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At least 235 records · Page 13Linked to original sources
USGS2013

Method to support Total Maximum Daily Load development using hydrologic alteration as a surrogate to address aquatic life impairment in New Jersey streams

More than 300 ambient monitoring sites in New Jersey have been identified by the New Jersey Department of Environmental Protection (NJDEP) in its integrated water-quality monitoring and assessment report (that is, the 305(b) Report on general water quality and 303(d) List of waters that do not support their designated uses) as being impaired with respect to aquatic life; however, no unambiguous stressors (for example, nutrients or bacteria) have been identified. Because of the indeterminate nature of the broad range of possible impairments, surrogate measures that more holistically encapsulate the full suite of potential environmental stressors need to be developed. Streamflow alteration resulting from anthropogenic changes in the landscape is one such surrogate. For example, increases in impervious surface cover (ISC) commonly cause increases in surface runoff, which can result in “flashy” hydrology and other changes in the stream corridor that are associated with streamflow alteration. The NJDEP has indicated that methodologies to support a hydrologically based Total Maximum Daily Load (hydro-TMDL) need to be developed in order to identify hydrologic targets that represent a minimal percent deviation from a baseline condition (“minimally altered”) as a surrogate measure to meet criteria in support of designated uses. The primary objective of this study was to develop an applicable hydro-TMDL approach to address aquatic-life impairments associated with hydrologic alteration for New Jersey streams. The U.S. Geological Survey, in cooperation with the NJDEP, identified 51 non- to moderately impaired gaged streamflow sites in the Raritan River Basin for evaluation. Quantile regression (QR) analysis was used to compare flow and precipitation records and identify baseline hydrographs at 37 of these sites. At sites without an appropriately long period of record (POR) or where a baseline hydrograph could not be identified with QR, a rainfall-runoff model was used to develop simulated baseline hydrographs. The hydro-TMDL approach provided an opportunity to evaluate proportional differences in flow attributes between observed and baseline hydrographs and to develop complementary flow-ecology response relations at a subset of Raritan River Basin sites where available flow and ecological information overlapped. The New Jersey Stream Classification Tool (NJSCT) was used to determine the stream class of all 51 study sites by using either an observed or a simulated baseline hydrograph. Two New Jersey stream classes (A and C) were evaluated to help characterize the unique hydrology of the Raritan River Basin. In general, class C streams (1.99–40.7 square miles) had smaller drainage areas than class A streams (0.7–785 square miles). Many of the non-impaired and moderately impaired class A and C streams in the Raritan River Basin were found to have significant hydrologic alteration as indicated by numerous flow values that fell outside the established 25th-to-75th- and the more conservative 40th-to-60th-percentile boundaries. However, percent deviations for the class C streams (defined as moderately stable streams with moderately high base-flow contributions) were, in general, much larger than those for the class A streams (defined as semiflashy streams characterized by moderately low base flow). The greater deviations for class C streams in the hydro-TMDL assessments likely resulted from comparisons that were based solely on simulated baseline hydrographs, which were developed without considering any anthropogenic influences in the basin. In contrast, comparisons for many of the class A streams were made by using an observed baseline, which already includes an implicit level of ISC and other human influences on the landscape. By using the hydro-TMDL approach, numerous flow deviations were identified that were indicative of streams that are highly regulated by reservoirs or dams, streams that are affected by increasing amounts of surface runoff resulting from ISC, and streams that are affected by water abstraction (that is, groundwater or surface-water withdrawals used for agricultural and human supply). Eight of the reservoir- and (or) dam-affected sites showed flow deviations that are indicative of flow-managed systems. For example, indices that account for the timing and magnitude of high and low flows were often found to fall outside the 25th-to-75th-percentile range. In general, at regulated class C streams, annual summer low flows are arriving later and tend to be lower, and high flows are arriving earlier with higher magnitudes of longer duration. At class A streams, high and low flows are arriving later with an overall increase in discharge with respect to the prereservoir baseline conditions. The drainage basins of eight of the study