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At least 1,009 records · Page 56Linked to original sources
USGS2026

Statewide agent-based model for management of chronic wasting disease in white-tailed deer: PAOvCWD

Chronic wasting disease (CWD) is an always-fatal disease infecting wild cervids globally. Ecologically and economically important, CWD presents a challenge for managing white-tailed deer ( Odocoileus virginianus ). We built an agent-based model to simulate CWD transmission and assess potential management actions that could slow disease spread: PAOvCWD . We developed PAOvCWD using contact rates and other behavioral and ecological metrics estimated from deer monitored in Pennsylvania, USA. We programmed potential management responses (e.g., culling, altered hunter harvest) for all 22 Pennsylvania wildlife management units and validated the efficacy of PAOvCWD using a deer population in south-central Pennsylvania infected with CWD for > 10 years. To support applications of our model, we developed a user-friendly R pipeline that allows implementation with relatively minor modifications. Our pipeline includes four steps: • Steps 1 and 2 generate regional percent forest cover rasters and curate population-level demographics. • Step 3 initializes landscapes and agents using our PAOvPOP model. • Step 4 assesses CWD transmission and management responses using our PAOvCWD model.

Pennsylvania↗
USGS2010

Determination of biologically significant hydrologic condition metrics in urbanizing watersheds: an empirical analysis over a range of environmental settings

We investigated the relations among 83 hydrologic condition metrics (HCMs) and changes in algal, invertebrate, and fish communities in five metropolitan areas across the continental United States. We used a statistical approach that employed Spearman correlation and regression tree analysis to identify five HCMs that are strongly associated with observed biological variation along a gradient of urbanization. The HCMs related to average flow magnitude, high-flow magnitude, high-flow event frequency, high-flow duration, and rate of change of stream cross-sectional area were most consistently associated with changes in aquatic communities. Although our investigation used an urban gradient design with short hydrologic periods of record (≤1 year) of hourly cross-sectional area time series, these five HCMs were consistent with previous investigations using long-term daily-flow records. The ecological sampling day often was included in the hydrologic period. Regression tree models explained up to 73, 92, and 79% of variance for specific algal, invertebrate, and fish community metrics, respectively. National models generally were not as statistically significant as models for individual metropolitan areas. High-flow event frequency, a hydrologic metric found to be transferable across stream type and useful for classifying habitat by previous research, was found to be the most ecologically relevant HCM; transformation by precipitation increased national-scale applicability. We also investigated the relation between measures of stream flashiness and land-cover indicators of urbanization and found that land-cover characteristic and pattern variables, such as road density, percent wetland, and proximity of developed land, were strongly related to HCMs at both a metropolitan and national scale and, therefore, may be effective land-use management options in addition to wholesale impervious-area reduction.

Milwaukee-Green Bay; Boston; Raleigh; Atlanta; Bir↗
USGS2015

Climate change: evaluating your local and regional water resources

The BCM is a fine-scale hydrologic model that uses detailed maps of soils, geology, topography, and transient monthly or daily maps of potential evapotranspiration, air temperature, and precipitation to generate maps of recharge, runoff, snow pack, actual evapotranspiration, and climatic water deficit. With these comprehensive environmental inputs and experienced scientific analysis, the BCM provides resource managers with important hydrologic and ecologic understanding of a landscape or basin at hillslope to regional scales. The model is calibrated using historical climate and streamflow data over the range of geologic materials specific to an area. Once calibrated, the model is used to translate climate-change data into hydrologic responses for a defined landscape, to provide managers an understanding of potential ecological risks and threats to water supplies and managed hydrologic systems. Although limited to estimates of unimpaired hydrologic conditions, estimates of impaired conditions, such as agricultural demand, diversions, or reservoir outflows can be incorporated into the calibration of the model to expand its utility. Additionally, the model can be linked to other models, such as groundwater-flow models (that is, MODFLOW) or the integrated hydrologic model (MF-FMP), to provide information about subsurface hydrologic processes. The model can be applied at a relatively small scale, but also can be applied to large-scale national and international river basins.

