The global wildland-urban interface.

The wildland-urban interface (WUI) is where buildings and wildland vegetation meet or intermingle1,2. It is where human-environmental conflicts and risks can be concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation and the spread of zoonotic diseases3. However, a global analysis of the WUI has been lacking. Here, we present a global map of the 2020 WUI at 10 m resolution using a globally consistent and validated approach based on remote sensing-derived datasets of building area4 and wildland vegetation5. We show that the WUI is a global phenomenon, identify many previously undocumented WUI hotspots and highlight the wide range of population density, land cover types and biomass levels in different parts of the global WUI. The WUI covers only 4.7% of the land surface but is home to nearly half its population (3.5 billion). The WUI is especially widespread in Europe (15% of the land area) and the temperate broadleaf and mixed forests biome (18%). Of all people living near 2003-2020 wildfires (0.4 billion), two thirds have their home in the WUI, most of them in Africa (150 million). Given that wildfire activity is predicted to increase because of climate change in many regions6, there is a need to understand housing growth and vegetation patterns as drivers of WUI change.

The potential of historical spy-satellite imagery to support research in ecology and conservation.

Remote sensing data are important for assessing ecological change, but their value is often restricted by their limited temporal coverage. Major historical events that affected the environment, such as those associated with colonial history, World War II, or the Green Revolution are not captured by modern remote sensing. In the present article, we highlight the potential of globally available black-and-white satellite photographs to expand ecological and conservation assessments back to the 1960s and to illuminate ecological concepts such as shifting baselines, time-lag responses, and legacy effects. This historical satellite photography can be used to monitor ecosystem extent and structure, species' populations and habitats, and human pressures on the environment. Even though the data were declassified decades ago, their use in ecology and conservation remains limited. But recent advances in image processing and analysis can now unlock this research resource. We encourage the use of this opportunity to address important ecological and conservation questions.

Mapping multiscale breeding bird species distributions across the United States and evaluating their conservation applications.

Species distribution models are vital to management decisions that require understanding habitat use patterns, particularly for species of conservation concern. However, the production of distribution maps for individual species is often hampered by data scarcity, and existing species maps are rarely spatially validated due to limited occurrence data. Furthermore, community-level maps based on stacked species distribution models lack important community assemblage information (e.g., competitive exclusion) relevant to conservation. Thus, multispecies, guild, or community models are often used in conservation practice instead. To address these limitations, we aimed to generate fine-scale, spatially continuous, nationwide maps for species represented in the North American Breeding Bird Survey (BBS) between 1992 and 2019. We developed ensemble models for each species at three spatial resolutions-0.5, 2.5, and 5 km-across the conterminous United States. We also compared species richness patterns from stacked single-species models with those of 19 functional guilds developed using the same data to assess the similarity between predictions. We successfully modeled 192 bird species at 5-km resolution, 160 species at 2.5-km resolution, and 80 species at 0.5-km resolution. However, the species we could model represent only 28%-56% of species found in the conterminous US BBSs across resolutions owing to data limitations. We found that stacked maps and guild maps generally had high correlations across resolutions (median = 84%), but spatial agreement varied regionally by resolution and was most pronounced between the East and West at the 5-km resolution. The spatial differences between our stacked maps and guild maps illustrate the importance of spatial validation in conservation planning. Overall, our species maps are useful for single-species conservation and can support fine-scale decision-making across the United States and support community-level conservation when used in tandem with guild maps. However, there remain data scarcity issues for many species of conservation concern when using the BBS for single-species models.

Beyond "greening" and "browning": Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation.

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.

Prioritizing global tall forests toward the 30 × 30 goals.

