Large-scale solar (LSS) deployment has accelerated rapidly across the United States, yet comprehensive understanding of its siting patterns remains limited. Here, we present a national-scale geospatial assessment of land characteristics converted for LSS development and apply statistical analyses to evaluate whether installations are disproportionately concentrated within specific land types. We find that siting patterns are strongly correlated with high solar irradiance, underscoring the role of resource quality in shaping deployment decisions. LSS projects are disproportionately concentrated on agricultural lands and shrubland. Within agricultural areas, installations predominantly occur on croplands and are strongly associated with irrigated systems. Notably, more than 90% of projects are deployed on agricultural land with Productive, Versatile, and Resilient (PVR) index values of land quality exceeding the national median. These findings highlight the need for strategic siting frameworks that reconcile solar development with the conservation of agriculturally valuable land.

AbstractThe extent of grasslands has contracted globally, leading to significant declines in grassland-obligate species, including birds. Grassland birds are threatened by multiple factors, including grassland conversion to cropland, and development, including energy infrastructure. In the USA, the federal Conservation Reserve Program (CRP) provides financial incentives for agricultural landowners to convert marginal cropland to vegetative covers, including grassland, making it a key approach to recovering grasslands. However, given the high percentage of wind turbines near Conservation Reserve Program land, it is critical to determine if wind energy and the Conservation Reserve Program interact to affect grassland birds. We used generalized additive models and community science eBird data to assess the interactive effects of wind and the Conservation Reserve Program on the abundances of 27 grassland-obligate bird species across their breeding ranges in the continental US. We found associations between bird relative abundances and interactions involving wind and Conservation Reserve Program variables, suggesting that wind energy development near Conservation Reserve Program fields could dampen the benefits of the Conservation Reserve Program for some species. These results indicate that decisions about where to install wind energy infrastructure that incorporate information about the presence of Conservation Reserve Program land may reduce the adverse effects of wind energy on grassland birds. This study provides information that can help maximize the benefits of the Conservation Reserve Program while allowing for environmentally friendly development of renewable energy.

The urban tree cover (UTC) is a key indicator for monitoring climate adaptation, guiding policy goals, and designing equitable cities. However, consistent UTC estimates remain limited. This study proposes a transferable UTC modelling framework using global open-source data to map fractional UTC. The method involves satellite spectral products and high-resolution canopy height data to build city-specific machine learning models of 19 Canadian cities. Model accuracy, assessed through cross-validation, ranged from R2 = 0.67-0.88 with RMSE values of 5.5-16.3%. Independent validation against airborne laser scanning in three cities demonstrated strong agreement (Pearson r = 0.82-0.92). Results suggest that UTC distributions are unevenly distributed, typically right skewed, with large parks and riparian corridors accounting for a disproportionate share of UTC. Greenness and moisture are the most influential predictors, while model residuals concentrate in areas with higher UTC. The resulting UTC maps and modelling framework provide a consistent and reproducible framework for UTC distribution assessments, benchmarking across diverse ecozones, and monitoring UTC change at urban scales over time.

Abstract The monarch butterfly (Danaus plexippus) is a species of iconic cultural interest. Thanks to annual overwintering monarch counts at hundreds of locations in coastal California, we are able to track fluctuations with high temporal and spatial resolution. Between 1997 and 2024, monarch populations at overwintering sites in the western United States experienced severe dips, at times (2018–2020, 2023–2024) giving the appearance of a population collapse. From 2018 to present, the Pismo State Beach Overwintering Monarch Grove has conducted multiple counts during overwintering and geolocated counts of individual monarch clusters to specific trees within the site. This study determined how annual monarch population variability is influenced by both climate and prior year population density at the state, region, and overwintering‐site scale. Furthermore, through a machine‐learning process, we investigated how overwintering site configuration and structure drive monarch winter space‐use dynamics within the Pismo Beach site. Our approach found monarchs exhibit a preference for specific overwintering sites in California, and that 64% of annual variability of counts across sites can be explained by climate and density dependence, with density dependence explaining 50% of total variability. Within the site we found very little regional climate effect, but individual trees, tree size, distance to boundary, and the amount of shade were all strong indicators of monarch presence. Additionally, only 11 out of 320 trees at the Pismo Beach site accounted for 83.6% of all counts over 6 years, highlighting how monarchs use specific trees and how tree structure may create preferred microclimates for clustering.

