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Results for “Ecological Modelling”

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At least 595 records · Page 33Linked to original sources
USGS2017

An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

Ecological invasions and colonizations occur dynamically through space and time. Estimating the distribution and abundance of colonizing species is critical for efficient management or conservation. We describe a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of a colonizing species. Our method accounts for several issues that are common when modeling spatiotemporal ecological data including multiple levels of detection probability, multiple data sources, and computational limitations that occur when making fine-scale inference over a large spatiotemporal domain. We apply the model to estimate the colonization dynamics of sea otters ( Enhydra lutris ) in Glacier Bay, in southeastern Alaska.

Ecology↗
USGS2006

Mussel dynamics model: A hydroinformatics tool for analyzing the effects of different stressors on the dynamics of freshwater mussel communities

A model for simulating freshwater mussel population dynamics is presented. The model is a hydroinformatics tool that integrates principles from ecology, river hydraulics, fluid mechanics and sediment transport, and applies the individual-based modelling approach for simulating population dynamics. The general model layout, data requirements, and steps of the simulation process are discussed. As an illustration, simulation results from an application in a 10 km reach of the Upper Mississippi River are presented. The model was used to investigate the spatial distribution of mussels and the effects of food competition in native unionid mussel communities, and communities infested by Dreissena polymorpha, the zebra mussel. Simulation results were found to be realistic and coincided with data obtained from the literature. These results indicate that the model can be a useful tool for assessing the potential effects of different stressors on long-term population dynamics, and consequently, may improve the current understanding of cause and effect relationships in freshwater mussel communities. ?? 2006 Elsevier B.V. All rights reserved.

Ecological Modelling↗
USGS2007

The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling

A wide variety of ecological applications require spatially explicit, historic, current, and projected land use and land cover data. The U.S. Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States. The newly developed FORE-SCE model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land cover change through 2020 for multiple plausible scenarios. Projected proportions of future land use were initially developed, and then sited on the lands with the highest potential for supporting that land use and land cover using a statistically based stochastic allocation procedure. Three scenarios of 2020 land cover were mapped for the western Great Plains in the US. The model provided realistic, high-resolution, scenario-based land-cover products suitable for multiple applications, including studies of climate and weather variability, carbon dynamics, and regional hydrology.

Journal of Land Use Science↗
USGS2019

The importance of simulation assumptions when evaluating detectability in population models

Population monitoring is important for investigating a variety of ecological questions, and N-mixture models are increasingly used to model population size (N) and trends (lambda) while estimating detectability (p) from repeated counts within primary periods (when populations are closed to changes). Extending these models to dynamic processes with serial dependence across primary periods may relax the closure assumption, but simulations to evaluate models and inform effort (e.g., number of repeated counts) typically assume p is constant or random across sites and years. Thus, it is unknown how these models perform under scenarios where trends in p confound inferences on N and lambda, and conclusions regarding effort may be overoptimistic. Here, we used global positioning system (GPS) data from greater sage-grouse (Centrocercus urophasianus) to inform simulations of the detection process for lek counts of this species, and we created scenarios with and without linear annual trends in p. We then compared estimates of N and lambda from hierarchical population models either fit with single maximum counts or with detectability estimated from repeated counts (dynamic N-mixture models). We also explored using auxiliary data to correct counts for variation in detectability. Uncorrected count models consistently underestimated N by >50% whereas N-mixture models without auxiliary data underestimated N to a lesser degree due to unmodeled heterogeneity in p such as age. Nevertheless, estimates of lambda from both types of models were unbiased and similar for scenarios without trends in p. When p declined systematically across years, uncorrected count models underestimated lambda whereas N-mixture models estimated lambda with little bias when all sites were counted repeatedly. Auxiliary data also reduced bias in parameter estimates. Evaluating population models using scenarios with systematic variation in p may better reveal potential biases and inform effort than simulations that assume p is constant or random. Dynamic N-mixture models can distinguish between trends in p and N, but also require repeated counts within primary periods for accurate estimates. Auxiliary data may be useful when researchers lack repeated counts, wish to monitor more sites less intensively, or require unbiased estimates of N.

