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At least 829 records · Page 46Linked to original sources
USGS2021

Transient disease dynamics across ecological scales

Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.

Theoretical Ecology↗
USGS2021

The river corridor’s evolving connectivity of lotic and lentic waters

River corridors supply a substantial proportion of the fresh water for societal and ecological needs. Individual functions of flowing (lotic) streams and rivers and ponded (lentic) waterbodies such as lakes and reservoirs are well-studied, but their collective functions are not as well understood. Here we bring together nationally consistent river corridor datasets to characterize the contributions of lotic and lentic features and to estimate changes over the past centuries. High-resolution datasets describing waterbodies across 10 million kilometers of the conterminous U.S. (CONUS) river network were classified by waterbody type and origin (historic vs. human-made or intensively managed), surface areal coverage, and degree of connectivity as estimated by a change in water residence timescale in river corridors. Four centuries of human disturbance drove large swings in river corridor makeup, with a transition toward more lotic systems caused by beaver extirpation and abandonment of waterwheel mill ponds by end of the nineteenth century. The twentieth century saw a vast expansion (49%) in river corridor areal coverage resulting from construction and management of small ponds and reservoirs for drinking water, hydropower, irrigation and livestock watering, and stormwater control. Water residence timescale in river corridors doubled or quadrupled over large areas, and more in specific locations, during the twentieth century as a result of the increased coverage of reservoirs and managed small ponds. Although reservoirs and lakes now dominate river corridor surface areas, we found that the growing number of small ponds impacts a greater proportion of network length through their influence on headwater streams where most water and chemical runoff enters the river corridor. We close with an agenda for integrated modeling of the physical, biogeochemical, and ecological drivers of river corridor functions, trajectories of change, and management opportunities.

Conterminous United States↗
USGS2010

New trends in species distribution modelling

Species distribution modelling has its origin in the late 1970s when computing capacity was limited. Early work in the field concentrated mostly on the development of methods to model effectively the shape of a species' response to environmental gradients (Austin 1987, Austin et al. 1990). The methodology and its framework were summarized in reviews 10–15 yr ago (Franklin 1995, Guisan and Zimmermann 2000), and these syntheses are still widely used as reference landmarks in the current distribution modelling literature. However, enormous advancements have occurred over the last decade, with hundreds – if not thousands – of publications on species distribution model (SDM) methodologies and their application to a broad set of conservation, ecological and evolutionary questions. With this special issue, originating from the third of a set of specialized SDM workshops (2008 Riederalp) entitled 'The Utility of Species Distribution Models as Tools for Conservation Ecology', we reflect on current trends and the progress achieved over the last decade.

Ecography↗
USGS2017

Multistressor predictive models of invertebrate condition in the Corn Belt, USA

Understanding the complex relations between multiple environmental stressors and ecological conditions in streams can help guide resource-management decisions. During 14 weeks in spring/summer 2013, personnel from the US Geological Survey and the US Environmental Protection Agency sampled 98 wadeable streams across the Midwest Corn Belt region of the USA for water and sediment quality, physical and habitat characteristics, and ecological communities. We used these data to develop independent predictive disturbance models for 3 macroinvertebrate metrics and a multimetric index. We developed the models based on boosted regression trees (BRT) for 3 stressor categories, land use/land cover (geographic information system [GIS]), all in-stream stressors combined (nutrients, habitat, and contaminants), and for GIS plus in-stream stressors. The GIS plus in-stream stressor models had the best overall performance with an average cross-validation R 2 across all models of 0.41. The models were generally consistent in the explanatory variables selected within each stressor group across the 4 invertebrate metrics modeled. Variables related to riparian condition, substrate size or embeddedness, velocity and channel shape, nutrients (primarily NH 3 ), and contaminants (pyrethroid degradates) were important descriptors of the invertebrate metrics. Models based on all measured in-stream stressors performed comparably to models based on GIS landscape variables, suggesting that the in-stream stressor characterization reasonably represents the dominant factors affecting invertebrate communities and that GIS variables are acting as surrogates for in-stream stressors that directly affect in-stream biota.