The Global Deal for Nature sets an ambitious goal to protect 30% of Earth's land and ocean by 2030. The 30 × 30 initiative is a way to allocate conservation resources and extend protection to conserve vulnerable and underprotected ecosystems while reducing carbon emissions to combat climate change. However, most prioritization methods for identifying high-value conservation areas are based on thematic attributes and do not consider vertical habitat structure. Global tall forests represent a rare vertical habitat structure that harbors high species richness in various taxonomic groups and is associated with large amounts of aboveground biomass. Global tall forests should be prioritized when planning global protected areas toward reaching the 30 × 30 goals. We examined the spatial distribution of global tall forests based on the Global Canopy Height 2020 product. We defined global tall forests as areas with the average canopy height above 3 thresholds (20, 25, and 30 m). We quantified the spatial distribution and protection level of global tall forests in high-protection zones, where the 30 × 30 goals are being met or are within reach, and low-protection zones, where there is a low chance of reaching 30 × 30 goals. We quantified the protection level by computing the percentage of global tall forest area protected based on the 2017 World Database on Protected Areas. We also determined the global extent and protection level of undisturbed, mature, tall forests based on the 2020 Global Intact Forest Landscapes mask. In most cases, the percentage of protection decreased as forest height reached the top strata. In the low-protection zones, <30% of forests were protected in almost all tall forest strata. In countries such as Brazil, tall forests had a higher percentage of protection (consistently >30%) compared to forests of lower height, presenting a more effective conservation model than in countries such as the United States, where forest protection was almost uniformly <30% across height strata. Our results show an urgent need to target forest conservation in the greatest height strata, particularly in high-protection areas, where most global tall forests are found. Vegetation vertical structure can inform the decision-making process toward the 30 × 30 goals because it can be used to identify areas of high conservation value for biodiversity protection which also contribute to carbon sequestration.

Priorización de bosques globales altos hacia las metas 30 por 30 Resumen El Tratado Global por la Naturaleza establece una meta ambiciosa de proteger 30% de los continentes y océanos de la Tierra para 2030. La iniciativa 30 por 30 es una forma de asignar recursos para la conservación y extender la protección para conservar ecosistemas vulnerables y sin protección al tiempo que se controlan las emisiones de carbono para combatir el cambio climático. Sin embargo, la mayoría de los métodos de priorización para identificar áreas de elevado valor de conservación se basan en atributos temáticos y no consideran la estructura vertical del hábitat. Los bosques altos globales representan un estructura de hábitat vertical rara que alberga alta riqueza de especies de varios grupos taxonómicos y se asocia con grandes cantidades de biomasa aérea. Los bosques altos globales deberían ser priorizados cuando se planifican áreas protegidas globales en el esfuerzo por alcanzar las metas 30 por 30. Examinamos la distribución espacial de bosques globales con base en el producto Altura de Dosel Global 2020. Definimos a los bosques altos globales como áreas con una altura de dosel promedio por arriba de 3 umbrales (20, 25 y 30 m). Cuantificamos la distribución espacial y el nivel de protección de los bosques altos globales en zonas con gran protección, donde se están alcanzando las metas 30 por 30. Cuantificamos el nivel de protección registrando el porcentaje de bosque alto global protegido con base en la Base de Datos Mundial de Áreas Protegidas 2017. También determinamos la extensión global y el nivel de protección de bosques altos, maduros, no perturbados con base en la mascarilla Paisajes Forestales Globales Intactos 2020. En la mayoría de los casos, el porcentaje de protección decreció a medida que la altura del bosque llegaba al estrato superior. En las zonas poco protegidas, >30% de los bosques estaban protegidos en casi todos los estratos de bosque alto. En países como Brasil, los bosques altos tuvieron un mayor porcentaje de protección (>30% consistentemente) que los bosques de menor altura, presentando un modelo de conservación más efectivo que en países como los Estados Unidos, donde la protección de bosques fue casi uniformemente >30% en los tres estratos de altura. Nuestros resultados muestran una urgente necesidad de enfocar la conservación de bosques en los estratos más altos, particularmente en las áreas muy protegidas, donde se encuentra la mayoría de bosques altos globales. La estructura vertical de la vegetación puede proporcionar información al proceso de toma de decisiones con miras a las metas 30 por 30 debido a que puede ser utilizada para identificar áreas de elevado valor de conservación para la protección de la biodiversidad que también contribuya al secuestro de carbono.

The wildland-urban interface in the United States based on 125 million building locations.

The wildland-urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human-wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous United States based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint data set from Microsoft. We classified WUI across the conterminous United States at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and census-based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6%-18.8% of the conterminous United States as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail and can be widely applied to take advantage of the growing availability of high-resolution building footprint data sets and classification methods.

