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At least 73 records · Page 4Linked to original sources
USGS2002

Generalized linear and generalized additive models in studies of species distributions: Setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.

Ecological Modelling↗
USGS2015

Progress and challenges in coupled hydrodynamic-ecological estuarine modeling

Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review, we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a “theory of everything” for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.

Estuaries and Coasts↗
USGS2026

An overview and participatory framework for choosing spatial boundaries in social–ecological systems modeling

A common challenge when modeling social–ecological systems (SESs) is defining the spatial extent of the system. Boundaries that do not adequately capture both social and ecological processes and their interactions can lead to mischaracterization of the system, while expanding boundaries too widely can impact model complexity and required resources. Socially, boundaries can invoke and influence identity, culture, power, and sense of place. Boundary decisions benefit from flexible, iterative approaches and the expertise of local communities. Here, we use a structured database search supplemented with citation searching to identify and review the literature that addresses choosing or defining spatial boundaries in SESs mapping or modeling and, when applicable, how participatory methods were used in the research process. In a review of the resulting 79 studies, we discovered that pre-existing social or ecological boundaries were used most frequently (36 and 18 publications, respectively). Twenty-one publications combined social and ecological boundaries or data to create custom boundaries, and four studies used an alternative approach to conventional boundaries. Informed by the literature review, we present a general framework for defining boundaries at the outset of SES research. We then connect the framework to a specific case study based on a collaborative project with Tribal, university, and federal scientists to develop a social–ecological climate adaptation plan. We present guiding questions alongside candidate boundaries for our study system and explore the tradeoffs of these boundary options, which can function as a useful template for other social–ecological research collaborations.

ISPRS International Journal of Geo-Information↗
USGS2015

Activity-specific ecological niche models for planning reintroductions of California condors (Gymnogyps californianus)

Ecological niche models can be a useful tool to identify candidate reintroduction sites for endangered species but have been infrequently used for this purpose. In this paper, we (1) develop activity-specific ecological niche models (nesting, roosting, and feeding) for the critically endangered California condor ( Gymnogyps californianus ) to aid in reintroduction planning in California, Oregon, and Washington, USA, (2) test the accuracy of these models using empirical data withheld from model development, and (3) integrate model results with information on condor movement ecology and biology to produce predictive maps of reintroduction site suitability. Our approach, which disentangles niche models into activity-specific components, has applications for other species where it is routinely assumed (often incorrectly) that individuals fulfill all requirements for life within a single environmental space. Ecological niche models conformed to our understanding of California condor ecology, had good predictive performance when tested with data withheld from model development, and aided in the identification of several candidate reintroduction areas outside of the current distribution of the species. Our results suggest there are large unoccupied regions of the California condor’s historical range that have retained ecological features similar to currently occupied habitats, and thus could be considered for future reintroduction efforts. Combining our activity-specific ENMs with ground reconnaissance and information on other threat factors that could not be directly incorporated into empirical ENMs will ultimately improve our ability to select successful reintroduction sites for the California condor.

California, Oregon, Washington↗
USGS2017

When mechanism matters: Bayesian forecasting using models of ecological diffusion

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer ( Odocoileus virginianus ). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Ecology Letters↗
USGS2010

Southeast Regional Assessment Project for the National Climate Change and Wildlife Science Center, U.S. Geological Survey