Illinois, Indiana, Iowa, Kansas, Kentucky, Minneso↗
USGS2019

Predicting species distributions: unifying model selection and scale optimization for multi-scale occupancy models

Geographic distributions are a basic component of a species’ ecology, and predicting distributions is a fundamental task of conservation and resource management. Reliable prediction depends on identification of appropriate scales of effect for environmental data, and scale-optimization techniques are thus desirable to identify optimal scales for predictor variables. Recent statistical developments have also advanced methods of model selection based explicitly on predictive ability, which differ from commonly-used methods that regulate model structures via anticipated predictive performance. Such methods are beginning to permeate into species distribution models (SDMs), yet there remains no consensus methodology for developing optimally-predictive multi-scale SDMs when covariate data are collected over a range of scales. Thus, we compared the performance of common approaches for scale optimization and model selection in terms of their ability to produce optimally predictive multi-scale Bayesian occupancy models for predicting a species distribution, using models of the breeding distribution for King Rails (Rallus elegans) as a case study. Our results demonstrate sizable gains in predictive performance for hierarchical occupancy models selected explicitly via their ability to predict out-of-sample data using the logarithmic scoring rule, as compared to models selected using information criteria (DIC and WAIC). Information criteria commonly selected individual covariates, as well as scales of effect for those covariates, with suboptimal predictive performance. Performance of models selected using the logarithmic scoring rule was robust across method of scale optimization, which was not true for models selected using DIC and WAIC. Thus, we empirically demonstrate benefits of study designs that enable covariate and scale selection based explicitly on predictive ability. Our results also imply that more careful consideration of what constitutes an optimal scale is warranted in many ecological studies, as the meaning of optimal is not independent of the technique used for scale selection.

Ecosphere↗
USGS2018

Urban growth and landscape connectivity threats assessment at Saguaro National Park, Arizona, USA

Urban and exurban expansion results in habitat and biodiversity loss globally. We hypothesize that a coupled-model approach could connect urban planning for future cities with landscape ecology to consider wildland habitat connectivity. Our work combines urban growth simulations with models of wildlife corridors to examine how species will be impacted by development to test this hypothesis. We leverage a land use change model (SLEUTH) with structural and functional landscape-connectivity modeling techniques to ascertain the spatial extent and locations of connectivity related threats to a national park in southern Arizona, USA, and describe how protected areas might be impacted by urban expansion. Results of projected growth significantly altered structural connectivity (80%) when compared to current (baseline) corridor conditions. Moreover, projected growth impacted functional connectivity differently amongst species, indicating resilience of some species and near-complete displacement of others. We propose that implementing a geospatial-design-based model will allow for a better understanding of the impacts management decisions have on wildlife populations. The application provides the potential to understand both human and environmental impacts of land-system dynamics, critical for long-term sustainability.

Arizona↗
USGS2004

Calibration strategies for a groundwater model in a highly dynamic alpine floodplain

Most surface flows to the 20-km-long Maggia Valley in Southern Switzerland are impounded and the valley is being investigated to determine environmental flow requirements. The aim of the investigation is the devel-opment of a modelling framework that simulates the dynamics of the ground-water, hydrologic, and ecologic systems. Because of the multi-scale nature of the modelling framework, large-scale models are first developed to provide the boundary conditions for more detailed models of reaches that are of eco-logical importance. We describe here the initial (large-scale) groundwa-ter/surface water model and its calibration in relation to initial and boundary conditions. A MODFLOW-2000 model was constructed to simulate the inter-action of groundwater and surface water and was developed parsimoniously to avoid modelling artefacts and parameter inconsistencies. Model calibration includes two steady-state conditions, with and without recharge to the aquifer from the adjoining hillslopes. Parameters are defined to represent areal re-charge, hydraulic conductivity of the aquifer (up to 5 classes), and streambed hydraulic conductivity. Model performance was investigated following two system representation. The first representation assumed unknown flow input at the northern end of the groundwater domain and unknown lateral inflow. The second representation used simulations of the lateral flow obtained by means of a raster-based, physically oriented and continuous in time rainfall-runoff (R-R) model. Results based on these two representations are compared and discussed.

