Search USGS⌕ Search

SEARCH · Search USGS

Results for “Ecological Modeling”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25Linked to original sources
USGS2020

Beyond sticks and stones: Integrating physical and ecological conditions into watershed restoration assessments using a food web modeling approach

Watershed assessments have become common for prioritizing restoration in river networks. These assessments primarily focus on geomorphic conditions of rivers but less frequently incorporate non-geomorphic abiotic factors such as water chemistry and temperature, and biotic factors such as the structure of food webs. Using a dynamic food web model that integrates physical and ecological environmental conditions of rivers, we simulated how juvenile salmon ( Oncorhynchus spp.) biomass responded to restoration at twelve sites distributed across the Methow River (Washington, USA), ranging from headwater tributaries to mainstem reaches. We explored responses to three common river restoration strategies: (1) physical habitat modification, (2) nutrient supplementation, and (3) increased riparian vegetation cover. We also simulated how different food web configurations that exist in salmon-bearing streams, such as the presence of ‘non-target’ fishes and ‘armored’ predation resistant invertebrates, could mediate restoration outcomes. Some locations in the river network experienced relatively large increases in modeled fish biomass with restoration, whereas other locations were almost entirely unresponsive. Spatial variation in restoration outcomes was primarily controlled by non-geomorphic environmental conditions, such as nutrient availability, water temperature, and stream canopy cover. Restoration responses also varied significantly with different food web configurations, suggesting that as the structure of food webs varies across river networks, so too could the outcome of restoration. These findings illustrate that ecological responses to restoration may exhibit substantial spatial variation within river networks, resulting from heterogeneity in environmental conditions that are commonly overlooked—but which can and should be considered—in restoration planning and prioritization.

Washington↗
USGS2022

Spatial dynamic N-mixture models with interspecific interactions

Interspecific interactions and movement are key factors that drive the coexistence of metapopulations in heterogenous landscapes. Yet, it is challenging to understand these factors because separating movement from local population processes relied on capture-based data that are difficult to collect. Recent development of spatial dynamic N-mixture models (SDNMs) made it possible to draw inference on local population growth and movement using count data of unmarked populations. However, no SDNMs have been developed to account for interspecific interactions and double counting observation errors. In this study we further developed SDNMs to account for interspecific interactions and both false negative and double counting observation errors. We conducted simulation studies to evaluate the inferential performance of these models under different ecological systems (competition, predator-prey), observation processes (binomial, Poisson), and sampling situations including the number of surveyed sites, detection, and the adoption of robust sampling design. We then illustrated the applications of these models with two case studies, one representing a competition system (mallard Anas platyrhynchos , northern pintail Anas acuta ) with binomial observations and the other representing a predator-prey system (bobcat Lynx rufus , wild turkey Meleagris gallopavo ) with Poisson observations. The results of the simulation studies showed that the models provide unbiased parameter estimates regardless of the ecological system, observation process, and sampling situation. Case studies further demonstrated the capabilities of these models in revealing important ecological processes. More specifically, the first case study revealed a negative effect of the superior competitor, Mallard, on the population growth of the inferior competitor, pintail as well as different movement patterns between these species, while the second case study reveal a top-down effect of bobcat abundance on wild turkey population growth and their differential habitat preferences. The models developed in this study could be used by themselves on count data or serve as population sub-models in integrated modeling frameworks to provide insights about metapopulation dynamics of interacting species in heterogeneous landscapes. The flexible structures of these models made them highly adaptive and relevant in population and community ecology.