sites had large values of ISC (>10 percent), most likely as a result of expanding urban development. In general, the magnitude and frequency of high flows at class A and C sites with high ISC are increasing and were commonly found to fall outside the 25th-to-75th-percentile range. Additionally, magnitudes of low flows are becoming lower and, although the timing of high flows was highly variable, low-flow events appeared to be arriving earlier than would be expected under normal low-flow conditions. Three of the study sites appeared to be affected by hydrologic changes associated with water abstraction. At these sites, the timing of flows appeared to be altered. For example, low flows tended to arrive earlier and high flows arrived later at two of the three sites. Additionally, the magnitude and duration of low flows were commonly less than the 25th-percentile value and the duration of high flows appeared to increase. A reduced set of hydrologic and ecological variables was used to develop univariate and multivariate flow-ecology response models for the aquatic-invertebrate assemblage. Many hydrologic variables accounting for the duration, magnitude, frequency, and timing of flows were significantly correlated with ecological response. Multiple linear regression (MLR) models were developed to provide a more holistic evaluation of the combined effects of hydrologic alteration and to identify models with two or three hydrologic variables that account for a significant proportion of the variability in invertebrate-assemblage condition as represented by assemblage metric scores. MLR models, derived on the basis of hydrologic attributes, accounted for 35 to 75 percent of the variability in assemblage condition. The hydro-TMDL method developed herein for non- to moderately impaired Raritan River Basin streams utilizes a “surrogate” approach in place of the traditional “pollutant of concern” approach commonly used for TMDL development. Managers can use the results obtained by using the hydro-TMDL method to offset the effects of impervious-surface runoff and altered streamflow and to implement measures designed to achieve the necessary load reductions for the “pollutant of concern” (that is, percentage deviations of stream-class-specific flow-index values outside the established 25th-to-75th-percentile range). In this case, such deviations could represent all or a subset of the altered flow indices that prevent the stream from meeting designated aquatic-life criteria. This hydro-TMDL uses a reference, or attainment stream approach for developing the TMDL endpoint. That is, either observed or simulated baseline hydrographs were selected as appropriate reference conditions on the basis of results of QR analysis and watershed modeling procedures, respectively. For any stream in the Raritan River Basin evaluated as part of this study, the hydro-TMDL can be expressed as the greatest amount of deviation in flow a stream can exhibit without violating the stream’s designated aquatic-life criteria. Use of this surrogate approach is appropriate because flows that fall outside the established percentile ranges are ultimately a function of many anthropogenic modifications of the landscape, including the amount of stormwater runoff generated from impervious surfaces within a given basin, the presence of manmade structures designed to retain or divert water, the magnitude of ground- and surface-water abstraction, and the presence of water-supply processes implemented to support human needs. In addition, the stream-type-specific flow indices used as the basis for the hydro-TMDL approach are useful for representing the hydrologic conditions of class A and C streams/basins because they incorporate the full spectrum of flow conditions (very low to very high) that occur in the stream system over a long period of time, as well as those flow properties that change as a result of seasonal variation. Ultimately, an estimate of the maximum percentage flow reduction that could be allowed will be needed to address the aquatic-life impairments in many of the study streams in the Raritan River Basin and will be necessary for identifying appropriate target flow conditions for hydro-TMDL implementation. As described in this report, a target flow value equal to the 25th- or 75th-percentile flow rate could be selected as the point useful for setting specific hydrologic targets. This selection, however, is a management decision that could vary depending on the designated use of the stream or other regulatory factors (for example, water-supply protection, trout production, antidegradation policies, or special protection designations). In New Jersey streams where no unambiguous stressors can be identified, State monitoring agencies, such as the NJDEP, could choose to require the implementation of a flow-based TMDL that not only supports designated uses, but meets the regulatory requirements under the Clean Water Act, and represents a balance between water supply intended to meet human needs and the conservation of ecosystem integrity.