Conference Paper↗
USGS2010

Sampling in ecology and evolution - bridging the gap between theory and practice

Sampling is a key issue for answering most ecological and evolutionary questions. The importance of developing a rigorous sampling design tailored to specific questions has already been discussed in the ecological and sampling literature and has provided useful tools and recommendations to sample and analyse ecological data. However, sampling issues are often difficult to overcome in ecological studies due to apparent inconsistencies between theory and practice, often leading to the implementation of simplified sampling designs that suffer from unknown biases. Moreover, we believe that classical sampling principles which are based on estimation of means and variances are insufficient to fully address many ecological questions that rely on estimating relationships between a response and a set of predictor variables over time and space. Our objective is thus to highlight the importance of selecting an appropriate sampling space and an appropriate sampling design. We also emphasize the importance of using prior knowledge of the study system to estimate models or complex parameters and thus better understand ecological patterns and processes generating these patterns. Using a semi-virtual simulation study as an illustration we reveal how the selection of the space (e.g. geographic, climatic), in which the sampling is designed, influences the patterns that can be ultimately detected. We also demonstrate the inefficiency of common sampling designs to reveal response curves between ecological variables and climatic gradients. Further, we show that response-surface methodology, which has rarely been used in ecology, is much more efficient than more traditional methods. Finally, we discuss the use of prior knowledge, simulation studies and model-based designs in defining appropriate sampling designs. We conclude by a call for development of methods to unbiasedly estimate nonlinear ecologically relevant parameters, in order to make inferences while fulfilling requirements of both sampling theory and field work logistics. ?? 2010 The Authors.

Ecography↗
USGS2013

Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in cold desert shrublands of western North America

Alien grass invasions in arid and semi-arid ecosystems are resulting in grass–fire cycles and ecosystem-level transformations that severely diminish ecosystem services. Our capacity to address the rapid and complex changes occurring in these ecosystems can be enhanced by developing an understanding of the environmental factors and ecosystem attributes that determine resilience of native ecosystems to stress and disturbance, and resistance to invasion. Cold desert shrublands occur over strong environmental gradients and exhibit significant differences in resilience and resistance. They provide an excellent opportunity to increase our understanding of these concepts. Herein, we examine a series of linked questions about (a) ecosystem attributes that determine resilience and resistance along environmental gradients, (b) effects of disturbances like livestock grazing and altered fire regimes and of stressors like rapid climate change, rising CO 2 , and N deposition on resilience and resistance, and (c) interacting effects of resilience and resistance on ecosystems with different environmental conditions. We conclude by providing strategies for the use of resilience and resistance concepts in a management context. At ecological site scales, state and transition models are used to illustrate how differences in resilience and resistance influence potential alternative vegetation states, transitions among states, and thresholds. At landscape scales management strategies based on resilience and resistance—protection, prevention, restoration, and monitoring and adaptive management—are used to determine priority management areas and appropriate actions.

Ecosystems↗
USGS2025

Increased soil greenhouse gas emissions from the combined use of cover crops and no‐tillage in producer‐ managed fields

Cover crop adoption offers multiple benefits and climate mitigation potential for agroecosystems, but is still an underutilized conservation practice. Recently, the combined use of cover cropping plus no-tillage (CCNT) has been increasingly promoted to achieve its synergistic effectiveness. Yet, how this combined practice affects soil greenhouse gas (GHG) emission remains a topic of debate. Existing studies are predominantly based on research-managed settings and often fail to assess all three major GHGs of carbon dioxide (CO 2 ), nitrous oxide (N 2 O), and methane (CH 4 ). To address these knowledge gaps, this study conducted a 30-month monitoring from producer-managed fields to quantify the soil greenhouse gas responses to CCNT compared to no-tillage (NT) alone. The findings showed that CCNT increased the soil global warming potential (GWP) by 15.2% relative to NT. CO 2 is the main contributor, accounting for over 91.7% of the total GWP. On average, the daily fluxes of CO 2 , N 2 O, and CH 4 were increased by 16.2%, 32.3%, and 55.6% under CCNT, respectively. Meteorological variables explained 85.3% of the CO 2 increase and 46.1% of the N 2 O increase associated with CCNT. Furthermore, two types of CCNT practices differed in GHG emission responses, though both strategies significantly reduced nitrogen losses. These quantitative results, derived from actual production systems, provide informed decision-making among local producers regarding the adoption of cover crops. Moreover, this field-based evidence offers a robust empirical foundation for future modeling efforts aimed at assessing the ecological benefits of cover crops under varying climatic and soil conditions.

