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Results for “Ecological Modeling”

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At least 55 records · Page 3Linked to original sources
USGS2006

Multimodeling: new approaches for linking ecological models

The Everglades region of South Florida presents one of the major natural system management challenges facing the United States. With its assortment of alligators, crocodiles, manatees, panthers, large mixed flocks of wading birds, highly diverse subtropical flora, and sea of sawgrass, the ecosystem is unique in this country (Davis and Ogden 1994). The region is also perhaps the largest human-controlled system on the planet in that the major environmental factor influencing the region is water, and water flows are managed on a daily basis--subject to the vagaries of rainfall--by a massive system of locks, pumps, canals, and levees constructed over the past century. The changes brought about by such control have led to extensive modifications of historical patterns and magnitudes of flow, causing large declines in many native species, extensive changes in nutrient cycling and vegetation across south Florida, and great increases in pollutants such as mercury. Constrained by the conflicting demands of agriculture, urban human populations, and wildlife for control of water resources, and the varying agendas of hosts of government agencies and nongovernmental organizations, there is now an ongoing effort to plan for major changes to the system with expenditure estimates of eight billion dollars or more over the next several decades (USACOE 1999). Carrying out such planning, particularly as it impacts the natural systems of the region, provides one of the major challenges to the new field of computational ecology.

Florida↗
USGS2011

Modelling ecological flow regime: an example from the Tennessee and Cumberland River basins

Predictive equations were developed for 19 ecologically relevant streamflow characteristics within five major groups of flow variables (magnitude, ratio, frequency, variability, and date) for use in the Tennessee and Cumberland River basins using stepbackward regression. Basin characteristics explain 50% or more of the variation for 12 of the 19 equations. Independent variables identified through stepbackward regression were statistically significant in 78 of 304 cases (α > 0.0001) and represent four major groups: climate, physical landscape features, regional indicators, and land use. Of these groups, the regional and climate variables were the most influential for determining hydrologic response. Daily temperature range, geologic factor, and rock depth were major factors explaining the variability in 17, 15, and 13 equations, respectively. The equations and independent datasets were used to explore the broad relation between basin properties and streamflow and the implication of streamflow to the study of ecological flow requirements. Key results include a high degree of hydrologic variability among least disturbed Blue Ridge streams, similar hydrologic behaviour for watersheds with widely varying degrees of forest cover, and distinct hydrologic profiles for streams in different geographic regions. Published in 2011. This article is a US Government work and is in the public domain in the USA.

Alabama;Georgia;Kentucky;Mississippi;North Carolin↗
USGS2025

Spatiotemporal causal inference with mechanistic ecological models: Evaluating targeted culling on chronic wasting disease dynamics in cervids

Spatiotemporal causal inference methods are needed to detect the effect of interventions on indirectly measured epidemiological outcomes that go beyond studying spatiotemporal correlations. Chronic wasting disease (CWD) causes neurological degeneration and eventual death to white-tailed deer ( Odocoileus virginianus ) in Wisconsin. Targeted culling involves removing deer after traditional hunting seasons in areas with high CWD prevalence. The evaluation of the causal effects of targeted culling in the spread and growth of CWD is an important unresolved research and CWD management question that can guide surveillance efforts. Reaction–diffusion partial differential equations (PDEs) can be used to mechanistically model the underlying spatiotemporal dynamics of wildlife diseases, like CWD, allowing researchers to make inference about unobserved epidemiological quantities. These models indirectly regress spatiotemporal covariates on diffusion and growth rates parameterizing such PDEs, obtaining associational conclusions. In this work we develop an innovative method to obtain causal estimators for the effect of targeted culling interventions on CWD epidemiological processes using an inverse-probability-of-treatment-weighted technique by means of marginal structural models embedded in the PDE fitting process. Additionally we establish a novel scheme for sensitivity analysis under unmeasured confounder for testing the hypothesis of a significant causal effect in the indirectly measured epidemiological outcomes. Our methods can be broadly used to study the impact of spatiotemporal interventions and treatment exposures in the epidemiological evolution of infectious diseases that can help to inform future efforts to mitigate public health implications and wildlife disease burden.

