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At least 721 records · Page 40Linked to original sources

USGS2021

Advancing estuarine ecological forecasts: Seasonal hypoxia in Chesapeake Bay

Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.

Chesapeake Bay↗

USGS2016

To manage inland fisheries is to manage at the social-ecological watershed scale

Approaches to managing inland fisheries vary between systems and regions but are often based on large-scale marine fisheries principles and thus limited and outdated. Rarely do they adopt holistic approaches that consider the complex interplay among humans, fish, and the environment. We argue that there is an urgent need for a shift in inland fisheries management towards holistic and transdisciplinary approaches that embrace the principles of social-ecological systems at the watershed scale. The interconnectedness of inland fisheries with their associated watershed (biotic, abiotic, and humans) make them extremely complex and challenging to manage and protect. For this reason, the watershed is a logical management unit. To assist management at this scale, we propose a framework that integrates disparate concepts and management paradigms to facilitate inland fisheries management and sustainability. We contend that inland fisheries need to be managed as social-ecological watershed system (SEWS). The framework supports watershed-scale and transboundary governance to manage inland fisheries, and transdisciplinary projects and teams to ensure relevant and applicable monitoring and research. We discuss concepts of social-ecological feedback and interactions of multiple stressors and factors within/between the social-ecological systems. Moreover, we emphasize that management, monitoring, and research on inland fisheries at the watershed scale are needed to ensure long-term sustainable and resilient fisheries.

Journal of Environmental Management↗

USGS2019

Identifying and characterizing extrapolation in multivariate response data

Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences.

PLoS ONE↗

USGS2019

Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests

Functional responses describe how changing resource availability affects consumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro‐ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and availability. We linked heterogeneous movement data across different agro‐ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs ( Sus scrofa ), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental‐scale movement data within agro‐ecosystems to quantify the functional response of agricultural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as distance to crops altered the magnitude of the functional response. There was a strong agricultural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non‐agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found significant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depending on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our understanding of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro‐ecosystems.

California. Florida, Georgia, Louisiana, Missouri,↗

USGS2018

Moving from eco-forecasts to eco-projections

Ecological models can provide estimates of future conditions that are useful for decision-making, including long-term planning and resource prioritization. However, these models often rely on assumptions about ecological relationships and trajectories, forcings (e.g., biophysical conditions), and management approaches that may not be explicitly considered. To make assumptions more transparent, disciplines such as economics, demographics, climatology, and national intelligence make a fairly clear and consistent distinction between “forecasts” and “projections”. Forecasts are typically more near-term and rely on extending existing relationships and trends to estimate the most likely future conditions; whereas projections evaluate conditions under multiple scenarios that are based on an array of assumptions, often going further out in time. Consistently referring to ecological models of future conditions as either “eco-forecasts” or “eco-projections” could help make modelling assumptions more transparent and thus more effectively focus their application across landscapes and through time. To the extent that ecological modelling is used to support management, policy, and programmatic decisions, practitioners can ask the following. If the modelling is an eco-forecast, is it worth considering different initial conditions, trajectories, or forcings based on alternative scenarios? If the modelling is an eco-projection, are the underlying assumptions and future scenarios explicit, and are decisions properly tempered with respect to those modelling specifications? We demonstrate these concepts and methods for conducting eco-projections through examples from invasion biology and climate adaptation.

Conference Paper↗

USGS2022

Statistical consideration of nonrandom treatment applications reveal region-wide benefits of widespread post-fire restoration action

Accurate predictions of ecological restoration outcomes are needed across the increasingly large landscapes requiring treatment following disturbances. However, observational studies often fail to account for nonrandom treatment application, which can result in invalid inference. Examining a spatiotemporally extensive management treatment-- post-fire seeding of declining sagebrush shrubs across the semiarid U.S. over two decades -- we quantify drivers and consequences of selection biases in restoration, using remotely sensed data. Treatments were disproportionately applied in more stressful, degraded ecological conditions. Failure to incorporate nonrandom treatment allocation led to the conclusion that costly, widespread seedings were unsuccessful; however, after considering biases, restoration positively affected sagebrush recovery. Treatment effect sizes varied with climate, indicating possible prioritization criteria for interventions. Our findings revise the perspective that widespread post-fire sagebrush seedings have been broadly “unsuccessful” and demonstrate how selection biases can pose substantive inferential hazards in observational studies of restoration efficacy and development of restoration theory.

