Geology Reports⌕ Search

SEARCH · Geology Reports

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 775 records · Page 43Linked to original sources
USGS2023

Modeled distribution shifts of North American birds over four decades based on suitable climate alone do not predict observed shifts

As climate change alters the global environment, it is critical to understand the relationship between shifting climate suitability and species distributions. Key questions include whether observed changes in population abundance are aligned with the velocity and direction of shifts predicted by climate suitability models and if the responses are consistent among species with similar ecological traits. We examined the direction and velocity of the observed abundance-based distribution centroids compared with the model-predicted bioclimatic distribution centroids of 250 bird species across the United States from 1969 to 2011. We hypothesized that there is a significant positive correlation in both direction and velocity between the observed and the modeled shifts. We then tested five additional hypotheses that predicted differential shifting velocity based on ecological adaptability and climate change exposure. Contrary to our hypotheses, we found large differences between the observed and modeled shifts among all studied bird species and within specific ecological guilds. However, temperate migrants and habitat generalist species tended to have higher velocity of observed shifts than other species. Neotropical migratory and wetland birds also had significantly different observed velocities than their counterparts, which may be due to their climate change exposure. The velocity based on modeled bioclimatic suitability did not exhibit significant differences among most guilds. Boreal forest birds were the only guild with significantly faster modeled-shifts than the other groups, suggesting an elevated conservation risk for high latitude and altitude species. The highly idiosyncratic species responses to climate and the mismatch between shifts in modeled and observed distribution centroids highlight the challenge of predicting species distribution change based solely on climate suitability and the importance of non-climatic factors traits in shaping species distributions.

Science of the Total Environment↗
USGS2022

A conceptual framework to integrate biodiversity, ecosystem function, and ecosystem service models

Global biodiversity and ecosystem service models typically operate independently. Ecosystem service projections thus may be overly optimistic because they do not account for the role of biodiversity in maintaining ecological functions underpinning their provision. We review models used in recent global model intercomparison projects and develop a novel model integration framework to more fully account for the role of biodiversity in ecosystem function, a key gap for linking biodiversity changes to ecosystem services. We propose two model integration pathways. The first uses empirical data on biodiversity-ecosystem function relationships to bridge biodiversity and ecosystem function models and could currently be implemented at the global scale. We also propose a trait-based approach involving greater incorporation of biodiversity into ecosystem function models that can be applied to more systems and taxa than the first pathway. Integrating biodiversity, ecosystem function, and ecosystem service modeling will enhance development of policies to meet global sustainability goals.

BioScience↗
USGS2016

Addressing potential local adaptation in species distribution models: implications for conservation under climate change

Species distribution models (SDMs) have been criticized for involving assumptions that ignore or categorize many ecologically relevant factors such as dispersal ability and biotic interactions. Another potential source of model error is the assumption that species are ecologically uniform in their climatic tolerances across their range. Typically, SDMs to treat a species as a single entity, although populations of many species differ due to local adaptation or other genetic differentiation. Not taking local adaptation into account, may lead to incorrect range prediction and therefore misplaced conservation efforts. A constraint is that we often do not know the degree to which populations are locally adapted, however. Lacking experimental evidence, we still can evaluate niche differentiation within a species' range to promote better conservation decisions. We explore possible conservation implications of making type I or type II errors in this context. For each of two species, we construct three separate MaxEnt models, one considering the species as a single population and two of disjunct populations. PCA analyses and response curves indicate different climate characteristics in the current environments of the populations. Model projections into future climates indicate minimal overlap between areas predicted to be climatically suitable by the whole species versus population-based models. We present a workflow for addressing uncertainty surrounding local adaptation in SDM application and illustrate the value of conducting population-based models to compare with whole-species models. These comparisons might result in more cautious management actions when alternative range outcomes are considered.

Ecological Applications↗
USGS2012

Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k -fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k -fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

Science of the Total Environment↗
USGS2017

Sustainability of utility-scale solar energy – critical ecological concepts

Renewable energy development is an arena where ecological, political, and socioeconomic values collide. Advances in renewable energy will incur steep environmental costs to landscapes in which facilities are constructed and operated. Scientists – including those from academia, industry, and government agencies – have only recently begun to quantify trade-offs in this arena, often using ground-mounted, utility-scale solar energy facilities (USSE, ≥1 megawatt) as a model. Here, we discuss five critical ecological concepts applicable to the development of more sustainable USSE with benefits over fossil-fuel-generated energy: (1) more sustainable USSE development requires careful evaluation of trade-offs between land, energy, and ecology; (2) species responses to habitat modification by USSE vary; (3) cumulative and large-scale ecological impacts are complex and challenging to mitigate; (4) USSE development affects different types of ecosystems and requires customized design and management strategies; and (5) long-term ecological consequences associated with USSE sites must be carefully considered. These critical concepts provide a framework for reducing adverse environmental impacts, informing policy to establish and address conservation priorities, and improving energy production sustainability.

