Geology topics

Alexa McKerrow

Publications and source records attributed to Alexa McKerrow.

At least 19 recordsLinked to original sources

USGS2023

Developing fluvial fish species distribution models across the conterminous United States—A framework for management and conservation

This report explains the steps and specific methods used to predict fluvial fish occurrences in their native ranges for the conterminous United States. In this study, boosted regression tree models predict distributions of 271 ecologically important fluvial fish species using relations between fish presence/absence and 22 natural and anthropogenic landscape variables. Models developed for the freshwater portions of the ranges for species represented 28 families. Cyprinidae was the family with the most species (87 of 271) modeled for this study, followed by Percidae (34) and Ictaluridae (17). Model predictive performance was evaluated using four metrics: area under the receiver operating characteristic curve, sensitivity, specificity, and True Skill Statistic, which are all from tenfold cross-validation results. The relative importance of the predictor variables in the boosted regression tree models was calculated and ranked for each species. The three strongest natural predictors of fish distributions were network catchment area, the mean annual air temperature of the local catchment, and the maximum elevation of the local catchment, while the three strongest anthropogenic predictors were downstream main stem dam density, distance to downstream main stem dam, and the percentage of pasture/hay land use area within network catchment boundaries. Study results showed 61 fish species were sensitive to climate variables, and 40 fish species were sensitive to anthropogenic stressors. The models developed in this study can be used to derive critical information regarding habitat protection priorities, anthropogenic threats, and potential effects of climate change on habitat suitability, aiding in efforts to conserve fluvial fishes now and into the future.

Conterminous United States↗

USGS2022

Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data

The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random. In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, Desmognathus quadramaculatus (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters. We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.

Alabama, Georgia, North Carolina, Tennessee, Virgi↗

USGS2022

Identifying monitoring information needs that support the management of fish in large rivers

Management actions intended to benefit fish in large rivers can directly or indirectly affect multiple ecosystem components. Without consideration of the effects of management on non-target ecosystem components, unintended consequences may limit management efficacy. Monitoring can help clarify the effects of management actions, including on non-target ecosystem components, but only if data are collected to characterize key ecosystem processes that could affect the outcome. Scientists from across the U.S. convened to develop a conceptual model that would help identify monitoring information needed to better understand how natural and anthropogenic factors affect large river fishes. We applied the conceptual model to case studies in four large U.S. rivers. The application of the conceptual model indicates the model is flexible and relevant to large rivers in different geographic settings and with different management challenges. By visualizing how natural and anthropogenic drivers directly or indirectly affect cascading ecosystem tiers, our model identified critical information gaps and uncertainties that, if resolved, could inform how to best meet management objectives. Despite large differences in the physical and ecological contexts of the river systems, the case studies also demonstrated substantial commonalities in the data needed to better understand how stressors affect fish in these systems. For example, in most systems information on river discharge and water temperature were needed and available. Conversely, information regarding trophic relationships and the habitat requirements of larval fishes were generally lacking. This result suggests that there is a need to better understand a set of common factors across large-river systems. We provide a stepwise procedure to facilitate the application of our conceptual model to other river systems and management goals.

Environmental Management↗

USGS2021

Applying biodiversity metrics as surrogates to a habitat conservation plan

Unabated urbanization has led to environmental degradation and subsequent biodiversity loss across the globe. As an outcome of unmitigated land use, multi-jurisdictional agencies have developed land use plans that attempt to protect threatened or endangered species across selected areas by which some trade-offs between harm to species and additional conservation approaches are allowed among the partnering organizations. Typical conservation plans can be created to focus on single or multiple species, and although they may protect a species or groups of species, they may not account for biodiversity or its protection across the given area. We applied an approach that clustered deductive habitat models for terrestrial vertebrates into metrics that serve as surrogates for biodiversity and relate to ecosystem services. In order to evaluate this process, we collaborated with the partnering agencies who are creating a Multi-Species Habitat Conservation Plan in southern California and compared it to the entire Mojave Desert Ecoregion. We focused on total terrestrial vertebrate species richness and taxon groupings representing amphibians, birds, mammals, and reptiles, and two special status species using the Normalized Index of Biodiversity (NIB). The conservation planning area had a lower NIB and was less species rich than the Mojave Desert Ecoregion, but the Mojave River riparian corridor had a higher NIB and was more species-rich, and while taxon analysis varied across the geographies, this pattern generally held. Additionally, we analyzed desert tortoise ( Gopherus agassizii ) and desert kit fox ( Vulpes macrotis arsipus ) as umbrella species and determined that both species are associated with increased NIB and large numbers of species for the conservation area. Our process provided the ability to incorporate value-added surrogate information into a formal land use planning process and used a metric, NIB, which allowed comparison of the various planning areas and geographic units. Although this process has been applied to Apple Valley, CA, and other geographies within the U.S., the approach has practical application for other global biodiversity initiatives.