Fact Sheet↗
USGS2006

Nest survival in dusky Canada geese ( Branta canadensis occidentalis ): Use of discrete-time models

The Dusky Canada Goose ( Branta canadensis occidentalis ) population that breeds in the Copper River Delta, Alaska, has declined substantially since the late 1970s. Persistent low numbers have been attributed to low productivity in recent years. We examined patterns in survival rates of 1,852 nests to better understand ecological processes that influenced productivity during 1997-2000. We compared 10 nonparametric models of daily survival rate of nests (DSR) that included variation among years, calendar dates, nest initiation dates, and nest ages with equivalent models based on parametric functions. The unequivocal best model included patterns of DSR that varied among discrete periods of years, calendar dates, and nest ages. Generally, DSR was low early in the nesting season and higher midseason. Across years, patterns in DSR were most variable early and late in the nesting season. Daily survival rates of nests declined between the first and second week after initiation, increased until the fourth week, and then declined during the last week before hatch. Nest survival probability estimates ranged from 0.07 to 0.71 across years and nest initiation dates. Mean rates of nest survival ranged between 0.21 and 0.31 each year. We suggest (1) considering models that do not limit estimates of daily nest survival to parametric forms; (2) placing greater emphasis on sample size when nests are rare, to obtain accurate estimates of nest survival; and (3) developing new techniques to estimate the number of nests initiated.

The Auk↗
USGS2020

Integrating multiple data sources and multi-scale land-cover data to model the distribution of a declining amphibian

Determining the spatial scale at which landscape features influence population persistence is an important task for conservation planning. One challenge is that sampling biases confound factors that influence species occurrence and survey effort. Recent developments in Point Process Models (PPMs) enable researchers to disentangle the sampling process from ecological drivers of species' distributions. Land-cover change is a driver of decline for the western spadefoot (Spea hammondii), which has been extirpated from much of its range in California. Assessing this species' status requires information on the current distribution of suitable habitat within its historical range, but little is known about the effect of the landscape surrounding breeding ponds on spadefoot occurrence. Critically, surveys for western spadefoots often occur along roads, potentially biasing data used to fit species distribution models. We created PPMs integrating historical presence/non-detection and presence-only data for western spadefoots and land-cover data at multiple spatial scales to model the distribution of this species while removing the influence of sampling bias. There was spatial sampling bias in presence-only data; records were more likely to be reported near roads and urban centers and PPMs that removed sampling bias outperformed models that ignored sampling bias. The occurrence of western spadefoots was positively related to the proportion of grassland within a 2000 m buffer. The remaining habitat for western spadefoots is largely found in the foothills surrounding California's Central Valley. Our study illustrates how PPMs can improve projections of habitat suitability and our understanding of the drivers of species' distributions.

California↗
USGS1989

Interaction assessment: Rationale and a test using plants

A non-manipulative method for deriving empirical expressions of population growth parameters from simple field data is presented. The derived expressions can be used to assess the intensity and form of density dependence and interspecies interactions, and have potential for parameterizing more mechanistic models of population dynamics and for use in applied ecology, e.g. land management or environmental risk assessment. The method is based on an assertion of invariant expected fitness across occupied microhabitats. Hence, its success depends upon the degree to which that assertion holds. The assertion, as used here, is broadly applicable. Thus, the method can be expected to yield reliable results even in nonequilibrium communities. Here, we apply the method to data on six desert plant species. Expressions generated from data in one stand, in one year, successfully predict plant cover values in other stands and years. The predicted patterns of plant species interactions are discussed in the light of current knowledge and theorics of desert succession.

Evolutionary Ecology↗
USGS2014

Life in the main channel: long-term hydrologic control of microbial mat abundance in McMurdo Dry Valley streams, Antarctica