Wisconsin↗
USGS2001

Some considerations on the use of ecological models to predict species' geographic distributions

Peterson (2001) used Genetic Algorithm for Rule-set Prediction (GARP) models to predict distribution patterns from Breeding Bird Survey (BBS) data. Evaluations of these models should consider inherent limitations of BBS data: (1) BBS methods may not sample species and habitats equally; (2) using BBS data for both model development and testing may overlook poor fit of some models; and (3) BBS data may not provide the desired spatial resolution or capture temporal changes in species distributions. The predictive value of GARP models requires additional study, especially comparisons with distribution patterns from independent data sets. When employed at appropriate temporal and geographic scales, GARP models show considerable promise for conservation biology applications but provide limited inferences concerning processes responsible for the observed patterns.

Condor↗
USGS2025

Reconstructing relative abundance indices for Atlantic sturgeon using hierarchical ecological models

Objective The Atlantic Sturgeon Acipenser oxyrinchus is a wide-ranging, long-lived diadromous fish that is endangered in most of its range. Our objective was to develop and apply long-term, detection-corrected indices of relative abundance for juvenile and adult Atlantic Sturgeon in the Hudson River, New York, United States, to support population monitoring and stock assessment. Methods We used long-term gill-net catches to estimate relative abundances of juvenile and adult Atlantic Sturgeon while accounting for imperfect detection within an N- mixture modeling framework. We validated the model framework using a simulation–estimation framework based on mean parameter estimates from the adult Atlantic Sturgeon relative abundance index. Results Simulation testing indicated that absolute abundance estimates may be biased low due to poor characterization of detection probabilities. However, model estimates of relative abundance tracked simulated abundance trends well. Juvenile relative abundance estimates followed similar trends as raw gill-net catches but were less variable among years when corrected for detection probability. Relative abundance of juveniles increased from 2004 to 2015 prior to declining through 2022, with little evidence for change between the start and end of the survey. Detection-corrected indices for adult sturgeon indicated a consistent increase in relative abundance that was not readily apparent in raw catch indices. Conclusions Detection-corrected catch indices can provide improved characterization of Atlantic Sturgeon relative abundance dynamics over raw gill-net catches through use of N- mixture models. The approach has broad applicability to data types that are commonly collected for understanding population trends in stock assessment. Estimation of absolute abundance and other population demographics germane to management would benefit from alternative or auxiliary data collected through approaches such as side-scan sonar or acoustic telemetry, which are increasingly common for monitoring sturgeon populations.

New York↗
USGS2021

The Everglades vulnerability analysis—Integrating ecological models and addressing uncertainty

The Everglades vulnerability analysis (EVA) is a project led by the U.S. Geological Survey in cooperation with the National Park Service and U.S. Army Corps of Engineers to accomplish one of the science goals of Restoration Coordination & Verification (RECOVER), a multiagency group responsible for providing scientific and technical evaluations and assessments for improving the ability of the Comprehensive Everglades Restoration Plan to restore, preserve, and protect the south Florida ecosystem while providing for the region’s other water-related needs. In 2016, RECOVER acknowledged the need for a tool that could synthesize the decades of Everglades ecosystem science and identify areas vulnerable to changing conditions on the landscape. The EVA tool answers this need through a landscape-scale modeling framework that provides annual responses and relative vulnerability for a suite of indicators of Everglades ecosystem health.

Florida↗
USGS2001

Uncertainty and spatial linear models for ecological data

Models are not perfect; they do not fit the data exactly and they do not allow exact prediction. Given that models are imperfect, we need to assess the uncertainties in the fits of the models and their ability to predict new outcomes. The goals of building models for scientific problems include (1) understanding and developing appropriate relationships between variables, and (2) predicting variables in the future or at locations where data have not been collected. Ecological models range in complexity from those that are relatively simple (e.g., linear regression) to those that are very complex (e.g., ecosystem models, forest-growth models, and nitrogen-cycling models). In a mathematical model, parameters control the relationships between variables in the model. In this framework of parametric modeling, inference is the process whereby we take output (data) and estimate model parameters, whereas deduction is the process whereby we take a parameterized model and obtain output (data) or deduce properties. We often add random components in both inference and deduction to reflect a model’s lack-of-fit and our uncertainty about predicting outcomes. Complex models in ecology have largely been of the deductive type, where the scientist takes some values of parameters (usually obtained from an independent data source or chosen from a reasonable range of values) and then simulates results based on model relationships. These models may be quite realistic, but the manner in which their parameters are obtained for the simulations is questionable.