Methods in Ecology and Evolution↗
USGS2009

Modeling Flood Plain Hydrology and Forest Productivity of Congaree Swamp, South Carolina

An ecological field and modeling study was conducted to examine the flood relations of backswamp forests and park trails of the flood plain portion of Congaree National Park, S.C. Continuous water level gages were distributed across the length and width of the flood plain portion - referred to as 'Congaree Swamp' - to facilitate understanding of the lag and peak flood coupling with stage of the Congaree River. A severe and prolonged drought at study start in 2001 extended into late 2002 before backswamp zones circulated floodwaters. Water levels were monitored at 10 gaging stations over a 4-year period from 2002 to 2006. Historical water level stage and discharge data from the Congaree River were digitized from published sources and U.S. Geological Survey (USGS) archives to obtain long-term daily averages for an upstream gage at Columbia, S.C., dating back to 1892. Elevation of ground surface was surveyed for all park trails, water level gages, and additional circuits of roads and boundaries. Rectified elevation data were interpolated into a digital elevation model of the park trail system. Regression models were applied to establish time lags and stage relations between gages at Columbia, S.C., and gages in the upper, middle, and lower reaches of the river and backswamp within the park. Flood relations among backswamp gages exhibited different retention and recession behavior between flood plain reaches with greater hydroperiod in the lower reach than those in the upper and middle reaches of the Congaree Swamp. A flood plain inundation model was developed from gage relations to predict critical river stages and potential inundation of hiking trails on a real-time basis and to forecast the 24-hour flood In addition, tree-ring analysis was used to evaluate the effects of flood events and flooding history on forest resources at Congaree National Park. Tree cores were collected from populations of loblolly pine (Pinus taeda), baldcypress (Taxodium distichum), water tupelo (Nyssa aquatica), green ash (Fraxinus pennslyvanica), laurel oak (Quercus laurifolia), swamp chestnut oak (Quercus michauxii), and sycamore (Plantanus occidentalis) within Congaree Swamp in highand low-elevation sites characteristic of shorter and longer flood duration and related to upriver flood controls and dam operation. Ring counts and dating indicated that all loblolly pine trees and nearly all baldcypress collections in this study are postsettlement recruits and old-growth cohorts, dating from 100 to 300 years in age. Most hardwood species and trees cored for age analysis were less than 100 years old, demonstrating robust growth and high site quality. Growth chronologies of loblolly pine and baldcypress exhibited positive and negative inflections over the last century that corresponded with climate history and residual effects of Hurricane Hugo in 1989. Stemwood production on average was less for trees and species on sites with longer flood retention and hydroperiod affected more by groundwater seepage and site elevation than river floods. Water level data provided evidence that stream regulation and operations of the Saluda Dam (post-1934) have actually increased the average daily water stage in the Congaree River. There was no difference in tree growth response by species or hydrogeomorphic setting to predam and postdam flood conditions and river stage. Climate-growth analysis showed that long-term growth variation is controlled more by spring/ summer temperatures in loblolly pine and by spring/summer precipitation in baldcypress than flooding history.

South Carolina↗
USGS2016

Effects of model complexity and priors on estimation using sequential importance sampling/resampling for species conservation

We examined the effects of complexity and priors on the accuracy of models used to estimate ecological and observational processes, and to make predictions regarding population size and structure. State-space models are useful for estimating complex, unobservable population processes and making predictions about future populations based on limited data. To better understand the utility of state space models in evaluating population dynamics, we used them in a Bayesian framework and compared the accuracy of models with differing complexity, with and without informative priors using sequential importance sampling/resampling (SISR). Count data were simulated for 25 years using known parameters and observation process for each model. We used kernel smoothing to reduce the effect of particle depletion, which is common when estimating both states and parameters with SISR. Models using informative priors estimated parameter values and population size with greater accuracy than their non-informative counterparts. While the estimates of population size and trend did not suffer greatly in models using non-informative priors, the algorithm was unable to accurately estimate demographic parameters. This model framework provides reasonable estimates of population size when little to no information is available; however, when information on some vital rates is available, SISR can be used to obtain more precise estimates of population size and process. Incorporating model complexity such as that required by structured populations with stage-specific vital rates affects precision and accuracy when estimating latent population variables and predicting population dynamics. These results are important to consider when designing monitoring programs and conservation efforts requiring management of specific population segments.

