Legacies of millennial-scale climate oscillations in contemporary biodiversity in eastern North America

The Atlantic meridional overturning circulation (AMOC) has caused significant climate changes over the past 90 000 years. Prior work has hypothesized that these millennial-scale climate variations effected past and contemporary biodiversity, but the effects are understudied. Moreover, few biogeographic models have accounted for uncertainties in palaeoclimatic simulations of millennial-scale variability. We examine whether refuges from millennial-scale climate oscillations have left detectable legacies in the patterns of contemporary species richness in eastern North America. We analyse 13 palaeoclimate estimates from climate simulations and proxy-based reconstructions as predictors for the contemporary richness of amphibians, passerine birds, mammals, reptiles and trees. Results suggest that past climate changes owing to AMOC variations have left weak but detectable imprints on the contemporary richness of mammals and trees. High temperature stability, precipitation increase, and an apparent climate fulcrum in the southeastern United States across millennial-scale climate oscillations aligns with high biodiversity in the region. These findings support the hypothesis that the southeastern United States may have acted as a biodiversity refuge. However, for some taxa, the strength and direction of palaeoclimate-richness relationships varies among different palaeoclimate estimates, pointing to the importance of palaeoclimatic ensembles and the need for caution when basing biogeographic interpretations on individual palaeoclimate simulations. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

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.

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.

Rising wildfire risk to houses in the United States, especially in grasslands and shrublands

Wildfire risks to homes are increasing, especially in the wildland-urban interface (WUI), where wildland vegetation and houses are in close proximity. Notably, we found that more houses are exposed to and destroyed by grassland and shrubland fires than by forest fires in the United States. Destruction was more likely in forest fires, but they burned less WUI. The number of houses within wildfire perimeters has doubled since the 1990s because of both housing growth (47% of additionally exposed houses) and more burned area (53%). Most exposed houses were in the WUI, which grew substantially during the 2010s (2.6 million new WUI houses), albeit not as rapidly as before. Any WUI growth increases wildfire risk to houses though, and more fires increase the risk to existing WUI houses.

Social vulnerability of the people exposed to wildfires in U.S. West Coast states

Understanding of the vulnerability of populations exposed to wildfires is limited. We used an index from the U.S. Centers for Disease Control and Prevention to assess the social vulnerability of populations exposed to wildfire from 2000-2021 in California, Oregon, and Washington, which accounted for 90% of exposures in the western United States. The number of people exposed to fire from 2000-2010 to 2011-2021 increased substantially, with the largest increase, nearly 250%, for people with high social vulnerability. In Oregon and Washington, a higher percentage of exposed people were highly vulnerable (>40%) than in California (~8%). Increased social vulnerability of populations in burned areas was the primary contributor to increased exposure of the highly vulnerable in California, whereas encroachment of wildfires on vulnerable populations was the primary contributor in Oregon and Washington. Our results emphasize the importance of integrating the vulnerability of at-risk populations in wildfire mitigation and adaptation plans.

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.

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.

Modeled distribution shifts of North American birds over four decades based on suitable climate alone do not predict observed shifts

As climate change alters the global environment, it is critical to understand the relationship between shifting climate suitability and species distributions. Key questions include whether observed changes in population abundance are aligned with the velocity and direction of shifts predicted by climate suitability models and if the responses are consistent among species with similar ecological traits. We examined the direction and velocity of the observed abundance-based distribution centroids compared with the model-predicted bioclimatic distribution centroids of 250 bird species across the United States from 1969 to 2011. We hypothesized that there is a significant positive correlation in both direction and velocity between the observed and the modeled shifts. We then tested five additional hypotheses that predicted differential shifting velocity based on ecological adaptability and climate change exposure. Contrary to our hypotheses, we found large differences between the observed and modeled shifts among all studied bird species and within specific ecological guilds. However, temperate migrants and habitat generalist species tended to have higher velocity of observed shifts than other species. Neotropical migratory and wetland birds also had significantly different observed velocities than their counterparts, which may be due to their climate change exposure. The velocity based on modeled bioclimatic suitability did not exhibit significant differences among most guilds. Boreal forest birds were the only guild with significantly faster modeled-shifts than the other groups, suggesting an elevated conservation risk for high latitude and altitude species. The highly idiosyncratic species responses to climate and the mismatch between shifts in modeled and observed distribution centroids highlight the challenge of predicting species distribution change based solely on climate suitability and the importance of non-climatic factors traits in shaping species distributions.