New Jersey↗
USGS2012

Species, functional groups, and thresholds in ecological resilience

The cross-scale resilience model states that ecological resilience is generated in part from the distribution of functions within and across scales in a system. Resilience is a measure of a system's ability to remain organized around a particular set of mutually reinforcing processes and structures, known as a regime. We define scale as the geographic extent over which a process operates and the frequency with which a process occurs. Species can be categorized into functional groups that are a link between ecosystem processes and structures and ecological resilience. We applied the cross-scale resilience model to avian species in a grassland ecosystem. A species’ morphology is shaped in part by its interaction with ecological structure and pattern, so animal body mass reflects the spatial and temporal distribution of resources. We used the log-transformed rank-ordered body masses of breeding birds associated with grasslands to identify aggregations and discontinuities in the distribution of those body masses. We assessed cross-scale resilience on the basis of 3 metrics: overall number of functional groups, number of functional groups within an aggregation, and the redundancy of functional groups across aggregations. We assessed how the loss of threatened species would affect cross-scale resilience by removing threatened species from the data set and recalculating values of the 3 metrics. We also determined whether more function was retained than expected after the loss of threatened species by comparing observed loss with simulated random loss in a Monte Carlo process. The observed distribution of function compared with the random simulated loss of function indicated that more functionality in the observed data set was retained than expected. On the basis of our results, we believe an ecosystem with a full complement of species can sustain considerable species losses without affecting the distribution of functions within and across aggregations, although ecological resilience is reduced. We propose that the mechanisms responsible for shaping discontinuous distributions of body mass and the nonrandom distribution of functions may also shape species losses such that local extinctions will be nonrandom with respect to the retention and distribution of functions and that the distribution of function within and across aggregations will be conserved despite extinctions.

Conservation Biology↗
USGS2012

Numerical models of salt marsh evolution: ecological, geomorphic, and climatic factors

Salt marshes are delicate landforms at the boundary between the sea and land. These ecosystems support a diverse biota that modifies the erosive characteristics of the substrate and mediates sediment transport processes. Here we present a broad overview of recent numerical models that quantify the formation and evolution of salt marshes under different physical and ecological drivers. In particular, we focus on the coupling between geomorphological and ecological processes and on how these feedbacks are included in predictive models of landform evolution. We describe in detail models that simulate fluxes of water, organic matter, and sediments in salt marshes. The interplay between biological and morphological processes often produces a distinct scarp between salt marshes and tidal flats. Numerical models can capture the dynamics of this boundary and the progradation or regression of the marsh in time. Tidal channels are also key features of the marsh landscape, flooding and draining the marsh platform and providing a source of sediments and nutrients to the marsh ecosystem. In recent years, several numerical models have been developed to describe the morphogenesis and long-term dynamics of salt marsh channels. Finally, salt marshes are highly sensitive to the effects of long-term climatic change. We therefore discuss in detail how numerical models have been used to determine salt marsh survival under different scenarios of sea level rise.

Reviews of Geophysics↗
USGS2013

Computationally efficient statistical differential equation modeling using homogenization

Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.

Journal of Agricultural, Biological, and Environme↗
USGS2014

The effect of adjusting model inputs to achieve mass balance on time-dynamic simulations in a food-web model of Lake Huron

