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At least 919 records · Page 51Linked to original sources
USGS2014

The southern megalopolis: using the past to predict the future of urban sprawl in the Southeast U.S.

The future health of ecosystems is arguably as dependent on urban sprawl as it is on human-caused climatic warming. Urban sprawl strongly impacts the urban ecosystems it creates and the natural and agro-ecosystems that it displaces and fragments. Here, we project urban sprawl changes for the next 50 years for the fast-growing Southeast U.S. Previous studies have focused on modeling population density, but the urban extent is arguably as important as population density per se in terms of its ecological and conservation impacts. We develop simulations using the SLEUTH urban growth model that complement population-driven models but focus on spatial pattern and extent. To better capture the reach of low-density suburban development, we extend the capabilities of SLEUTH by incorporating street-network information. Our simulations point to a future in which the extent of urbanization in the Southeast is projected to increase by 101% to 192%. Our results highlight areas where ecosystem fragmentation is likely, and serve as a benchmark to explore the challenging tradeoffs between ecosystem health, economic growth and cultural desires.

PLoS ONE↗
USGS2014

Estimating habitat carrying capacity for migrating and wintering waterfowl: Considerations, pitfalls and improvements

Population-based habitat conservation planning for migrating and wintering waterfowl in North America is carried out by habitat Joint Venture (JV) initiatives and is based on the premise that food can limit demography (i.e. food limitation hypothesis). Consequently, planners use bioenergetic models to estimate food (energy) availability and population-level energy demands at appropriate spatial and temporal scales, and translate these values into regional habitat objectives. While simple in principle, there are both empirical and theoretical challenges associated with calculating energy supply and demand including: 1) estimating food availability, 2) estimating the energy content of specific foods, 3) extrapolating site-specific estimates of food availability to landscapes for focal species, 4) applicability of estimates from a single species to other species, 5) estimating resting metabolic rate, 6) estimating cost of daily behaviours, and 7) estimating costs of thermoregulation or tissue synthesis. Most models being used are daily ration models (DRMs) whose set of simplifying assumptions are well established and whose use is widely accepted and feasible given the empirical data available to populate such models. However, DRMs do not link habitat objectives to metrics of ultimate ecological importance such as individual body condition or survival, and largely only consider food-producing habitats. Agent-based models (ABMs) provide a possible alternative for creating more biologically realistic models under some conditions; however, ABMs require different types of empirical inputs, many of which have yet to be estimated for key North American waterfowl. Decisions about how JVs can best proceed with habitat conservation would benefit from the use of sensitivity analyses that could identify the empirical and theoretical uncertainties that have the greatest influence on efforts to estimate habitat carrying capacity. Development of ABMs at restricted, yet biologically relevant spatial scales, followed by comparisons of their outputs to those generated from more simplistic, deterministic models can provide a means of assessing degrees of dissimilarity in how alternative models describe desired landscape conditions for migrating and wintering waterfowl.

Wildfowl↗
USGS2023

Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin

Quantifying the effects of streamflow alteration on assemblage response is central to understanding the role humans play in shaping aquatic environments. These changes represent a level of complexity that impedes developing quantitative links between flow and ecological response because stream hydrology is strongly intertwined with natural and anthropogenic factors. Better management outcomes require disentangling these linkages. Benthic macroinvertebrate data were combined with GIS-derived natural and anthropogenic basin characteristics to identify factors associated with changes in flow processes and assemblage characteristics. Models linking streamflow metrics and macroinvertebrate response at basin and subregion scales were developed using boosted regression tree (BRT) analysis. Basin-scale BRT analyses revealed that links between macroinvertebrate response and flow metrics were often obscured, whereas more homogeneous subregions were better able to discern relations with flow. Urban land cover was the primary factor accounting for changes in flow characteristics. Elevation, land cover, and high flow frequency were the principal variables driving changes in assemblage structure within subregions. Assemblage metrics and traits were equally useful for building response models and were affected similarly by streamflow alteration. Results indicate that response models should be developed based on upland and coastal subregions. However, when defining subregions, care should be taken to maintain data sufficiency. Developing practical flow-protection standards that support a balance between human water requirements and ecological integrity requires models that reduce uncertainty and identify management-relevant drivers. However, effective management often differs by location and models developed at the subregion level may be more applicable to management and stakeholder interests.

