Geology topics

John M. Tirpak

Publications and source records attributed to John M. Tirpak.

15 recordsLinked to original sources

USGS2025

Decision support tools for brown pelican management in the northern Gulf of America (Gulf of Mexico)

Management plans with clear priorities can help to achieve brown pelican Pelecanus occidentalis conservation objectives in the northern Gulf of America (Gulf of Mexico). Efforts to establish clear priorities can be hindered by information gaps, especially those related to the uncertainty associated with changing conditions that influence brown pelican populations. We addressed these gaps by creating a model that uses island-specific conditions (e.g., geomorphology; predator-related conditions; brown pelican terrestrial nesting, roosting, and loafing habitats) to predict the nest count as a proxy for breeding pairs on the island. We used the model and 2000–2015 brown pelican nest count data to estimate if breeding pair targets that we identified or estimated for 10 U.S Fish and Wildlife Service Gulf Coast Biological Planning Units were met while accounting for uncertainty. Our results indicate that breeding pair targets were met in 7 of the 10 units by existing conditions. Our confidence in judging nest deficits tended to decrease from west to east because the model over-predicted total nests in the east Gulf Coast. Using an island from our data, we show how the model could be used to quantify the uncertainty of nest count outcomes under simulated changes in island conditions. The model indicated that the island's existing conditions most probably result in nests (probability = 0.51) and that increasing the area of nesting habitat (shrubs) could increase the probability of nests from 49% to 70%. Increasing shrub habitat in the model also increased nest count uncertainty by 60%, but this was due to a greater probability of larger nest counts. Our model suggests that nest count uncertainty could be reduced by improving data on island size, shrub area, and predator presence, depending on the unit and how isolated the island is from the mainland. These tools could help managers understand and incorporate the uncertainty associated with creating island conditions that are intended to help achieve brown pelican conservation objectives.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2023

Structured decision making to prioritize regional bird monitoring needs

Conservation planning for large ecosystems has multiple benefits but is often challenging to implement because of the multiple jurisdictions, species, and habitats involved. In addition, decision making at large spatial scales can be hampered because many approaches do not explicitly incorporate potentially competing values and concerns of stakeholders. After the Deepwater Horizon oil spill, establishing baselines was challenging because of (1) variation in study designs, (2) inconsistent use of explicit objectives and hypotheses, (3) inconsistent use of standardized monitoring protocols, and (4) variation in spatial and temporal scope associated with avian monitoring projects before the spill. Herein, we show how the Gulf of Mexico Avian Monitoring Network members used structured decision making to identify bird monitoring priorities. We used multiple tools and techniques to clearly define the problem and stakeholder objectives and to identify bird monitoring priorities at the scale of the entire northern Gulf of Mexico region. Although our example is specific to the northern Gulf of Mexico, this approach provides an example of how stakeholder values can be incorporated into the coordination process of broad-scale monitoring programs to address management, restoration, and scientific questions in other ecosystems and for other taxa.

INFORMS Journal on Applied Analytics↗

USGS2021

Structured decision making and optimal bird monitoring in the northern Gulf of Mexico