Because urban landscapes are heterogeneous, the methods and spatial resolution used to depict the land surface greatly influence the representation of urban features. Land cover products such as tree canopy cover (TCC) are particularly sensitive to the methodology and resolution used in their creation. Differences in TCC mapping have implications on the outcomes of ecosystem service (ES) models, including those underlying natural capital accounting. Here, we quantify the sensitivity of physical rainfall interception and local climate regulation ES models for 189 U.S. cities to TCC inputs from four TCC products: a) National Land Cover Database (NLCD), b) Enhanced NLCD TCC, c) aggregated city-specific composite, and d) global tree canopy height dataset. We find both city-level and aggregate differences in TCC estimates, from a 38% decrease to a 3% increase relative to an aggregated high-resolution product. These differences result in up to 3% overestimations and 27% underestimation of rainfall interception and 2–56% underestimation of local climate regulation ES. City size, population, and greenness in addition to climatic variation drove differences between TCC products, and this variation requires users to carefully consider the choice of input data for any planned analysis. Though high-resolution data can offer greater nuance and accuracy, more limited spatiotemporal availability can hinder their usefulness for long-term monitoring applications such as natural capital accounting. The differences found in this study provide valuable insights for making informed decisions on data inputs for use in urban ecosystem research and for contextualizing model outcomes.

Migratory species, and the many cultural and economic values they provide, are declining worldwide. Previous research has quantified ecosystem service flows for single migratory species to provide valuable information on the dynamics of socio-ecological systems to inform cross-jurisdictional management. The urgency of the conservation challenge suggests there is value in exploring the potential management efficiencies to be gained from considering the dynamics of multiple species (e.g., ecological guilds) simultaneously. As proof of concept, this paper demonstrates how ecosystem service flows can be aggregated spatially for a group of three North American migratory species. We further present a flexible approach to estimating how conservation funding can be allocated equitably between the socio-ecological regions of the migratory range. Results show that most benefits from these three migratory species are supported locally by habitat within each region, but net interregional flows exist, including a $33.1 M USD annual subsidy from Mexico to the U.S. and Canada. Results also suggest that directing 18% of net conservation funding raised for these species in the U.S. and Canada toward conservation projects in Mexico would redress inequities in shared management responsibility. Considering the values provided by multiple species simultaneously has the potential to broaden coalitions and expedite negotiation of international agreements by providing a better shared understanding of the benefits to each party.

Moderate-resolution (30-m) national map products have limited capacity to represent fine-scale, heterogeneous urban forms and processes, yet improvements from incorporating higher resolution predictor data remain rare. In this study, we applied random forest models to high-resolution land cover data for 71 U.S. urban areas, moderate-resolution National Land Cover Database (NLCD) Tree Canopy Cover (TCC), and additional explanatory climatic and structural data to develop an enhanced urban TCC dataset for U.S. urban areas. With a coefficient of determination (R2) of 0.747, our model estimated TCC within 3% for 62 urban areas and added 13.4% more city-level TCC on average, compared to the native NLCD TCC product. Cross validations indicated model stability suitable for building a national-scale TCC dataset (median R2 of 0.752, 0.675, and 0.743 for 1,000-fold cross validation, urban area leave-one-out cross validation, and cross validation by Census block group median year built, respectively). Additionally, our model code can be used to improve moderate-resolution TCC in other parts of the world where high-resolution land cover data have limited spatiotemporal availability.