Delaware, New Jersey, New York, Pennsylvania↗
USGS2010

Assessing conservation relevance of organism-environment relations using predicted changes in response variables

1. Organism–environment models are used widely in conservation. The degree to which they are useful for informing conservation decisions – the conservation relevance of these relations – is important because lack of relevance may lead to misapplication of scarce conservation resources or failure to resolve important conservation dilemmas. Even when models perform well based on model fit and predictive ability, conservation relevance of associations may not be clear without also knowing the magnitude and variability of predicted changes in response variables. 2. We introduce a method for evaluating the conservation relevance of organism–environment relations that employs confidence intervals for predicted changes in response variables. The confidence intervals are compared to a preselected magnitude of change that marks a threshold (trigger) for conservation action. To demonstrate the approach, we used a case study from the Chihuahuan Desert involving relations between avian richness and broad-scale patterns of shrubland. We considered relations for three winters and two spatial extents (1- and 2-km-radius areas) and compared predicted changes in richness to three thresholds (10%, 20% and 30% change). For each threshold, we examined 48 relations. 3. The method identified seven, four and zero conservation-relevant changes in mean richness for the 10%, 20% and 30% thresholds respectively. These changes were associated with major (20%) changes in shrubland cover, mean patch size, the coefficient of variation for patch size, or edge density but not with major changes in shrubland patch density. The relative rarity of conservation-relevant changes indicated that, overall, the relations had little practical value for informing conservation decisions about avian richness. 4. The approach we illustrate is appropriate for various response and predictor variables measured at any temporal or spatial scale. The method is broadly applicable across ecological environments, conservation objectives, types of statistical predictive models and levels of biological organization. By focusing on magnitudes of change that have practical significance, and by using the span of confidence intervals to incorporate uncertainty of predicted changes, the method can be used to help improve the effectiveness of conservation efforts.

Methods in Ecology and Evolution↗
USGS2025

Streamflow as a stressor: Disentangling hydrology and water quality impacts to characterize flow-ecology relationships for two stream assemblages across two southeastern landscapes

Disassociating the independent effects of flow and water quality on the ecology of flowing waters is an overarching goal in water resource science needed to improve the efficacy of watershed management. However, the interrelatedness of these gradients and their subsequent alteration due to land use change has constrained progress made on this front. The objective of this study was to use benthic macroinvertebrate and fish assemblage data to characterize flow-ecology relationships that were unchanged by water quality impacts across two southeastern landscapes in the USA to help detect ecological change driven by flow alteration. General linear latent models were used to identify taxa that were responsive to high or low flow metrics and water quality gradients. Bayesian hierarchical generalized additive models were then developed using these indicator taxa and three biological metrics to identify flow-specific relationships that were unaffected by water quality impacts. Three low flow-specific relationships were identified, illustrating how potential agricultural or urban impacts to hydrology reduced stream biological health. Importantly, flow-ecology relationships developed using indicator taxa in this study effectively captured hydrology-specific impacts while biological metrics typical of state monitoring and assessment programs did not. Therefore, developing flow-specific biological metrics is a critical step when developing management strategies targeting flow alteration. Implementing standardized frameworks such as the one characterized here can limit contradictory findings and improve streamflow enhancement and restoration project efficacy. These low flow-specific relationships will enhance managers' capacity to develop environmental flow standards, monitor their success, and better understand urban and agricultural impacts on stream assemblages.

Alabama↗
USGS2012

Tools for quantifying isotopic niche space and dietary variation at the individual and population level.

