Conserving biodiversity requires identifying and prioritizing critical habitats at a fine scale, as coarse-scale approaches often fail to address the needs of specialized and threatened species. This study applies a fine-scale prioritization approach across temperate and tropical regions of North America and the Caribbean using a detailed map of 636 ecosystem types and high-resolution Area of Habitat (AOH) data. We then evaluated the current protection status and risk of future land use changes for each habitat type and prioritized them for conservation. Our results revealed that 38% of the area was identified in the top quartile of high-priority habitats, with 56 (33%) of identified IUCN threatened ecosystem types captured within these areas. Top priority habitats include the Meso-American Premontane Semi-deciduous Forest, Central American Caribbean Evergreen Lowland Forest, and Guerreran Dry Deciduous Forest, all characterized by low protection, high projected land-use conversion, and large numbers of threatened and habitat-specialist species, highlighting their urgent conservation importance in Meso-American and Caribbean tropical forests. Our findings emphasize the need for targeted conservation strategies that consider finer-scale habitat classifications and species requirements to improve the precision of conservation planning, especially where already at-risk species and ecosystems are located, and human land use intensities are high.

Restoration practitioners specify goals that describe how the focal ecosystem will look or function upon reaching recovery goals. Goals may be influenced by the level of degradation, surrounding landscape conditions, societal choice, and a changing climate regime. The Society for Ecological Restoration’s International Principles and Standards for the Practice of Ecological Restoration recommend that goals should be informed by reference models of site conditions, which include the biotic composition, the environmental setting, and dynamic processes—had anthropogenic degradation not occurred—while accounting for anticipated changes. The SER principles address many aspects of ecological restoration, and practical steps include conceptualizing the structure and function of the natural system, measuring ecological integrity, and assessing potential climate change effects and adaptations. Models optimally reflect a variety of information sources and are based, where possible, on multiple reference sites of similar native ecological conditions. Using a project site from the Colorado National Monument in the USA, we illustrate a stepwise process to address these principles and standards by compiling and synthesizing map, text, and tabular information from reference materials and sites. By addressing these principles and systematically utilizing existing frameworks and locally available data, practitioners can streamline the establishment of reference models for ecological restoration.

Restoration practitioners specify targets for what the ecosystem will look like to reach recovery goals. Targets may be influenced by the level of degradation, surrounding landscape conditions, societal choice, and a changing and uncertain climate regime. The Society for Ecological Restoration’s International Principles and Standards for the Practice of Ecological Restoration recommends that targets be informed by reference models of site conditions that include biotic composition, environmental setting, and dynamic processes—had anthropogenic degradation not occurred—while accounting for anticipated change. Models optimally reflect a variety of information sources and are based where possible on multiple reference sites of similar native ecological conditions. Using a project site from Colorado National Monument, we illustrate a stepwise process for compiling and synthesizing map, text, and tabular information from reference materials and sites. Reference materials include multiple ecosystem classifications and site inventories to describe composition, structure, and dynamics of the target ecosystems. An ecological integrity framework aids in identifying key ecological attributes and indicators for site measurement. Climate change vulnerability assessment specifies risks to anticipate, while adaptation frameworks point to appropriate strategies. By systematically utilizing existing frameworks and available data, practitioners can streamline the establishment of reference models for ecological restoration.