Given alterations in global hydrologic regime, we examine the role of hydrology in regulating stream microbial mat abundance in the McMurdo Dry Valleys, Antarctica. Here, perennial mats persist as a desiccated crust until revived by summer streamflow, which varies inter-annually, and has increased since the 1990s. We predicted high flows to scour mats, and intra-seasonal drying to slow growth. Responses were hypothesized to differ based on mat location within streams, along with geomorphology, which may promote (high coverage) or discourage (low coverage) accrual. We compared hydrologic trends with the biomass of green and orange mats, which grow in the channel, and black mats growing at stream margins for 16 diverse stream transects over two decades. We found mat biomass collectively decreased during first decade coinciding with low flows, and increased following elevated discharges. Green mat biomass showed the greatest correlations with hydrology and was stimulated by discharge in high coverage transects, but negatively correlated in low coverage due to habitat scour. In contrast, orange mat biomass was negatively related to flow in high coverage transects, but positively correlated in low coverage because of side-channel expansion. Black mats were weakly correlated with all hydrologic variables regardless of coverage. Lastly, model selection indicated the best combination of predictive hydrologic variables for biomass differed between mat types, but also high and low coverage transects. These results demonstrate the importance of geomorphology and species composition to modeling primary production, and will be useful in predicting ecological responses of benthic habitats to altered hydrologic regimes.

Ecosystems↗
USGS2016

Study of biological communities subject to imperfect detection: Bias and precision of community N -mixture abundance models in small-sample situations

Community N -mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance ( λ ¯ : 0.1, 0.5, 1, 5), detection probability ( p ¯ : 0.1, 0.2, 0.5), and number of sampling sites ( n site : 10, 20, 40) and visits ( n visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables ( λ ¯ , p ¯ , n site , n visit ) increased. Detection probability p ¯ was most important for the estimates of mean abundance, while λ ¯ was most influential for covariate effect and species richness estimates. For all parameters, increasing n site was more beneficial than increasing n visit . Minimal conditions for obtaining adequate performance of community abundance models were n site ≥ 20, p¯ ≥ 0.2, and λ ¯ ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N -mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.

Ecological Research↗
USGS2024

Fish bioenergetics modeling

A bioenergetics model for a fish is defined as a quantitative description of the fish's energy budget. Bioenergetics modeling can be applied to free-ranging fish in rivers, lakes, and oceans to estimate annual food consumption by fish populations; such applications have proven useful in managing fisheries. In addition, bioenergetics models have been used to better understand fish growth and consumption in ecosystems, to guide conservation management of endangered or threatened fish populations, to determine the importance of the role of fish in cycling nutrients within ecosystems, to predict fish growth and consumption under climate change , and to identify the important factors regulating contaminant accumulation in free-ranging fish. Over the past 60 years, fish bioenergetics modeling has led to significant advancements in both aquatic ecology, especially fish ecology, and fisheries management.

Book chapter↗
USGS2018

Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.

Colorado Plateau↗
USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗
USGS2018

Juvenile coho salmon growth and health in streams across an urbanization gradient

Expanding human population and urbanization alters freshwater systems through structural changes to habitat, temperature effects from increased runoff and reduced canopy cover, altered flows, and increased toxicants. Current stream assessments stop short of measuring health or condition of species utilizing these freshwater habitats and fail to link specific stressors mechanistically to the health of organisms in the stream. Juvenile fish growth integrates both external and internal conditions providing a useful indicator of habitat quality and ecosystem health. Thus, there is a need to account for ecological and environmental influences on fish growth accurately. Bioenergetics models can simulate changes in growth and consumption in response to environmental conditions and food availability to account for interactions between an organism's environmental experience and utilization of available resources. The bioenergetics approach accounts for how thermal regime, food supply, and food quality affect fish growth. This study used a bioenergetics modeling approach to evaluate the environmental factors influencing juvenile coho salmon growth among ten Pacific Northwest streams spanning an urban gradient. Urban streams tended to be warmer, have earlier emergence dates and stronger early season growth. However, fish in urban streams experienced increased stress through lower growth efficiencies, especially later in the summer as temperatures warmed, with as much as a 16.6% reduction when compared to fish from other streams. Bioenergetics modeling successfully characterized salmonid growth in small perennial streams as part of a more extensive monitoring program and provides a powerful assessment tool for characterizing mixed life-stage specific responses in urban streams.