Ecopath with Ecosim (EwE) is a widely used modeling tool in fishery research and management. Ecopath requires a mass-balanced snapshot of a food web at a particular point in time, which Ecosim then uses to simulate changes in biomass over time. Initial inputs to Ecopath, including estimates for biomasses, production to biomass ratios, consumption to biomass ratios, and diets, rarely produce mass balance, and thus ad hoc changes to inputs are required to balance the model. There has been little previous research of whether ad hoc changes to achieve mass balance affect Ecosim simulations. We constructed an EwE model for the offshore community of Lake Huron, and balanced the model using four contrasting but realistic methods. The four balancing methods were based on two contrasting approaches; in the first approach, production of unbalanced groups was increased by increasing either biomass or the production to biomass ratio, while in the second approach, consumption of predators on unbalanced groups was decreased by decreasing either biomass or the consumption to biomass ratio. We compared six simulation scenarios based on three alternative assumptions about the extent to which mortality rates of prey can change in response to changes in predator biomass (i.e., vulnerabilities) under perturbations to either fishing mortality or environmental production. Changes in simulated biomass values over time were used in a principal components analysis to assess the comparative effect of balancing method, vulnerabilities, and perturbation types. Vulnerabilities explained the most variation in biomass, followed by the type of perturbation. Choice of balancing method explained little of the overall variation in biomass. Under scenarios where changes in predator biomass caused large changes in mortality rates of prey (i.e., high vulnerabilities), variation in biomass was greater than when changes in predator biomass caused only small changes in mortality rates of prey (i.e., low vulnerabilities), and was amplified when environmental production was increased. When standardized to mean changes in biomass within each scenario, scenarios when vulnerabilities were low and when fishing mortality was increased explained the most variation in biomass. Our findings suggested that approaches to balancing Ecopath models have relatively little effect on changes in biomass over time, especially when compared to assumptions about how mortality rates of prey change in response to changes in predator biomass. We concluded that when constructing food-web models using EwE, determining the effect of changes in predator biomass on mortality rates of prey should be prioritized over determining the best way to balance the model.

Lake Huron↗
USGS2014

Modeling regeneration responses of big sagebrush ( Artemisia tridentata ) to abiotic conditions

Ecosystems dominated by big sagebrush, Artemisia tridentata Nuttall (Asteraceae), which are the most widespread ecosystems in semiarid western North America, have been affected by land use practices and invasive species. Loss of big sagebrush and the decline of associated species, such as greater sage-grouse, are a concern to land managers and conservationists. However, big sagebrush regeneration remains difficult to achieve by restoration and reclamation efforts and there is no regeneration simulation model available. We present here the first process-based, daily time-step, simulation model to predict yearly big sagebrush regeneration including relevant germination and seedling responses to abiotic factors. We estimated values, uncertainty, and importance of 27 model parameters using a total of 1435 site-years of observation. Our model explained 74% of variability of number of years with successful regeneration at 46 sites. It also achieved 60% overall accuracy predicting yearly regeneration success/failure. Our results identify specific future research needed to improve our understanding of big sagebrush regeneration, including data at the subspecies level and improved parameter estimates for start of seed dispersal, modified wet thermal-time model of germination, and soil water potential influences. We found that relationships between big sagebrush regeneration and climate conditions were site specific, varying across the distribution of big sagebrush. This indicates that statistical models based on climate are unsuitable for understanding range-wide regeneration patterns or for assessing the potential consequences of changing climate on sagebrush regeneration and underscores the value of this process-based model. We used our model to predict potential regeneration across the range of sagebrush ecosystems in the western United States, which confirmed that seedling survival is a limiting factor, whereas germination is not. Our results also suggested that modeled regeneration suitability is necessary but not sufficient to explain sagebrush presence. We conclude that future assessment of big sagebrush responses to climate change will need to account for responses of regenerative stages using a process-based understanding, such as provided by our model.

Ecological Modelling↗
USGS2017

A dynamic spatio-temporal model for spatial data

Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-temporal data generating process. In many applications, a generalized linear mixed model (GLMM) is used with a random effect to account for spatial dependence and to provide optimal spatial predictions. Location-specific covariates are often included as fixed effects in a GLMM and may be collinear with the spatial random effect, which can negatively affect inference. We propose a dynamic approach to account for spatial dependence that incorporates scientific knowledge of the spatio-temporal data generating process. Our approach relies on a dynamic spatio-temporal model that explicitly incorporates location-specific covariates. We illustrate our approach with a spatially varying ecological diffusion model implemented using a computationally efficient homogenization technique. We apply our model to understand individual-level and location-specific risk factors associated with chronic wasting disease in white-tailed deer from Wisconsin, USA and estimate the location the disease was first introduced. We compare our approach to several existing methods that are commonly used in spatial statistics. Our spatio-temporal approach resulted in a higher predictive accuracy when compared to methods based on optimal spatial prediction, obviated confounding among the spatially indexed covariates and the spatial random effect, and provided additional information that will be important for containing disease outbreaks.