Frontiers in Ecology and the Environment↗
USGS2017

Social-ecological outcomes in recreational fisheries: The interaction of lakeshore development and stocking

Many ecosystems continue to experience rapid transformations due to processes like land use change and resource extraction. A systems approach to maintaining natural resources focuses on how interactions and feedbacks among components of complex social‐ecological systems generate social and ecological outcomes. In recreational fisheries, residential shoreline development and fish stocking are two widespread human behaviors that influence fisheries, yet emergent social‐ecological outcomes from these potentially interacting behaviors remain under explored. We applied a social‐ecological systems framework using a simulation model and empirical data to determine whether lakeshore development is likely to promote stocking through its adverse effects on coarse woody habitat and thereby also on survival of juvenile and adult fish. We demonstrate that high lakeshore development is likely to generate dependency of the ecosystem on the social system, in the form of stocking. Further, lakeshore development can interact with social‐ecological processes to create deficits for state‐level governments, which threatens the ability to fund further ecosystem subsidies. Our results highlight the value of a social‐ecological framework for maintaining ecosystem services like recreational fisheries.

Ecological Applications↗
USGS2003

A Framework for Long-term Ecological Monitoring in Olympic National Park: Prototype for the Coniferous Forest Biome

This report is the result of a five-year collaboration between scientists of the U.S. Geological Survey Forest and Rangeland Ecosystem Science Center, Olympic Field Station, and the natural resources staff of Olympic National Park to develop a comprehensive strategy for monitoring natural resources of Olympic National Park. Olympic National Park is the National Park Serviceʼs prototype monitoring park, representing parks in the coniferous forest biome. Under the umbrella of the National Park Serviceʼs prototype parks program, U.S. Geological Survey and Olympic National Park staffs are obligated to: develop strategies and designs for monitoring the long-term health and integrity of national park ecosystems with a significant coniferous forest component. design exportable monitoring protocols that can be used by other parks within the coniferous forest biome (i.e., parks having similar environments), and create a demonstration area and ʻcenter of excellenceʼ for assisting other parks in developing ecological monitoring programs. Olympic National Park is part of the North Coast and Cascades Network, a network of seven Pacific Northwestern park units created recently by the National Park Serviceʼs Inventory and Monitoring Program to extend the monitoring of ʻvital signsʼ of park health to all National Park Service units. It is our intent and hope that the monitoring strategies and conceptual models described here will meet the overall purpose of the prototype parks monitoring program in proving useful not only to Olympic National Park, but also to parks within the North Coast and Cascades Network and elsewhere. Part I contains the conceptual design and sampling framework for the prototype long-term monitoring program in Olympic National Park. In this section, we explore key elements of monitoring design that help to ensure the spatial, ecological, and temporal integration of monitoring program elements and discuss approaches used to design an ecosystem-based monitoring program. Basic monitoring components include ecosystem drivers, (e.g., climate, atmospheric inputs, human pressures), indicators of ecosystem integrity (e.g., biogeochemical indicators), known threats (e.g., impacts of introduced mountain goats), and focal or ʻkeyʼ species (e.g., rare or listed species, Roosevelt elk). Monitoring system drivers and key indicators of ecosystem integrity provide the long-term baseline needed to judge what constitutes ʻunnaturalʼ variation in park resources and provide the earliest possible warning of unacceptable change. Monitoring effects of known threats and the status of focal species will provide information useful to park managers for dealing with current park issues. In Part I we describe the process of identifying potential indicators of ecological condition and present conceptual models of park ecosystems. In addition we report results from several workshops held in conjunction with Olympic National Park aimed at identifying potential indicators of change in the parkʼs ecosystem. First, we describe the responses of Olympic National Park staff to the generic question, “What is the most important resource to monitor in Olympic National Park and why?” followed by the responses from resource and land managers from areas adjoining the park. We also catalogue the responses of various expert groups that we asked to help identify the most appropriate system drivers and indicators of change in the Olympic National Park ecosystems. Results of the workshops provided the justification for selecting basic indicators of ecosystem integrity, effects of current threats to park resources, and focal resources of parks to detect both the currently evident and unforeseeable changes in park resources. We conclude Part I by exploring several generic statistical issues relevant to monitoring natural resources in Olympic National Park. Specifically we discuss trade-offs associated with sampling extensively versus sampling intensively in smaller geographic regions and describe a conceptual framework to guide development of a generic sampling frame for monitoring. We recommend partitioning Olympic National Park into three zones of decreasing accessibility to maximize monitoring efficiency. We present examples of how the generic sampling frame could be used to help ensure spatial integration of individual monitoring projects. Part II of the report is a record of the potential monitoring questions and indicators identified to date in our workshops. The presentation is organized according to the major system drivers, components, and processes identified in the intermediate-level working model of the Olympic National Park ecosystem. For each component of the park system, we develop the need and justification for monitoring, articulate park management issues, and describe key resources and ecosystem functions. We also present a pictorial conceptual model of each ecological subsystem, identify monitoring questions, and list potential indicators for each monitoring question. We conclude each section by identifying linkages of indicators to other ecological subsystems in our general ecosystem model, spatial and temporal contexts for monitoring (where and how often to monitor), and research and development needs. Part II represents the most current detailed listing of potential indicators—the material for subsequent discussions of monitoring priorities and selection of indicators for protocol development. Collectively, the sections of this report contain a comprehensive list of the important monitoring questions and potential indicators as well as recommendations for designing an integrated monitoring program. In Part I, Chapter 6 we provide recommendations on how to proceed with the important next steps in the design process: establishing priorities among the many possible monitoring questions and indicators, and beginning to research and design effective long-term monitoring protocols.