The avian conservation community struggles to design and implement large scale, long-term coordinated bird monitoring programs within the northern Gulf of Mexico due to the complexity of the conservation enterprise in the region; this complexity arises from the diverse stakeholders, multiple jurisdictions, complex ecological processes, myriad habitats, and over 500 species of birds using the region for at least some part of their annual cycle. In addition, long-term monitoring over large spatial scales is difficult because of the need for monitoring data to both (1) evaluate management and restoration outcomes, and (2) provide reliable information about the status and trends of bird populations over time. To address these challenges, the Gulf of Mexico Avian Monitoring Network developed a problem statement: “How can a cost-effective monitoring strategy for the Gulf Coast bird community and ecosystem be developed that evaluates ongoing conservation activities and chronic and acute threats; maximizes learning; and is flexible and holistic enough to detect novel ecological threats and evaluate new and emerging conservation activities?” A structured decision-making framework was then used to articulate and quantify stakeholder values related to the problem statement. One use of the stakeholder values was to develop a regional, strategic plan for bird monitoring, which is presented elsewhere. A formal and complete decision support tool for conservation investments in monitoring and research guided by the stakeholder values is presented in this report. The technical aspects of the stakeholder value model and a portfolio analysis that could be used to guide decision making when allocating resources for monitoring activities is described. Whereas the decision analysis presented here could be useful to any decision maker faced with difficult choices about resource allocation, it is designed for decision makers who request monitoring study proposals and then determine which combination of proposals to fund. The portfolio decision support tool is designed to help funding agencies and organizations identify resource allocation strategies to maximize stated objectives. To begin the decision analysis, an objectives hierarchy and quantitative performance metrics from the values of the Gulf of Mexico bird conservation community were created by a panel of regional stakeholders. Each fundamental objective and sub-objective in the hierarchy is composed of several performance metrics. To test the decision support tool, the authors evaluated a combination of monitoring study proposals written for the region and simulated proposals. Each proposal was scored against the performance metrics and used multi-attribute utility theory to combine the multiple objectives into a measure of total monitoring benefit. The total monitoring benefit and costs of each proposal were then used in a constrained optimization routine to identify optimal monitoring portfolios, that is, a combination of activities that maximizes monitoring benefits while meeting cost and other constraints of interest to stakeholders. A graphical solution based on the concept of Pareto efficiency, which is useful in situations when cost constraints and exact budgets are not known, is also provided. Finally, an evaluation of the sensitivity of the decision-making framework to the weights assigned to objectives by stakeholders is included. This decision support tool allows decision makers to identify an optimal suite of monitoring proposals with a transparent portfolio analysis that includes user-defined constraints (such as costs).

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2021

Identifying information gaps in predicting winter foraging habitat for juvenile Gulf Sturgeon

The Gulf Sturgeon Acipenser oxyrinchus desotoi is an anadromous species that inhabits Gulf of Mexico coastal waters from Louisiana to Florida and is listed as threatened under the U.S. Endangered Species Act. Seasonal cues (e.g., freshwater discharge) determine the timing of spawning and migration and may influence the availability of critical habitat during winter months in six estuaries. Large information gaps, especially related to critical estuarine habitat for juveniles, hinder recovery efforts to protect these habitats and assess risks from emerging threats. Using Apalachicola Bay, Florida, as a model system, we developed and analyzed a preliminary Bayesian network model so that we could identify knowledge gaps (i.e., where expert knowledge was lacking) and data gaps (i.e., where data were unavailable) that limit the ability to assess the quantity of critical estuarine habitat for juvenile Gulf Sturgeon. The model hypothesized habitat availability per winter month in estuarine habitat under alternative scenarios of river discharge and length of the winter foraging season. A search for geospatial data sets revealed that the largest gap involved salinity, temperature, and oxygen (i.e., water condition) monitoring data, with data available only for Apalachicola Bay. For the Apalachicola Bay model, data gaps prevented the development of 53% of water condition geospatial data sets and a sensitivity analysis showed that water condition data most limited the ability to predict habitat availability. Expert knowledge was low, and conditional certainty scores showed that the relationships with the lowest certainty were abiotic suitability and habitat availability. Reducing information gaps could aid the development of a model that is appropriate for informing management. Future efforts could prioritize the expansion of water monitoring within critical habitat estuaries and predicting abiotic suitability and habitat availability. Bayesian network models can easily incorporate prior and new information for complex systems. Thus, our model could be updated as future research and monitoring efforts close these information gaps.