The Americas contain highly biodiverse yet vulnerable ecosystems, with many threatened species inadequately protected. Finer-scale, localized habitat assessments are crucial for effective conservation planning, but continental-scale high-resolution vegetation maps remain limited. This study addresses this gap by identifying critical vegetation types across the Americas using the standardized framework of the International Vegetation Classification (IVC) system at the macrogroup level, representing the finest vegetation classification available across the region, as well as the highest-resolution Area of Habitat (AOH) maps currently available for birds and mammals. By combining these high-resolution IVC macrogroup maps with detailed AOH maps, we highlight at-risk vegetation types based on 1) threatened and macrogroup-associated species (species that have at least 50% of their AOH in one macrogroup), 2) current protection levels, and 3) projected threats from land use changes, and 4) develop a conservation value index (CVI) that accounts for all these factors. The results highlighted the remarkable diversity of high conservation value macrogroups across the Americas, emphasizing their significance in regions such as the Andes, montane Mesoamerica, the Caribbean, Brazil's Cerrado, and the Atlantic Forest. Among the highest-scoring macrogroups, the Northern Andean Montane & Upper Montane Humid Forest emerged as critically important, harboring a high number of threatened and macrogroup-associated species. Other macrogroups of immediate conservation concern include the Brazilian Atlantic Montane Humid Forest, Pacific Mesoamerican Seasonal Dry Forest, Caribbean Lowland Humid Forest, and Central Midwest Oak Forest, Woodland and Savanna. However, the study revealed that nearly three-quarters of the over 300 macrogroups in the Americas fall below the global target of 30% protection. Notably, a fifth of all species were macrogroup-associated species, including over 40% of threatened species. Our findings emphasize the need for targeted conservation strategies that consider finer-scale habitat classifications and paired with high-quality species distribution data to guide conservation strategies for biodiversity across the Americas.

Decision support tools are needed to ensure that appropriately timed and place-based adaptation is deployed in natural resource policy, planning, and management. Driven by accelerating climate change, analytical frameworks for adaptation are emerging to assist with these decisions. There is a natural relationship between climate change vulnerability assessments and adaptation responses, where low to high relative climate change vulnerability suggests "resistance" to "transformation" strategies for adaptation. The NatureServe Habitat Climate Change Vulnerability Index (HCCVI) embodies a process for ecosystem assessment that integrates both climate and non-climate data and knowledge to document the relative vulnerability of a given habitat or ecosystem type. The framework addresses climate exposure and ecosystem resilience. Since most measures of exposure and resilience are mapped, they can be utilized to create map zones that suggest climate-smart adaptation. We applied the HCCVI to a cross-section of 10 pinyon pine and juniper woodland ecosystem types in western North America. We then demonstrate the application of these outputs to adaptation zonation. Climate exposure defines relative adaptation strategies, while measures of resilience suggest specific priorities for habitat restoration and maintenance. By the mid-21st century, 3% and 23% of the combined area of these types in the United States was categorized as Directed Transformation or Autonomous Transformation, respectively. In just 10% of the combined areas for these types, Passive Resistance strategies are suggested.

In celebration of the Natural Areas Association’s 50 years of supporting practitioners who conserve and steward natural areas across North America

Abstract ContextInformation on the maturity of forests is important for conservation planning. However, available information for the USA is inadequate to support national conservation assessment and planning.ObjectivesThe main objective was to spatially model at a high resolution the relative level of maturity and stand development for forests across conterminous USA. A secondary objective was to explore which younger forests could be attributed to the impacts of severe natural disturbances. MethodsWe modelled the relative level of maturity for forests at a 30 m pixel resolution using spatial data for forest cover, height and biomass, stratified by forest types and ecoregions. National plot data were used to validate modelled results. The impact on Young forest from severe wildfire, insects and disease, and tornados was examined for the years 2000-2019.ResultsOf a total forest area of 248.9M ha, Young forest covered 52.9 M ha (22%); Intermediate 100.3 M ha (42%); and Mature 86.0 M ha (36%). Results suggest that the modelled data are tracking observed forest structure and stand development. 1.4 M ha (2.67%) of modelled Young forest was impacted by severe natural disturbances, with 51.5 (97.33%) M ha of Young forest unimpacted. The distribution of the disturbance factors varied geographically. The unimpacted Young plus Mature forest are where primary forests are most likely found. ConclusionsThe forest maturity data can assist forest decision makers in meeting environmental commitments regarding mitigating forest sector emissions, biodiversity conservation and water quality, including through prioritizing land for meeting protected area and ecosystem restoration targets.