Ecological Modelling↗
USGS2018

Increasing connectivity between metapopulation ecology and landscape ecology

Metapopulation ecology and landscape ecology aim to understand how spatial structure influences ecological processes, yet these disciplines address the problem using fundamentally different modeling approaches. Metapopulation models describe how the spatial distribution of patches affects colonization and extinction, but often do not account for the heterogeneity in the landscape between patches. Models in landscape ecology use detailed descriptions of landscape structure, but often without considering colonization and extinction dynamics. We present a novel spatially explicit modeling framework for narrowing the divide between these disciplines to advance understanding of the effects of landscape structure on metapopulation dynamics. Unlike previous efforts, this framework allows for statistical inference on landscape resistance to colonization using empirical data. We demonstrate the approach using 11 yr of data on a threatened amphibian in a desert ecosystem. Occupancy data for Lithobates chiricahuensis (Chiricahua leopard frog) were collected on the Buenos Aires National Wildlife Refuge (BANWR), Arizona, USA from 2007 to 2017 following a reintroduction in 2003. Results indicated that colonization dynamics were influenced by both patch characteristics and landscape structure. Landscape resistance increased with increasing elevation and distance to the nearest streambed. Colonization rate was also influenced by patch quality, with semi‐permanent and permanent ponds contributing substantially more to the colonization of neighboring ponds relative to intermittent ponds. Ponds that only hold water intermittently also had the highest extinction rate. Our modeling framework can be widely applied to understand metapopulation dynamics in complex landscapes, particularly in systems in which the environment between habitat patches influences the colonization process

Ecology↗
USGS2010

Ecological hierarchies and self-organisation - Pattern analysis, modelling and process integration across scales

A continuing discussion in applied and theoretical ecology focuses on the relationship of different organisational levels and on how ecological systems interact across scales. We address principal approaches to cope with complex across-level issues in ecology by applying elements of hierarchy theory and the theory of complex adaptive systems. A top-down approach, often characterised by the use of statistical techniques, can be applied to analyse large-scale dynamics and identify constraints exerted on lower levels. Current developments are illustrated with examples from the analysis of within-community spatial patterns and large-scale vegetation patterns. A bottom-up approach allows one to elucidate how interactions of individuals shape dynamics at higher levels in a self-organisation process; e.g., population development and community composition. This may be facilitated by various modelling tools, which provide the distinction between focal levels and resulting properties. For instance, resilience in grassland communities has been analysed with a cellular automaton approach, and the driving forces in rodent population oscillations have been identified with an agent-based model. Both modelling tools illustrate the principles of analysing higher level processes by representing the interactions of basic components.The focus of most ecological investigations on either top-down or bottom-up approaches may not be appropriate, if strong cross-scale relationships predominate. Here, we propose an 'across-scale-approach', closely interweaving the inherent potentials of both approaches. This combination of analytical and synthesising approaches will enable ecologists to establish a more coherent access to cross-level interactions in ecological systems. ?? 2010 Gesellschaft f??r ??kologie.

Basic and Applied Ecology↗
USGS2015

Dynamic reserve design in the face of climate change and urbanization

Reserve design is a process that must address many ecological, social, and political factors to successfully identify parcels of land in need of protection to sustain wildlife populations and other natural resources. Making land acquisition choices for a large, terrestrial protected area is difficult because it occurs over a long timeframe and may involve consideration future conditions such as climate and urbanization changes. Decision makers need to consider factors including: order of parcel purchasing given budget constraints, future uncertainty, potential future landscape‐scale changes from urbanization and climate. In central Florida, two new refuges and the expansion of a third refuge are in various stages of USFWS planning. The Everglades Headwaters National Wildlife Refuge (EHNWR) has recently been established, is at the top of the Presidential Administration’s priority conservation areas, and is cited by the Secretary of DOI routinely in the context of conservation. The new refuges were strategically located for both for species adaptation from climate change impacts as well as currently being host to a number of important threatened and endangered species and habitats. We plan to combine a structured decision making framework, optimal solution theory, and output from ecological and sociological models (these modeling efforts were previously funded by DOI partners) that incorporate climate change to provide guidance for EHNWR reserve design. Utilizing a SDM approach and optimal solution theory, decision support tools will be developed that will incorporate stakeholder and agency objectives into targeting conservation lands both through fee simple purchase and other incentives such as easements based on ecological and socioeconomic modeling outputs driven by climate change.