The Southeastern United States spans a broad range of physiographic settings and maintains exceptionally high levels of faunal diversity. Unfortunately, many of these ecosystems are increasingly under threat due to rapid human development, and management agencies are increasingly aware of the potential effects that climate change will have on these ecosystems. Natural resource managers and conservation planners can be effective at preserving ecosystems in the face of these stressors only if they can adapt current conservation efforts to increase the overall resilience of the system. Climate change, in particular, challenges many of the basic assumptions used by conservation planners and managers. Previous conservation planning efforts identified and prioritized areas for conservation based on the current environmental conditions, such as habitat quality, and assumed that conditions in conservation lands would be largely controlled by management actions (including no action). Climate change, however, will likely alter important system drivers (temperature, precipitation, and sea-level rise) and make it difficult, if not impossible, to maintain recent historic conditions in conservation lands into the future. Climate change will also influence the future conservation potential of non-conservation lands, further complicating conservation planning. Therefore, there is a need to develop and adapt effective conservation strategies to cope with the effects of climate and landscape change on future environmental conditions. Congress recognized this important issue and authorized the U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC; http://nccw.usgs.gov/) in the Fiscal Year 2008. The NCCWSC will produce science that will help resource management agencies anticipate and adapt to climate change impacts to fish, wildlife, and their habitats. With the release of Secretarial Order 3289 on September 14, 2009, the mandate of the NCCWSC was expanded to address climate change-related impacts on all Department of the Interior (DOI) resources. The NCCWSC will establish a network of eight DOI Regional Climate Science Centers (RCSCs) that will work with a variety of partners to provide natural resource managers with tools and information that will help them anticipate and adapt conservation planning and design for projected climate change. The forecasting products produced by the RCSCs will aid fish, wildlife, and land managers in designing suitable adaptive management approaches for their programs. The DOI also is developing Landscape Conservation Cooperatives (LCCs) as science and conservation action partnerships at subregional scales. The USGS is working with the Southeast Region of the U.S. Fish and Wildlife Service (FWS) to develop science collaboration between the future Southeast RCSC and future LCCs. The NCCWSC Southeast Regional Assessment Project (SERAP) will begin to develop regional downscaled climate models, land cover change models, regional ecological models, regional watershed models, and other science tools. Models and data produced by SERAP will be used in a collaborative process between the USGS, the FWS (LCCs), State and federal partners, nongovernmental organizations, and academia to produce science at appropriate scales to answer resource management questions. The SERAP will produce an assessment of climate change, and impacts on land cover, ecosystems, and priority species in the region. The predictive tools developed by the SERAP project team will allow end users to better understand potential impacts of climate change and sea level rise on terrestrial and aquatic populations in the Southeastern United States. The SERAP capitalizes on the integration of five existing projects: (1) the Multi-State Conservation Grants Program project "Designing Sustainable Landscapes," (2) the USGS multidisciplinary Science Thrust project "Water Availability for Ecological Needs," (3) the USGS Southeast Pilot Project "Climate Change in the Southeastern U.S. and its Impacts on Bird Distributions and Habitats," (4) a sea-level rise impacts study envisioned jointly with the National Oceanic and Atmospheric Administration (NOAA), and (5) two USGS sea-level rise impact assessment projects that address inundation hazards and provide probabilistic forecasts of coastal geomorphic change. The SERAP will expand on these existing projects and include the following tasks, which were initiated in summer 2009: * Regionally downscaled probabilistic climate-change projections * Integrated coastal assessment * Integrated terrestrial assessment * Multi-resolution assessment of potential climate change effects on biological resources: aquatic and hydrologic dynamics * Optimal conservation strategies to cope with climate change The SERAP seeks to formally integrate these tasks to aid conservation planning and design so that ecosystem management decisions can be optimized for providing desirable outcomes across a range of species and environments. The following chapters detail SERAP's efforts in providing a suite of regional climate, watershed, and landscape-change analyses and develop the interdisciplinary framework required for the biological planning phases of adaptive management and strategic conservation. The planning phase will include the identification of conservation alternatives, development of predictive models and decision support tools, and development of a template to address similar challenges and goals in other regions. The project teams will explore and develop ways to link the various ecological models arising from each component. The SERAP project team also will work closely with members of the LCCs and other partnerships throughout the life of the project to ensure that the objectives of the project meet resources mangers needs in the Southeast.

Open-File Report↗
USGS2023

Recognizing political influences in participatory social-ecological systems modeling

Stakeholder participation in social-ecological systems (SES) modeling is increasingly considered a desirable way to elicit diverse sources of knowledge about SES behavior and to promote inclusive decision-making in SES. Understanding how participatory modeling processes function in the context of long-term adaptive management of SES may allow for better design of participatory processes to achieve the intended outcomes of inclusionary knowledge, representativeness, and social learning, while avoiding unintended outcomes. Long-term adaptive management contexts often include political influences -- attempts to shift or preserve power structures and authority, and efforts to represent the political and economic interests of stakeholders -- in the computer models that are used to shape policy making and implementation. In this research, we examine a period that included a major transition in the watershed model used for management of the Chesapeake Bay in the United States. The Chesapeake Bay watershed model has been in development since the 1980s, and is considered by many to be an exemplary case of participatory modeling. We use documentary analysis and interviews with participants involved in the model application and development transition to reveal a variety of ways in which participatory modeling may be subject to different kinds of political influences, some of which resulted in unintended outcomes, including: perceptions of difficulty updating the model in substantive ways, “gaming” of the model/participatory process by stakeholders, and increasing resistance against considering uncertainty in the system not captured by the model. This research suggests unintended or negative outcomes may be associated with both participatory decision-making and stakeholder learning even though they are so often touted as the benefits of participatory modeling. We end with a hypothesis that further development of a theory of computer model governance to bridge model impact and broader theories of environmental governance at the science-policy interface may result in improved SES modeling outcomes.

