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At least 469 records · Page 26Linked to original sources
USGS2017

The Wetland and Aquatic Research Center strategic science plan

Introduction The U.S. Geological Survey (USGS) Wetland and Aquatic Research Center (WARC) has two primary locations (Gainesville, Florida, and Lafayette, Louisiana) and field stations throughout the southeastern United States and Caribbean. WARC’s roots are in U.S. Fish and Wildlife Service (USFWS) and National Park Service research units that were brought into the USGS as the Biological Research Division in 1996. Founded in 2015, WARC was created from the merger of two long-standing USGS biology science Centers—the Southeast Ecological Science Center and the National Wetlands Research Center—to bring together expertise in biology, ecology, landscape science, geospatial applications, and decision support in order to address issues nationally and internationally. WARC scientists apply their expertise to a variety of wetland and aquatic research and monitoring issues that require coordinated, integrated efforts to better understand natural environments. By increasing basic understanding of the biology of important species and broader ecological and physiological processes, this research provides information to policymakers and aids managers in their stewardship of natural resources and in regulatory functions. This strategic science plan (SSP) was developed to guide WARC research during the next 5–10 years in support of Department of the Interior (DOI) partnering bureaus such as the USFWS, the National Park Service, and the Bureau of Ocean Energy Management, as well as other Federal, State, and local natural resource management agencies. The SSP demonstrates the alignment of the WARC goals with the USGS mission areas, associated programs, and other DOI initiatives. The SSP is necessary for workforce planning and, as such, will be used as a guide for future needs for personnel. The SSP also will be instrumental in developing internal funding priorities and in promoting WARC’s capabilities to both external cooperators and other groups within the USGS.

Open-File Report↗
USGS2017

At the foot of the smoking mountains: The 2014 scientific investigations in the Islands of the Four Mountains

An interdisciplinary research team conducted archaeological, geological, and biological investigations in the Islands of the Four Mountains, Alaska during the summer of 2014 as part of a three-year project to study long-term geological and ecological patterns and processes with respect to human settlement. Researchers investigated three archaeological sites on Chuginadak Island (SAM-0014, SAM-0016 and SAM-0047) and two archaeological sites on Carlisle Island (AMK-0003 and SAM-0034) as well as peat, tephra, and lava deposition on those islands. These investigations resulted in the delineation of archaeological sites, documentation of geological and cultural stratigraphy, excavation of house-pit features, visual characterization and sampling of potential lithic sources, and documentation of Unangan occupation in the Islands of the Four Mountains from roughly 3,800 years ago to Russian contact.

Arctic Anthropology↗
USGS2021

Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups

Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1 & 2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have >17% of their extent in Status 1 & 2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.

Forests↗
USGS2012

Temporal genetic monitoring of hybridization between native westslope cutthroat trout and introduced rainbow trout in the Stehekin River, Washington

Introgressive hybridization with introduced rainbow trout (RBT) ( Oncorhynchus mykiss ) has led to the loss of native cutthroat trout species ( O. clarkii ) throughout their range, creating conservation concerns. Monitoring temporal hybridization trends provides resource managers with a tool for determining population status and information for establishing conservation goals for native cutthroat trout. In this study, we re-sampled six locations in 2010 within the Stehekin River watershed, North Cascades National Park, which were originally sampled between 1999 and 2003. We used genetic markers to monitor changes in hybridization levels between sampling periods in the native westslope cutthroat trout (WCT) ( O. c. lewisi ) stemming from past RBT introductions. Additionally, two new locations from the lower Stehekin drainage were added to the baseline data. We found that the frequency of WCT, RBT, and their hybrids was not significantly different between monitoring periods, but that RBT allele frequencies decreased in two locations and increased in one location. We also found a consistent, substantial reduction in the frequency of RBT alleles over the monitoring period in the Stehekin River upstream of Bridge Creek (SR3) compared to the Stehekin River downstream of Bridge Creek (SR1 -2) and within lower Bridge Creek (BR1) although these three locations are confined to a small geographic area (approximately 5 km). Ecological and/or evolutionary processes likely restrict the dispersal of RBT alleles in the Stehekin River upstream of Bridge Creek.