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.

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.

Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate

Hundreds of millions of hectares of cropland have been abandoned globally since 1950 due to demographic, economic, and environmental changes. This abandonment has been seen as an important opportunity for carbon sequestration and habitat restoration; yet those benefits depend on the persistence of abandonment, which is poorly known. Here, we track abandonment and recultivation at 11 sites across four continents using annual land-cover maps for 1987-2017. We find that abandonment is largely fleeting, lasting on average only 14.22 years (SD = 1.44). At most sites, we project that >50% of abandoned croplands will be recultivated within 30 years, precluding the accumulation of substantial amounts of carbon and biodiversity. Recultivation resulted in 30.84% less abandonment and 35.39% less carbon accumulated by 2017 than expected without recultivation. Unless policymakers take steps to reduce recultivation or provide incentives for regeneration, abandonment will remain a missed opportunity to reduce biodiversity loss and climate change.

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.

Early warning sign of forest loss in protected areas

As humanity is facing the double challenge of species extinctions and climate change, designating parts of forests as protected areas is a key conservation strategy.^1-4 Protected areas, encompassing 14.9% of the Earth's land surface and 19% of global forests, can prevent forest loss but do not do so perfectly everywhere.^5-12 The reasons why protection only works in some areas are difficult to generalize: older and newer parks, protected areas with higher and lower suitability for agriculture, and more and less strict protection can be more effective at preventing forest loss than their counterparts.^6,8,9,12-16 Yet predicting future forest loss within protected areas is crucial to proactive conservation. Here, we identify an early warning sign of subsequent forest loss, based on forest loss patterns in strict protected areas and their surrounding landscape worldwide, from 2000 to 2018.^17,18 We found that a low level in the absolute forest cover immediately outside of a protected area signals a high risk of future forest loss inside the protected area itself. When the amount of forest left outside drops to <20%, the protected area is likely to experience rates of forest loss matching those in the wider landscape, regardless of its protection status (e.g., 5% loss outside will be matched by 5% loss inside). This knowledge could be used to direct funding to protected areas threatened by imminent forest loss, helping to proactively bolster protection to prevent forest loss, especially in countries where detailed information is lacking.

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.

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.

Responses to land cover and grassland management vary across life-history stages for a grassland specialist

Grassland birds have exhibited dramatic and widespread declines since the mid-20th century. Greater prairie chickens (Tympanuchus cupido pinnatus) are considered an umbrella species for grassland conservation and are frequent targets of management, but their responses to land use and management can be quite variable. We used data collected during 2007-2009 and 2014-2015 to investigate effects of land use and grassland management practices on habitat selection and survival rates of greater prairie chickens in central Wisconsin, USA. We examined habitat, nest-site, and brood-rearing site selection by hens and modeled effects of land cover and management on survival rates of hens, nests, and broods. Prairie chickens consistently selected grassland over other cover types, but selection or avoidance of management practices varied among life-history stages. Hen, nest, and brood survival rates were influenced by different land cover types and management practices. At the landscape scale, hens selected areas where brush and trees had been removed during the previous year, which increased hen survival. Hens selected nest sites in hay fields and brood-rearing sites in burned areas, but prescribed fire had a negative influence on hen survival. Brood survival rates were positively associated with grazing and were highest when home ranges contained ≈15%-20% shrub/tree cover. The effects of landscape composition on nest survival were ambiguous. Collectively, our results highlight the importance of evaluating responses to management efforts across a range of life-history stages and suggest that a variety of management practices are likely necessary to provide structurally heterogeneous, high-quality habitat for greater prairie chickens. Brush and tree removal, grazing, hay cultivation, and prescribed fire may be especially beneficial for prairie chickens in central Wisconsin, but trade-offs among life-history stages and the timing of management practices must be considered carefully.