Mature and old-growth forests (MOG) of the conterminous United States collectively support exceptional levels of biodiversity but have declined substantially from logging and development. National-scale proposals to protect 30 and 50% of all lands and waters are useful in assessing MOG conservation targets given the precarious status of these forests. We present the first coast to coast spatially explicit MOG assessment based on three structural development measures—canopy height, canopy cover, and above-ground living biomass to assess relative maturity. MOG were displayed by major forest types (n = 22), landownerships (federal, state, private, and tribal), and Gap Analysis Project (GAP) management status overlaid on the NatureServe’s Red-listed Ecosystems and species, above-ground living biomass, and drinking water source areas. MOG total ∼67.2 M ha (35.9%) of all forest structural classes and were scattered across 8 regions with most in western regions. All federal lands combined represented the greatest (35%) concentrations of MOG, ∼92% of which is on national forest lands with ∼9% on Bureau of Land Management (BLM) and ∼3% on national park lands (totals do not sum to 100% due to minor mapping errors in the datasets). MOG on national forest lands supported the highest concentration of conservation values. However, national forests and BLM lands did not meet lower bound (30%) targets with only ∼24% of MOG in GAP1,2 (5.9 M ha) protection status. The vast majority (76%, 20.8 M ha) of MOG on federal lands that store 10.64 Gt CO2 (e) are vulnerable to logging (GAP3). If federal MOG are logged over a decade, and half their carbon stock emitted, there would be an estimated 0.5 ppm increase in atmospheric CO2 by 2030, which is equivalent to ∼9% of United States total annual emissions. We recommend upper bound (100%) protection of federal MOG, including elevating the conservation status of Inventoried Roadless Areas. This would avoid substantial CO2 emissions while allowing ongoing carbon sequestration to act as natural climate solutions to aid compliance with the Paris Climate Agreement and presidential executive orders on MOG and 30% of all lands and waters in protection by 2030. On non-federal lands, which have fewer MOG, regulatory improvements and conservation incentives are needed.

Documenting temporal trends in the extent of ecosystems is essential to monitoring their status but combining this information with the degree of protection helps us assess the effectiveness of societal actions for conserving ecosystem diversity and related ecosystem services. We demonstrated indicators in the Tropical Andes using both potential (pre-industrial) and recent (~2010) distribution maps of terrestrial ecosystem types. We measured long-term ecosystem loss, representation of ecosystem types within the current protected areas, quantifying the additional representation offered by protecting Key Biodiversity Areas. Six (4.8%) ecosystem types (i.e., measured as 126 distinct vegetation macrogroups) have lost >50% in extent across four Andean countries since pre-industrial times. For ecosystem type representation within protected areas, regarding the pre-industrial extent of each type, a total of 32 types (25%) had higher representation (>30%) than the post-2020 Convention on Biological Diversity (CBD) draft target in existing protected areas. Just 5 of 95 types (5.2%) within the montane Tropical Andes hotspot are currently represented with >30% within the protected areas. Thirty-nine types (31%) within these countries could cross the 30% CBD 2030 target with the addition of Key Biodiversity Areas. This indicator is based on the Essential Biodiversity Variables (EBV) and responds directly to the needs expressed by the users of these countries.

The IUCN Red List of Ecosystems (RLE) is a global standard for ecosystem risk assessment that integrates data and knowledge to document the relative risk status of ecosystem types as critically endangered (CR), endangered (EN), and vulnerable (VU). A series of indicators for each type gauge the probability of range wide "collapse". Climate change vulnerability can factor into RLE assessments, especially as indicators of climate change severity under the criteria for environmental degradation over the recent and upcoming 50 years. We applied a new framework to assess climate change vulnerability-and thus, severity of climate change degradation-to a cross-section of 33 upland ecosystem types in the United States to demonstrate this input to the RLE. The framework addressed climate exposure and ecosystem resilience. Measures of climate change exposure used climate projections for the mid-21st century compared against a 20th century baseline. Augmenting measures in use for RLE assessment, measures of resilience included several for adaptive capacity, including topoclimate variability, diversity with functional species groups, and vulnerability of any keystone species. All 33 types were listed as VU (n = 22), EN (n = 9), or CR (n = 2) and 51% scored at least one step higher (e.g., LC up to VU) from climate change severity.