Ecosphere↗
USGS2004

Salt-Pond Box Model (SPOOM) and Its Application to the Napa-Sonoma Salt Ponds, San Francisco Bay, California

A box model to simulate water volume and salinity of a salt pond has been developed by the U.S. Geological Survey to obtain water and salinity budgets. The model, SPOOM, uses the principle of conservation of mass to calculate daily pond volume and salinity and includes a salt crystallization and dissolution algorithm. Model inputs include precipitation, evaporation, infiltration, and water transfers. Salinity and water-surface-elevation data were collected monthly in the Napa-Sonoma Salt-Pond Complex from February 1999 through September 2001 and were used to calibrate and validate the model. The months when water transfers occurred were known but the magnitudes were unknown, so the magnitudes of water transfers were adjusted in the model to calibrate simulated pond volumes to measured pond volumes for three ponds. Modeled salinity was then compared with measured salinity, which remained a free parameter, in order to validate the model. Comparison showed good correlation between modeled and measured salinity. Deviations can be attributed to lack of water-transfer information. Water and salinity budgets obtained through modeling will be used to help interpret ecological data from the ponds. This model has been formulated to be applicable to the Napa-Sonoma salt ponds, but can be applied to other salt ponds.

Water-Resources Investigations Report↗
USGS2018

Impacts of temporal revisit designs on the power to detect trend with a linear mixed model: An application to long-term monitoring of Sierra Nevada lakes

Long-term ecological monitoring programs often use linear mixed models to estimate trend in an ecological indicator sampled across large landscapes. A linear mixed model is versatile for estimating a linear trend in time as well as components of spatial and temporal variationin the case of unbalanced data structures , which are common in complex monitoring designs where limited sampling effort must be optimized over time and space. A power analysis was used to inform a lake chemistry monitoring design, including selecting the most appropriate temporal revisit design. Pilot data from surveys of lakes across large wilderness national parks (Sequoia, Kings Canyon, and Yosemite national parks) were used to obtain variance components for a Monte Carlo power simulation. Using a linear mixed model for a range of temporal revisit designs, sample sizes, and trend magnitudes, we evaluated the power to detect trend, the trend test size, and the relative bias of trend coefficient estimates for four continuous and normally distributed indicators. Contrary to prior research based on large-sample approximations that identified a single panel of sites visited annually as the revisit design generating the highest power, we found that the power to detect a 12-year trend based on the Wald t-test from a linear mixed model may be optimized by obtaining unbalanced data sets with limited to no annual replication. We emphasize the importance of examining variance composition, sample size, and the power and size of the trend test with Monte Carlo simulation when allocating sampling effort over time and space.

Ecological Indicators↗
USGS2019

The Laurentian Great Lakes: A case study in ecological disturbance and climate change

Climate change effects are already significant, but can also magnify other ecological problems. This can be clearly seen in the Laurentian Great Lakes, which have suffered habitat degradation, fishery overharvest and dramatic alterations by invasive species. Thermal changes are expected to cause extensive loss of suitable fish habitat, and changing precipitation patterns will aggravate the problems with our highly modified lotic and lentic systems. A brief summary of the historic ecological context provided by the Great Lakes case is presented, followed by the descriptions of selected tools that help to understand and evaluate both ecological and climate change problems. Species distribution models and habitat classification combined with climate change predictions can identify the distribution and extent of optimal habitats, and identify which are most vulnerable to climate change. Ecological flow modelling can help to identify when critical flow changes are likely. Mechanistic simulation modelling specifies understanding of how aquatic systems function and can reveal cause and effect relationships. These tools can be used to help managers to protect optimal habitat, resist climate change effects to other habitats and adapt cultural systems to climate‐altered aquatic systems.