Florida↗

USGS2020

Strategic habitat conservation for beach mice: Estimating management scenario efficiencies

The Perdido Key beach mouse ( Peromyscus polionotus trissyllepsis ), Choctawhatchee beach mouse ( P. p. allophrys ), and St. Andrew beach mouse ( P. p. peninsularis ) are 3 federally endangered subspecies that inhabit coastal dunes of Alabama and Florida, USA. Conservation opportunities for these subspecies are limited and costly. Consequently, well‐targeted efforts are required to achieve their downlisting criteria. To aid the development of targeted management scenarios that are designed to achieve downlisting criteria, we developed a Bayesian network model that uses habitat characteristics to predict the probability of beach mouse presence at a 30‐m resolution across a portion of the Florida Panhandle. We then designed alternative management scenarios for a variety of habitat conditions for coastal dunes. Finally, we estimated how much area is needed to achieve the established downlisting criterion (i.e., habitat objective) and the amount of effort needed to achieve the habitat objective (i.e., management efficiency). The results suggest that after 7 years of post‐storm recolonization, habitat objectives were met for Perdido Key (within its Florida critical habitat) and Choctawhatchee beach mice. The St. Andrew beach mouse required 5.14 km 2 of additional critical habitat to be protected and occupied. The St. Andrew beach mouse habitat objective might be achieved by first restoring protected critical habitat to good dune conditions and then protecting or restoring the unprotected critical habitat with the highest predicted probability of beach mouse presence. This scenario provided a 28% increase in management efficiency compared to a scenario that randomly protected or restored undeveloped unprotected critical habitat. In total, when coupled with established downlisting criteria, these quantitative and spatial decision support tools could provide insight into how much habitat is available, how much more is needed, and targeted conservation or restoration efforts that might efficiently achieve habitat objectives.

Alabama, Florida↗

USGS2020

Local sea level rise information sheets for Texas, Louisiana, Mississippi, Alabama and Florida

Two Pagers for Federally Managed Lands The Northern Gulf of Mexico Sentinel Site Cooperative partnered with individuals at the U.S. Geological Survey’s Wetland and Aquatic Research Center and the U.S. Fish and Wildlife Service to produce customized two-pager information sheets for federal coastal refuges, parks, and reserves across the northern Gulf of Mexico (i.e., sea-level rise sensitive federally-managed lands within Texas, Louisiana, Alabama, Mississippi, and Florida). The project was funded by the Department of the Interior Southeast Climate Adaptation Science Center, and the project team included Bogdan Chivoiu (Cherokee Nation Technologies at USGS), Michael Osland (USGS), Renee Collini (Northern Gulf of Mexico Sentinel Site Cooperative), Sara Martin (Northern Gulf of Mexico Sentinel Site Cooperative), Benjamin Wilson (NAS Science Policy Fellow at USFWS) and John Tirpak (USFWS). The project team is grateful to the many individuals that provided helpful comments on earlier drafts of these two pagers.

Texas, Louisiana, Mississippi, Alabama, Florida↗

USGS2020

Louisiana Adaptive Management Status and Improvement Report: Vision and Recommendations

As part of the process to increase implementation of adaptive management for ecosystem-based coastal restoration within Louisiana, we aim for this report to be broadly applicable across planning processes as well as funding and implementing entities. It compiles technical knowledge and guidance summarized as key findings through the text which lead to eight priority recommendations to improve application of adaptive management in Louisiana. This report identifies critical linkage points and opportunities for knowledge and data transfers within, and among, agencies in Louisiana. The primary focus is on CPRA and others affiliated with the LA TIG. This report presents a consensus based common vision for adaptive management of coastal restoration implementation in Louisiana. As CPRA is the primary agency responsible for coordinating and facilitating coastal restoration projects within the state of Louisiana, the primary source of information and experience was collated from personnel and processes carried out at CPRA. To capture needs and mechanisms ,across agencies, extensive input was also received from the LA TIG, representing the other state and federal Trustees.

Louisiana↗

USGS2018

A review of coastal management approaches to support the integration of ecological and human community planning for climate change

The resilience of socio-ecological systems to sea level rise, storms and flooding can be enhanced when coastal habitats are used as natural infrastructure. Grey infrastructure has long been used for coastal flood protection but can lead to unintended negative impacts. Natural infrastructure often provides similar services as well as added benefits that support short- and long-term biological, cultural, social, and economic goals. While natural infrastructure is becoming more widespread in practice, it often represents a relatively small fraction within portfolios of coastal risk-reducing strategies compared to more traditional grey infrastructure. This study provides a comprehensive review of how natural infrastructure is being used along the United States Atlantic, Gulf of Mexico, and Caribbean coasts related to four habitats – tidal marshes, beaches and barrier islands, mangroves, and biogenic reefs. We compare information on the benefits, opportunities and challenges of implementing natural, grey and hybrid infrastructure in the coastal zone. In addition, we present a suite of actions to increase information and reduce uncertainty so that coastal mangers and planners are aware of the full suite of options for restoration, conservation and planning that maximize ecosystem services over short- and long-term planning horizons.