Ecologists are increasingly using stable isotope analysis to inform questions about variation in resource and habitat use from the individual to community level. In this study we investigate data sets from 2 California sea otter ( Enhydra lutris nereis ) populations to illustrate the advantages and potential pitfalls of applying various statistical and quantitative approaches to isotopic data. We have subdivided these tools, or metrics, into 3 categories: IsoSpace metrics, stable isotope mixing models, and DietSpace metrics. IsoSpace metrics are used to quantify the spatial attributes of isotopic data that are typically presented in bivariate (e.g., δ 13 C versus δ 15 N) 2-dimensional space. We review IsoSpace metrics currently in use and present a technique by which uncertainty can be included to calculate the convex hull area of consumers or prey, or both. We then apply a Bayesian-based mixing model to quantify the proportion of potential dietary sources to the diet of each sea otter population and compare this to observational foraging data. Finally, we assess individual dietary specialization by comparing a previously published technique, variance components analysis, to 2 novel DietSpace metrics that are based on mixing model output. As the use of stable isotope analysis in ecology continues to grow, the field will need a set of quantitative tools for assessing isotopic variance at the individual to community level. Along with recent advances in Bayesian-based mixing models, we hope that the IsoSpace and DietSpace metrics described here will provide another set of interpretive tools for ecologists.

Journal of Mammalogy↗
USGS2019

Cohesive framework for modeling plant cover class data

The study of plant distribution and abundance is a fundamental pursuit in ecology and conservation biology. Measuring plant abundance by visually assessing percent cover and recording a cover class is a common field method that yields ordinal data. Statistical models for ordinal data exist but entail cumbersome interpretations and sometimes restrictive assumptions. We propose a Bayesian hierarchical framework for analysing cover class data that allows for linking ordinal observations to a latent beta distribution and accounts for zero inflation. Harnessing a latent beta distribution supports interpreting changes in abundance in terms of mean percent cover rather than odds ratios of cumulative cover classes as for cumulative link models. The zero augmentation allows for simultaneous inferences on both occurrence (distribution) and abundance. We show how our model can account for true and false zeros, misclassification of cover classes, multiple species and hierarchical sampling designs, using empirical examples and simulations. Simulated observation errors, when ignored, led to models overestimating abundance and underestimating occurrence. Based on simulations, we found no substantial difference between mean percent cover estimates when analyzing ordinal cover classes versus continuous percent cover as the response. Our empirical datasets displayed high probability of detection (>0.85 on average for all species), likely due to the sampling design used and training of observers. Probability of occurrence was slightly underestimated for bare ground, Artemisia tridentata , Elycap medusae , and Poa secunda using a model that ignored imperfect detection. Estimated mean percent cover was not substantially impacted by ignoring measurement error for five plant species and bare ground. Our modelling framework for cover class data allows for an explicit separation of distribution from abundance and, importantly, allows for interpreting species–environment relationships in terms of variation in mean percent cover as compared to cumulative odds ratios. The beta distribution inherently accommodates heteroscedasticity and skewness, statistical properties that are a consequence of spatially aggregated patterns common to plant survey data. Recording cover classes provides a reliable, efficient way to measure plants and our simulations suggest little loss of information compared to assuming continuous percent cover. We provide JAGS and Stan model code for implementation.

Methods in Ecology and Evolution↗
USGS2012

Modeling habitat dynamics accounting for possible misclassification

Land cover data are widely used in ecology as land cover change is a major component of changes affecting ecological systems. Landscape change estimates are characterized by classification errors. Researchers have used error matrices to adjust estimates of areal extent, but estimation of land cover change is more difficult and more challenging, with error in classification being confused with change. We modeled land cover dynamics for a discrete set of habitat states. The approach accounts for state uncertainty to produce unbiased estimates of habitat transition probabilities using ground information to inform error rates. We consider the case when true and observed habitat states are available for the same geographic unit (pixel) and when true and observed states are obtained at one level of resolution, but transition probabilities estimated at a different level of resolution (aggregations of pixels). Simulation results showed a strong bias when estimating transition probabilities if misclassification was not accounted for. Scaling-up does not necessarily decrease the bias and can even increase it. Analyses of land cover data in the Southeast region of the USA showed that land change patterns appeared distorted if misclassification was not accounted for: rate of habitat turnover was artificially increased and habitat composition appeared more homogeneous. Not properly accounting for land cover misclassification can produce misleading inferences about habitat state and dynamics and also misleading predictions about species distributions based on habitat. Our models that explicitly account for state uncertainty should be useful in obtaining more accurate inferences about change from data that include errors.