Indiana↗
USGS2022

African penguins and localized fisheries management: Response to Butterworth and Ross-Gillespie

We present a response to Butterworth and Ross-Gillespie's (2022) comment on our perspectives on how forage fish fisheries are impacting the endangered African penguin ( Sphenicus demersus ), and corresponding management options. Butterworth and Ross-Gillespie overstate model uncertainties and downplay the clear ecological and conservation significance of the fisheries closure experiment. We demonstrate that their criticism of “pseudo-replication” is weak, and not in line with their own analyses nor with the interpretations of many international scientific review panels commissioned by the government of South Africa to evaluate experimental results. Their comment does not alter our fundamental conclusions that forage fisheries operating near penguin breeding colonies compete with the birds for food resources, are detrimental to the penguin's population health, and are impeding recovery. Given that sardines are depleted (DFFE, 2021) and the African penguin is approaching a conservation crisis, we reiterate our position that continuing the precautionary approach of closures at the local scale of central-place foraging penguins is warranted to facilitate their population growth under fisheries management goals to conserve and maintain ecosystem functions.

ICES Journal of Marine Science↗
USGS2015

Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publicly available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Wyoming↗
USGS2005

Exposing extinction risk analysis to pathogens: Is disease just another form of density dependence?

In the United States and several other countries, the development of population viability analyses (PVA) is a legal requirement of any species survival plan developed for threatened and endangered species. Despite the importance of pathogens in natural populations, little attention has been given to host-pathogen dynamics in PVA. To study the effect of infectious pathogens on extinction risk estimates generated from PVA, we review and synthesize the relevance of host-pathogen dynamics in analyses of extinction risk. We then develop a stochastic, density-dependent host-parasite model to investigate the effects of disease on the persistence of endangered populations. We show that this model converges on a Ricker model of density dependence under a suite of limiting assumptions, including a high probability that epidemics will arrive and occur. Using this modeling framework, we then quantify: (1) dynamic differences between time series generated by disease and Ricker processes with the same parameters; (2) observed probabilities of quasi-extinction for populations exposed to disease or self-limitation; and (3) bias in probabilities of quasi-extinction estimated by density-independent PVAs when populations experience either form of density dependence. Our results suggest two generalities about the relationships among disease, PVA, and the management of endangered species. First, disease more strongly increases variability in host abundance and, thus, the probability of quasi-extinction, than does self-limitation. This result stems from the fact that the effects and the probability of occurrence of disease are both density dependent. Second, estimates of quasi-extinction are more often overly optimistic for populations experiencing disease than for those subject to self-limitation. Thus, although the results of density-independent PVAs may be relatively robust to some particular assumptions about density dependence, they are less robust when endangered populations are known to be susceptible to disease. If potential management actions involve manipulating pathogens, then it may be useful to model disease explicitly. ?? 2005 by the Ecological Society of America.

Ecological Applications↗
USGS1985

Foraging decisions, patch use, and seasonality in egrets (Aves: ciconiiformes)

Feeding Snowy (Egretta thula) and Great (Casmerodius albus) egrets were observed during two breeding seasons in coastal New Jersey and two brief winter periods in northeast Florida. A number of tests based on assumptions of foraging models, predictions from foraging theory, and earlier empirical tests concerning time allocation and movement in foraging patches was made. Few of the expectations based on foraging theory and/or assumptions were supported by the empirical evidence. Snowy Egrets fed with greater intensity and efficiency during the breeding season (when young were being fed) than during winter. They also showed some tendency to leave patches when their capture rate declined, and they spent more time foraging in patches when other birds were present nearby. Great Egrets, in Contrast, showed few of these tendencies, although they did leave patches when their intercapture intervals increased. Satiation differences had some influence on feeding rates in Snowy Egrets, but only at the end of feeding bouts. Some individuals of both species revisited areas in patches that had recently been exploited, and success rates were usually higher after the second visit. Apparently, for predators of active prey, short—term changes in resource availability ( r esource depression ) may be more important than resource depletion, a common assumption in most OFT models.

Florida, New Jersey↗
USGS2001

The robust design for capture-recapture studies: analysis using program MARK

Collecting capture-recapture data under Pollock?s robust design provides an additional source of information on capture probability that can be used to provide less biased and more efficient estimates of population dynamics parameters. In addition, it can be used to estimate the probability of being available for capture, which in some cases (e.g., breeding proportion) has ecological significance. This phenomenon can be modeled as a completely random process, Markovian, or with temporary trap dependence. Analysis of this type of data is one of the options in program MARK. By using MARK the relationship between parameters and covariates can be modeled, and various approaches to goodness of fit, model selection, and model averaging can be implemented.