Maryland, Virginia↗
USGS2018

Moving from eco-forecasts to eco-projections

Ecological models can provide estimates of future conditions that are useful for decision-making, including long-term planning and resource prioritization. However, these models often rely on assumptions about ecological relationships and trajectories, forcings (e.g., biophysical conditions), and management approaches that may not be explicitly considered. To make assumptions more transparent, disciplines such as economics, demographics, climatology, and national intelligence make a fairly clear and consistent distinction between “forecasts” and “projections”. Forecasts are typically more near-term and rely on extending existing relationships and trends to estimate the most likely future conditions; whereas projections evaluate conditions under multiple scenarios that are based on an array of assumptions, often going further out in time. Consistently referring to ecological models of future conditions as either “eco-forecasts” or “eco-projections” could help make modelling assumptions more transparent and thus more effectively focus their application across landscapes and through time. To the extent that ecological modelling is used to support management, policy, and programmatic decisions, practitioners can ask the following. If the modelling is an eco-forecast, is it worth considering different initial conditions, trajectories, or forcings based on alternative scenarios? If the modelling is an eco-projection, are the underlying assumptions and future scenarios explicit, and are decisions properly tempered with respect to those modelling specifications? We demonstrate these concepts and methods for conducting eco-projections through examples from invasion biology and climate adaptation.

Conference Paper↗
USGS2016

Spatially explicit modeling in ecology: A review

The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.

Ecosystems↗
USGS2011

Homogenization of large-scale movement models in ecology

A difficulty in using diffusion models to predict large scale animal population dispersal is that individuals move differently based on local information (as opposed to gradients) in differing habitat types. This can be accommodated by using ecological diffusion. However, real environments are often spatially complex, limiting application of a direct approach. Homogenization for partial differential equations has long been applied to Fickian diffusion (in which average individual movement is organized along gradients of habitat and population density). We derive a homogenization procedure for ecological diffusion and apply it to a simple model for chronic wasting disease in mule deer. Homogenization allows us to determine the impact of small scale (10–100 m) habitat variability on large scale (10–100 km) movement. The procedure generates asymptotic equations for solutions on the large scale with parameters defined by small-scale variation. The simplicity of this homogenization procedure is striking when compared to the multi-dimensional homogenization procedure for Fickian diffusion,and the method will be equally straightforward for more complex models.

Bulletin of Mathematical Biology↗
USGS2018

Modeling water quality in the Anthropocene: Directions for the next-generation aquatic ecosystem models

“Everything changes and nothing stands still” (Heraclitus). Here we review three major improvements to freshwater aquatic ecosystem models — and ecological models in general — as water quality scenario analysis tools towards a sustainable future. To tackle the rapid and deeply connected dynamics characteristic of the Anthropocene, we argue for the inclusion of eco-evolutionary, novel ecosystem and social-ecological dynamics. These dynamics arise from adaptive responses in organisms and ecosystems to global environmental change and act at different integration levels and different time scales. We provide reasons and means to incorporate each improvement into aquatic ecosystem models. Throughout this study we refer to Lake Victoria as a microcosm of the evolving novel social-ecological systems of the Anthropocene. The Lake Victoria case clearly shows how interlinked eco-evolutionary, novel ecosystem and social-ecological dynamics are, and demonstrates the need for transdisciplinary research approaches towards global sustainability.