California, Colorado, Idaho, Nevada, Oregon, Utah,↗

USGS2025

A generalization error bound of physics-informed neural networks for ecological diffusion models

Ecological diffusion equations (EDEs) are partial differential equations (PDEs) that model spatiotemporal dynamics, often applied to wildlife diseases. Derived from ecological mechanisms, EDEs are useful for forecasting, inference, and decision-making, such as guiding surveillance strategies for wildlife diseases. Physics-informed neural networks (PINNs) offer a cutting-edge approach to approximating the solution of EDEs by constraining the loss function. PINNs have shown empirical success in forecasting and inference in wildlife disease problems. However, theoretical properties of PINNs in this context remain unexplored. In this paper, we derive a generalization error bound for PINNs applied to forward problems in EDEs, providing new crucial theoretical results for statistical analysis. Furthermore, we compare the performance of PINNs with commonly used numerical solvers, showing that PINNs achieve competitive accuracy while providing important modelling flexibility. Our results help connecting theory and application in EDE-based statistical models for ecological systems.

Stat↗

USGS2012

Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k -fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k -fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

Science of the Total Environment↗

USGS2015

Adapting California’s ecosystems to a changing climate

Significant efforts are underway to translate improved understanding of how climate change is altering ecosystems into practical actions for sustaining ecosystem functions and benefits. We explore this transition in California, where adaptation and mitigation are advancing relatively rapidly, through four case studies that span large spatial domains and encompass diverse ecological systems, institutions, ownerships, and policies. The case studies demonstrate the context specificity of societal efforts to adapt ecosystems to climate change and involve applications of diverse scientific tools (e.g., scenario analyses, downscaled climate projections, ecological and connectivity models) tailored to specific planning and management situations (alternative energy siting, wetland management, rangeland management, open space planning). They illustrate how existing institutional and policy frameworks provide numerous opportunities to advance adaptation related to ecosystems and suggest that progress is likely to be greatest when scientific knowledge is integrated into collective planning and when supportive policies and financing enable action.

BioScience↗

USGS2016

Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey

The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

Ecology↗

USGS2011

Homogenization of large-scale movement models in ecology

A difficulty in using diffusion models to predict large scale animal population dispersal is that individuals move differently based on local information (as opposed to gradients) in differing habitat types. This can be accommodated by using ecological diffusion. However, real environments are often spatially complex, limiting application of a direct approach. Homogenization for partial differential equations has long been applied to Fickian diffusion (in which average individual movement is organized along gradients of habitat and population density). We derive a homogenization procedure for ecological diffusion and apply it to a simple model for chronic wasting disease in mule deer. Homogenization allows us to determine the impact of small scale (10–100 m) habitat variability on large scale (10–100 km) movement. The procedure generates asymptotic equations for solutions on the large scale with parameters defined by small-scale variation. The simplicity of this homogenization procedure is striking when compared to the multi-dimensional homogenization procedure for Fickian diffusion,and the method will be equally straightforward for more complex models.

Bulletin of Mathematical Biology↗

USGS2015

Natural and unnatural oil slicks in the Gulf of Mexico

When wind speeds are 2 – 10 m s −1 , reflective contrasts in the ocean surface make oil slicks visible to synthetic aperture radar (SAR) under all sky conditions. Neural network analysis of satellite SAR images quantified the magnitude and distribution of surface oil in the Gulf of Mexico from persistent, natural seeps and from the Deepwater Horizon (DWH) discharge. This analysis identified 914 natural oil seep zones across the entire Gulf of Mexico in pre-2010 data. Their &sim;0.1 µm slicks covered an aggregated average of 775 km 2 . Assuming an average volume of 77.5 m 3 over an 8 – 24 h lifespan per oil slick, the floating oil indicates a surface flux of 2.5 – 9.4 × 10 4 m 3 y −1 . Oil from natural slicks was regionally concentrated: 68%, 25%, 7%, and <1% of the total was observed in the NW, SW, NE and SE Gulf, respectively. This reflects differences in basin history and hydrocarbon generation. SAR images from 2010 showed that the 87-day DWH discharge produced a surface-oil footprint fundamentally different from background seepage, with an average ocean area of 11,200 km 2 (SD 5,028) and a volume of 22,600 m 3 (SD 5,411). Peak magnitudes of oil were detected during equivalent, &sim;14-day intervals around 23 May and 18 June, when wind speeds remained <5 m s −1 . Over this interval, aggregated volume of floating oil decreased by 21%; area covered increased by 49% ( p  < .1), potentially altering its ecological impact. The most likely causes were increased applications of dispersant and surface burning operations.