Book chapter↗
USGS2010

Using GIS and Google Earth for the creation of the Going-to-the-Sun Road Avalanche Atlas, Glacier National Park, Montana, USA

Snow avalanche paths are key geomorphologic features in Glacier National Park, Montana, and an important component of mountain ecosystems: they are isolated within a larger ecosystem, they are continuously disturbed, and they contain unique physical characteristics (Malanson and Butler, 1984). Avalanches impact subalpine forest structure and function, as well as overall biodiversity (Bebi et al., 2009). Because avalanches are dynamic phenomena, avalanche path geometry and spatial extent depend upon climatic regimes. The USGS/GNP Avalanche Program formally began in 2003 as an avalanche forecasting program for the spring opening of the ever-popular Going-to-the-Sun Road (GTSR), which crosses through 37 identified avalanche paths. Avalanche safety and forecasting is a necessary part of the GTSR spring opening procedures. An avalanche atlas detailing topographic parameters and oblique photographs was completed for the GTSR corridor in response to a request from GNP personnel for planning and resource management. Using ArcMap 9.2 GIS software, polygons were created for every avalanche path affecting the GTSR using aerial imagery, field-based observations, and GPS measurements of sub-meter accuracy. Spatial attributes for each path were derived within the GIS. Resulting products include an avalanche atlas book for operational use, a geoPDF of the atlas, and a Google Earth flyover illustrating each path and associated photographs. The avalanche atlas aids park management in worker safety, infrastructure planning, and natural resource protection by identifying avalanche path patterns and location. The atlas was created for operational and planning purposes and is also used as a foundation for research such as avalanche ecology projects and avalanche path runout modeling.

Conference Paper↗
USGS2018

Assessing and communicating the impacts of climate change on the Southern California coast

Over the course of this and the next century, the combination of rising sea levels, severe storms, and coastal erosion will threaten the sustainability of coastal communities, development, and ecosystems as we currently know them. To clearly identify coastal vulnerabilities and develop appropriate adaptation strategies for projected increased levels of coastal flooding and erosion, coastal managers need user-friendly planning tools based on the best available climate and coastal science. In anticipation of these climate change impacts, many communities are in the early stages of climate change adaptation planning but lack the scientific information and tools to adequately address the potential impacts. In collaboration with leading scientists worldwide, the USGS designed the Coastal Storm Modeling System (CoSMoS) to assess the coastal impacts of climate change for the California coast, including the combination of sea level rise, storms, and coastal change. In this project, we directly address the needs of coastal resource managers in Southern California by integrating a vast range of global climate change projections and translate that information using sophisticated physical process models into planning-scale physical, ecological, and economic exposure, shoreline change, and impact assessments, all delivered in two simple, user-friendly, online tools. Our results show that by the end of the 21st century, over 250,000 residents and nearly $40 billion in building value across Southern California could be exposed to coastal flooding from storms, sea level rise, and coastal change. Results for the other major population center in California (the greater San Francisco Bay Area) are also available but not explicitly discussed in this report. Together, CoSMoS has now assessed the exposure of 95% of the 26 million coastal residents of the State (17 million in Southern California).

California↗
USGS2024

Climate vulnerability assessment of Oregon hatchery programs

The goal of this project was to assess the vulnerability to climate change impacts for a sample set of hatchery programs representing different geographic areas and primary anadromous species raised in state-managed Oregon hatcheries (Summary Figure 1). Freshwater and marine ecosystem processes can significantly influence salmon and steelhead survival, and understanding how these factors have affected historical returns can help managers evaluate the climate vulnerability of hatchery stocks. We examined stock-specific trends in smolt-to-adult returns (SARs), which represent the proportion of smolts released from the hatchery that are recovered in fisheries or as returning adult spawners. SARs are among the most consistent long term estimators of survival for hatchery-origin stocks. Depending on the stock, adult recoveries could occur in marine fisheries, freshwater fisheries, returns to the hatchery or another collection facility, and spawning ground surveys. We collected time series data on relevant ecological indicators and used generalized additive models (GAMs) to explore both univariate and multivariate relationships with SARs for each hatchery stock. An additional aspect of this assessment was to evaluate the climate vulnerability of resident trout stocking programs in the Department's East and West regions, incorporating insights from Oregon Department of Fish and Wildlife (ODFW) staff interviews and published data on the thermal tolerance of hatchery trout stocks. This assessment is provided in the ‘Climate vulnerability of trout stocking programs’ subsection below.

Oregon↗