Journal of Coastal Conservation↗

USGS2018

A multiscale natural community and species-level vulnerability assessment of the Gulf Coast, USA

Vulnerability assessments combine quantitative and qualitative evaluations of the exposure, sensitivity, and adaptive capacity of species or natural communities to current and future threats. When combined with the economic, ecological or evolutionary value of the species, vulnerability assessments quantify the relative risk to regional species and natural communities and can enable informed prioritization of conservation efforts. Vulnerability assessments are common practice in conservation biology, including the potential impacts of future climate scenarios. However, geographic variation in scenarios and vulnerabilities is rarely quantified. This gap is particularly limiting for informing ecosystem management given that conservation practices typically vary by sociopolitical boundaries rather than by ecological boundaries. To support prioritization of conservation actions across a range of spatial scales, we conducted the Gulf Coast Vulnerability Assessment (GCVA) for four natural communities and eleven focal species around the Gulf of Mexico based on current and future threats from climate change and land-use practices out to 2060. We used the Standardized Index of Vulnerability and Value (SIVVA) tool to assess both natural community and species vulnerabilities. We observed greater variation across ecologically delineated subregions within the Gulf Coast of the U.S. than across climate scenarios. This novel finding suggests that future vulnerability assessments incorporate regional variation and that conservation prioritization may vary across ecological subregions. Across subregions and climate scenarios the most prominent threats were legacy effects, primarily from habitat loss and degradation, that compromised the adaptive capacity of species and natural communities. The second most important threats were future threats from sea-level rise. Our results suggest that the substantial threats species and natural communities face from climate change and sea-level rise would be within their adaptive capacity were it not for historic habitat loss, fragmentation, and degradation.

Alabama, Florida, Georgia, Louisiana, Mississippi,↗

USGS2017

Assessing coastal wetland vulnerability to sea-level rise along the northern Gulf of Mexico coast: Gaps and opportunities for developing a coordinated regional sampling network

Coastal wetland responses to sea-level rise are greatly influenced by biogeomorphic processes that affect wetland surface elevation. Small changes in elevation relative to sea level can lead to comparatively large changes in ecosystem structure, function, and stability. The surface elevation table-marker horizon (SET-MH) approach is being used globally to quantify the relative contributions of processes affecting wetland elevation change. Historically, SET-MH measurements have been obtained at local scales to address site-specific research questions. However, in the face of accelerated sea-level rise, there is an increasing need for elevation change network data that can be incorporated into regional ecological models and vulnerability assessments. In particular, there is a need for long-term, high-temporal resolution data that are strategically distributed across ecologically-relevant abiotic gradients. Here, we quantify the distribution of SET-MH stations along the northern Gulf of Mexico coast (USA) across political boundaries (states), wetland habitats, and ecologically-relevant abiotic gradients (i.e., gradients in temperature, precipitation, elevation, and relative sea-level rise). Our analyses identify areas with high SET-MH station densities as well as areas with notable gaps. Salt marshes, intermediate elevations, and colder areas with high rainfall have a high number of stations, while salt flat ecosystems, certain elevation zones, the mangrove-marsh ecotone, and hypersaline coastal areas with low rainfall have fewer stations. Due to rapid rates of wetland loss and relative sea-level rise, the state of Louisiana has the most extensive SET-MH station network in the region, and we provide several recent examples where data from Louisiana’s network have been used to assess and compare wetland vulnerability to sea-level rise. Our findings represent the first attempt to examine spatial gaps in SET-MH coverage across abiotic gradients. Our analyses can be used to transform a broadly disseminated and unplanned collection of SET-MH stations into a coordinated and strategic regional network. This regional network would provide data for predicting and preparing for the responses of coastal wetlands to accelerated sea-level rise and other aspects of global change.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2017