Home Owners' Loan Corporation (HOLC) maps illustrated patterns of segregation in United States cites in the 1930s. As the causes and drivers of demographic and land-use segregation vary over years, these maps provide an important spatial lens in determining how patterns of segregation spatially and temporally developed during the past century. Using a high-resolution land-use time series (1937-2018) of Denver, Colorado, USA, in conjunction with 80 years of U.S. Census data, we found divergent land-use and demographics patterns across HOLC categories were both pre-existent to the establishment of HOLC mapping and continued to develop over time. Over this period, areas deemed "declining" or "hazardous" had more diverse land use compared to "desirable" areas. "Desirable" areas were dominated by one land-use type (single-family residential), while single-family residential diminished in prominence in the "declining/hazardous" areas. This divergence became more established decades after HOLC mapping, with impact to racial metrics and low-income households. We found changes in these demographic patterns also occurred between 2000 and 2019, highlighting how processes like gentrification can develop from both rapid demographic and land-use changes. This study demonstrates how the legacy of urban segregation develops over decades and can simultaneously persist in some neighborhoods while providing openings for fast-paced gentrification in others.

Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.

Global expansion in wind energy development is a notable achievement of the international community's effort to reduce carbon emissions during energy production. However, the increasing number of wind turbines have unintended consequences for migratory birds and bats. Wind turbine curtailment and other mitigation strategies can reduce fatalities, but improved spatial and temporal data are needed to identify the most effective way for wind energy development and volant migratory species to coexist. Mexican free-tailed bats (Tadarida brasiliensis mexicana) account for a large proportion of known bat fatalities at wind facilities in the southwestern US. We examined the geographic concordance between existing wind energy generation facilities, areas of high wind potential amenable for future deployment of wind facilities, and seasonally suitable habitat for these bats. We used ecological niche modeling to determine species distribution during each of 4 seasons. We used a multi-criteria GIS-based approach to produce a wind turbine siting suitability map. We identified seasonal locations with highest and lowest potential for the species' probability of occurrence, providing a potential explanation for the higher observed fatalities during fall migration. Thirty percent of 33,606 wind turbines within the southwestern US occurred in highly suitable areas for Mexican free-tailed bats, primarily in west Texas. There is also broad spatial overlap between areas of high wind potential and areas of suitable habitat for Mexican free-tailed bats. Because of this high degree of overlap, our results indicate that post-construction strategies, such as curtailing the timing of operations and deterrents, would be more effective for bat conservation than strategic siting of new wind energy installations.

Returning monarch butterflies (Danaus plexippus) to sustainable levels of abundance will require an array of contributors to protect and restore habitat over broad areas. Due to the diversity and scale of land managed by electric power companies across the monarch range, plus an additional 32 million hectares needed for new solar arrays by 2050 to meet renewable energy goals, the industry may have potential to contribute to monarch conservation. However, it is challenging to clearly understand an individual company’s potential for monarch conservation because of the scale and distribution of their specific land assets (ranging from 4,800 to 240,000 hectares in this study alone), the complexity of monarch science, and the lack of a science-based approach for evaluating large land assets for monarch habitat. With monarchs potentially being protected under the United States Endangered Species Act in the future and thereby limiting land management approaches, there is interest from electric power companies to understand how their lands relate to monarchs. In collaboration with companies, we developed a GIS-based model to identify company landholdings that contain high-quality monarch habitat and applied the model to specific landholdings of eight power companies in the United States. We then facilitated discussions with company teams to balance conservation goals, corporate risk, and social opinion. This paper describes non-confidential results for developing a national GIS-based monarch habitat model and applying it to electric power companies who are considering monarch conservation while simultaneously transitioning to a new clean energy future. The model and applied experience may be useful for other organizations working across large landscapes to manage monarchs.