Wisconsin↗
USGS1989

Animal population dynamics: Identification of critical components

There is a growing interest in the use of population dynamics models in environmental risk assessment and the promulgation of environmental regulatory policies. Unfortunately, because of species and areal differences in the physical and biotic influences on population dynamics, such models must almost inevitably be both complex and species- or site-specific. Given the emormous variety of species and sites of potential concern, this fact presents a problem; it simply is not possible to construct models for all species and circumstances. Therefore, it is useful, before building predictive population models, to discover what input parameters are of critical importance to the desired output. This information should enable the construction of simpler and more generalizable models. As a first step, it is useful to consider population models as composed to two, partly separable classes, one comprising the purely mechanical descriptors of dynamics from given demographic parameter values, and the other describing the modulation of the demographic parameters by environmental factors (changes in physical environment, species interactions, pathogens, xenobiotic chemicals). This division permits sensitivity analyses to be run on the first of these classes, providing guidance for subsequent model simplification. We here apply such a sensitivity analysis to network models of mammalian and avian population dynamics.

Ecological Modelling↗
USGS2019

Energetic constraints and the paradox of a diffusing population in a heterogeneous environment

Previous mathematical analyses have shown that, for certain parameter ranges, a population, described by logistic equations on a set of connected patches, and diffusing among them, can reach a higher equilibrium total population when the local carrying capacities are heterogeneously distributed across patches, than when carrying capacities having the same total sum are homogeneously distributed across the patches. It is shown here that this apparently paradoxical result is explained when the resultant differences in energy inputs to the whole multi-patch system are taken into account. We examine both Pearl–Verhulst and Original Verhulst logistic models and show that, when total input of energy or limiting resource, is constrained to be the same in the homogeneous and heterogeneous cases, the total population in the heterogeneous patches can never reach an asymptotic equilibrium that is greater than the sum of the carrying capacities over the homogeneous patches. We further show that, when the dynamics of the limiting resources are explicitly modeled, as in a chemostat model, the paradoxical result of the logistic models does not occur. These results have implications concerning the use of some ubiquitous equations of population ecology in modeling populations in space.

Theoretical Population Biology↗
USGS2015

Equation-free modeling unravels the behavior of complex ecological systems

Ye et al. (1) address a critical problem confronting the management of natural ecosystems: How can we make forecasts of possible future changes in populations to help guide management actions? This problem is especially acute for marine and anadromous fisheries, where the large interannual fluctuations of populations, arising from complex nonlinear interactions among species and with varying environmental factors, have defied prediction over even short time scales. The empirical dynamic modeling (EDM) described in Ye et al.’s report, the latest in a series of papers by Sugihara and his colleagues, offers a promising quantitative approach to building models using time series to successfully project dynamics into the future. With the term “equation-free” in the article title, Ye et al. (1) are suggesting broader implications of their approach, considering the centrality of equations in modern science. From the 1700s on, nature has been increasingly described by mathematical equations, with differential or difference equations forming the basic framework for describing dynamics. The use of mathematical equations for ecological systems came much later, pioneered by Lotka and Volterra, who showed that population cycles might be described in terms of simple coupled nonlinear differential equations. It took decades for Lotka–Volterra-type models to become established, but the development of appropriate differential equations is now routine in modeling ecological dynamics. There is no question that the injection of mathematical equations, by forcing “clarity and precision into conjecture” (2), has led to increased understanding of population and community dynamics. As in science in general, in ecology equations are a key method of communication and of framing hypotheses. These equations serve as compact representations of an enormous amount of empirical data and can be analyzed by the powerful methods of mathematics.