Landscape Ecology↗
USGS2005

Compilation of geologic, hydrologic, and ground-water flow modeling information for the Spokane Valley-Rathdrum Prairie aquifer, Spokane County, Washington, and Bonner and Kootenai Counties, Idaho

The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources and Washington Department of Ecology compiled and described geologic, hydrologic, and ground-water flow modeling information about the Spokane Valley-Rathdrum Prairie (SVRP) aquifer in northern Idaho and northeastern Washington. Descriptions of the hydrogeologic framework, water-budget components, ground- and surface-water interactions, computer flow models, and further data needs are provided. The SVRP aquifer, which covers about 370 square miles including the Rathdrum Prairie, Idaho and the Spokane valley and Hillyard Trough, Washington, was designated a Sole Source Aquifer by the U.S. Environmental Protection Agency in 1978. Continued growth, water management issues, and potential effects on water availability and water quality in the aquifer and in the Spokane and Little Spokane Rivers have illustrated the need to better understand and manage the region's water resources. The SVRP aquifer is composed of sand, gravel, cobbles, and boulders primarily deposited by a series of catastrophic glacial outburst floods from ancient Glacial Lake Missoula. The material deposited in this high-energy environment is coarser-grained than is typical for most basin-fill deposits, resulting in an unusually productive aquifer with well yields as high as 40,000 gallons per minute. In most places, the aquifer is bounded laterally by bedrock composed of granite, metasedimentary rocks, or basalt. The lower boundary of the aquifer is largely unknown except along the margins or in shallower parts of the aquifer where wells have penetrated its entire thickness and reached bedrock or silt and clay deposits. Based on surface geophysics, the thickness of the aquifer is about 500 ft near the Washington-Idaho state line, but more than 600 feet within the Rathdrum Prairie and more than 700 feet in the Hillyard trough based on drilling records. Depth to water in the aquifer is greatest in the northern Rathdrum Prairie (about 500 feet) and least near the city of Spokane along the Spokane River (less than about 50 feet). Ground-water flow is south from near the southern end of Lake Pend Oreille and Hoodoo Valley, through the Rathdrum Prairie, then west toward Spokane. In Spokane, the aquifer splits and water moves north through the Hillyard Trough as well as west through the Trinity Trough. From the Trinity Trough water flows north along the western arm of the aquifer. The aquifer's discharge area is along the Little Spokane River and near Long Lake, Washington. A compilation of estimates of water-budget components, including recharge (precipitation, irrigation, canal leakage, septic tank effluent, inflow from tributary basins, and flow from the Spokane River) and discharge (withdrawals from wells, flow to the Spokane and Little Spokane Rivers, evapotranspiration, and underflow to Long Lake) illustrates that these estimated values should be compared with caution due to several variables including the area and time period of interest as well as methods employed in making the estimates. Numerous studies have documented the dynamic ground-water and surface-water interaction between the SVRP aquifer and the Spokane and Little Spokane Rivers. Gains and losses vary throughout the year, as well as the locations of gains and losses. September 2004 streamflow measurements indicated that the upper reach of the Spokane River between Post Falls and downstream at Flora Road lost 321 cubic feet per second. A gain of 736 cubic feet per second was measured between the Flora Road site and downstream at Green Street Bridge. A loss of 124 cubic feet per second was measured for the reach between the Green Street Bridge and the Spokane River at Spokane gaging station. The river gained about 87 cubic feet per second between the Spokane River at Spokane gaging station and the TJ Meenach Bridge. Overall, the Spokane River gained about 284 cubic feet per second between the Post Falls,

Scientific Investigations Report↗
USGS2012

Estimated probability of arsenic in groundwater from bedrock aquifers in New Hampshire, 2011