Aims: Natural resource management and biodiversity conservation rely on inventories of vegetation that span multiple management or political jurisdictions. However, while remote sensing data and analytical tools have enabled production of maps at increasing spatial resolution and reliability, there are limited examples where national or continental-scaled maps are produced to represent vegetation at high thematic detail. We illustrate two examples that have bridged the gap between traditional land cover mapping and modern vegetation classification. Study area: Our two case studies include national (USA) and continental (North and South America) vegetation and land cover mapping. These studies span conditions from subpolar to tropical latitudes of the Americas. Methods: Both case studies used a supervised modeling approach with the International Vegetation Classification (IVC) to produce maps that provide for greater thematic detail. Georeferenced locations for these vegetation types are used by machine learning algorithms to train a predictive model and generate a distribution map. Results: The USA LANDFIRE (Landscape Fire and Resource Management Planning Tools Project) case study illustrates how a history of vegetation-based classification and availability of key inputs can come together to generate standard map products covering more than 9.8 million km2 that are unsurpassed anywhere in the world in terms of spatial and thematic resolution. That being said, it also remains clear that mapping at the thematic resolution of the IVC Group and finer resolution require very large and spatially balanced inputs of georeferenced samples. Even with extensive prior data collection efforts, these remain a key limitation. The NatureServe effort for the Americas - encompassing 22% of the global land surface - demonstrates methods and outputs suitable for worldwide application at continental scales. Conclusions: Continued collection of input data used in the case studies could enable mapping at these spatial and thematic resolutions around the globe. Abbreviations: CART = Classification and Regression Tree; CONUS = Conterminous United States; DSWE = Dynamic Surface Water Extent; EPA = United States Environmental Protection Agency; FGDC = Federal Geographic Data Committee; IVC = International Vegetation Classification; LANDFIRE = Landscape Fire and Resource Management Planning Tools Project; LFRDB = LANDFIRE Reference Database; LiDAR = Light Detection and Ranging; NDVI = Normalized Difference Vegetation Index; NLCD = National Land Cover Database; USNVC = United States National Vegetation Classification; USA = United States of America; WWF = World Wildlife Fund or Worldwide Fund for Nature.

The IUCN Red List of Ecosystems (RLE) is an emerging global standard for ecosystem risk assessment that integrates data and knowledge to document the relative risk status of ecosystem types. Here we summarize initial findings from the applying IUCN criteria to 107 grassland and savanna ecosystems in temperate North America. Determining threatened status first requires type description and mapping. Then, a series of measures are applied addressing trends in ecosystem extent, the relative restricted nature of its distribution, and the relative severity of environmental degradation and disruption of biotic processes. All measures aim to gauge the probability of range wide collapse. Ecosystems may be scored as collapsed, critically endangered, endangered, vulnerable, near threatened, least concern, data deficient, or not evaluated. From this analysis, and taking uncertainty into account, 67 (63%) of grassland and savanna ecosystem types scored as threatened (i.e., either critically endangered (25%), endangered (14%), or vulnerable (24%)). Examples of threatened ecosystems include Midwestern tallgrass prairies and oak savannas, Pine barrens of the Northeast, California Central Valley grasslands and oak savannas, Palouse prairie, savannas and wet prairies of Washington and Oregon, Southeastern glades and barrens, and Florida dry prairie and inland scrub. These ecosystems historically occupied about 2.5 million km2 of North America but today account for only 834,000 km2, a loss of two-thirds of historical extent. Those listed in the most severely threatened categories of critically endangered or endangered today account for about 110,000 km2 or an 88% loss of their historical extent of about 930,000 km2. This result reflects conditions at temperate latitudes where, such as in the United States and Canada, advanced industrial economies have actively transformed ecosystems over recent centuries. The RLE provides governments with a means to prioritize conservation effort and for researchers to advance knowledge critical to natural resource conservation assessment, planning, management, and monitoring.