PNAS↗
USGS2013

Host range, host ecology, and distribution of more than 11800 fish parasite species

Our data set includes 38 008 fish parasite records (for Acanthocephala, Cestoda, Monogenea, Nematoda, Trematoda) compiled from the scientific literature, Internet databases, and museum collections paired to the corresponding host ecological, biogeographical, and phylogenetic traits (maximum length, growth rate, life span, age at maturity, trophic level, habitat preference, geographical range size, taxonomy). The data focus on host features, because specific parasite traits are not consistently available across records. For this reason, the data set is intended as a flexible resource able to extend the principles of ecological niche modeling to the host–parasite system, providing researchers with the data to model parasite niches based on their distribution in host species and the associated host features. In this sense, the database offers a framework for testing general ecological, biogeographical, and phylogenetic hypotheses based on the identification of hosts as parasite habitat. Potential applications of the data set are, for example, the investigation of species–area relationships or the taxonomic distribution of host-specificity. The provided host–parasite list is that currently used by Fish Parasite Ecology Software Tool (FishPEST, http://purl.oclc.org/fishpest ), which is a website that allows researchers to model several aspects of the relationships between fish parasites and their hosts. The database is intended for researchers who wish to have more freedom to analyze the database than currently possible with FishPEST. However, for readers who have not seen FishPEST, we recommend using this as a starting point for interacting with the database.

Ecology↗
USGS2009

Modeling utilization distributions in space and time

W. Van Winkle defined the utilization distribution (UD) as a probability density that gives an animal's relative frequency of occurrence in a two-dimensional (x, y) plane. We extend Van Winkle's work by redefining the UD as the relative frequency distribution of an animal's occurrence in all four dimensions of space and time. We then describe a product kernel model estimation method, devising a novel kernel from the wrapped Cauchy distribution to handle circularly distributed temporal covariates, such as day of year. Using Monte Carlo simulations of animal movements in space and time, we assess estimator performance. Although not unbiased, the product kernel method yields models highly correlated (Pearson's r - 0.975) with true probabilities of occurrence and successfully captures temporal variations in density of occurrence. In an empirical example, we estimate the expected UD in three dimensions (x, y, and t) for animals belonging to each of two distinct bighorn sheep {Ovis canadensis) social groups in Glacier National Park, Montana, USA. Results show the method can yield ecologically informative models that successfully depict temporal variations in density of occurrence for a seasonally migratory species. Some implications of this new approach to UD modeling are discussed. ?? 2009 by the Ecological Society of America.

Ecology↗
USGS2024

An integrated analysis for estimation of survival, growth, and movement of unmarked juvenile anadromous fish

Managers invest substantial resources to promote recovery of declining anadromous fish stocks. Recovery strategies are manifold and often include management actions intended to stimulate somatic growth, increase in-river survival, and motivate juvenile outmigration during favorable environmental conditions. Evaluating the efficacy of these management actions is difficult, however, because monitoring data that explicitly track individuals from egg deposition to juvenile outmigration are typically lacking. We developed an integrated population model that links two different and often collected types of anadromous fish monitoring data: spawning ground surveys and rotary screw trap juvenile catch data. The integrated model accounts for incomplete detection and uses the two sources of data to estimate juvenile demographic parameters in a multistate framework. We evaluated the model's performance using simulated data under a range of conditions typically encountered in similar surveys. Simulation results indicated that the model estimated juvenile survival, growth, and movement with no-to-minimal bias (i.e., ≥ 50 % of simulations ± 0–0.05). As an example case study, we fit the model to empirical fall-run Chinook Salmon ( Oncorhynchus tshawytscha ) monitoring data collected in California's Central Valley, U.S.A. In doing so, we evaluated the influence of environmental conditions (e.g., discharge, water temperature) and habitat availability on juvenile demographic rates. We demonstrated that through our integrated approach we could estimate state transition probabilities that are typically inestimable for naturally produced, unmarked juvenile fish when using traditional statistical approaches to analyze these types of monitoring data. Furthermore, the structure of our model can serve as a useful foundation for decision-support models within adaptive management programs by directly linking management actions, decision-support-model predictions, and monitoring.