Bird populations are declining globally. Wind and solar energy can reduce emissions of fossil fuels that drive anthropogenic climate change, yet renewable-energy production represents a potential threat to bird species. Surveys to assess potential effects at renewable-energy facilities are exclusively local, and the geographic extent encompassed by birds killed at these facilities is largely unknown, which creates challenges for minimizing and mitigating the population-level and cumulative effects of these fatalities. We performed geospatial analyses of stable hydrogen isotope data obtained from feathers of 871 individuals of 24 bird species found dead at solar- and wind-energy facilities in California (USA). Most species had individuals with a mix of origins, ranging from 23% to 98% nonlocal. Mean minimum distances to areas of likely origin for nonlocal individuals were as close as 97 to >1250 km, and these minimum distances were larger for species found at solar-energy facilities in deserts than at wind-energy facilities in grasslands (Cohen's d = 6.5). Fatalities were drawn from an estimated 30-100% of species' desingated ranges, and this percentage was significantly smaller for species with large ranges found at wind facilities (Pearson's r = -0.67). Temporal patterns in the geographic origin of fatalities suggested that migratory movements and nonmigratory movements, such as dispersal and nomadism, influence exposure to fatality risk for these birds. Our results illustrate the power of using stable isotope data to assess the geographic extent of renewable-energy fatalities on birds. As the buildout of renewable-energy facilities continues, accurate assessment of the geographic footprint of wildlife fatalities can be used to inform compensatory mitigation for their population-level and cumulative effects.

Projections for deep decarbonization require large amounts of solar energy, which may compete with other land uses such as agriculture, urbanization, and conservation of natural lands. Existing capacity expansion models do not integrate land use land cover change (LULC) dynamics into projections. We explored the interaction between projected LULC, solar photovoltaic (PV) deployment, and solar impacts on natural lands and croplands by integrating projections of LULC with a model that can project future deployment of solar PV with high spatial resolution for the conterminous United States. We used scenarios of LULC projections from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios from 2010 to 2050 and two electricity grid scenarios to model future PV deployment and compared those results against a baseline that held 2010 land cover constant through 2050. Though solar PV's overall technical potential was minimally impacted by LULC scenarios, deployed PV varied by -16.5 to 11.6 % in 2050 from the baseline scenario. Total land requirements for projected PV were similar to other studies, but measures of PV impacts on natural systems depended on the underlying land change dynamics occurring in a scenario. The solar PV deployed through 2050 resulted in 1.1 %-2.4 % of croplands and 0.3 %-0.7 % of natural lands being converted to PV. However, the deepest understanding of PV impacts and interactions with land cover emerged when the complete net gains and losses from all land cover change dynamics, including PV, were integrated. For example, one of the four LULC projections allows for high solar development and a net gain in natural lands, even though PV drives a larger percentage of natural land conversion. This paper shows that integrating land cover change dynamics with energy expansion models generates new insights into trade offs between decarbonization, impacts of renewables, and ongoing land cover change.

The decline of the iconic monarch butterfly ( Danaus plexippus ) in North America has motivated research on the impacts of land use and land cover (LULC) change and climate variability on monarch habitat and population dynamics. We investigated spring and fall trends in LULC, milkweed and nectar resources over a 20-year period, and ~ 30 years of climate variables in Mexico and Texas, U.S. This region supports spring breeding, and spring and fall migration during the annual life cycle of the monarch. We estimated a − 2.9% decline in milkweed in Texas, but little to no change in Mexico. Fall and spring nectar resources declined < 1% in both study extents. Vegetation greenness increased in the fall and spring in Mexico while the other climate variables did not change in both Mexico and Texas. Monarch habitat in Mexico and Texas appears relatively more intact than in the midwestern, agricultural landscapes of the U.S. Given the relatively modest observed changes in nectar and milkweed, the relatively stable climate conditions, and increased vegetation greenness in Mexico, it seems unlikely that habitat loss (quantity or quality) in Mexico and Texas has caused large declines in population size or survival during migration.