Report↗
USGS2021

Hawai‘i forest review: Synthesizing the ecology, evolution, and conservation of a model system

As the most remote archipelago in the world, the Hawaiian Islands are home to a highly endemic and disharmonic biota that has fascinated biologists for centuries. Forests are the dominant terrestrial biome in Hawai‘i, spanning complex, heterogeneous climates across substrates that vary tremendously in age, soil structure, and nutrient availability. Species richness is low in Hawaiian forests compared to other tropical forests, as a consequence of dispersal limitation from continents and adaptive radiations in only some lineages, and forests are dominated by the widespread Metrosideros species complex. Low species richness provides a relatively tractable model system for studies of community assembly, local adaptation, and species interactions. Moreover, Hawaiian forests provide insights into predicted patterns of evolution on islands, revealing that while some evidence supports “island syndromes,” there are exceptions to them all. For example, Hawaiian plants are not as a whole less defended against herbivores, less dispersible, more conservative in resource use, or more slow-growing than their continental relatives. Clearly, more work is needed to understand the drivers, sources, and constraints on phenotypic variation among Hawaiian species, including both widespread and rare species, and to understand the role of this variation for ecological and evolutionary processes, which will further contribute to conservation of this unique biota. Today, Hawaiian forests are among the most threatened globally. Resource management failures – the proliferation of non-native species in particular – have led to devastating declines in native taxa and resulted in dominance by novel species assemblages. Conservation and restoration of Hawaiian forests now rely on managing threats including climate change, ongoing species introductions, novel pathogens, lost mutualists, and altered ecosystem dynamics through the use of diverse tools and strategies grounded in basic ecological, evolutionary, and biocultural principles. The future of Hawaiian forests thus depends on the synthesis of ecological and evolutionary research, which will continue to inform future conservation and restoration practices.

Hawaii↗
USGS2019

Conceptualizing ecological responses to dam removal: If you remove it, what's to come?

One of the desired outcomes of dam decommissioning and removal is the recovery of aquatic and riparian ecosystems. To investigate this common objective, we synthesized information from empirical studies and ecological theory into conceptual models that depict key physical and biological links driving ecological responses to removing dams. We define models for three distinct spatial domains: upstream of the former reservoir, within the reservoir, and downstream of the removed dam. Emerging from these models are response trajectories that clarify potential pathways of ecological transitions in each domain. We illustrate that the responses are controlled by multiple causal pathways and feedback loops among physical and biological components of the ecosystem, creating recovery trajectories that are dynamic and nonlinear. In most cases, short-term effects are typically followed by longer-term responses that bring ecosystems to new and frequently predictable ecological condition, which may or may not be similar to what existed prior to impoundment.

BioScience↗
USGS2018

The relative importance of intrinsic and extrinsic drivers to population growth vary among local populations of Greater Sage-Grouse: An integrated population modeling approach

Consideration of ecological scale is fundamental to understanding and managing avian population growth and decline. Empirically driven models for population dynamics and demographic processes across multiple spatial scales can be powerful tools to help guide conservation actions. Integrated population models (IPMs) provide a framework for better parameter estimation by unifying multiple sources of data (e.g., count and demographic data). Hierarchical structure within such models that include random effects allow for varying degrees of data sharing across different spatiotemporal scales. We developed an IPM to investigate Greater Sage-Grouse ( Centrocercus urophasianus ) on the border of California and Nevada, known as the Bi-State Distinct Population Segment. Our analysis integrated 13 years of lek count data ( n > 2,000) and intensive telemetry (VHF and GPS; n > 350 individuals) data across 6 subpopulations. Specifically, we identified the most parsimonious models among varying random effects and density-dependent terms for each population vital rate (e.g., nest survival). Using a joint likelihood process, we integrated the lek count data with the demographic models to estimate apparent abundance and refine vital rate parameter estimates. To investigate effects of climatic conditions, we extended the model to fit a precipitation covariate for instantaneous rate of change ( r ). At a metapopulation extent (i.e. Bi-State), annual population rate of change λ ( e r ) did not favor an overall increasing or decreasing trend through the time series. However, annual changes in λ were driven by changes in precipitation (one-year lag effect). At subpopulation extents, we identified substantial variation in λ and demographic rates. One subpopulation clearly decoupled from the trend at the metapopulation extent and exhibited relatively high risk of extinction as a result of low egg fertility. These findings can inform localized, targeted management actions for specific areas, and status of the species for the larger Bi-State.