California↗
USGS2016

Potential distribution of the viral haemorrhagic septicaemia virus in the Great Lakes region

Viral haemorrhagic septicaemia virus (VHSV) genotype IVb has been responsible for large-scale fish mortality events in the Great Lakes of North America. Anticipating the areas of potential VHSV occurrence is key to designing epidemiological surveillance and disease prevention strategies in the Great Lakes basin. We explored the environmental features that could shape the distribution of VHSV, based on remote sensing and climate data via ecological niche modelling. Variables included temperature measured during the day and night, precipitation, vegetation, bathymetry, solar radiation and topographic wetness. VHSV occurrences were obtained from available reports of virus confirmation in laboratory facilities. We fit a Maxent model using VHSV-IVb reports and environmental variables under different parameterizations to identify the best model to determine potential VHSV occurrence based on environmental suitability. VHSV reports were generated from both passive and active surveillance. VHSV occurrences were most abundant near shore sites. We were, however, able to capture the environmental signature of VHSV based on the environmental variables employed in our model, allowing us to identify patterns of VHSV potential occurrence. Our findings suggest that VHSV is not at an ecological equilibrium and more areas could be affected, including areas not in close geographic proximity to past VHSV reports.

Great Lakes↗
USGS2020

Predicting multi-species foraging hotspots for marine turtles in the Gulf of Mexico

Quantifying the distribution of animals and identifying underlying characteristics that define suitable habitat are essential for effective conservation of free-ranging species. Prioritizing areas for conservation is important in managing a geographic extent that has a high level of disturbance and limited conservation resources. We examined the potential use of a species distribution model ensemble for multi-species conservation in marine habitats. Using satellite telemetry locations during foraging as input data, and ensemble ecological niche models, we predicted foraging areas for 2 nesting marine turtle species within the Gulf of Mexico (GoM): Kemp’s ridley Lepidochelys kempii (n = 63) and loggerhead Caretta caretta (n = 63). We considered 7 geophysical, biological, and climatic variables and compared contributing factors for each species’ foraging habitat selection. For both species, predicted suitable foraging habitats encompassed large areas along the GoM coast, but only intersected with each other in relatively small areas. Highly parameterized models resulted in overall greater fits, suggesting that multiple factors influence habitat selection by these species. Model validation results were mixed: cross-validation resulted in high prediction accuracy for both species, but an evaluation against independent data resulted in a low omission rate (5%) for Kemp’s ridleys and a high omission rate (72%) for loggerheads. The relatively small intersection of model-predicted foraging areas for these 2 species within the study area may indicate possible niche differentiations. The high omission rate for loggerheads indicates our samples likely underrepresent the population and illustrates the challenges in predicting suitable foraging extents for species that make dynamic movements and have greater individual variability.

Gulf of Mexico↗
USGS2018

Synthesizing models useful for ecohydrology and ecohydraulic approaches: An emphasis on integrating models to address complex research questions