Anticipating where an invasive species could become abundant can help guide prevention and control efforts aimed at reducing invasion impacts. Information on potential abundance can be combined with information on the current status of an invasion to guide management towards currently uninvaded locations where the threat of invasion is high. We aimed to support management by developing predictive maps of potential cover for cheatgrass (Bromus tectorum), a problematic invader that can transform plant communities. We integrated our predictions of potential abundance with mapped estimates of current cover to quantify invasion potential within lightly invaded areas. We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios. We developed a species-specific set of covariates and validated model performance using spatially and temporally independent data. Potential cheatgrass abundance was higher in areas that had burned, at low elevations, and when fall germination conditions were more favorable. Our results highlight the extensive areas across the Great Basin where cheatgrass abundance could increase to levels that can alter fire behavior and cause other ecological impacts. We predict potential cheatgrass abundance to quantify relative invasion risk. Our model results provide high and low scenarios of cheatgrass abundance to guide resource allocation and planning efforts across shrubland ecosystems of the Great Basin that remain relatively uninvaded. Combining information on an invasive species’ current and potential abundance can yield spatial predictions to guide resource allocation and management action.

The International Union for Conservation of Nature (IUCN) Red List of Ecosystems (RLE) is an emerging global standard for ecosystem risk assessment that integrates data and knowledge to document the relative risk status of ecosystem types. Here, we summarize initial findings from applying four IUCN RLE criteria to 655 terrestrial ecosystems in temperate and tropical North America, or 8.5% of the global land surface. A series of indicators are measured for each criterion to address trends in ecosystem extent (A), the relative restricted nature of its distribution (B), and the extent and relative severity of environmental degradation (C), and the extent and relative severity of disruption of biotic processes (D); all to gauge the probability of range wide “collapse.” Ecosystems are listed as collapsed, critically endangered, endangered, vulnerable, near threatened, least concern, data deficient, or not evaluated. Taking uncertainty into account, 219 (33%) of terrestrial ecosystem types were listed as threatened (i.e., either critically endangered, [7%], endangered [14%], or vulnerable [13%]). Examples include tallgrass prairies, oak savannas, longleaf pine woodlands, floodplain forests, mesic hardwood forests, and dry tropical forests. Historically, these threatened ecosystems occurred across about 45% of the continental study area, and today account for about 30%. The RLE provides one important focus for prioritizing conservation effort.

In 2021, the Biden administration signed an executive order to protect 30% of American lands by 2030. Accomplishing this ambitious goal in the U.S. requires understanding the relative contribution of public and private lands toward supporting biodiversity. New approaches are needed because existing approaches focus on quantity of habitat without incorporating quality. To fill this need, we developed a 30 m resolution national habitat condition index (HCI) that integrates quality and quantity measures of habitat. We hypothesized that including an evaluation of the quality of habitat at landscape scales, both in conservation-focused preserves and working lands would provide a better assessment of the value of geographies for conservation. We divided the conterminous U.S. by major land cover type and into natural and cultivated lands and then spatially mapped multiple anthropogenic stressors, proximity to aquatic habitat, and vegetation departure from expected natural disturbance regimes. Each map layer was then scored for site impact and distance decay and combined into a final national index. Field observations providing scored relative ecological conditions were used for HCI calibration and validation at both CONUS and regional scales. Finally, we evaluate lands by management (conservation versus working lands) and ownership (public versus private) testing the value of these lands for conservation. While we found regional differences across CONUS, functional habitat was largely independent of protection status: working lands provide clear habitat and other values. These results are relevant for guiding strategies to achieve the U.S. 30 by 30 goals. Where similar data exist in other countries, analogous modeling could be used to meet their national conservation commitments.