Washington↗
USGS2013

Phenology monitoring protocol: Northeast Temperate Network

Phenology is critical to many aspects of human life and nearly all ecological relationships and processes. Recent climate change has already led to widespread changes in phenological patterns across the globe, and more change is inevitable. This protocol has been developed to provide standardized methods for monitoring phenology within the National Park Service (NPS) Northeast Temperate Network (NETN), as part of the NPS Inventory and Monitoring Program (I&M). NETN encompasses the Appalachian National Scenic Trail (APPA), Acadia National Park (ACAD), the Boston Harbor Islands National Recreation Area (BOHA), and 10 national historical parks and national historic sites in the northeastern US. This protocol was developed in collaboration with and relies upon the procedures and infrastructure of the USA National Phenology Network (USA-NPN), including Nature’s Notebook , USA-NPN’s online plant and animal phenology observation program (www.nn.usanpn.org). Organized in 2007, USA-NPN is a nation-wide partnership among federal agencies, schools and universities, citizen volunteers, and others to monitor and understand the influence of seasonal cycles on the nation’s biological resources. The overall goal of NETN’s phenology monitoring program is to determine trends in the phenology of key species in order to assist park managers with the detection and mitigation of the effects of climate change on park resources. An additional programmatic goal is to interest and educate park visitors and staff, as well as a cadre of volunteer monitors.

National Park Service Natural Resource Report↗
USGS2011

Simulating effects of microtopography on wetland specific yield and hydroperiod

Specific yield and hydroperiod have proven to be useful parameters in hydrologic analysis of wetlands. Specific yield is a critical parameter to quantitatively relate hydrologic fluxes (e.g., rainfall, evapotranspiration, and runoff) and water level changes. Hydroperiod measures the temporal variability and frequency of land-surface inundation. Conventionally, hydrologic analyses used these concepts without considering the effects of land surface microtopography and assumed a smoothly-varying land surface. However, these microtopographic effects could result in small-scale variations in land surface inundation and water depth above or below the land surface, which in turn affect ecologic and hydrologic processes of wetlands. The objective of this chapter is to develop a physically-based approach for estimating specific yield and hydroperiod that enables the consideration of microtopographic features of wetlands, and to illustrate the approach at sites in the Florida Everglades. The results indicate that the physically-based approach can better capture the variations of specific yield with water level, in particular when the water level falls between the minimum and maximum land surface elevations. The suggested approach for hydroperiod computation predicted that the wetlands might be completely dry or completely wet much less frequently than suggested by the conventional approach neglecting microtopography. One reasonable generalization may be that the hydroperiod approaches presented in this chapter can be a more accurate prediction tool for water resources management to meet the specific hydroperiod threshold as required by a species of plant or animal of interest.

Book chapter↗
USGS1995

Landscape-scale fire history studies support fire management action at Bandelier

Fire has long been recognized as a key process determining the ecological structure and function of many southwestern forests (Weaver 1951). Major changes in southwestern fire regimes over the past century (Swetnam 1990) are having correspondingly large ecological effects on southwestern forests, including those of Bandelier National Monument in the Jemez Mountains of northern New Mexico (Allen 1989). Ecologists and managers who seek to understand current landscapes require accurate information on the spatial and temporal variability in past fire regimes. This kind of information provides essential historical context that is needed to properly manage our modern park landscapes.

Park Science↗
USGS2013

Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?

There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

Ecology and Society↗
USGS2011

The interactions between vegetation and erosion: new directions for research at the interface of ecology and geomorphology

Vegetation and processes of erosion and deposition are interactive. An objective of this paper is to review selected studies that emphasize the interdependencies. The reviews suggest new directions for research uniting ecology and geomorphology – the sub‐discipline of biogeomorphology. The research, which recently has become vigorous, includes the sources, movement, and fates of fluvial loads of sediment, organic carbon, nutrients, contaminants, and woody debris to low‐energy storage sites; the function of biota in causing soil evolution, stability, and sequestration of carbon; the development of new methods to characterize watersheds based on edaphic conditions; and the refinement of current empirical and conceptual models and dendrochronological techniques to measure landscape change. These well acknowledged topics and others less well anticipated ensure that biogeomorphology will remain vibrant.

Earth Surface Processes and Landforms↗
USGS2011

Semiparametric bivariate zero-inflated Poisson models with application to studies of abundance for multiple species

Ecological studies involving counts of abundance, presence–absence or occupancy rates often produce data having a substantial proportion of zeros. Furthermore, these types of processes are typically multivariate and only adequately described by complex nonlinear relationships involving externally measured covariates. Ignoring these aspects of the data and implementing standard approaches can lead to models that fail to provide adequate scientific understanding of the underlying ecological processes, possibly resulting in a loss of inferential power. One method of dealing with data having excess zeros is to consider the class of univariate zero-inflated generalized linear models. However, this class of models fails to address the multivariate and nonlinear aspects associated with the data usually encountered in practice. Therefore, we propose a semiparametric bivariate zero-inflated Poisson model that takes into account both of these data attributes. The general modeling framework is hierarchical Bayes and is suitable for a broad range of applications. We demonstrate the effectiveness of our model through a motivating example on modeling catch per unit area for multiple species using data from the Missouri River Benthic Fishes Study, implemented by the United States Geological Survey.