Current Opinion in Environmental Sustainability↗
USGS2014

An ecological response model for the Cache la Poudre River through Fort Collins

The Poudre River Ecological Response Model (ERM) is a collaborative effort initiated by the City of Fort Collins and a team of nine river scientists to provide the City with a tool to improve its understanding of the past, present, and likely future conditions of the Cache la Poudre River ecosystem. The overall ecosystem condition is described through the measurement of key ecological indicators such as shape and character of the stream channel and banks, streamside plant communities and floodplain wetlands, aquatic vegetation and insects, and fishes, both coolwater trout and warmwater native species. The 13- mile-long study area of the Poudre River flows through Fort Collins, Colorado, and is located in an ecological transition zone between the upstream, cold-water, steep-gradient system in the Front Range of the Southern Rocky Mountains and the downstream, warm-water, low-gradient reach in the Colorado high plains. The City wanted to better understand the ecological response of the Poudre River ecosystem to potential changes in stream flow and other physical parameters through the conceptual framework of a multivariable integrated model. This goal was met through the use of a probabilistic model based on Bayesian concepts. This construct allowed the integration of a wide range of data and expert opinion (as informed by local data) to predict potential changes to ecosystem conditions under various flow scenarios. Nine flow scenarios representing past, present, and possible future hydrology were developed as the primary model input. Both reach-scale drivers such as stream channel conditions and pollutant loads, as well as ecological conditions, including species composition, interactions, and habitat requirements influenced model-predicted ecosystem outcomes. Model output consisted of probability distributions for eight ecological indicators collectively representing the physical setting, aquatic life, and riparian habitats of the river ecosystem. We are confident in model predictions related to probable trends, relative magnitude of changes and potential ecosystem responses to changing flow conditions, though data availability and the process of converting diverse data types into a common unit (probabilities) limit precision of individual results. Key findings suggest that: The present ecological function of the Poudre River is altered as a result of more than 150 years of human influences that include highly managed flows, urbanization, gravel mining, channelization and urban and industrial encroachment in the floodplain, underscoring the vulnerable and complex character of the Poudre River; A continuation of today’s flow management will lead to ongoing changes in ecosystem condition, and additional water depletions will compromise ecological conditions; High flows play an essential role in maintaining and improving the aquatic and riparian condition of the river; Adequate flows in base-flow periods are critical to desirable water quality, and thriving fish and insect populations; Improvement of native aquatic life is possible if issues related to channel modifications, siltation, invasive species, and base and high flow conditions are managed properly; The present confined river channel and modified flows has reduced the potential for a keystone and iconic species, plains cottonwood, to be self-sustaining in the study area; The streamside corridor retains the potential to support a functioning riparian forest that provides important ecological services if periodic floodplain inundation occurs. Environmental flows that combine stable and adequate flows in base-flow periods with occasional rejuvenating high flows that meet target levels defined in this study are likely improve all biological indicators across the system. ERM test scenarios that include both stable base flows and rejuvenating high flows indicate that substantial improvements in the river ecosystem can be achieved with improved management of flow volumes similar to those observed in the river during the last half century of intensive water development. These results underscore the possibility of improving the river ecosystem through active management while still maintaining the Poudre’s diverse economic benefits and role as a working river. The ERM was designed to represent the multi-dimensional ecological character of the contemporary urban Poudre River. It provides a scientific foundation that can serve as a decision support tool and foster a more informed community discussion about the future of the river as it provides a better understanding of the likely response of the Poudre River ecosystem to environmental flow management and other stewardship activities. In particular, model results can assist managers in developing specific management actions to achieve desirable goals for key indicators of river health.

Colorado↗
USGS2015

A comparison of hydrologic models for ecological flows and water availability

Robust hydrologic models are needed to help manage water resources for healthy aquatic ecosystems and reliable water supplies for people, but there is a lack of comprehensive model comparison studies that quantify differences in streamflow predictions among model applications developed to answer management questions. We assessed differences in daily streamflow predictions by four fine-scale models and two regional-scale monthly time step models by comparing model fit statistics and bias in ecologically relevant flow statistics (ERFSs) at five sites in the Southeastern USA. Models were calibrated to different extents, including uncalibrated (level A), calibrated to a downstream site (level B), calibrated specifically for the site (level C) and calibrated for the site with adjusted precipitation and temperature inputs (level D). All models generally captured the magnitude and variability of observed streamflows at the five study sites, and increasing level of model calibration generally improved performance. All models had at least 1 of 14 ERFSs falling outside a +/−30% range of hydrologic uncertainty at every site, and ERFSs related to low flows were frequently over-predicted. Our results do not indicate that any specific hydrologic model is superior to the others evaluated at all sites and for all measures of model performance. Instead, we provide evidence that (1) model performance is as likely to be related to calibration strategy as it is to model structure and (2) simple, regional-scale models have comparable performance to the more complex, fine-scale models at a monthly time step.

Ecohydrology↗
USGS2014

Identifying objectives and alternative actions to frame a decision problem.

In this chapter, we discuss the role of objectives and alternative actions in framing a natural resource management decision problem, with particular attention to thresholds. We outline a number of considerations in developing objectives and measurable attributes, including when utility thresholds may be needed to express the decision-makers’ values.We also discuss the development of a set of alternative actions, and how these might give rise to decision thresholds, particularly when the predictive models contain ecological thresholds. Framing of a decision problem plays a central role in decision analysis because it helps determine the needs for a predictive ecological model, the type of solution method required, and the value and structure of a monitoring system.