A synthesis of thresholds for focal species along the U.S. Atlantic and Gulf Coasts: A review of research and applications

The impacts from climate change are increasing the possibility of vulnerable coastal species and habitats crossing critical thresholds that could spur rapid and possibly irreversible changes. For species of high conservation concern, improved knowledge of quantitative thresholds could greatly improve management. To meet this need, we synthesized information pertaining to biological responses as tipping points to sea level rise (SLR) and coastal storms for 45 fish, wildlife, and plant species along the U.S. Atlantic and Gulf Coasts and Caribbean through a literature review and expert elicitation. Although these species were selected based on their ecological, economic, and cultural importance, just over half (56%, n = 25) have quantitative threshold data currently available that can be used to assess the effects of SLR and storms during some aspect of their life history. Birds, reptiles, and plants represent the best studied coastal species. Thirteen of the species (29%) are projected to lose at least 50% of their population or habitat (e.g., foraging, nesting, spawning, or resting habitat) in some areas with a 0.5 m or greater rise in sea levels by 2100. Two species (a bird and reptile) may gain habitat from projected SLR and be resilient to future impacts. Numeric thresholds were not available for the remaining 20 species we searched for. Coastal fishes, mammals, and amphibians were among the groups representing a major information gap in this field of research. In addition, quantitative threshold responses to coastal storms were scarce for all taxa. While vulnerability assessments and qualitative research related to the impacts of SLR and storms on coastal species and habitats are increasing, work that incorporates quantitative thresholds as response and impact metrics remains limited. Additional monitoring, modeling, and research that provides multiple quantitative thresholds across species' life stages and/or latitudinal gradients is ideal to support robust coastal management and decision-making across spatio-temporal scales in the face of climate change.

Ocean and Coastal Management↗

USGS2017

Five-year external reviews of the eight Department of Interior Climate Science Centers: Southeast Climate Science Center

In 2008, the U.S. Congress authorized the establishment of the National Climate Change and Wildlife Science Center (NCCWSC) within the U.S. Department of Interior (DOI). Housed administratively within the U.S. Geological Survey (USGS), NCCWSC is part of the DOI’s ongoing mission to meet the challenges of climate change and its effects on wildlife and aquatic resources. From 2010 through 2012, NCCWSC established eight regional DOI Climate Science Centers (CSCs). Each of these regional CSCs operated with the mission to “synthesize and integrate climate change impact data and develop tools that the Department’s managers and partners can use when managing the Department’s land, water, fish and wildlife, and cultural heritage resources” (Salazar 2009). The model developed by NCCWSC for the regional CSCs employed a dual approach of a federal USGS-staffed component and a parallel host-university component established competitively through a 5-year cooperative agreement with NCCWSC. At the conclusion of this 5-year agreement, a review of each CSC was undertaken, with the Southeast Climate Science Center (SE CSC) review in February 2016. The SE CSC is hosted by North Carolina State University (NCSU) in Raleigh, North Carolina, and is physically housed within the NCSU Department of Applied Ecology along with the Center for Applied Aquatic Ecology, the North Carolina Cooperative Fish and Wildlife Research Unit (CFWRU), and the North Carolina Agromedicine Institute. The U.S. Department of Agriculture Southeast Regional Climate Hub is based at NCSU as is the National Oceanic and Atmospheric Administration (NOAA) Southeast Regional Climate Center, the North Carolina Institute for Climate Studies, the North Carolina Wildlife Resources Commission, the NOAA National Weather Service, the State Climate Office of North Carolina, and the U.S. Forest Service Eastern Forest Environmental Threat Assessment Center. This creates a strong core of organizations operating in close proximity focused on climate issues. The geographic area covered by the SE CSC represents all or part of 16 states and the Caribbean Islands and has overlapping boundaries with seven Landscape Conservation Cooperatives (LCCs): Appalachian LCC, Eastern Tallgrass Prairie and Big Rivers LCC, Gulf Coast Prairie LCC, Gulf Coastal Plains and Ozarks LCC, Peninsular Florida LCC, South Atlantic LCC, and Caribbean LCC. The SE CSC region also encompasses 134 U.S. Fish and Wildlife Service refuges and 89 National Park Service (NPS) units and is home to 11 federally recognized and 54 state recognized tribes.