Information and Technology Report↗
USGS2010

Occurrence and distribution of Indian primates

Global and regional species conservation efforts are hindered by poor distribution data and range maps. Many Indian primates face extinction, but assessments of population status are hindered by lack of reliable distribution data. We estimated the current occurrence and distribution of 15 Indian primates by applying occupancy models to field data from a country-wide survey of local experts. We modeled species occurrence in relation to ecological and social covariates (protected areas, landscape characteristics, and human influences), which we believe are critical to determining species occurrence in India. We found evidence that protected areas positively influence occurrence of seven species and for some species are their only refuge. We found evergreen forests to be more critical for some primates along with temperate and deciduous forests. Elevation negatively influenced occurrence of three species. Lower human population density was positively associated with occurrence of five species, and higher cultural tolerance was positively associated with occurrence of three species. We find that 11 primates occupy less than 15% of the total land area of India. Vulnerable primates with restricted ranges are Golden langur, Arunachal macaque, Pig-tailed macaque, stump-tailed macaque, Phayre's leaf monkey, Nilgiri langur and Lion-tailed macaque. Only Hanuman langur and rhesus macaque are widely distributed. We find occupancy modeling to be useful in determining species ranges, and in agreement with current species ranking and IUCN status. In landscapes where monitoring efforts require optimizing cost, effort and time, we used ecological and social covariates to reliably estimate species occurrence and focus species conservation efforts. ?? Elsevier Ltd.

Biological Conservation↗
USGS2006

Model weights and the foundations of multimodel inference

Statistical thinking in wildlife biology and ecology has been profoundly influenced by the introduction of AIC (Akaike?s information criterion) as a tool for model selection and as a basis for model averaging. In this paper, we advocate the Bayesian paradigm as a broader framework for multimodel inference, one in which model averaging and model selection are naturally linked, and in which the performance of AIC-based tools is naturally evaluated. Prior model weights implicitly associated with the use of AIC are seen to highly favor complex models: in some cases, all but the most highly parameterized models in the model set are virtually ignored a priori. We suggest the usefulness of the weighted BIC (Bayesian information criterion) as a computationally simple alternative to AIC, based on explicit selection of prior model probabilities rather than acceptance of default priors associated with AIC. We note, however, that both procedures are only approximate to the use of exact Bayes factors. We discuss and illustrate technical difficulties associated with Bayes factors, and suggest approaches to avoiding these difficulties in the context of model selection for a logistic regression. Our example highlights the predisposition of AIC weighting to favor complex models and suggests a need for caution in using the BIC for computing approximate posterior model weights.

Ecology↗
USGS2014

GIS-based rapid-assessment of bighead carp Hypophthalmichthys nobilis (Richardson, 1845) suitability in reservoirs

Broad-scale niche models are good for examining the potential for invasive species occurrences, but can fall short in providing managers with site-specific locations for monitoring. Using Oklahoma as an example, where invasive bighead carp (Hypophthalmichthys nobilis) are established in certain reservoirs, but predicted to be widely distributed based on broad-scale niche models, we cast bighead carp reproductive ecology in a site-specific geospatial framework to determine their potential establishment in additional reservoirs. Because bighead carp require large, long free-flowing rivers with suitable hydrology for reproduction but can persist in reservoirs, we considered reservoir tributaries with mean annual daily discharge ≥8.5 cubic meters per second (m3 /s) and quantified the length of their unimpeded portions. In contrast to published broad-scale niche models that identified nearly the entire state as susceptible to invasion, our site-specific models showed that few reservoirs in Oklahoma (N = 9) were suitable for bighead carp establishment. Moreover, this method was rapid and identified sites that could be prioritized for increased study or scrutiny. Our results highlight the importance of considering the environmental characteristics of individual sites, which is often the level at which management efforts are implemented when assessing susceptibility to invasion.