Great Lakes↗
USGS2002

Artificial intelligence based decision support for trumpeter swan management

The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-State area where Montana, Idaho, and Wyoming come together has declined to just a few hundred pairs. However, these birds are part of the Rocky Mountain Population which additionally has over 3,500 birds breeding in Alberta, British Columbia, Northwest Territories, and Yukon Territory. To a large degree, these birds seem to have abandoned traditional migratory pathways in the flyway. Waterfowl managers have been interested in decision support tools that would help them explore simulated management scenarios in their quest towards reaching population recovery and the reestablishment of traditional migratory pathways. I have developed a decision support system to assist biologists with such management, especially related to wetland ecology. Decision support systems use a combination of models, analytical techniques, and information retrieval to help develop and evaluate appropriate alternatives. Swan management is a domain that is ecologically complex, and this complexity is compounded by spatial and temporal issues. As such, swan management is an inherently distributed problem. Therefore, the ecological context for modeling swan movements in response to management actions was built as a multiagent system of interacting intelligent agents that implements a queuing model representing swan migration. These agents accessed ecological knowledge about swans, their habitats, and flyway management principles from three independent expert systems. The agents were autonomous, had some sensory capability, and could respond to changing conditions. A key problem when developing ecological decision support systems is empirically determining that the recommendations provided are valid. Because Rocky Mountain trumpeter swans have been surveyed for a long period of time, I was able to compare simulated distributions provided by the system with actual field observations across 20 areas for the period 1988-2000. Applying the Matched Pairs Multivariate Permutation Test as a statistical tool was a new approach for comparing flyway distributions of waterfowl over time that seemed to work well. Based on this approach, the empirical evidence that I gathered led me to conclude that the base queuing model does accurately simulate swan distributions in the flyway. The system was insensitive to almost all model parameters tested. That remains perplexing, but might result from the base queuing model, itself, being particularly effective at representing the actual ecological diversity in the world of Rocky Mountain trumpeter swans, both spatial and temporally.

Idaho, Montana, Utah, Wyoming↗
USGS2026

Understanding co-invading species’ ecological strategies and interactions with causal inference and simple dynamic models: Bighead (Hypophthalmichthys nobilis) and Silver carp (H. molitrix) in the Illinois River, USA

Species interactions among multiple invasive species can present hidden challenges for management and eradication efforts. Bighead and Silver carp ( Hypophthalmichthys nobilis and H. molitrix , respectively) are closely related species that have co-invaded the Mississippi River and many of its tributaries, including the Illinois River. We used a combination of causal inference and dynamic models to test for interspecific interactions in a 10-year data set of hydroacoustic density data from the Illinois River. The results suggest that Bighead and Silver carp are exploiting different ecological strategies; where Bighead carp shows fast initial growth but low equilibrium density and Silver carp had a lower low-density growth rate but larger equilibrium population density. Further, we found evidence for a positive interspecific effect of Silver carp on Bighead carp. The mechanism leading to the observed positive interactions is unknown, but could be an effect of ecological facilitation, differential responses to harvest of both species, or hybridization and introgression. Finally, while these results provide valuable information in the ecological strategies and relationships between these species, simulations of the resulting models suggest that the magnitude of the positive interaction is likely too small to be exploited for innovative multispecies management interventions.

Illinois↗
USGS2020

Assessment of existing groundwater quality data in the Green-Duwamish watershed, Washington

The United States Geological Survey (USGS) provided technical support to the Washington Department of Ecology (Ecology) in their assessment of the role groundwater plays in contributing pollutant loading to the Green-Duwamish River near Seattle, Washington. Ecology is developing watershed hydrology models of the Green-Duwamish watershed, and need to assign realistic contaminant concentrations to the various Hydrologic Response Units represented in their models. The USGS compiled existing groundwater quality data in the Green-Duwamish watershed, and this report summarizes results and interpretation of the dataset, including identifying data gaps and needs for further research and monitoring. The sources of existing data were the USGS’s National Water Information System, Ecology’s Environmental Information Management System, and a compilation of several studies by Leidos, a scientific research company. The water-quality parameters of interest included polychlorinated biphenyl (PCB) Aroclors and congeners, phthalates, carcinogenic polycyclic aromatic hydrocarbons (cPAHs), arsenic, copper, and zinc. Results were grouped into the four subwatersheds delineated in Ecology’s hydrology models: Duwamish, Lower Green, Soos, and Upper Green. Results from the Duwamish subwatershed were further sub-divided by the USGS into the Lower Duwamish, containing land adjacent to the Lower Duwamish Waterway Superfund site, and the Upper Duwamish, containing the remaining area of the Duwamish subwatershed. Groundwater quality data in the Lower Duwamish were treated separately because there is known contamination in this area. The availability of water quality data varied by subwatershed as follows: phthalate data was only available within the Duwamish, PCB data was available within the Duwamish and Lower Green, cPAH data was available within the Duwamish, Lower Green, and Soos, and data for arsenic, copper, and zinc were available within all four subwatersheds. More than 99 percent of the available data was within the Duwamish subwatershed, identifying a need for additional monitoring of groundwater quality in the other subwatersheds.