Mapping breeding bird species richness at management-relevant resolutions across the United States.

Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life-history-based traits across the conterminous United States at three resolutions (0.5, 2.5, and 5 km) and quantify the tradeoff between resolution and accuracy and, hence, relevance for management of the resulting biodiversity maps. We summarized 18 years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percentage variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using ~24 predictor variables based on percentage variance explained, symmetric mean absolute percentage error, and root mean square error values. However, our 2.5-km-resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.

Forest phenoclusters for Argentina based on vegetation phenology and climate.

Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2 ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions.

Global mitigation potential of carbon stored in harvested wood products.

Carbon stored in harvested wood products (HWPs) can affect national greenhouse gas (GHG) inventories, in which the production and end use of HWPs play a key role. The Intergovernmental Panel on Climate Change (IPCC) provides guidance on HWP carbon accounting, which is sensitive to future developments of socioeconomic factors including population, income, and trade. We estimated the carbon stored within HWPs from 1961 to 2065 for 180 countries following IPCC carbon-accounting guidelines, consistent with Food and Agriculture Organization of the United Nations (FAOSTAT) historical data and plausible futures outlined by the shared socioeconomic pathways. We found that the global HWP pool was a net annual sink of 335 Mt of CO2 equivalent (CO2e)⋅y-1 in 2015, offsetting substantial amounts of industrial processes within some countries, and as much as 441 Mt of CO2e⋅y-1 by 2030 under certain socioeconomic developments. Furthermore, there is a considerable sequestration gap (71 Mt of CO2e⋅y-1 of unaccounted carbon storage in 2015 and 120 Mt of CO2e⋅y-1 by 2065) under current IPCC Good Practice Guidance, as traded feedstock is ineligible for national GHG inventories. However, even under favorable socioeconomic conditions, and when accounting for the sequestration gap, carbon stored annually in HWPs is <1% of global emissions. Furthermore, economic shocks can turn the HWP pool into a carbon source either long-term-e.g., the collapse of the USSR-or short-term-e.g., the US economic recession of 2008/09. In conclusion, carbon stored within end-use HWPs varies widely across countries and depends on evolving market forces.

Recent collapse of crop belts and declining diversity of US agriculture since 1840.

Over the last century, US agriculture greatly intensified and became industrialized, increasing in inputs and yields while decreasing in total cropland area. In the industrial sector, spatial agglomeration effects are typical, but such changes in the patterns of crop types and diversity would have major implications for the resilience of food systems to global change. Here, we investigate the extent to which agricultural industrialization in the United States was accompanied by agglomeration of crop types, not just overall cropland area, as well as declines in crop diversity. Based on county-level analyses of individual crop land cover area in the conterminous United States from 1840 to 2017, we found a strong and abrupt spatial concentration of most crop types in very recent years. For 13 of the 18 major crops, the widespread belts that characterized early 20th century US agriculture have collapsed, with spatial concentration increasing 15-fold after 2002. The number of counties producing each crop declined from 1940 to 2017 by up to 97%, and their total area declined by up to 98%, despite increasing total production. Concomitantly, the diversity of crop types within counties plummeted: in 1940, 88% of counties grew >10 crops, but only 2% did so in 2017, and combinations of crop types that once characterized entire agricultural regions are lost. Importantly, declining crop diversity with increasing cropland area is a recent phenomenon, suggesting that corresponding environmental effects in agriculturally dominated counties have fundamentally changed. For example, the spatial concentration of agriculture has important consequences for the spread of crop pests, agrochemical use, and climate change. Ultimately, the recent collapse of most agricultural belts and the loss of crop diversity suggest greater vulnerability of US food systems to environmental and economic change, but the spatial concentration of agriculture may also offer environmental benefits in areas that are no longer farmed.

Rapid growth of the US wildland-urban interface raises wildfire risk.