Environmetrics↗
USGS2019

Negative frequency-dependent foraging behaviour in a generalist herbivore (Alces alces) and its stabilizing influence on food-web dynamics

1. Resource selection is widely appreciated to be context‐dependent and shaped by both biological and abiotic factors. However, few studies have empirically assessed the extent to which selective foraging behaviour is dynamic and varies in response to environmental conditions for free‐ranging animal populations. 2. Here, we assessed the extent that forage selection fluctuated in response to different environmental conditions for a free‐ranging herbivore, moose (Alces alces), in Isle Royale National Park, over a 10‐year period. More precisely, we assessed how moose selection for coniferous versus deciduous forage in winter varied between geographic regions and in relation to (a) the relative frequency of forage types in the environment (e.g. frequency‐dependent foraging behaviour), (b) moose abundance, (c) predation rate (by grey wolves) and (d) snow depth. These factors are potentially important for their influence on the energetics of foraging. We also built a series of food‐chain models to assess the influence of dynamic foraging strategies on the stability of food webs. 3. Our analysis indicates that moose exhibited negative frequency dependence, by selectively exploiting rare resources. Frequency‐dependent foraging was further mediated by density‐dependent processes, which are likely to be predation, moose abundance or some combination of both. In particular, frequency dependence was weaker in years when predation risk was high (i.e. when the ratio of moose to wolves was relatively low). Selection for conifers was also slightly weaker during deep snow years. 4. The food‐chain analysis indicates that the type of frequency‐dependent foraging strategy exhibited by herbivores had important consequences for the stability of ecological communities. In particular, the dynamic foraging strategy that we observed in the empirical analysis (i.e. negative frequency dependence being mediated by density‐dependent processes) was associated with more stable food web dynamics compared to fixed foraging strategies. 5. The results of this study indicated that forage selection is a complex ecological process, varying in response to both biological (predation and moose density) and abiotic factors (snow depth) and over relatively small spatial scales (between regions). This study also provides a useful framework for assessing the influence of other aspects of foraging behaviour on the stability of food web dynamics.

Michigan↗
USGS2024

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

Spatial Statistics↗
USGS1999

Of elephants and blind men: Deer management in the U.S. National Parks

Overabundant populations of white-tailed deer ( Odocoileus virginianus ) are becoming common in the eastern United States. Faced with burgeoning deer populations in eastern parks, the National Park Service (NPS) formulated policy based on its long experience with ungulate management in western parks. That the NPS failed to find a management solution acceptable to its many constituencies was inevitable. Like blind men touching different parts of an elephant and disagreeing about its form, those engaged in the debate about deer management in parks are viewing different parts of the ecological system. None has seen the entire system, and consequently, there is neither common agreement on the nature of the problem nor on the solutions. We explore the quandary of deer management in eastern parks by addressing three questions: (1) Can the National Park Service reconcile its management goals with those of its neighbors? (2) Can thresholds be identified for determining when to intervene in natural processes? (3) Is there a scientific foundation for proceeding with effective management of deer? We argue that reconciling the NPS management with that of state conservation agencies is not possible because management policy guides these agencies in opposite directions: the NPS is charged with limiting human impact on ecological processes, and state agencies are charged with exerting human control over population abundance. Questions about thresholds and a scientific basis for management arise from concern that irrupting deer populations are a manifestation of disrupted natural processes. Several population growth paradigms are at the heart of this ecological question. The science provides no consensus about which of these paradigms are appropriate to deer in eastern ecosystems. Thus, it is premature to expect science to identify if or when natural processes have been disrupted. While the NPS cannot effectively achieve its goals without better science, neither can it wait for science to fully understand the dynamics of plant–herbivore interactions. The best hope for resolving both the biological and political dilemmas surrounding deer management is through an adaptive management approach.

Ecological Applications↗
USGS2012

Using assemblage data in ecological indicators: A comparison and evaluation of commonly available statistical tools

Ecological indicators are science-based tools used to assess how human activities have impacted environmental resources. For monitoring and environmental assessment, existing species assemblage data can be used to make these comparisons through time or across sites. An impediment to using assemblage data, however, is that these data are complex and need to be simplified in an ecologically meaningful way. Because multivariate statistics are mathematical relationships, statistical groupings may not make ecological sense and will not have utility as indicators. Our goal was to define a process to select defensible and ecologically interpretable statistical simplifications of assemblage data in which researchers and managers can have confidence. For this, we chose a suite of statistical methods, compared the groupings that resulted from these analyses, identified convergence among groupings, then we interpreted the groupings using species and ecological guilds. When we tested this approach using a statewide stream fish dataset, not all statistical methods worked equally well. For our dataset, logistic regression (Log), detrended correspondence analysis (DCA), cluster analysis (CL), and non-metric multidimensional scaling (NMDS) provided consistent, simplified output. Specifically, the Log, DCA, CL-1, and NMDS-1 groupings were ≥60% similar to each other, overlapped with the fluvial-specialist ecological guild, and contained a common subset of species. Groupings based on number of species (e.g., Log, DCA, CL and NMDS) outperformed groupings based on abundance [e.g., principal components analysis (PCA) and Poisson regression]. Although the specific methods that worked on our test dataset have generality, here we are advocating a process (e.g., identifying convergent groupings with redundant species composition that are ecologically interpretable) rather than the automatic use of any single statistical tool. We summarize this process in step-by-step guidance for the future use of these commonly available ecological and statistical methods in preparing assemblage data for use in ecological indicators.