Book chapter↗
USGS2014

An online database for informing ecological network models: http://kelpforest.ucsc.edu

Ecological network models and analyses are recognized as valuable tools for understanding the dynamics and resiliency of ecosystems, and for informing ecosystem-based approaches to management. However, few databases exist that can provide the life history, demographic and species interaction information necessary to parameterize ecological network models. Faced with the difficulty of synthesizing the information required to construct models for kelp forest ecosystems along the West Coast of North America, we developed an online database ( http://kelpforest.ucsc.edu/ ) to facilitate the collation and dissemination of such information. Many of the database's attributes are novel yet the structure is applicable and adaptable to other ecosystem modeling efforts. Information for each taxonomic unit includes stage-specific life history, demography, and body-size allometries. Species interactions include trophic, competitive, facilitative, and parasitic forms. Each data entry is temporally and spatially explicit. The online data entry interface allows researchers anywhere to contribute and access information. Quality control is facilitated by attributing each entry to unique contributor identities and source citations. The database has proven useful as an archive of species and ecosystem-specific information in the development of several ecological network models, for informing management actions, and for education purposes (e.g., undergraduate and graduate training). To facilitate adaptation of the database by other researches for other ecosystems, the code and technical details on how to customize this database and apply it to other ecosystems are freely available and located at the following link ( https://github.com/kelpforest-cameo/databaseui ).

PLoS ONE↗
USGS2016

State-and-transition models: Conceptual versus simulation perspectives, usefulness and breadth of use, and land management applications

State-and-Transition Simulation Modeling (STSM) is a quantitative analysis method that can consolidate a wide array of resource management issues under a “what-if” scenario exercise. STSM can be seen as an ensemble of models, such as climate models, ecological models, and economic models that incorporate human dimensions and management options. This chapter presents STSM as a tool to help synthesize information on social–ecological systems and to investigate some of the management issues associated with exotic annual Bromus species, which have been described elsewhere in this book. Definitions, terminology, and perspectives on conceptual and computer-simulated stochastic state-and-transition models are given first, followed by a brief review of past STSM studies relevant to the management of Bromus species. A detailed case study illustrates the usefulness of STSM for land management. As a whole, this chapter is intended to demonstrate how STSM can help both managers and scientists: (a) determine efficient resource allocation for monitoring nonnative grasses; (b) evaluate sources of uncertainty in model simulation results involving expert opinion, and their consequences for management decisions; and (c) provide insight into the consequences of predicted local climate change effects on ecological systems invaded by exotic annual Bromus species.

Book chapter↗
USGS2020

Hierarchical computing for hierarchical models in ecology

Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade-offs between capturing ecological complexity in statistical models and implementing these models. We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation-assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual- and population-level migratory characteristics. Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm. For complex ecological statistical models, like those for animal movement, multi-species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation-assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena.

Article↗
USGS2026

Ecological diffusion models are still useful in ecology

Context Connecting animal movement across scales—both among spatial scales and between individual and population level processes—is a central theme in ecology, including the sub-fields of movement and landscape ecology. However, most modeling frameworks operate at either the individual (Lagrangian) or population (Eulerian) level. Objectives We aimed to review how a partial differential equation (PDE) known as the ecological diffusion equation (EDE) offers connections among the scales of animal movement and how it can be embedded in commonly used statistical frameworks (e.g., hierarchical models) and fit to multiple types of data, such as count, presence-only, presence-absence, and telemetry data. In doing so, we also highlight emerging and promising avenues of research related to statistical EDE models. Methods We present how the EDE is derived from a simple set of first principles, can be used to represent both individual animal movement and spatiotemporal population dynamics, and can be fit to data by being applied in a hierarchical statistical framework. Finally, we review literature on recent applications and advances related to EDE models. Results In presenting the derivation of the EDE and reviewing recent advances and applications of EDE models, we established that the EDE can enable a mechanistic understanding of animal movement across scales, as well as the integration of multiple types of data. We also identified a number of topics where EDE models can be advanced to gain additional ecological insight. Conclusions Ecological diffusion is a process that emerges naturally from first principles governing animal movement and can be used to model both individual and population-level spatiotemporal dynamics. Statistical EDE models are related to other modeling frameworks, such as species distribution models, occupancy models, and step-selection analysis. The flexibility of hierarchical statistical modeling allows combining different types of data collected on movement and population dynamics, and the theoretical underpinnings of the EDE allow it to connect ecological processes occurring at fine and coarse spatial scales, as well as individual animal movement and population dynamics.