The wildland-urban interface (WUI) is the area where houses and wildland vegetation meet or intermingle, and where wildfire problems are most pronounced. Here we report that the WUI in the United States grew rapidly from 1990 to 2010 in terms of both number of new houses (from 30.8 to 43.4 million; 41% growth) and land area (from 581,000 to 770,000 km2; 33% growth), making it the fastest-growing land use type in the conterminous United States. The vast majority of new WUI areas were the result of new housing (97%), not related to an increase in wildland vegetation. Within the perimeter of recent wildfires (1990-2015), there were 286,000 houses in 2010, compared with 177,000 in 1990. Furthermore, WUI growth often results in more wildfire ignitions, putting more lives and houses at risk. Wildfire problems will not abate if recent housing growth trends continue.

Habitat heterogeneity captured by 30-m resolution satellite image texture predicts bird richness across the United States.

Species loss is occurring globally at unprecedented rates, and effective conservation planning requires an understanding of landscape characteristics that determine biodiversity patterns. Habitat heterogeneity is an important determinant of species diversity, but is difficult to measure across large areas using field-based methods that are costly and logistically challenging. Satellite image texture analysis offers a cost-effective alternative for quantifying habitat heterogeneity across broad spatial scales. We tested the ability of texture measures derived from 30-m resolution Enhanced Vegetation Index (EVI) data to capture habitat heterogeneity and predict bird species richness across the conterminous United States. We used Landsat 8 satellite imagery from 2013-2017 to derive a suite of texture measures characterizing vegetation heterogeneity. Individual texture measures explained up to 21% of the variance in bird richness patterns in North American Breeding Bird Survey (BBS) data during the same time period. Texture measures were positively related to total breeding bird richness, but this relationship varied among forest, grassland, and shrubland habitat specialists. Multiple texture measures combined with mean EVI explained up to 41% of the variance in total bird richness, and models including EVI-based texture measures explained up to 10% more variance than those that included only EVI. Models that also incorporated topographic and land cover metrics further improved predictive performance, explaining up to 51% of the variance in total bird richness. A texture measure contributed predictive power and characterized landscape features that EVI and forest cover alone could not, even though the latter two were overall more important variables. Our results highlight the potential of texture measures for mapping habitat heterogeneity and species richness patterns across broad spatial extents, especially when used in conjunction with vegetation indices or land cover data. By generating 30-m resolution texture maps and modeling bird richness at a near-continental scale, we expand on previous applications of image texture measures for modeling biodiversity that were either limited in spatial extent or based on coarse-resolution imagery. Incorporating texture measures into broad-scale biodiversity models may advance our understanding of mechanisms underlying species richness patterns and improve predictions of species responses to rapid global change.

Global priorities for conservation across multiple dimensions of mammalian diversity.

Conservation priorities that are based on species distribution, endemism, and vulnerability may underrepresent biologically unique species as well as their functional roles and evolutionary histories. To ensure that priorities are biologically comprehensive, multiple dimensions of diversity must be considered. Further, understanding how the different dimensions relate to one another spatially is important for conservation prioritization, but the relationship remains poorly understood. Here, we use spatial conservation planning to (i) identify and compare priority regions for global mammal conservation across three key dimensions of biodiversity-taxonomic, phylogenetic, and traits-and (ii) determine the overlap of these regions with the locations of threatened species and existing protected areas. We show that priority areas for mammal conservation exhibit low overlap across the three dimensions, highlighting the need for an integrative approach for biodiversity conservation. Additionally, currently protected areas poorly represent the three dimensions of mammalian biodiversity. We identify areas of high conservation priority among and across the dimensions that should receive special attention for expanding the global protected area network. These high-priority areas, combined with areas of high priority for other taxonomic groups and with social, economic, and political considerations, provide a biological foundation for future conservation planning efforts.

Land-use and climatic causes of environmental novelty in Wisconsin since 1890.