Report↗

USGS2014

Estimating habitat carrying capacity for migrating and wintering waterfowl: Considerations, pitfalls and improvements

Population-based habitat conservation planning for migrating and wintering waterfowl in North America is carried out by habitat Joint Venture (JV) initiatives and is based on the premise that food can limit demography (i.e. food limitation hypothesis). Consequently, planners use bioenergetic models to estimate food (energy) availability and population-level energy demands at appropriate spatial and temporal scales, and translate these values into regional habitat objectives. While simple in principle, there are both empirical and theoretical challenges associated with calculating energy supply and demand including: 1) estimating food availability, 2) estimating the energy content of specific foods, 3) extrapolating site-specific estimates of food availability to landscapes for focal species, 4) applicability of estimates from a single species to other species, 5) estimating resting metabolic rate, 6) estimating cost of daily behaviours, and 7) estimating costs of thermoregulation or tissue synthesis. Most models being used are daily ration models (DRMs) whose set of simplifying assumptions are well established and whose use is widely accepted and feasible given the empirical data available to populate such models. However, DRMs do not link habitat objectives to metrics of ultimate ecological importance such as individual body condition or survival, and largely only consider food-producing habitats. Agent-based models (ABMs) provide a possible alternative for creating more biologically realistic models under some conditions; however, ABMs require different types of empirical inputs, many of which have yet to be estimated for key North American waterfowl. Decisions about how JVs can best proceed with habitat conservation would benefit from the use of sensitivity analyses that could identify the empirical and theoretical uncertainties that have the greatest influence on efforts to estimate habitat carrying capacity. Development of ABMs at restricted, yet biologically relevant spatial scales, followed by comparisons of their outputs to those generated from more simplistic, deterministic models can provide a means of assessing degrees of dissimilarity in how alternative models describe desired landscape conditions for migrating and wintering waterfowl.

Wildfowl↗

USGS2011

Comparison of statistical and theoretical habitat models for conservation planning: The benefit of ensemble prediction

Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and regression tree (CRT), habitat suitability index (HSI), forest structure database (FS), and habitat association database (HA). We focused our comparison on models for five priority forest-breeding species in the Central Hardwoods Bird Conservation Region: Acadian Flycatcher, Cerulean Warbler, Prairie Warbler, Red-headed Woodpecker, and Worm-eating Warbler. Lacking complete knowledge on the distribution and abundance of each species with which we could illuminate differences between approaches and provide strong grounds for recommending one approach over another, we used two approaches to compare models: rank correlations among model outputs and comparison of spatial correspondence. In general, rank correlations were significantly positive among models for each species, indicating general agreement among the models. Worm-eating Warblers had the highest pairwise correlations, all of which were significant ( P , 0.05). Red-headed Woodpeckers had the lowest agreement among models, suggesting greater uncertainty in the relative conservation value of areas within the region. We assessed model uncertainty by mapping the spatial congruence in priorities (i.e., top ranks) resulting from each model for each species and calculating the coefficient of variation across model ranks for each location. This allowed identification of areas more likely to be good targets of conservation effort for a species, those areas that were least likely, and those in between where uncertainty is higher and thus conservation action incorporates more risk. Based on our results, models developed independently for the same purpose (conservation planning for a particular species in a particular geography) yield different answers and thus different conservation strategies. We assert that using only one habitat model (even if validated) as the foundation of a conservation plan is risky. Using multiple models (i.e., ensemble prediction) can reduce uncertainty and increase efficacy of conservation action when models corroborate one another and increase understanding of the system when they do not.

Ecological Applications↗

USGS2010

Change in avian abundance predicted from regional forest inventory data

An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963-2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuckwills- widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will increase.

Forest Ecology and Management↗