Ecohydrology combines empiricism, data analytics, and the integration of models to characterize linkages between ecological and hydrological processes. A challenge for practitioners is determining which models best generalizes heterogeneity in hydrological behaviour, including water fluxes across spatial and temporal scales, integrating environmental and socio‐economic activities to determine best watershed management practices and data requirements. We conducted a literature review and synthesis of hydrologic, hydraulic, water quality, and ecological models designed for solving interdisciplinary questions. We reviewed 1,275 papers and identified 178 models that have the capacity to answer an array of research questions about ecohydrology or ecohydraulics. Of these models, 43 were commonly applied due to their versatility, accessibility, user‐friendliness, and excellent user‐support. Forty‐one of 43 reviewed models were linked to at least 1 other model especially: Water Quality Analysis Simulation Program (linked to 21 other models), Soil and Water Assessment Tool (19), and Hydrologic Engineering Center's River Analysis System (15). However, model integration was still relatively infrequent. There was substantial variation in model applications, possibly an artefact of the regional focus of research questions, simplicity of use, quality of user‐support efforts, or a limited understanding of model applicability. Simply increasing the interoperability of model platforms, transformation of models to user‐friendly forms, increasing user‐support, defining the reliability and risk associated with model results, and increasing awareness of model applicability may promote increased use of models across subdisciplines. Nonetheless, the current availability of models allows an array of interdisciplinary questions to be addressed, and model choice relates to several factors including research objective, model complexity, ability to link to other models, and interface choice.

Ecohydrology↗
USGS2013

Inferential consequences of modeling rather than measuring snow accumulation in studies of animal ecology

Abstract. It is increasingly common for studies of animal ecology to use model-based predictions of environmental variables as explanatory or predictor variables, even though model prediction uncertainty is typically unknown. To demonstrate the potential for misleading inferences when model predictions with error are used in place of direct measurements, we compared snow water equivalent (SWE) and snow depth as predicted by the Snow Data Assimilation System (SNODAS) to field measurements of SWE and snow depth. We examined locations on elk (Cervus canadensis) winter ranges in western Wyoming, because modeled data such as SNODAS output are often used for inferences on elk ecology. Overall, SNODAS predictions tended to overestimate field measurements, prediction uncertainty was high, and the difference between SNODAS predictions and field measurements was greater in snow shadows for both snow variables compared to non-snow shadow areas. We used a simple simulation of snow effects on the probability of an elk being killed by a predator to show that, if SNODAS prediction uncertainty was ignored, we might have mistakenly concluded that SWE was not an important factor in where elk were killed in predatory attacks during the winter. In this simulation, we were interested in the effects of snow at finer scales (<1 km 2 ) than the resolution of SNODAS. If bias were to decrease when SNODAS predictions are averaged over coarser scales, SNODAS would be applicable to population-level ecology studies. In our study, however, averaging predictions over moderate to broad spatial scales (9–2200 km 2 ) did not reduce the differences between SNODAS predictions and field measurements. This study highlights the need to carefully evaluate two issues when using model output as an explanatory variable in subsequent analysis: (1) the model’s resolution relative to the scale of the ecological question of interest and (2) the implications of prediction uncertainty on inferences when using model predictions as explanatory or predictor variables.

Wyoming↗
USGS1998

Simulating cholinesterase inhibition in birds caused by dietary insecticide exposure

We describe a stochastic simulation model that simulates avian foraging in an agricultural landscape to evaluate factors affecting dietary insecticide exposure and to predict post-exposure cholinesterase (ChE) inhibition. To evaluate the model, we simulated published field studies and found that model predictions of insecticide decay and ChE inhibition reasonably approximated most observed results. Sensitivity analysis suggested that foraging location usually influenced ChE inhibition more than diet preferences or daily intake rate. Although organophosphorus insecticides usually caused greater inhibition than carbamate insecticides, insecticide toxicity appeared only moderately important. When we simulated impact of heavy insecticide applications during breeding seasons of 15 wild bird species, mean maximum ChE inhibition in most species exceeded 20% at some point. At this level of inhibition, birds may experience nausea and/or may exhibit minor behavioral changes. Simulated risk peaked in April–May and August–September and was lowest in July. ChE inhibition increased with proportion of vegetation in the diet. This model, and ones like it, may help predict insecticide exposure of and sublethal ChE inhibition in grassland animals, thereby reducing dependence of ecological risk assessments on field studies alone.

Ecological Modelling↗