Ecological Indicators↗
USGS2013

Ecological prediction with nonlinear multivariate time-frequency functional data models

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Lower Missouri River↗
USGS2008

Some guidelines for helping natural resources adapt to climate change

The changes occurring in mountain regions are an epitome of climate change. The dramatic shrinkage of major glaciers over the past century – and especially in the last 30 years – is one of several iconic images that have come to symbolize climate change. Climate creates the context for ecosystems, and climate variables strongly influence the structure, composition, and processes that characterize distinct ecosystems. Climate change, therefore, is having direct and indirect effects on species attributes, ecological interactions, and ecosystem processes. Because changes in the climate system will continue regardless of emissions mitigation, management strategies to enhance the resilience of ecosystems will become increasingly important. It is essential that management responses to climate change proceed using the best available science despite uncertainties associated with the future path of climate change, the response of ecosystems to climate effects, and the effects of management. Given these uncertainties, management adaptation will require flexibility to reflect our growing understanding of climate change impacts and management effectiveness.

IHDP Update↗
USGS2016

The role of habitat filtering in the leaf economics spectrum and plant susceptibility to pathogen infection

1.The Leaf Economics Spectrum (LES) describes global covariation in the traits of plant leaves. The LES is thought to arise from biophysical constraints and habitat filtering (ecological selection against unfit trait combinations along environmental gradients). However, the role of habitat filtering in generating the LES has not been tested experimentally. 2.If the process of habitat filtering plays a role in generating the LES, the LES could weaken in communities that have yet to be filtered by the current environment, for example after abiotic environmental change. LES traits are commonly used to predict community and ecosystem processes, and if the LES weakens in unfiltered communities, LES-based models may no longer apply. 3.In the greenhouse, we experimentally simulated three stages of habitat filtering in response to abiotic change: from unfiltered, to semi-filtered, to completely filtered communities. In each stage, we quantified the strength of the LES and assessed the accuracy of trait-based models of an important ecological process, pathogen infection. 4.The strength of the LES increased with the completeness of habitat filtering, as did the accuracy of trait-based models of plant susceptibility to pathogen infection. 5. Synthesis . Our results suggest that habitat filtering plays a fundamental role in strengthening the trait correlations of the LES, and that trait-based models may be less accurate when communities have not been filtered by the current environment, for example, following rapid environmental change.

Journal of Ecology↗
USGS2015

Assessing local population vulnerability to wind energy development with branching process models: an application to wind energy development

Quantifying the impact of anthropogenic development on local populations is important for conservation biology and wildlife management. However, these local populations are often subject to demographic stochasticity because of their small population size. Traditional modeling efforts such as population projection matrices do not consider this source of variation whereas individual-based models, which include demographic stochasticity, are computationally intense and lack analytical tractability. One compromise between approaches is branching process models because they accommodate demographic stochasticity and are easily calculated. These models are known within some sub-fields of probability and mathematical ecology but are not often applied in conservation biology and applied ecology. We applied branching process models to quantitatively compare and prioritize species locally vulnerable to the development of wind energy facilities. Specifically, we examined species vulnerability using branching process models for four representative species: A cave bat (a long-lived, low fecundity species), a tree bat (short-lived, moderate fecundity species), a grassland songbird (a short-lived, high fecundity species), and an eagle (a long-lived, slow maturation species). Wind turbine-induced mortality has been observed for all of these species types, raising conservation concerns. We simulated different mortality rates from wind farms while calculating local extinction probabilities. The longer-lived species types (e.g., cave bats and eagles) had much more pronounced transitions from low extinction risk to high extinction risk than short-lived species types (e.g., tree bats and grassland songbirds). High-offspring-producing species types had a much greater variability in baseline risk of extinction than the lower-offspring-producing species types. Long-lived species types may appear stable until a critical level of incidental mortality occurs. After this threshold, the risk of extirpation for a local population may rapidly increase with only minimal increases in wind mortality. Conservation biologists and wildlife managers may need to consider this mortality pattern when issuing take permits and developing monitoring protocols for wind facilities. We also describe how our branching process models may be generalized across a wider range of species for a larger assessment project and then describe how our methods may be applied to other stressors in addition to wind.

Ecosphere↗