Washington↗
USGS2010

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

Journal of Applied Ecology↗
USGS2012

Ecological context for the North Pacific Landscape Conservation Cooperative

The North Pacific Landscape Conservation Cooperative (NPLCC) encompasses the temperate coastal rainforest and extends from the coastal mountains to the near-shore from the Kenai Peninsula, Alaska to Bodega Bay, California. The area spans multiple agency, state, and international boundaries over more than 22 degrees of latitude, including a wide range of type and intensity of human land-use activities. Development of NPLCC goals and administrative structures will be facilitated by a shared ecological context for discussing this expansive, diverse, and complex landscape. In support of activities to organize the NPLCC, we provided conceptual models to describe the ecological structure of the NPLCC. Recognizing that the boundaries of LCCs were primarily based on Level 2 of the hierarchical ecoregional classification of Omernik (Comission for Environmental Cooperation 1997), we used nested Level 3 ecoregions to define subregions within the NPLCC. Rather than develop conceptual models for all nine constituent subregions, we opted to consider five groups: Puget-Georgia Basin Lowland and Willamette Valley, Alaska-British Columbia Coast, Alaska-British Columbia Mountains, Klamath-Olympic-Cascade Mountains, and Washington-Oregon-Northern California Coast. At the conclusion of the project, we felt that the close relationship between mountain and coastal areas support combining them to create three major subregions: Alaska-British Columbia coast and mountains, Washington-Oregon-Northern California coast and mountains, and the lowlands of the Georgia Basin and Willamette Valley. The following figures present the Omernik Level 3 ecoregions comprising the NPLCC; how the ecoregions were grouped to create conceptual models; and conceptual models for each group. The five models each consist of a table listing resources, stressors, potential climate change impacts; a landcover map; and a cartoon to summarize the table and evoke the landscape. A final figure summarizes resources, stressors, and climate change impacts that are common across the NPLCC.

Alaska;British Columbia;California;Oregon;Washingt↗
USGS2010

Linking microbial and ecosystem ecology using ecological stoichiometry: a synthesis of conceptual and empirical approaches

Currently, one of the biggest challenges in microbial and ecosystem ecology is to develop conceptual models that organize the growing body of information on environmental microbiology into a clear mechanistic framework with a direct link to ecosystem processes. Doing so will enable development of testable hypotheses to better direct future research and increase understanding of key constraints on biogeochemical networks. Although the understanding of phenotypic and genotypic diversity of microorganisms in the environment is rapidly accumulating, how controls on microbial physiology ultimately affect biogeochemical fluxes remains poorly understood. We propose that insight into constraints on biogeochemical cycles can be achieved by a more rigorous evaluation of microbial community biomass composition within the context of ecological stoichiometry. Multiple recent studies have pointed to microbial biomass stoichiometry as an important determinant of when microorganisms retain or recycle mineral nutrients. We identify the relevant cellular components that most likely drive changes in microbial biomass stoichiometry by defining a conceptual model rooted in ecological stoichiometry. More importantly, we show how X-ray microanalysis (XRMA), nanoscale secondary ion mass spectroscopy (NanoSIMS), Raman microspectroscopy, and in situ hybridization techniques (for example, FISH) can be applied in concert to allow for direct empirical evaluation of the proposed conceptual framework. This approach links an important piece of the ecological literature, ecological stoichiometry, with the molecular front of the microbial revolution, in an attempt to provide new insight into how microbial physiology could constrain ecosystem processes.