First posted May 20, 2021 For additional information, contact: Director, Lower Mississippi-Gulf Water Science CenterU.S. Geological Survey640 Grassmere Park Drive Nashville, TN 37211 The unglaciated southeastern United States is a biodiversity hotspot, with a disproportionate amount of this biodiversity concentrated in grasslands. Like most hotspots, the Southeast is also threatened by human activities, with the total reduction of southeastern grasslands estimated as 90 percent (upwards to 100 percent for some types) and with many threats escalating today. This report summarizes the results of a multistakeholder workshop organized by the Southeastern Grasslands Initiative and the U.S. Geological Survey, held in January 2020 to provide a scientific needs assessment to help inform the Species Status Assessment (SSA) process under the U.S. Endangered Species Act, with a focus on grassland species and communities of conservation concern in the southeastern United States. This report reviews the ecology of southeastern grasslands, including influences on their origin, maintenance, and high species richness and endemism; presents findings from the workshop; and discusses science questions, hypotheses, and possibilities for future research projects to help fill key knowledge gaps.Participants in the January 2020 workshop, representing diverse expertise in various topics in southeastern grassland ecology, were tasked with identifying major threats to grassland species in the Southeast as well as potential ways to make the SSA process more efficient and effective. An underlying assumption and starting place for workshop discussion was that an ecosystem-based approach to the SSA process is more cost-efficient than a species-by-species approach, in large part because many species with similar biological requirements can be addressed by the same actions. Nevertheless, one partner in this effort, the U.S. Fish and Wildlife Service, does require specific attention be given to taxa that have been petitioned for Federal listing, though as often as possible these taxa are considered alongside a larger group of priority taxa with an ecosystem approach.For group discussions, workshop participants followed a modified “World Café” method, a structured conversational approach for knowledge sharing. Group discussions focused on five categories of threats to grassland communities and species: (1) habitat loss, fragmentation, and disruption of functional population connectivity; (2) climate change, especially changes in temperature and precipitation, including intensity and seasonality, and impacts on soil moisture, groundwater levels, and other ecosystem parameters; (3) changes to disturbance regimes, as influenced by climate and land-use change, extinctions, and human attitudes and behaviors; (4) invasive species (not limited to nonnative species); and (5) localized or subregional impacts such as sea-level rise. In addition to group discussions, workshop participants—as well as other grassland experts who were unable to attend the workshop—completed a preworkshop survey concerning challenges and opportunities for grassland conservation. Findings reported here under each of these topics represent ideas, problems, hypotheses, and questions identified by a diverse community of grassland managers and researchers which may be addressed by future research and monitoring in southeastern grassland ecosystems to help guide science-based conservation of grassland-dependent species.

Observed ecological responses to climate change are highly individualistic across species and locations, and understanding the drivers of this variability is essential for management and conservation efforts. While it is clear that differences in exposure, sensitivity, and adaptive capacity all contribute to heterogeneity in climate change vulnerability, predicting these features at macroecological scales remains a critical challenge. We explore multiple drivers of heterogeneous vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US, using data on observed climate trends from 1948 to 2014 to highlight emerging patterns of change. We ask three novel questions about factors potentially shaping vulnerability across the region: (a) How does sensitivity to different climate variables vary geographically and across vegetation classes? (b) How do multivariate climate exposure patterns interact with these sensitivities to shape vulnerability patterns? (c) How different are these vulnerability patterns according to three widely implemented vulnerability paradigms-niche novelty (decline in modeled suitability), temporal novelty (standardized anomaly), and spatial novelty (inbound climate velocity)-each of which uses a distinct frame of reference to quantify climate departure? We propose that considering these three novelty paradigms in combination could help improve our understanding and prediction of heterogeneous climate change responses, and we discuss the distinct climate adaptation strategies connected with different combinations of high and low novelty across the three metrics. Our results reveal a diverse mosaic of climate change vulnerability signatures across the region's plant communities. Each of the above factors contributes strongly to this heterogeneity: climate variable sensitivity exhibits clear patterns across vegetation types, multivariate climate change data reveal highly diverse exposure signatures across locations, and the three novelty paradigms diverge widely in their climate change vulnerability predictions. Together, these results shed light on potential drivers of individualistic climate change responses and may help to inform effective management strategies.