Book chapter↗
USGS2014

Distribution of soil organic carbon in the conterminous United States

The U.S. Soil Survey Geographic (SSURGO) database provides detailed soil mapping for most of the conterminous United States (CONUS). These data have been used to formulate estimates of soil carbon stocks, and have been useful for environmental models, including plant productivity models, hydrologic models, and ecological models for studies of greenhouse gas exchange. The data were compiled by the U.S. Department of Agriculture Natural Resources Conservation Service (NRCS) from 1:24,000-scale or 1:12,000-scale maps. It was found that the total soil organic carbon stock in CONUS to 1 m depth is 57 Pg C and for the total profile is 73 Pg C, as estimated from SSURGO with data gaps filled from the 1:250,000-scale Digital General Soil Map. We explore the non-linear distribution of soil carbon on the landscape and with depth in the soil, and the implications for sampling strategies that result from the observed soil carbon variability.

Book chapter↗
USGS2023

Informing management of recovering predators and their prey with ecological diffusion models

The reintroduction and recovery of predators can be ecologically beneficial as well as socially and economically controversial. However, the growth and expansion of predator populations, and thus their ecological, social, and economic impacts, are not static but rather they vary in space and time. We propose a spatiotemporal statistical modeling framework based on ecological diffusion to better inform the ecology and management of recovering predators and their prey. We demonstrate its utility by applying it to a recovering sea otter ( Enhydra lutris ) population in Southeast Alaska, where sea otters were reintroduced in the late 1960s and have exhibited unprecedented population growth. Estimated parameters yield inferences about movement and population ecology, and our approach provides useful derived quantities, such as local abundance and carrying capacity as well as a quantity we term the equilibrium differential. We used our model to examine how density dependence and carrying capacity of sea otters vary spatially across a region. The diffusion modeling approach we present can be generalized for use in other instances of (re)colonization across taxa to inform management and conservation efforts.

Alaska↗
USGS2010

On the specification of structural equation models for ecological systems

The use of structural equation modeling (SEM) is often motivated by its utility for investigating complex networks of relationships, but also because of its promise as a means of representing theoretical concepts using latent variables. In this paper, we discuss characteristics of ecological theory and some of the challenges for proper specification of theoretical ideas in structural equation models (SE models). In our presentation, we describe some of the requirements for classical latent variable models in which observed variables (indicators) are interpreted as the effects of underlying causes. We also describe alternative model specifications in which indicators are interpreted as having causal influences on the theoretical concepts. We suggest that this latter nonclassical specification (which involves another variable type—the composite) will often be appropriate for ecological studies because of the multifaceted nature of our theoretical concepts. In this paper, we employ the use of meta‐models to aid the translation of theory into SE models and also to facilitate our ability to relate results back to our theories. We demonstrate our approach by showing how a synthetic theory of grassland biodiversity can be evaluated using SEM and data from a coastal grassland. In this example, the theory focuses on the responses of species richness to abiotic stress and disturbance, both directly and through intervening effects on community biomass. Models examined include both those based on classical forms (where each concept is represented using a single latent variable) and also ones in which the concepts are recognized to be multifaceted and modeled as such. To address the challenge of matching SE models with the conceptual level of our theory, two approaches are illustrated, compositing and aggregation. Both approaches are shown to have merits, with the former being preferable for cases where the multiple facets of a concept have widely differing effects in the system and the latter being preferable where facets act together consistently when influencing other parts of the system. Because ecological theory characteristically deals with concepts that are multifaceted, we expect the methods presented in this paper will be useful for ecologists wishing to use SEM.

Ecological Monographs↗
USGS2005

Individual-based modeling of ecological and evolutionary processes

Individual-based models (IBMs) allow the explicit inclusion of individual variation in greater detail than do classical differential-equation and difference-equation models. Inclusion of such variation is important for continued progress in ecological and evolutionary theory. We provide a conceptual basis for IBMs by describing five major types of individual variation in IBMs: spatial, ontogenetic, phenotypic, cognitive, and genetic. IBMs are now used in almost all subfields of ecology and evolutionary biology. We map those subfields and look more closely at selected key papers on fish recruitment, forest dynamics, sympatric speciation, metapopulation dynamics, maintenance of diversity, and species conservation. Theorists are currently divided on whether IBMs represent only a practical tool for extending classical theory to more complex situations, or whether individual-based theory represents a radically new research program. We feel that the tension between these two poles of thinking can be a source of creativity in ecology and evolutionary theory.

Annual Review of Ecology, Evolution, and Systemati↗