Science of the Total Environment↗
USGS2002

Landscape patterns as habitat predictors: Building and testing models for cavity-nesting birds in the Uinta Mountains of Utah, USA

The ability to predict species occurrences quickly is often crucial for managers and conservation biologists with limited time and funds. We used measured associations with landscape patterns to build accurate predictive habitat models that were quickly and easily applied (i.e., required no additional data collection in the field to make predictions). We used classification trees (a nonparametric alternative to discriminant function analysis, logistic regression, and other generalized linear models) to model nesting habitat of red-naped sapsuckers (Sphyrapicus nuchalis), northern flickers (Colaptes auratus), tree swallows (Tachycineta bicolor), and mountain chickadees (Parus gambeli) in the Uinta Mountains of northeastern Utah, USA. We then tested the predictive capability of the models with independent data collected in the field the following year. The models built for the northern flicker, red-naped sapsucker, and tree swallow were relatively accurate (84%, 80%, and 75% nests correctly classified, respectively) compared to the models for the mountain chickadee (50% nests correctly classified). All four models were more selective than a null model that predicted habitat based solely on a gross association with aspen forests. We conclude that associations with landscape patterns can be used to build relatively accurate, easy to use, predictive models for some species. Our results stress, however, that both selecting the proper scale at which to assess landscape associations and empirically testing the models derived from those associations are crucial for building useful predictive models.

Landscape Ecology↗
USGS2018

Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed

The U.S. Geological Survey monthly water balance model (MWBM) was enhanced with the capability to simulate glaciers in order to make it more suitable for simulating cold region hydrology. The new model, MWBMglacier, is demonstrated in the heavily glacierized and ecologically important Copper River watershed in Southcentral Alaska. Simulated water budget components compared well to satellite‐based observations and ground measurements of streamflow, evapotranspiration, snow extent, and total water storage, with differences ranging from 0.2% to 7% of the precipitation flux. Nash Sutcliffe efficiency for simulated and observed streamflow was greater than 0.8 for six of eight stream gages. Snow extent matched satellite‐based observations with Nash Sutcliffe efficiency values of greater than 0.89 in the four Copper River ecoregions represented. During the simulation period 1949 to 2009, glacier ice melt contributed 25% of total runoff, ranging from 12% to 45% in different tributaries, and glacierized area was reduced by 6%. Statistically significant ( p < 0.05) decreasing and increasing trends in annual glacier mass balance occurred during the multidecade cool and warm phases of the Pacific Decadal Oscillation, respectively, reinforcing the link between climate perturbations and glacier mass balance change. The simulations of glaciers and total runoff for a large, remote region of Alaska provide useful data to evaluate hydrologic, cryospheric, ecologic, and climatic trends. MWBM glacier is a valuable tool to understand when, and to what extent, streamflow may increase or decrease as glaciers respond to a changing climate.

Journal of Geophysical Research F: Earth Surface↗
USGS2020

Coordinated river infrastructure decisions improve net social-ecological benefits

We explore the social, ecological, economic, and technical dimensions of sustainable river infrastructure development and the potential benefits of coordinating decisions such as dam removal and stream crossing improvement. Dam removal is common practice for restoring river habitat connectivity and ecosystem health. However, stream crossings such as culverts are often 15 times more abundant than dams and may pose similar ecological impacts. Using multi-objective optimization for a model system of 6100 dams and culverts in Maine, USA, we demonstrate substantial benefit-cost improvements provided by coordinating habitat connectivity decisions. Benefit-cost efficiency improves by two orders of magnitude when coordinating more decisions across wider areas, but this approach may cause inequitable resource distribution. Culvert upgrades improve roadway safety and habitat connectivity, creating cost-effective opportunities for coordinating and cost-sharing projects between conservationists and safety managers. Benefit-cost trends indicate significant overlaps in habitat and safety goals, encouraging flexible stakeholder collaborations and cost-sharing strategies.

Maine↗
USGS2023

Evaluating population trends of juvenile Atlantic Sturgeon at low abundance in a dynamic estuarine environment (Hudson River, New York)

Evaluating population trends in dynamic estuarine environments can be challenging, especially when survey data include a high percentage of zero observations. In fishery-independent surveys, zeros that come from reduced susceptibility to sample gears and reduced availability of the population to the survey impact survey catchability and negatively bias relative abundance indices. A zero-inflated negative binomial model was used to standardize a juvenile Atlantic Sturgeon ( Acipenser oxyrinchus oxyrinchus ) relative abundance index (Hudson River, New York) that included a high proportion (42%) of zero observations and intra- and interannually variable covariates. Reduced susceptibility was related to low water temperature, with the percentage of zeroes increasing rapidly below 7°C. Availability was influenced by temperature and distance to salt front, as catch rates increased with temperature and peaked in mesohaline waters ~27 km downstream of the predicted salt front. An alternative index suggested significant population growth ( r = 0.15; p -value = 0.007) occurred from 2004 to 2015. The zero-inflated model helped better understand Hudson River juvenile Atlantic Sturgeon ecology and relative trends in abundance, to better inform future management and monitoring decisions along the Atlantic Coast.