Oklahoma↗
USGS2018

Consequences of gas flux model choice on the interpretation of metabolic balance across 15 lakes

Ecosystem metabolism and the contribution of carbon dioxide from lakes to the atmosphere can be estimated from free-water gas measurements through the use of mass balance models, which rely on a gas transfer coefficient ( k ) to model gas exchange with the atmosphere. Theoretical and empirically based models of k range in complexity from wind-driven power functions to complex surface renewal models; however, model choice is rarely considered in most studies of lake metabolism. This study used high-frequency data from 15 lakes provided by the Global Lake Ecological Observatory Network (GLEON) to study how model choice of k influenced estimates of lake metabolism and gas exchange with the atmosphere. We tested 6 models of k on lakes chosen to span broad gradients in surface area and trophic states; a metabolism model was then fit to all 6 outputs of k data. We found that hourly values for k were substantially different between models and, at an annual scale, resulted in significantly different estimates of lake metabolism and gas exchange with the atmosphere.

Inland Waters↗
USGS2015

Taking a systems approach to ecological systems

Increasingly, there is interest in a systems-level understanding of ecological problems, which requires the evaluation of more complex, causal hypotheses. In this issue of the Journal of Vegetation Science, Soliveres et al. use structural equation modeling to test a causal network hypothesis about how tree canopies affect understorey communities. Historical analysis suggests structural equation modeling has been under-utilized in ecology.

Journal of Vegetation Science↗
USGS2002

Narrowing historical uncertainty: probabilistic classification of ambiguously identified tree species in historical forest survey data

Historical data have increasingly become appreciated for insight into the past conditions of ecosystems. Uses of such data include assessing the extent of ecosystem change; deriving ecological baselines for management, restoration, and modeling; and assessing the importance of past conditions on the composition and function of current systems. One historical data set of this type is the Public Land Survey (PLS) of the United States General Land Office, which contains data on multiple tree species, sizes, and distances recorded at each survey point, located at half-mile (0.8 km) intervals on a 1-mi (1.6 km) grid. This survey method was begun in the 1790s on US federal lands extending westward from Ohio. Thus, the data have the potential of providing a view of much of the US landscape from the mid-1800s, and they have been used extensively for this purpose. However, historical data sources, such as those describing the species composition of forests, can often be limited in the detail recorded and the reliability of the data, since the information was often not originally recorded for ecological purposes. Forest trees are sometimes recorded ambiguously, using generic or obscure common names. For the PLS data of northern Wisconsin, USA, we developed a method to classify ambiguously identified tree species using logistic regression analysis, using data on trees that were clearly identified to species and a set of independent predictor variables to build the models. The models were first created on partial data sets for each species and then tested for fit against the remaining data. Validations were conducted using repeated, random subsets of the data. Model prediction accuracy ranged from 81% to 96% in differentiating congeneric species among oak, pine, ash, maple, birch, and elm. Major predictor variables were tree size, associated species, landscape classes indicative of soil type, and spatial location within the study region. Results help to clarify ambiguities formerly present in maps of historic ecosystems for the region and can be applied to PLS datasets elsewhere, as well as other sources of ambiguous historical data. Mapping the newly classified data with ecological land units provides additional information on the distribution, abundance, and associations of tree species, as well as their relationships to environmental gradients before the industrial period, and clarifies the identities of species formerly mapped only to genus. We offer some caveats on the appropriate use of data derived in this way, as well as describing their potential.

Ecosystems↗
USGS2017

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis , and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua . Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea . We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

Mojave Desert↗
USGS2020

Use of multiple temperature logger models can alter conclusions

Remote temperature loggers are often used to measure water temperatures for ecological studies and by regulatory agencies to determine whether water quality standards are being maintained. Equipment specifications are often given a cursory review in the methods; however, the effect of temperature logger model is rarely addressed in the discussion. In a laboratory environment, we compared measurements from three models of temperature loggers at 5 to 40 °C to better understand the utility of these devices. Mean water temperatures recorded by logger models differed statistically even for those with similar accuracy specifications, but were still within manufacturer accuracy specifications. Maximum mean temperature difference between models was 0.4 °C which could have regulatory and ecological implications, such as when a 0.3 °C temperature change triggers a water quality violation or increases species mortality rates. Additionally, precision should be reported as the overall precision (including a consideration of significant digits) for combined model types which in our experiment was 0.7 °C, not the ≤0.4 °C for individual models. Our results affirm that analyzing data collected by different logger models can result in potentially erroneous conclusions when <1 °C difference has regulatory compliance or ecological implications and that combining data from multiple logger models can reduce the overall precision of results.