California↗

USGS2021

Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups

Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1 & 2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have >17% of their extent in Status 1 & 2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.

Forests↗

USGS2021

U.S. Geological Survey wildland fire science strategic plan, 2021–26

The U.S. Geological Survey (USGS) Wildland Fire Science Strategic Plan defines critical, core fire science capabilities for understanding fire-related and fire-responsive earth system processes and patterns, and informing management decision making. Developed by USGS fire scientists and executive leadership, and informed by conversations with external stakeholders, the Strategic Plan is aligned with the needs of the fire science stakeholder community–fire, land, natural resource, and emergency managers from Federal, State, Tribal, and community organizations, as well as members of the scientific community. The Strategic Plan is composed of four integrated priorities, each with associated goals and specific strategies for accomplishing the goals: Priority 1: Produce state-of-the-art, actionable fire science; Priority 2: Engage stakeholders in science production and science delivery; Priority 3: Effectively communicate USGS fire science capacity, products, and information to a broad audience; and Priority 4: Enhance USGS organizational structure and advance support for fire science. The priorities of this Strategic Plan define the USGS’s commitment to producing and delivering cutting edge fire science, information, and decision-support tools in support of national, regional, and local priorities and stakeholder needs.

Circular↗

USGS2021

Refining the coarse filter approach: Using habitat-based species models to identify rarity and vulnerabilities in the protection of U.S. biodiversity

Preserving biodiversity and its many components is a priority of conservation science and how to efficiently allocate resources to preserve healthy populations of as many species, habitats, and ecosystems as possible. We used the U.S. Geological Survey (USGS) Gap Analysis Project (GAP) species models released in 2018, which identify predicted habitats for terrestrial vertebrates in the conterminous United States, to illustrate hotspots of biodiversity for the major taxonomic groups. This collection represents the first complete compilation of terrestrial vertebrate species models for the conterminous United States (U.S. Geological Survey (USGS), 2018a). We used the species models but not the available subspecies models; this resulted in the inclusion of 282 amphibian models, 621 bird models, 365 mammal models, and 322 reptiles in our analysis. We also used population trend information and made spatial queries to characterize species in three dimensions: geographic range (small or large), habitat breadth (narrow or wide), and population trend (decreasing vs stable or increasing). This characterization allowed us to divide the species into eight groups (A-H) with similar characteristics. Group A species (large geographic range, wide habitat breadth, and stable or increasing population trend) are species that are common now with no indication of becoming rare. Species B-H have theoretical or known characteristics that could lead them to become rare with the H species exhibiting small geographic range, narrow habitat breadth, and decreasing population trend. Finally, we evaluated the prevalence of mapped habitat on protected lands for each species, exploring the patterns of representation in the rare species groups by ecoregion. The species we identified with population and habitat use characteristics that potentially predispose them to being or becoming rare represented a large percentage of each taxon. Potentially rare species were widely distributed among ecoregions. Of the 20 ecoregions in the country, 14 have a greater number of rare species than the national average for at least one taxon. Protection of the habitat for the majority of these rare species is below that recommended (17% of available habitat) by the Convention on Biological Diversity (CBD). The Everglades ecoregion was the only ecoregion that protected more than half of its rare or potentially rare species.

Global Ecology and Conservation↗

USGS2019

Gap Analysis Project (GAP) Terrestrial Vertebrate Species Richness Maps for the Conterminous U.S.