Ecosystems↗
USGS2012

Predicting ecological flow regime at ungaged sites: A comparison of methods

Nineteen ecologically relevant streamflow characteristics were estimated using published rainfall–runoff and regional regression models for six sites with observed daily streamflow records in Kentucky. The regional regression model produced median estimates closer to the observed median for all but two characteristics. The variability of predictions from both models was generally less than the observed variability. The variability of the predictions from the rainfall–runoff model was greater than that from the regional regression model for all but three characteristics. Eight characteristics predicted by the rainfall–runoff model display positive or negative bias across all six sites; biases are not as pronounced for the regional regression model. Results suggest that a rainfall–runoff model calibrated on a single characteristic is less likely to perform well as a predictor of a range of other characteristics (flow regime) when compared with a regional regression model calibrated individually on multiple characteristics used to represent the flow regime. Poor model performance may misrepresent hydrologic conditions, potentially distorting the perceived risk of ecological degradation. Without prior selection of streamflow characteristics, targeted calibration, and error quantification, the widespread application of general hydrologic models to ecological flow studies is problematic. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Kentucky, Tennessee↗
USGS2025

Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm

Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R 2 for annual 7-day maximum = 0.76; R 2 for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.

contiguous United States↗
USGS2017

Estimating occupancy and abundance using aerial images with imperfect detection

Species distribution and abundance are critical population characteristics for efficient management, conservation, and ecological insight. Point process models are a powerful tool for modelling distribution and abundance, and can incorporate many data types, including count data, presence-absence data, and presence-only data. Aerial photographic images are a natural tool for collecting data to fit point process models, but aerial images do not always capture all animals that are present at a site. Methods for estimating detection probability for aerial surveys usually include collecting auxiliary data to estimate the proportion of time animals are available to be detected. We developed an approach for fitting point process models using an N -mixture model framework to estimate detection probability for aerial occupancy and abundance surveys. Our method uses multiple aerial images taken of animals at the same spatial location to provide temporal replication of sample sites. The intersection of the images provide multiple counts of individuals at different times. We examined this approach using both simulated and real data of sea otters ( Enhydra lutris kenyoni ) in Glacier Bay National Park, southeastern Alaska. Using our proposed methods, we estimated detection probability of sea otters to be 0.76, the same as visual aerial surveys that have been used in the past. Further, simulations demonstrated that our approach is a promising tool for estimating occupancy, abundance, and detection probability from aerial photographic surveys. Our methods can be readily extended to data collected using unmanned aerial vehicles, as technology and regulations permit. The generality of our methods for other aerial surveys depends on how well surveys can be designed to meet the assumptions of N -mixture models.

Methods in Ecology and Evolution↗
USGS2013

Mapping behavioral landscapes for animal movement: a finite mixture modeling approach

Because of its role in many ecological processes, movement of animals in response to landscape features is an important subject in ecology and conservation biology. In this paper, we develop models of animal movement in relation to objects or fields in a landscape. We take a finite mixture modeling approach in which the component densities are conceptually related to different choices for movement in response to a landscape feature, and the mixing proportions are related to the probability of selecting each response as a function of one or more covariates. We combine particle swarm optimization and an Expectation-Maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. We use this approach to analyze data for movement of three bobcats in relation to urban areas in southern California, USA. A behavioral interpretation of the models revealed similarities and differences in bobcat movement response to urbanization. All three bobcats avoided urbanization by moving either parallel to urban boundaries or toward less urban areas as the proportion of urban land cover in the surrounding area increased. However, one bobcat, a male with a dispersal-like large-scale movement pattern, avoided urbanization at lower densities and responded strictly by moving parallel to the urban edge. The other two bobcats, which were both residents and occupied similar geographic areas, avoided urban areas using a combination of movements parallel to the urban edge and movement toward areas of less urbanization. However, the resident female appeared to exhibit greater repulsion at lower levels of urbanization than the resident male, consistent with empirical observations of bobcats in southern California. Using the parameterized finite mixture models, we mapped behavioral states to geographic space, creating a representation of a behavioral landscape. This approach can provide guidance for conservation planning based on analysis of animal movement data using statistical models, thereby linking connectivity evaluations to empirical data.

Ecological Applications↗
USGS2011

Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages

Issues with ecological data (e.g. non-normality of errors, nonlinear relationships and autocorrelation of variables) and modelling (e.g. overfitting, variable selection and prediction) complicate regression analyses in ecology. Flexible models, such as generalized additive models (GAMs), can address data issues, and machine learning techniques (e.g. gradient boosting) can help resolve modelling issues. Gradient boosted GAMs do both. Here, we illustrate the advantages of this technique using data on benthic macroinvertebrates and fish from 1573 small streams in Maryland, USA.

Methods in Ecology and Evolution↗