range of headings) provide a quantitative, information-dense representation of environmental structure that can be used for comparison or detection of subtle differences in the physical-biological feedbacks driving pattern formation. Case-study application of the DCI to the Everglades in south Florida revealed that loss of directional hydrologic connectivity occurs more rapidly and is a more sensitive indicator of declining ecosystem function than other metrics (e.g., habitat area) used previously. Here and elsewhere, directional connectivity can provide insight into landscape drivers and processes, act as an early-warning indicator of environmental degradation, and serve as a planning tool or performance measure for conservation and restoration efforts.

Ecological Applications↗
USGS2007

Plumbing the global carbon cycle: Integrating inland waters into the terrestrial carbon budget

Because freshwater covers such a small fraction of the Earth's surface area, inland freshwater ecosystems (particularly lakes, rivers, and reservoirs) have rarely been considered as potentially important quantitative components of the carbon cycle at either global or regional scales. By taking published estimates of gas exchange, sediment accumulation, and carbon transport for a variety of aquatic systems, we have constructed a budget for the role of inland water ecosystems in the global carbon cycle. Our analysis conservatively estimates that inland waters annually receive, from a combination of background and anthropogenically altered sources, on the order of 1.9 Pg C y-1 from the terrestrial landscape, of which about 0.2 is buried in aquatic sediments, at least 0.8 (possibly much more) is returned to the atmosphere as gas exchange while the remaining 0.9 Pg y-1 is delivered to the oceans, roughly equally as inorganic and organic carbon. Thus, roughly twice as much C enters inland aquatic systems from land as is exported from land to the sea. Over prolonged time net carbon fluxes in aquatic systems tend to be greater per unit area than in much of the surrounding land. Although their area is small, these freshwater aquatic systems can affect regional C balances. Further, the inclusion of inland, freshwater ecosystems provides useful insight about the storage, oxidation and transport of terrestrial C, and may warrant a revision of how the modern net C sink on land is described. ?? 2007 Springer Science+Business Media, LLC.

Ecosystems↗
USGS2019

Spatially-structured statistical network models for landscape genetics

A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape‐genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non‐binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape‐genetic data. We further explore a set of spatial‐statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape‐genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape‐genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.

Ecological Monographs↗
USGS2023

Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery

Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%–96% and balanced accuracy of 52%–54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.

Indiana, Michigan, Ohio, Wisconsin↗
USGS2014

Identifying polar bear resource selection patterns to inform offshore development in a dynamic and changing Arctic

Although sea ice loss is the primary threat to polar bears ( Ursus maritimus ), little can be done to mitigate its effects without global efforts to reduce greenhouse gas emissions. Other factors, however, could exacerbate the impacts of sea ice loss on polar bears, such as exposure to increased industrial activity. The Arctic Ocean has enormous oil and gas potential, and its development is expected to increase in the coming decades. Estimates of polar bear resource selection will inform managers how bears use areas slated for oil development and to help guide conservation planning. We estimated temporally-varying resource selection patterns for non-denning adult female polar bears in the Chukchi Sea population (2008–2012) at two scales (i.e., home range and weekly steps) to identify factors predictive of polar bear use throughout the year, before any offshore development. From the best models at each scale, we estimated scale-integrated resource selection functions to predict polar bear space use across the population's range and determined when bears were most likely to use the region where offshore oil and gas development in the United States is slated to occur. Polar bears exhibited significant intra-annual variation in selection patterns at both scales but the strength and annual patterns of selection differed between scales for most variables. Bears were most likely to use the offshore oil and gas planning area during ice retreat and growth with the highest predicted use occurring in the southern portion of the planning area. The average proportion of predicted high-value habitat in the planning area was >15% of the total high-value habitat for the population during sea ice retreat and growth and reached a high of 50% during November 2010. Our results provide a baseline on which to judge future changes to non-denning adult female polar bear resource selection in the Chukchi Sea and help guide offshore development in the region. Lastly, our study provides a framework for assessing potential impacts of offshore oil and gas development to other polar bear populations around the Arctic.

Ecosphere↗