The mission of the Gap Analysis Project (GAP) is to support national and regional assessments of the conservation status of vertebrate species and plant communities. This report explains conterminous United States species richness maps created by the U.S. Geological Survey for four major classes in the phylum Chordata: mammals, birds, reptiles, and amphibians. In this work, we focus on terrestrial vertebrate species and the spatial patterns of richness derived from species’ habitat distribution models. We created species’ habitat distribution models for 1,590 species (282 amphibians, 621 birds, 365 mammals, 322 reptiles) and an additional 129 subspecies (2 amphibians, 28 birds, 94 mammals, 5 reptiles) that occur in the conterminous United States. The 1,590 species level models were spatially combined to create the taxa richness maps at a spatial resolution of 30 meters. Based on those maps we identified the maximum species richness for each of the taxa (43 amphibians, 163 birds, 72 mammals, and 54 reptiles) and show variation in richness across the conterminous United States. Because these habitat models remove unsuitable areas within the range of the species, the patterns of richness presented here are different from the coarse-resolution species’ habitat distribution models commonly presented in the literature. These maps provide a new, more spatially refined richness map. In addition, since these models are logically linked to mapped data layers that constitute habitat suitability, this suite of data can provide an intuitive data system for further exploration of biodiversity and implications for change at ecosystem and landscape scales.

Scientific Investigations Report↗

USGS2019

Protected areas lacking for many common fluvial fishes of the conterminous USA

Aim To assess the effectiveness of protected areas in two catchment scales (local and network) in conserving regionally common fluvial fishes using modelled species distributions. Location Conterminous United States. Methods A total of 150 species were selected that were geographically widespread, abundant, non‐habitat specialists and native within nine large ecoregions. Species distribution models were developed using boosted regression trees, and modelled distributions were assessed for protection status under two alternatives: lands strictly managed for biodiversity (Highly Restricted Use) and those allowing multiple uses (Multiple Use), with protection target levels (i.e., the amount of protected area required for protection) for local and network catchments being developed from ecoregion‐based urban and agricultural land use thresholds from fish responses. Results Overall, less than 2% of fluvial catchments in the conterminous USA are meeting both local and network catchment protection target levels under the Highly Restricted Use alternative, whereas 16% of catchments met protection levels for the Multiple Use alternative, with protection largely concentrated in the western USA. For common native species distributions within ecoregions, only one species had >10% of streams meeting combined local and network catchment protection target levels under the Highly Restricted Use alternative, whereas 50 distributions (~14% of species distribution models) met this level under the Multiple Use alternative. Main conclusions Even for fishes considered widespread and abundant, protection levels are lacking, particularly when considering only lands that are actively managed for biodiversity. Given the increasing intensification of anthropogenic activities and substantial uncertainty associated with climate change, considering the conservation status for all species, including those currently considered common, is warranted.

Diversity and Distributions↗

USGS2018

Patterns of species richness hotspots and estimates of their protection are sensitive to spatial resolution

Aim Species richness is a measure of biodiversity often used in spatial conservation assessments and mapped by summing species distribution maps. Commission errors inherent those maps influence richness patterns and conservation assessments. We sought to further the understanding of the sensitivity of hotspot delineation methods and conservation assessments to commission errors, and choice of threshold for hotspot delineation. Location United States. Methods We created range maps and 30‐m and 1‐km resolution habitat maps for terrestrial vertebrates in the United States and generated species richness maps with each dataset. With the richness maps and the GAP Protected Areas Dataset, we created species richness hotspot maps and calculated the proportion of hotspots within protected areas; calculating protection under a range of thresholds for defining hotspots. Our method allowed us to identify the influence of commission errors by comparing hotspot maps. Results Commission errors from coarse spatial grain data and lack of porosity in the range data inflated richness estimates and altered their spatial patterns. Coincidence of hotspots from different data types was low. The 30‐m hotspots were spatially dispersed, and some were very long distances from the hotspots mapped with coarser data. Estimates of protection were low for each of the taxa. The relationship between estimates of hotspot protection and threshold choice was nonlinear and inconsistent among data types (habitat and range) and grain size (30‐m and 1‐km). Main conclusions Coarse mapping methods and grain sizes can introduce commission errors into species distribution data that could result in misidentifications of the regions where hotspots occur and affect estimates of hotspot protection. Hotspot conservation assessments are also sensitive to choice of threshold for hotspot delineation. There is value in developing species distribution maps with high resolution and low rates of commission error for conservation assessments.