Multiple global change drivers are increasing the present and future novelty of environments and ecological communities. However, most assessments of environmental novelty have focused only on future climate and were conducted at scales too broad to be useful for land management or conservation. Here, using historical county-level data sets of agricultural land use, forest composition, and climate, we conduct a regional-scale assessment of environmental novelty for Wisconsin landscapes from ca. 1890 to 2012. Agricultural land-use data include six cropland types, livestock densities for four livestock species, and human populations. Forestry data comprise biomass-weighted relative abundances for 15 tree genera. Climate data comprise seasonal means for temperature and precipitation. We found that forestry and land use are the strongest cause of environmental novelty (NoveltyForest  = 3.66, NoveltyAg  = 2.83, NoveltyClimate  = 1.60, with Wisconsin's forests transformed by early 20th-century logging and its legacies and multiple waves of agricultural innovation and obsolescence. Climate change is the smallest contributor to contemporary novelty, with precipitation signals stronger than temperature. Magnitudes and causes of environmental novelty are strongly spatially patterned, with novelty in southern Wisconsin roughly twice that in northern Wisconsin. Forestry is the most important cause of novelty in the north, land use and climate change are jointly important in the southwestern Wisconsin, and land use and forest composition are most important in central and eastern Wisconsin. Areas of high regional novelty tend also to be areas of high local change, but local change has not pushed all counties beyond regional baselines. Seven counties serve as the best historical analogues for over one-half of contemporary Wisconsin counties (40/72), and so can offer useful historical counterparts for contemporary systems and help managers coordinate to tackle similar environmental challenges. Multi-dimensional environmental novelty analyses, like those presented here, can help identify the best historical analogues for contemporary ecosystems, places where new management rules and practices may be needed because novelty is already high, and the main causes of novelty. Separating regional novelty clearly from local change and measuring both across many dimensions and at multiple scales thus helps advance ecology and sustainability science alike.

Future changes in fire weather, spring droughts, and false springs across U.S. National Forests and Grasslands.

Public lands provide many ecosystem services and support diverse plant and animal communities. In order to provide these benefits in the future, land managers and policy makers need information about future climate change and its potential effects. In particular, weather extremes are key drivers of wildfires, droughts, and false springs, which in turn can have large impacts on ecosystems. However, information on future changes in weather extremes on public lands is lacking. Our goal was to compare historical (1950-2005) and projected mid-century (2041-2070) changes in weather extremes (fire weather, spring droughts, and false springs) on public lands. This case study looked at the lands managed by the U.S. Forest Service across the conterminous United States including 501 ranger district units. We analyzed downscaled projections of daily records from 19 Coupled Model Intercomparison Project 5 General Circulation Models for two climate scenarios, with either medium-low or high CO2 - equivalent concentration (RCPs 4.5 and 8.5). For each ranger district, we estimated: (1) fire potential, using the Keetch-Byram Drought Index; (2) frequency of spring droughts, using the Standardized Precipitation Index; and (3) frequency of false springs, using the extended Spring Indices. We found that future climates could substantially alter weather conditions across Forest Service lands. Under the two climate scenarios, increases in wildfire potential, spring droughts, and false springs were projected in 32-72%, 28-29%, and 13-16% of all ranger districts, respectively. Moreover, a substantial number of ranger districts (17-30%), especially in the Southwestern, Pacific Southwest, and Rocky Mountain regions, were projected to see increases in more than one type of weather extreme, which may require special management attention. We suggest that future changes in weather extremes could threaten the ability of public lands to provide ecosystem services and ecological benefits to society. Overall, our results highlight the value of spatially-explicit weather projections to assess future changes in key weather extremes for land managers and policy makers.

Forests, houses, or both? Relationships between land cover, housing characteristics, and resident socioeconomic status across ecoregions.

Residential development is one of the most intensive and widespread land uses in the United States, with substantial environmental impacts, including changes in forest cover. However, the relationships between forest cover and residential development are complex. Contemporary forest cover reflects multiple factors, including housing density, time since development, historical land cover, and land management since development. We investigated how forest cover varies with housing density, housing age, and household income over a range of development intensities, in six ecoregions within New York State, Wisconsin, and Colorado. We find areas with residential development do retain important forest resources: across landscapes they are typically more forested than areas that remain undeveloped. However, forest cover consistently had a negative, inverse relationship with housing density, across study areas. Relationships between forest cover and housing age and household income were less common and often restricted to only portions of a given region, according to geographically weighted regression analyses. A better understanding of how forest cover varies with residential development, outside of the typically studied urban areas, will be essential to maintaining ecosystem function and services in residential landscapes.

Evolutionary time drives global tetrapod diversity.