Water↗
USGS2013

Circuit theory and model-based inference for landscape connectivity

Circuit theory has seen extensive recent use in the field of ecology, where it is often applied to study functional connectivity. The landscape is typically represented by a network of nodes and resistors, with the resistance between nodes a function of landscape characteristics. The effective distance between two locations on a landscape is represented by the resistance distance between the nodes in the network. Circuit theory has been applied to many other scientific fields for exploratory analyses, but parametric models for circuits are not common in the scientific literature. To model circuits explicitly, we demonstrate a link between Gaussian Markov random fields and contemporary circuit theory using a covariance structure that induces the necessary resistance distance. This provides a parametric model for second-order observations from such a system. In the landscape ecology setting, the proposed model provides a simple framework where inference can be obtained for effects that landscape features have on functional connectivity. We illustrate the approach through a landscape genetics study linking gene flow in alpine chamois (Rupicapra rupicapra) to the underlying landscape.

Journal of the American Statistical Association↗
USGS2009

Sensitivity of system stability to model structure

A community is stable, and resilient, if the levels of all community variables can return to the original steady state following a perturbation. The stability properties of a community depend on its structure, which is the network of direct effects (interactions) among the variables within the community. These direct effects form feedback cycles (loops) that determine community stability. Although feedback cycles have an intuitive interpretation, identifying how they form the feedback properties of a particular community can be intractable. Furthermore, determining the role that any specific direct effect plays in the stability of a system is even more daunting. Such information, however, would identify important direct effects for targeted experimental and management manipulation even in complex communities for which quantitative information is lacking. We therefore provide a method that determines the sensitivity of community stability to model structure, and identifies the relative role of particular direct effects, indirect effects, and feedback cycles in determining stability. Structural sensitivities summarize the degree to which each direct effect contributes to stabilizing feedback or destabilizing feedback or both. Structural sensitivities prove useful in identifying ecologically important feedback cycles within the community structure and for detecting direct effects that have strong, or weak, influences on community stability. The approach may guide the development of management intervention and research design. We demonstrate its value with two theoretical models and two empirical examples of different levels of complexity. ?? 2009 Elsevier B.V. All rights reserved.

Ecological Modelling↗
USGS2022

Exploring metapopulation-scale suppression alternatives for a global invader in a river network experiencing climate change

Invasive species can dramatically alter ecosystems, but eradication is difficult, and suppression is expensive once they are established. Uncertainties in the potential for expansion and impacts by an invader can lead to delayed and inadequate suppression, allowing for establishment. Metapopulation viability models can aid in planning strategies to improve responses to invaders and lessen invasive species’ impacts, which may be particularly important under climate change. We used a spatially explicit metapopulation viability model to explore suppression strategies for ecologically damaging invasive brown trout ( Salmo trutta ), established in the Colorado River and a tributary in Grand Canyon National Park. Our goals were to estimate the effectiveness of strategies targeting different life stages and subpopulations within a metapopulation; quantify the effectiveness of a rapid response to a new invasion relative to delaying action until establishment; and estimate whether future hydrology and temperature regimes related to climate change and reservoir management affect metapopulation viability and alter the optimal management response. Our models included scenarios targeting different life stages with spatially varying intensities of electrofishing, redd destruction, incentivized angler harvest, piscicides, and a weir. Quasi-extinction (QE) was obtainable only with metapopulation-wide suppression targeting multiple life stages. Brown trout population growth rates were most sensitive to changes in age 0 and large adult mortality. The duration of suppression needed to reach QE for a large established subpopulation was 12 years compared with 4 with a rapid response to a new invasion. Isolated subpopulations were vulnerable to suppression; however, connected tributary subpopulations enhanced metapopulation persistence by serving as climate refuges. Water shortages driving changes in reservoir storage and subsequent warming would cause brown trout declines, but metapopulation QE was achieved only through refocusing and increasing suppression. Our modeling approach improves understanding of invasive brown trout metapopulation dynamics, which could lead to more focused and effective invasive species suppression strategies and, ultimately, maintenance of populations of endemic fishes.

Arizona↗