Journal of Geophysical Research C: Oceans↗

USGS2024

Geographic distribution of feather δ34S in Europe

Geographic distribution models of environmentally stable isotopes (the so-called “isoscapes”) are widely employed in animal ecology, and wildlife forensics and conservation. However, the application of isoscapes is limited to elements and regions for which the spatial patterns have been estimated. Here, we focused on the ubiquitous yet less commonly used stable sulfur isotopes (δ 34 S). To predict the European δ 34 S isoscape, we used 242 feather samples from Eurasian Reed Warbler ( Acrocephalus scirpaceus ) formed at 69 European wetland sites. We quantified the relationships between sample δ 34 S and environmental covariates using a random forest regression model and applied the model to predict the geographic distribution of δ 34 S. We also quantified within-site variation in δ 34 S and complementarity with other isotopes on both individual and isoscape levels. The predicted feather δ 34 S isoscape shows only slight differences between the central and southern parts of Europe while the coastal regions were most enriched in 34 S. The most important covariates of δ 34 S were distance to coastline, surface elevation, and atmospheric concentrations of SO 2 gases. The absence of a systematic spatial pattern impedes the application of the δ 34 S isoscape, but high complementarity with other isoscapes advocates the combination of multiple isoscapes to increase the precision of animal tracing. Feather δ 34 S compositions showed considerable within-site variation with highest values in inland parts of Europe, likely attributed to wetland anaerobic conditions and redox sensitivity of sulfur. The complex European geography and topography as well as using δ 34 S samples from wetlands may contribute to the absence of a systematic spatial gradient of δ 34 S values in Europe. We thus encourage future studies to focus on the geographic distribution of δ 34 S using tissues from diverse taxa collected in various habitats over large land masses in the world (i.e., Africa, South America, or East Asia).

Ecosphere↗

USGS2014

Panarchy: theory and application

The concept of panarchy provides a framework that characterizes complex systems of people and nature as dynamically organized and structured within and across scales of space and time. It has been more than a decade since the introduction of panarchy. Over this period, its invocation in peer-reviewed literature has been steadily increasing, but its use remains primarily descriptive and abstract. Here, we discuss the use of the concept in the literature to date, highlight where the concept may be useful, and discuss limitations to the broader applicability of panarchy theory for research in the ecological and social sciences. Finally, we forward a set of testable hypotheses to evaluate key propositions that follow from panarchy theory.

Ecosystems↗

USGS2022

Climate refugia for Pinus spp. in topographic and bioclimatic environments of the Madrean sky islands of México and the United States

Climate refugia, or places where habitats are expected to remain relatively buffered from regional climate extremes, provide an important focus for science and conservation planning. Within high-priority, multi-jurisdictional landscapes like the Madrean sky islands of the United States and México, efforts to identify and manage climate refugia are hindered by the lack of high-quality and consistent transboundary datasets. To fill these data gaps, we assembled a bi-national field dataset ( n = 1416) for five pine species ( Pinus spp.) and used generalized boosted regression to model pine habitats in relation to topographic variability as a basis for identifying potential microrefugia at local scales in the context of current species’ distribution patterns. We developed additional models to quantify climatic refugial attributes using coarse scale bioclimatic variables and finer scale seasonal remote sensing indices. Terrain metrics including ruggedness, slope position, and aspect defined microrefugia for pines within elevation ranges preferred by each species. Response to bioclimatic variables indicated that small shifts in climate were important to some species (e.g., P. chihuahuana, P. strobiformis ), but others exhibited a broader tolerance (e.g., P. arizonica ). Response to seasonal climate was particularly important in modeling microrefugia for species with open canopy structure and where regular fires occur (e.g., P. engelmannii and P. chihuahuana ). Hotspots of microrefugia differed among species and were either limited to northern islands or occurred across central or southern latitudes. Mapping and validation of refugia and their ecological functions are necessary steps in developing regional conservation strategies that cross jurisdictional boundaries. A salient application will be incorporation of climate refugia in management of fire to restore and maintain pine ecology.