The Auk↗
USGS2013

Evaluating model structure adequacy: The case of the Maggia Valley groundwater system, southern Switzerland

Model adequacy is evaluated with alternative models rated using model selection criteria (AICc, BIC, and KIC) and three other statistics. Model selection criteria are tested with cross-validation experiments and insights for using alternative models to evaluate model structural adequacy are provided. The study is conducted using the computer codes UCODE_2005 and MMA (MultiModel Analysis). One recharge alternative is simulated using the TOPKAPI hydrological model. The predictions evaluated include eight heads and three flows located where ecological consequences and model precision are of concern. Cross-validation is used to obtain measures of prediction accuracy. Sixty-four models were designed deterministically and differ in representation of river, recharge, bedrock topography, and hydraulic conductivity. Results include: (1) What may seem like inconsequential choices in model construction may be important to predictions. Analysis of predictions from alternative models is advised. (2) None of the model selection criteria consistently identified models with more accurate predictions. This is a disturbing result that suggests to reconsider the utility of model selection criteria, and/or the cross-validation measures used in this work to measure model accuracy. (3) KIC displayed poor performance for the present regression problems; theoretical considerations suggest that difficulties are associated with wide variations in the sensitivity term of KIC resulting from the models being nonlinear and the problems being ill-posed due to parameter correlations and insensitivity. The other criteria performed somewhat better, and similarly to each other. (4) Quantities with high leverage are more difficult to predict. The results are expected to be generally applicable to models of environmental systems.

Water Resources Research↗
USGS2016

Estimating abundance: Chapter 27

This chapter provides a non-technical overview of ‘closed population capture–recapture’ models, a class of well-established models that are widely applied in ecology, such as removal sampling, covariate models, and distance sampling. These methods are regularly adopted for studies of reptiles, in order to estimate abundance from counts of marked individuals while accounting for imperfect detection. Thus, the chapter describes some classic closed population models for estimating abundance, with considerations for some recent extensions that provide a spatial context for the estimation of abundance, and therefore density. Finally, the chapter suggests some software for use in data analysis, such as the Windows-based program MARK, and provides an example of estimating abundance and density of reptiles using an artificial cover object survey of Slow Worms ( Anguis fragilis ).

Book chapter↗
USGS2020

The Everglades Depth Estimation Network (EDEN) surface-water interpolation model, version 3

The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that estimate daily water-level data at ungaged locations, and applications that generate derived hydrologic data across the freshwater part of the Greater Everglades landscape. Version 3 (V3) of the EDEN interpolation surface-water model is the most recent update, replacing the version 2 (V2) model released in 2011. The primary revision for the V3 model is the switch to the R programming language to create a more efficient and portable EDEN code relative to V2, without reliance on proprietary software. Using R, the interpolation script runs over 10 times faster and is more easily updated, for example, to accommodate changes in the gage network or to incorporate R software updates. Additional revisions made for the V3 model include updates to the interpolation model, the gage network, and groundwater-level estimations. The EDEN model domain in the Greater Everglades and Big Cypress National Preserve is divided into subdomains that are based on hydrologic boundaries. In the V3 model, the number of subdomains was increased from five to eight, which allows hydrologic boundaries, such as levees and canals, to be better represented in the interpolation scheme. Five pseudogages were added to constrain the water-level surface at subdomain boundaries. Changes made to the water-level gage network between the implementation of the V2 and V3 models are incorporated, and groundwater-level estimations are added, which are important information for hydrologic and ecological studies. Summary model performance statistics indicate similar accuracy in water-level surfaces generated by the V3 and V2 models, with a root mean square error of 4.78 centimeters for both interpolation models against independent water-level measurements. Providing stability and continuity for the EDEN user community, the V3 model closely replicates the V2 model, with a root mean square difference of 3.87 centimeters for interpolated surfaces from April 1, 2014, to March 31, 2018. The additional groundwater levels provide a realistic estimate of the saturated groundwater surface continuous with the surface-water surface for Water Conservation Areas 2A and 2B from 2000 to 2011. This continuous surface is a more accurate estimation of the spatial distribution of water in the hydrologic system than before, providing needed information for ecological studies in areas where depth to water table affects habitats. Development of the EDEN V3 model advances the tools available to scientists and resource managers for guiding large-scale field operations, describing hydrologic changes, and supporting biological and ecological assessments.