Rapid and unplanned development can diminish the social values for ecosystem services associated with urban landscapes. Tropical Global South cities, such as Greater Kuala Lumpur (GKL), Malaysia, that are highly biodiverse, are particularly vulnerable to the fragmentation and loss of natural ecosystems. This study investigates the social values for ecosystem services in GKL, a rapidly urbanising metropolitan area in Southeast Asia and presents the novel application of the SolVES (Social Values for Ecosystem Services) tool to an urban area. A public participatory GIS (PPGIS) survey was conducted to assess residents’ development preferences and perceived distribution of social values. The study revealed two groups of respondents, using cluster analysis, each with unique development preferences - a group favouring both green and grey developments and another favouring green development but strongly opposing grey development. The two groups differed statistically in their sociodemographic characteristics, and also how they mapped different social values. Our spatial analysis of their preferences towards future urban expansion in GKL highlighted locations where there is potential for land-use conflict. For example, the favour-balanced-development group tended to value built areas over green spaces. These findings support the need to consider residents’ preferences and socioecological traits through greater public engagement in urban landscape management.

The migration of species, often across continents, makes it difficult to quantify the cumulative effects of localand regional-scale conservation actions. Further, variation in stakeholder interests, differing jurisdictional governance processes, priorities, and monitoring abilities across the migratory range shapes place-specific differences in management actions. These differences may lead management of migratory species to benefit both species and stakeholders in some places more than others. In the case of North American waterfowl, possible reduction of wetland protection in breeding areas may lead to substantive shifts in benefits among stakeholders across their range by adversely affecting recreational viewing and hunting opportunities for these species. To understand possible consequences of wetland loss in the U.S. Prairie Pothole Region, the breeding region for 12 focal species of waterfowl, on the recreation economics for these species, we modeled a causal pathway linking wetland loss in the breeding grounds to changes in duck abundance and then assessed the consequences of that change in abundance on recreational hunting and viewing within migratory flyways. Under a scenario where wetland protections cease, we find annual economic activity associated with recreation may decrease as much as $489 million at the highest levels of predicted wetland loss, the majority of it coming from impacts to viewing behavior in the Mississippi Flyway. The number of hunters may decline by as much as 18,000, leading to $32 million less in annual economic activity. At highest levels of wetland loss, viewing value is expected to decline by more than one-quarter. Lost economic value associated with reductions in recreation in the Mississippi and Central Flyway states is not likely to be overcome by increases in agricultural economic output in drained wetlands of the Prairie Pothole Region. Our analyses indicate local effects of national water policies likely have far-reaching consequences because of the multi-dimensional connections arising from place-specific differences in management action, global and national agricultural economic drivers of crop expansion, and the biotic phenomena of transcontinental avian migration. Reductions in habitat in one location could ramify to economic consequences throughout the continent through connections fostered by migrating waterfowl.

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MWdc) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.

Many pressing conservation issues are complex problems caused by multiple social and environmental drivers; their resolution is aided by interdisciplinary teams of scientists, decision makers, and stakeholders working together. In these situations, how do we generate science to effectively guide conservation (resource management and policy) decisions? This paper describes elements of successful big-team science in conservation, as well as shortcomings and lessons learned, based on our work with the monarch butterfly ( Danaus plexippus ) in North America. We summarize literature on effective science teams, extracting information about elements of success, effective implementation approaches, and barriers or pitfalls. We then describe recent and ongoing conservation science for the monarch butterfly in North America. We focus primarily on the activities of the Monarch Conservation Science Partnership–an international collaboration of interdisciplinary scientists, policy experts and natural resource managers spanning government, non-governmental and academic institutions—which developed science to inform imperilment status, recovery options, and monitoring strategies. We couch these science efforts in the adaptative management framework of Strategic Habitat Conservation, the business model for conservation employed by the US Fish and Wildlife Service to inform decision-making needs identified by stakeholders from Canada, the United States, and Mexico. We conclude with elements critical to effective big-team conservation science, discuss why science teams focused on applied conservation problems are unique relative to science teams focusing on traditional or theoretical research, and list benefits of big team science in conservation.