New York↗
USGS2009

Climate alters response of an endemic island plant to removal of invasive herbivores

Islands experience higher rates of species extinction than mainland ecosystems, with biological invasions among the leading causes; they also serve as important model systems for testing ideas in basic and applied ecology. Invasive removal programs on islands are conservation efforts that can also be viewed as powerful manipulative experiments, but few data are available to evaluate their effects. We collected demographic and herbivore damage data for Castilleja mollis Pennell, an endangered plant endemic to Santa Rosa Island, California, over a 12-year period before, during, and after the implementation of control for introduced cattle, deer, and elk. We used these long-term data to explore mechanisms underlying herbivore effects, assess the results of herbivore reduction at the scales of both individual plants and populations, and determine how temporal variability in herbivory and plant demography influenced responses to herbivore removals. For individual plants, herbivore effects mediated by disturbance were greater than those of grazing. Deer and elk scraping of the ground substantially increased plant mortality and dormancy and reduced flowering and growth. Stem damage from browsing did not affect survivorship but significantly reduced plant growth and flower production. Herbivore control successfully lowered damage rates, which declined steeply between 1997 and 2000 and have remained relatively low. Castilleja mollis abundances rose sharply after 1997, suggesting a positive effect of herbivore control, but then began to decline steadily again after 2003. The recent decline appears to be driven by higher mean growing season temperatures; interestingly, not only reductions in scraping damage but a period of cooler conditions were significant in explaining increases in C. mollis populations between 1997 and 2002. Our results demonstrate strong effects of introduced herbivores on both plant demography and population dynamics and show that climate-driven variation may counteract and mask positive responses to herbivore removal. Regional mean temperatures already have risen significantly over the last 50 years, suggesting that climate change could increasingly swamp the effects of management targeted at other environmental problems. Similar interactions between climate and invasive species will play an even greater role in future management, with long-term data sets like this critical to quantifying such effects. ?? 2009 by the Ecological Society of America.

Ecological Applications↗
USGS2020

Evaluation of the impacts of radio-marking devices on feral horses and burros in a captive setting

Radio-collars and other radio-marking devices have been invaluable tools for wildlife managers for >40 years. These marking devices have improved our understanding of wildlife spatial ecology and demographic parameters and provided new data facilitating model development for species conservation and management. Although these tools have been used on virtually all North American ungulates, their deployment on feral horses ( Equus ferus caballus ) or burros ( E. asinus ) has been limited. To determine if radio-collars and radio-tags could be safely deployed on feral equids, we conducted a 1-year observational study in 2015 to investigate fit and wear of radio-collars on feral horses and burros kept in pastures/pens at the Bureau of Land Management contracted adoption facility in Pauls Valley, Oklahoma, USA. We assessed the impact of radio-collars and transmitter tags on individual behavior, body condition, and evaluated neck surface for effects. We tested 2 radio-collar shapes (teardrop and oval) and a radio-tag (i.e., avian backpack) braided into the mane and tail of horses. Behavior of mares did not differ between radio-collared ( n = 12) and control (uncollared; n = 12) individuals. Despite the small sample size, collared burro jennies ( n = 4) spent more time standing than controls ( n = 4). Stallions wearing radio-collars ( n = 9) fed less, moved less, and stood more than controls ( n = 8). During the study, we did not detect injuries to the necks of mares or burro jennies, but stallions developed small sores (that healed while still wearing radio-collars and re-haired within 3 months). Two radio-collars occasionally flipped forward over the ears onto the foreheads of stallions. Although our study confirmed that radio-collars could be safely deployed on captive mares and jennies, stallions proved challenging for a variety of reasons. While our conclusions were optimistic, longer studies will be required to ensure radio-collar safety on free-ranging feral horses and burros.

Oklahoma↗