Global variation in species richness is widely recognized, but the explanation for what drives it continues to be debated. Previous efforts have focused on a subset of potential drivers, including evolutionary rate, evolutionary time (maximum clade age of species restricted to a region), dispersal (migration from one region to another), ecological factors and climatic stability. However, no study has evaluated these competing hypotheses simultaneously at a broad spatial scale. Here, we examine their relative contribution in determining the richness of the most comprehensive dataset of tetrapods to our knowledge (84% of the described species), distinguishing between the direct influences of evolutionary rate, evolutionary time and dispersal, and the indirect influences of ecological factors and climatic stability through their effect on direct factors. We found that evolutionary time exerted a primary influence on species richness, with evolutionary rate being of secondary importance. By contrast, dispersal did not significantly affect richness patterns. Ecological and climatic stability factors influenced species richness indirectly by modifying evolutionary time (i.e. persistence time) and rate. Overall, our findings suggest that global heterogeneity in tetrapod richness is explained primarily by the length of time species have had to diversify.

Payments for ecosystem services in Mexico reduce forest fragmentation.

Forest fragmentation can lead to habitat reduction, edge increase, and exposure to disturbances. A key emerging policy to protect forests is payments for ecosystem services (PES), which offers compensation to landowners for environmental stewardship. Mexico was one of the first countries to implement a broad-scale PES program, enrolling over 2.3 Mha by 2010. However, Mexico's PES did not completely eliminate deforestation in enrolled parcels and could have increased incentives to hide deforestation in ways that increased fragmentation. We studied whether Mexican forests enrolled in the PES program had less forest fragmentation than those not enrolled, and whether the PES effects varied among forest types, among socioeconomic zones, or compared to the protected areas system. We analyzed forest cover maps from 2000 to 2012 to calculate forest fragmentation. We summarized fragmentation for different forest types and in four socioeconomic zones. We then used matching analysis to investigate the possible causal impacts of the PES on forests across Mexico and compared the effects of the PES program with that of protected areas. We found that the area covered by forest in Mexico decreased by 3.4% from 2000 to 2012, but there was 9.3% less forest core area. Change in forest cover was highest in the southern part of Mexico, and high-stature evergreen tropical forest lost the most core areas (-17%), while oak forest lost the least (-2%). Our matching analysis found that the PES program reduced both forest cover loss and forest fragmentation. Low-PES areas increased twice as much of the number of forest patches, forest edge, forest islets, and largest area of forest lost compared to high-PES areas. Compared to the protected areas system in Mexico, high-PES areas performed similarly in preventing fragmentation, but not as well as biosphere reserve core zones. We conclude that the PES was successful in slowing forest fragmentation at the regional and country level. However, the program could be improved by targeting areas where forest changes are more frequent, especially in southern Mexico. Fragmentation analyses should be implemented in other areas to monitor the outcomes of protection programs such as REDD+ and PES.

Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery.

Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter (<1 m) resolution aerial imagery, and identify social-ecological relationships of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution (0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3) investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planning in tropical cities, and emphasizes the need for more studies in tropical cities.

Sprawling and diverse: The changing U.S. population and implications for public lands in the 21st Century.

Public lands are typically established in recognition of their unique ecological value, yet both ecological and social values of public lands change over time, along with human distribution and land use. These transformations are evident even in developed countries with long histories of public land management, such as the United States. The 20th Century saw dramatic changes in the American population, in distribution and in racial and ethnic diversity, leading to new challenges and new roles for public lands. Our goal with this paper is to review changing demographics and implications for terrestrial protected areas in the U.S. We overview the fundamentals of population change and data, review past trends in population change and housing growth and their impacts on public lands, and then analyze the most recent decade of demographic change (2000-2010) relative to public lands. Discussions of demographic change and public lands commonly focus on the rural West, but we show that the South is also experiencing substantial change in rural areas with public lands, including Hispanic population growth. We identify those places, rural and urban, where demographic change (2000-2010), including diversification and housing growth, coincide with public lands. Understanding the current trends and long-term demographic context for recent changes in populations can help land managers and conservation scientists mitigate the effects of residential development near public lands, serve a more diverse population, and anticipate future population changes.