Diversity and Distributions↗

USGS2017

Long-term fish monitoring in large rivers: Utility of “benchmarking” across basins

In business, benchmarking is a widely used practice of comparing your own business processes to those of other comparable companies and incorporating identified best practices to improve performance. Biologists and resource managers designing and conducting monitoring programs for fish in large river systems tend to focus on single river basins or segments of large rivers, missing opportunities to learn from those conducting fish monitoring in other rivers. We briefly examine five long-term fish monitoring programs in large rivers in the United States (Colorado, Columbia, Mississippi, Illinois, and Tallapoosa rivers) and identify opportunities for learning across programs by detailing best monitoring practices and why these practices were chosen. Although monitoring objectives, methods, and program maturity differ between each river system, examples from these five case studies illustrate the important role that long-term monitoring programs play in interpreting temporal and spatial shifts in fish populations for both established objectives and newly emerging questions. We suggest that deliberate efforts to develop a broader collaborative network through benchmarking will facilitate sharing of ideas and development of more effective monitoring programs.

Colorado River, Columbia River, Illinois River, Mi↗

USGS2017

Integrating multiple data sources in species distribution modeling: A framework for data fusion

The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species’ occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches (“Shared,” “Correlation,” “Covariates”) for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source (“Single”). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.

Ecology↗

USGS2016

Uncertainty quantification and propagation for projections of extremes in monthly area burned under climate change: A case study in the coastal plain of Georgia, USA

Human‐caused climate change is predicted to affect the frequency of hazard‐linked extremes. Unusually large wildfires are a type of extreme event that is constrained by climate and can be a hazard to society but also an important ecological disturbance. This chapter focuses on changes in the frequency of extreme monthly area burned by wildfires for the end of the 21st century for a wildfire‐prone region in the southeast United States. Predicting changes in area burned is complicated by the large and varied uncertainties in how the climate will change and in the models used to predict those changes. The chapter characterizes and quantifies multiple sources of uncertainty and propagate the expanded prediction intervals of future area burned. It illustrates that while accounting for multiple sources of uncertainty in global change science problems is a difficult task, it will be necessary in order to properly assess the risk of increased exposure to these society‐relevant events.

Georgia↗

USGS2016

Normalized burn ratios link fire severity with patterns of avian occurrence

Context Remotely sensed differenced normalized burn ratios (DNBR) provide an index of fire severity across the footprint of a fire. We asked whether this index was useful for explaining patterns of bird occurrence within fire adapted xeric pine-oak forests of the southern Appalachian Mountains. Objectives We evaluated the use of DNBR indices for linking ecosystem process with patterns of bird occurrence. We compared field-based and remotely sensed fire severity indices and used each to develop occupancy models for six bird species to identify patterns of bird occurrence following fire. Methods We identified and sampled 228 points within fires that recently burned within Great Smoky Mountains National Park. We performed avian point counts and field-assessed fire severity at each bird census point. We also used Landsat™ imagery acquired before and after each fire to quantify fire severity using DNBR. We used non-parametric methods to quantify agreement between fire severity indices, and evaluated single season occupancy models incorporating fire severity summarized at different spatial scales. Results Agreement between field-derived and remotely sensed measures of fire severity was influenced by vegetation type. Although occurrence models using field-derived indices of fire severity outperformed those using DNBR, summarizing DNBR at multiple spatial scales provided additional insights into patterns of occurrence associated with different sized patches of high severity fire. Conclusions DNBR is useful for linking the effects of fire severity to patterns of bird occurrence, and informing how high severity fire shapes patterns of bird species occurrence on the landscape.

Landscape Ecology↗

USGS2016

Projected gains and losses of wildlife habitat from bioenergy-induced landscape change

Domestic and foreign renewable energy targets and financial incentives have increased demand for woody biomass and bioenergy in the southeastern United States. This demand is expected to be met through purpose-grown agricultural bioenergy crops, short-rotation tree plantations, thinning and harvest of planted and natural forests, and forest harvest residues. With results from a forest economics model, spatially explicit state-and-transition simulation models, and species–habitat models, we projected change in habitat amount for 16 wildlife species caused by meeting a renewable fuel target and expected demand for wood pellets in North Carolina, USA. We projected changes over 40 years under a baseline ‘business-as-usual’ scenario without bioenergy production and five scenarios with unique feedstock portfolios. Bioenergy demand had potential to influence trends in habitat availability for some species in our study area. We found variation in impacts among species, and no scenario was the ‘best’ or ‘worst’ across all species. Our models projected that shrub-associated species would gain habitat under some scenarios because of increases in the amount of regenerating forests on the landscape, while species restricted to mature forests would lose habitat. Some forest species could also lose habitat from the conversion of forests on marginal soils to purpose-grown feedstocks. The conversion of agricultural lands on marginal soils to purpose-grown feedstocks increased habitat losses for one species with strong associations with pasture, which is being lost to urbanization in our study region. Our results indicate that landscape-scale impacts on wildlife habitat will vary among species and depend upon the bioenergy feedstock portfolio. Therefore, decisions about bioenergy and wildlife will likely involve trade-offs among wildlife species, and the choice of focal species is likely to affect the results of landscape-scale assessments. We offer general principals to consider when crafting lists of focal species for bioenergy impact assessments at the landscape scale.