Probabilities of arsenic occurrence in groundwater from bedrock aquifers at concentrations of 1, 5, and 10 micrograms per liter (µg/L) were estimated during 2011 using multivariate logistic regression. These estimates were developed for use by the New Hampshire Environmental Public Health Tracking Program. About 39 percent of New Hampshire bedrock groundwater was identified as having at least a 50 percent chance of containing an arsenic concentration greater than or equal to 1 µg/L. This compares to about 7 percent of New Hampshire bedrock groundwater having at least a 50 percent chance of containing an arsenic concentration equaling or exceeding 5 µg/L and about 5 percent of the State having at least a 50 percent chance for its bedrock groundwater to contain concentrations at or above 10 µg/L. The southeastern counties of Merrimack, Strafford, Hillsborough, and Rockingham have the greatest potential for having arsenic concentrations above 5 and 10 µg/L in bedrock groundwater. Significant predictors of arsenic in groundwater from bedrock aquifers for all three thresholds analyzed included geologic, geochemical, land use, hydrologic, topographic, and demographic factors. Among the three thresholds evaluated, there were some differences in explanatory variables, but many variables were the same. More than 250 individual predictor variables were assembled for this study and tested as potential predictor variables for the models. More than 1,700 individual measurements of arsenic concentration from a combination of public and private water-supply wells served as the dependent (or predicted) variable in the models. The statewide maps generated by the probability models are not designed to predict arsenic concentration in any single well, but they are expected to provide useful information in areas of the State that currently contain little to no data on arsenic concentration. They also may aid in resource decision making, in determining potential risk for private wells, and in ecological-level analysis of disease outcomes. The approach for modeling arsenic in groundwater could also be applied to other environmental contaminants that have potential implications for human health, such as uranium, radon, fluoride, manganese, volatile organic compounds, nitrate, and bacteria.

New Hampshire↗
USGS2015

Landscape characteristics influencing the genetic structure of greater sage-grouse within the stronghold of their range: a holistic modeling approach

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high- and low-quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage-grouse ( Centrocercus urophasianus ) across Wyoming. We found that pairwise resistance derived from the distribution of low-quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33-km-diameter moving windows were preferred, suggesting small-scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.

Ecology and Evolution↗
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↗
USGS2023

Mapping landslide susceptibility over large regions with limited data

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

California, Kentucky, North Carolina, West Virgini↗
USGS2012

Scenarios of land use and land cover change in the conterminous United States: Utilizing the special report on emission scenarios at ecoregional scales

Global environmental change scenarios have typically provided projections of land use and land cover for a relatively small number of regions or using a relatively coarse resolution spatial grid, and for only a few major sectors. The coarseness of global projections, in both spatial and thematic dimensions, often limits their direct utility at scales useful for environmental management. This paper describes methods to downscale projections of land-use and land-cover change from the Intergovernmental Panel on Climate Change's Special Report on Emission Scenarios to ecological regions of the conterminous United States, using an integrated assessment model, land-use histories, and expert knowledge. Downscaled projections span a wide range of future potential conditions across sixteen land use/land cover sectors and 84 ecological regions, and are logically consistent with both historical measurements and SRES characteristics. Results appear to provide a credible solution for connecting regionalized projections of land use and land cover with existing downscaled climate scenarios, under a common set of scenario-based socioeconomic assumptions.

Global Environmental Change↗
USGS2019

Incorporating citizen science data in spatially explicit integrated population models

Information about population abundance, distribution, and demographic rates is critical for understanding a species’ ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence–absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture–recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi‐season simulations with parameters informed by North American black bear ( Ursus americanus ) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population‐level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost‐efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