Landscape Ecology↗
USGS2023

The unmarked R package: Twelve years of advances in occurrence and abundance modelling in ecology

Species distribution models (SDMs) are widely applied to understand the processes governing spatial and temporal variation in species abundance and distribution but often do not account for measurement errors such as false negatives and false positives. We describe unmarked , a package for the freely available and open-source R software that provides a complete workflow for modelling species distribution and abundance while explicitly accounting for measurement errors. Here we focus on recent advances in unmarked functionality to support multi-species, multi-state, and multi-season data, as well as support for fitting models with random effects. For illustration, we present an analysis of Acadian Flycatcher Empidonax virescens abundance on Roanoke River National Wildlife Refuge, North Carolina, USA, over 18 years. We found that Acadian Flycatcher abundance was initially greater in hardwood plantation habitat relative to bottomland hardwood forest along river levees but that abundance declined over time in both habitats. We plan for unmarked development to keep pace with advances in hierarchical modelling in ecology, including better handling of continuous-time data from camera trap and automated recording units and integrated models for multiple data streams.

Methods in Ecology & Evolution↗
USGS2014

Biological soil crusts (biocrusts) as a model system in community, landscape and ecosystem ecology

Model systems have had a profound influence on the development of ecological theory and general principles. Compared to alternatives, the most effective models share some combination of the following characteristics: simpler, smaller, faster, general, idiosyncratic or manipulable. We argue that biological soil crusts (biocrusts) have unique combinations of these features that should be more widely exploited in community, landscape and ecosystem ecology. In community ecology, biocrusts are elucidating the importance of biodiversity and spatial pattern for maintaining ecosystem multifunctionality due to their manipulability in experiments. Due to idiosyncrasies in their modes of facilitation and competition, biocrusts have led to new models on the interplay between environmental stress and biotic interactions and on the maintenance of biodiversity by competitive processes. Biocrusts are perhaps one of the best examples of micro-landscapes—real landscapes that are small in size. Although they exhibit varying patch heterogeneity, aggregation, connectivity and fragmentation, like macro-landscapes, they are also compatible with well-replicated experiments (unlike macro-landscapes). In ecosystem ecology, a number of studies are imposing small-scale, low cost manipulations of global change or state factors in biocrust micro-landscapes. The versatility of biocrusts to inform such disparate lines of inquiry suggests that they are an especially useful model system that can enable researchers to see ecological principles more clearly and quickly.

Biodiversity and Conservation↗
USGS2003

Measuring and mapping the topography of the Florida Everglades for ecosystem restoration

One of the major issues facing ecosystem restoration and management of the Greater Everglades is the availability and distribution of clean, fresh water. The South Florida ecosystem encompasses an area of approximately 28,000 square kilometers and supports a human population that exceeds 5 million and is continuing to grow. The natural systems of the Kissimmee-Okeechobee-Everglades watershed compete for water resources primarily with the region's human population and urbanization, and with the agricultural and tourism industries. Surface water flow modeling and ecological modeling studies are important means of providing scientific information needed for ecosystem restoration planning and modeling. Hydrologic and ecological models provide much-needed predictive capabilities for evaluating management options for parks, refuges, and land acquisition and for understanding the impacts of land management practices in surrounding areas. These models require various input data, including elevation data that very accurately define the topography of the Florida Everglades.

Florida↗