GCB Bioenergy↗

USGS2016

Bioenergy production and forest landscape change in the southeastern United States

Production of woody biomass for bioenergy, whether wood pellets or liquid biofuels, has the potential to cause substantial landscape change and concomitant effects on forest ecosystems, but the landscape effects of alternative production scenarios have not been fully assessed. We simulated landscape change from 2010 to 2050 under five scenarios of woody biomass production for wood pellets and liquid biofuels in North Carolina, in the southeastern United States, a region that is a substantial producer of wood biomass for bioenergy and contains high biodiversity. Modeled scenarios varied biomass feedstocks, incorporating harvest of ‘conventional’ forests, which include naturally regenerating as well as planted forests that exist on the landscape even without bioenergy production, as well as purpose-grown woody crops grown on marginal lands. Results reveal trade-offs among scenarios in terms of overall forest area and the characteristics of the remaining forest in 2050. Meeting demand for biomass from conventional forests resulted in more total forest land compared with a baseline, business-as-usual scenario. However, the remaining forest was composed of more intensively managed forest and less of the bottomland hardwood and longleaf pine habitats that support biodiversity. Converting marginal forest to purpose-grown crops reduced forest area, but the remaining forest contained more of the critical habitats for biodiversity. Conversion of marginal agricultural lands to purpose-grown crops resulted in smaller differences from the baseline scenario in terms of forest area and the characteristics of remaining forest habitats. Each scenario affected the dominant type of land-use change in some regions, especially in the coastal plain that harbors high levels of biodiversity. Our results demonstrate the complex landscape effects of alternative bioenergy scenarios, highlight that the regions most likely to be affected by bioenergy production are also critical for biodiversity, and point to the challenges associated with evaluating bioenergy sustainability.

GCB Bioenergy↗

USGS2016

A comparison of NLCD 2011 and LANDFIRE EVT 2010: Regional and national summaries.

In order to provide the land cover user community a summary of the similarity and differences between the 2011 National Land Cover Dataset (NLCD) and the Landscape Fire and Resource Management Planning Tools Program Existing Vegetation 2010 Data (LANDFIRE EVT), the two datasets were compared at a national (conterminous U.S.) and regional (Eastern, Midwestern, and Western) extents (Figure 1). The comparisons were done by generalizing the LANDFIRE data to be consistent with mapped land cover classes in the NLCD (i.e., crosswalked). Summaries of the comparisons were based on areal extent including 1) the total extent of each land cover class, and 2) land cover classes in corresponding 900-m2 areas. The results from the comparisons provide the user community information regarding the utility of both datasets relative to their intended uses.

Report↗

USGS2015

Modeling climate change, urbanization, and fire effects on Pinus palustris ecosystems of the southeastern U.S.

Managing ecosystems for resilience and sustainability requires understanding how they will respond to future anthropogenic drivers such as climate change and urbanization. In fire-dependent ecosystems, predicting this response requires a focus on how these drivers will impact fire regimes. Here, we use scenarios of climate change, urbanization and management to simulate the future dynamics of the critically endangered and fire-dependent longleaf pine ( Pinus palustris ) ecosystem. We investigated how climate change and urbanization will affect the ecosystem, and whether the two conservation goals of a 135% increase in total longleaf area and a doubling of fire-maintained open-canopy habitat can be achieved in the face of these drivers. Our results show that while climatic warming had little effect on the wildfire regime, and thus on longleaf pine dynamics, urban growth led to an 8% reduction in annual wildfire area. The management scenarios we tested increase the ecosystem's total extent by up to 62% and result in expansion of open-canopy longleaf by as much as 216%, meeting one of the two conservation goals for the ecosystem. We find that both conservation goals for this ecosystem, which is climate-resilient but vulnerable to urbanization, are only attainable if a greater focus is placed on restoration of non-longleaf areas as opposed to maintaining existing longleaf stands. Our approach demonstrates the importance of accounting for multiple relevant anthropogenic threats in an ecosystem-specific context in order to facilitate more effective management actions.

Journal of Environmental Management↗