Ecology↗
USGS2017

Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta

A linked modeling approach has been undertaken to understand the impacts of climate and infrastructure on aquatic ecology and water quality in the San Francisco Bay-Delta region. The Delft3D Flexible Mesh modeling suite is used in this effort for its 3D hydrodynamics, salinity, temperature and sediment dynamics, phytoplankton and water-quality coupling infrastructure, and linkage to a habitat suitability model. The hydrodynamic model component of the suite is D-Flow FM, a new 3D unstructured finite-volume model based on the Delft3D model. In this paper, D-Flow FM is applied to the San Francisco Bay-Delta to investigate tidal, seasonal and annual dynamics of water levels, river flows and salinity under historical environmental and infrastructural conditions. The model is driven by historical winds, tides, ocean salinity, and river flows, and includes federal, state, and local freshwater withdrawals, and regional gate and barrier operations. The model is calibrated over a 9-month period, and subsequently validated for water levels, flows, and 3D salinity dynamics over a 2 year period. Model performance was quantified using several model assessment metrics and visualized through target diagrams. These metrics indicate that the model accurately estimated water levels, flows, and salinity over wide-ranging tidal and fluvial conditions, and the model can be used to investigate detailed circulation and salinity patterns throughout the Bay-Delta. The hydrodynamics produced through this effort will be used to drive affiliated sediment, phytoplankton, and contaminant hindcast efforts and habitat suitability assessments for fish and bivalves. The modeling framework applied here will serve as a baseline to ultimately shed light on potential ecosystem change over the current century.

California↗
USGS2021

Modeling effects of disturbance across life history strategies of stream fishes

A central goal of population ecology is to establish linkages between life history strategy, disturbance, and population dynamics. Globally, disturbance events such as drought and invasive species have dramatically impacted stream fish populations and contributed to sharp declines in freshwater biodiversity. Here, we used RAMAS-Metapop to construct stage-based demographic metapopulation models for stream fishes with periodic, opportunistic, and equilibrium life history strategies and assessed their responses to disturbance scenarios that approximated the effects of invasion, drought, and the additive effects of both disturbances. Our models indicated that populations respond differentially to disturbance based on life history strategy. Equilibrium strategists were best able to deal with simulated invasion. Periodic strategists did well under lower levels of drought, whereas opportunistic strategists outperformed other life histories under extreme seasonal drought. When we modeled additive effects scenarios, these disturbances interacted synergistically, dramatically increasing terminal extinction risk for all three life history strategies. Modeling exercises that examine broad life history categories can help to answer fundamental ecological questions about the relationship between disturbance resilience and life history, as well as help managers to develop generalized conservation strategies when species-specific data are lacking. Our results indicate that life history strategy is a fundamental determinant of population trajectories, and that disturbances can interact synergistically to dramatically impact extinction outcomes.

Oecologia↗
USGS2006

The fitting of general force-of-infection models to wildlife disease prevalence data

Researchers and wildlife managers increasingly find themselves in situations where they must deal with infectious wildlife diseases such as chronic wasting disease, brucellosis, tuberculosis, and West Nile virus. Managers are often charged with designing and implementing control strategies, and researchers often seek to determine factors that influence and control the disease process. All of these activities require the ability to measure some indication of a disease's foothold in a population and evaluate factors affecting that foothold. The most common type of data available to managers and researchers is apparent prevalence data. Apparent disease prevalence, the proportion of animals in a sample that are positive for the disease, might seem like a natural measure of disease's foothold, but several properties, in particular, its dependency on age structure and the biasing effects of disease-associated mortality, make it less than ideal. In quantitative epidemiology, the a??force of infection,a?? or infection hazard, is generally the preferred parameter for measuring a disease's foothold, and it can be viewed as the most appropriate way to a??adjusta?? apparent prevalence for age structure. The typical ecology curriculum includes little exposure to quantitative epidemiological concepts such as cumulative incidence, apparent prevalence, and the force of infection. The goal of this paper is to present these basic epidemiological concepts and resulting models in an ecological context and to illustrate how they can be applied to understand and address basic epidemiological questions. We demonstrate a practical approach to solving the heretofore intractable problem of fitting general force-of-infection models to wildlife prevalence data using a generalized regression approach. We apply the procedures to Mycobacterium bovis (bovine tuberculosis) prevalence in bison (Bison bison) in Wood Buffalo National Park, Canada, and demonstrate strong age dependency in the force of infection as well as an increased mortality hazard in positive animals.

Ecology↗