Florida↗
USGS2016

Nonlinear relationships can lead to bias in biomass calculations and drift-foraging models when using summaries of invertebrate drift data

Drift-foraging models offer a mechanistic description of how fish feed in flowing water and the application of drift-foraging bioenergetics models to answer both applied and theoretical questions in aquatic ecology is growing. These models typically include nonlinear descriptions of ecological processes and as a result may be sensitive to how model inputs are summarized because of a mathematical property of nonlinear equations known as Jensen’s inequality. In particular, we show that the way in which continuous size distributions of invertebrate prey are represented within foraging models can lead to biases within the modeling process. We begin by illustrating how different equations common to drift-foraging models are sensitive to invertebrate inputs. We then use two case studies to show how different representations of invertebrate prey can influence predictions of energy intake and lifetime growth. Greater emphasis should be placed on accurate characterizations of invertebrate drift, acknowledging that inferences from drift-foraging models may be influenced by how invertebrate prey are represented.

Environmental Biology of Fishes↗
USGS2024

Rayleigh step-selection functions and connections to continuous-time mechanistic movement models

Background The process known as ecological diffusion emerges from a first principles view of animal movement, but ecological diffusion and other partial differential equation models can be difficult to fit to data. Step-selection functions (SSFs), on the other hand, have emerged as powerful practical tools for ecologists studying the movement and habitat selection of animals. Methods SSFs typically involve comparing resources between a set of used and available points at each step in a sequence of observed positions. We use change of variables to show that ecological diffusion implies certain distributions for available steps that are more flexible than others commonly used. We then demonstrate advantages of these distributions with SSF models fit to data collected for a mountain lion in Colorado, USA. Results We show that connections between ecological diffusion and SSFs imply a Rayleigh step-length distribution and uniform turning angle distribution, which can accommodate data collected at irregular time intervals. The results of fitting an SSF model with these distributions compared to a set of commonly used distributions revealed how precision and inference can vary between the two approaches. Conclusions Our new continuous-time step-length distribution can be integrated into various forms of SSFs, making them applicable to data sets with irregular time intervals between successive animal locations.

Movement Ecology↗
USGS2014

Identifying legal, ecological and governance obstacles and opportunities for adapting to climate change

Current governance of regional scale water management systems in the United States has not placed them on a path toward sustainability, as conflict and gridlock characterize the social arena and ecosystem services continue to erode. Changing climate may continue this trajectory, but it also provides a catalyst for renewal of ecosystems and a window of opportunity for change in institutions. Resilience provides a bridging concept that predicts that change in ecological and social systems is often dramatic, abrupt, and surprising. Adapting to the uncertainty of climate driven change must be done in a manner perceived as legitimate by the participants in a democratic society. Adaptation must begin with the current hierarchical and fragmented social-ecological system as a baseline from which new approaches must be applied. Achieving a level of integration between ecological concepts and governance requires a dialogue across multiple disciplines, including ecologists with expertise in ecological resilience, hydrologists and climate experts, with social scientists and legal scholars. Criteria and models that link ecological dynamics with policies in complex, multi-jurisdictional water basins with adaptive management and governance frameworks may move these social-ecological systems toward greater sustainability.

Sustainability↗