Arizona, Chihuahua, New Mexico, Sonora↗

USGS2017

Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software

Among the most pressing concerns of land managers in post-wildfire landscapes are the establishment and spread of invasive species. Land managers need accurate maps of invasive species cover for targeted management post-disturbance that are easily transferable across space and time. In this study, we sought to develop an iterative, replicable methodology based on limited invasive species occurrence data, freely available remotely sensed data, and open source software to predict the distribution of Bromus tectorum (cheatgrass) in a post-wildfire landscape. We developed four species distribution models using eight spectral indices derived from five months of Landsat 8 Operational Land Imager (OLI) data in 2014. These months corresponded to both cheatgrass growing period and time of field data collection in the study area. The four models were improved using an iterative approach in which a threshold for cover was established, and all models had high sensitivity values when tested on an independent dataset. We also quantified the area at highest risk for invasion in future seasons given 2014 distribution, topographic covariates, and seed dispersal limitations. These models demonstrate the effectiveness of using derived multi-date spectral indices as proxies for species occurrence on the landscape, the importance of selecting thresholds for invasive species cover to evaluate ecological risk in species distribution models, and the applicability of Landsat 8 OLI and the Software for Assisted Habitat Modeling for targeted invasive species management.

International Journal of Applied Earth Observation↗

USGS2017

Estimating the per-capita contribution of habitats and pathways in a migratory network: A modelling approach

Every year, migratory species undertake seasonal movements along different pathways between discrete regions and habitats. The ability to assess the relative demographic contributions of these different habitats and pathways to the species’ overall population dynamics is critical for understanding the ecology of migratory species, and also has practical applications for management and conservation. Metrics for assessing habitat contributions have been well-developed for metapopulations, but an equivalent metric is not currently available for migratory populations. Here, we develop a framework for estimating the demographic contributions of the discrete habitats and pathways used by migratory species throughout the annual cycle by estimating the per capita contribution of cohorts using these locations. Our framework accounts for seasonal movements between multiple breeding and non-breeding habitats and for both resident and migratory cohorts. We illustrate our framework using a hypothetical migratory network of four habitats, which allows us to better understand how variations in habitat quality affect per capita contributions. Results indicate that per capita contributions for any habitat or pathway are dependent on habitat-specific survival probabilities in all other areas used as part of the migratory circuit, and that contribution metrics are spatially linked (e.g. reduced survival in one habitat also decreases the contribution metric for other habitats). Our framework expands existing theory on the dynamics of spatiotemporally structured populations by developing a generalized approach to estimate the habitat- and pathway-specific contributions of species migrating between multiple breeding and multiple non-breeding habitats for a range of life histories or migratory strategies. Most importantly, it provides a means of prioritizing conservation efforts towards those migratory pathways and habitats that are most critical for the population viability of migratory species.

Ecography↗

USGS2021

The reuse of avian samples: Opportunities, pitfalls and a solution

Tissue samples are frequently collected to study various aspects of avian biology, but in many cases these samples are not used in their entirety and are stored by the collector. The already collected samples provide a largely overlooked opportunity because they can be used by different researchers in different biological fields. Broad reuse of samples could result in multispecies or large-scale studies, interdisciplinary collaborations, and the generation of new ideas, thereby increasing the quality and impact of research. Sample reuse could also reduce the number of new samples needed for a study, which is especially pertinent to endangered species where sample collection is necessarily limited. Importantly, reusing samples may be mutually beneficial for both the researchers providing samples and those reusing them. Here, we identify the benefits of sample reuse, describe currently available sources of already collected samples and their limitations, and highlight the wide range of potential applications in a single research field – avian isotopic ecology. To facilitate the reuse of avian samples worldwide and across research fields, we introduce the AviSample Network metadata repository. The main aims of this metadata repository are to collate and provide access to descriptions of available avian tissue samples. We contend that the creation of the AviSample Network metadata repository will provide the opportunity for new collaborations and studies. Moreover, we believe that this will help create research connections between ornithologists across the globe and encourage sample reuse in other fields.

Ibis↗