Pine plantations and five decades of land use change in central Chile

The expansion of forest plantations is cause for concern because of their environmental effects, and the loss of native forests and agricultural land. Our goal was to quantify the increase in pine plantation, and concomitant loss of native forests, in central Chile since ca. 1960, and to identify in which settings native forests were lost most rapidly. We analyzed aerial photographs from 1955 and 1961, Landsat images from 1975 and 1998, and Google Earth high-resolution satellite images from 2014. To ensure high classification accuracy, we visually interpreted images for a systematic 3-km grid and assigned each point as either ‘pine plantation’, ‘native forest’, ‘agricultural-livestock lands’, or ‘other’. We also calculated latitude, longitude, slope, Euclidean distance to the nearest road and to the nearest pulp mill, and the frequency of land use surrounding each point as potential variables to explain observed land use changes. Pine plantations expansion started even before 1960, when 12% of all points were already pine plantations, was particularly rapid from 1975 (18% of sample points) to 1998 (38%), and stabilized thereafter (37% by 2014). From 1975 to 1998 alone, 40% of native forests were replaced by pine plantations, and agricultural-livestock lands declined by 0.7%, 0.9%, 1% per year before 1975, from 1975 to 1998, and after 1998 respectively. Native forests that were surrounded by pine plantations, were most likely to be converted to plantations, and from 1960 to 1975, also native forests near pulp mills. The probability of change from agricultural-livestock lands to pine plantations was mainly influenced by slope, with most agricultural-livestock lands remaining in areas with low slopes.

Vegetation productivity summarized by the Dynamic Habitat Indices explains broad-scale patterns of moose abundance across Russia

Identifying the factors that determine habitat suitability and hence patterns of wildlife abundances over broad spatial scales is important for conservation. Ecosystem productivity is a key aspect of habitat suitability, especially for large mammals. Our goals were to a) explain patterns of moose (Alces alces) abundance across Russia based on remotely sensed measures of vegetation productivity using Dynamic Habitat Indices (DHIs), and b) examine if patterns of moose abundance and productivity differed before and after the collapse of the Soviet Union. We evaluated the utility of the DHIs using multiple regression models predicting moose abundance by administrative regions. Univariate models of the individual DHIs had lower predictive power than all three combined. The three DHIs together with environmental variables, explained 79% of variation in moose abundance. Interestingly, the predictive power of the models was highest for the 1980s, and decreased for the two subsequent decades. We speculate that the lower predictive power of our environmental variables in the later decades may be due to increasing human influence on moose densities. Overall, we were able to explain patterns in moose abundance in Russia well, which can inform wildlife managers on the long-term patterns of habitat use of the species.

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 (Novelty_Forest  = 3.66, Novelty_Ag  = 2.83, Novelty_Climate  = 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.

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.

Combined effects of night warming and light pollution on predator-prey interactions

Interactions between multiple anthropogenic environmental changes can drive non-additive effects in ecological systems, and the non-additive effects can in turn be amplified or dampened by spatial covariation among environmental changes. We investigated the combined effects of night-time warming and light pollution on pea aphids and two predatory ladybeetle species. As expected, neither night-time warming nor light pollution changed the suppression of aphids by the ladybeetle species that forages effectively in darkness. However, for the more-visual predator, warming and light had non-additive effects in which together they caused much lower aphid abundances. These results are particularly relevant for agriculture near urban areas that experience both light pollution and warming from urban heat islands. Because warming and light pollution can have non-additive effects, predicting their possible combined consequences over broad spatial scales requires knowing how they co-occur. We found that night-time temperature change since 1949 covaried positively with light pollution, which has the potential to increase their non-additive effects on pea aphid control by 70% in US alfalfa. Our results highlight the importance of non-additive effects of multiple environmental factors on species and food webs, especially when these factors co-occur.

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.

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.

Geography of current and future global mammal extinction risk

Identifying which species are at greatest risk, what makes them vulnerable, and where they are distributed are central goals for conservation science. While knowledge of which factors influence extinction risk is increasingly available for some taxonomic groups, a deeper understanding of extinction correlates and the geography of risk remains lacking. Here, we develop a predictive random forest model using both geospatial and mammalian species' trait data to uncover the statistical and geographic distributions of extinction correlates. We also explore how this geography of risk may change under a rapidly warming climate. We found distinctive macroecological relationships between species-level risk and extinction correlates, including the intrinsic biological traits of geographic range size, body size and taxonomy, and extrinsic geographic settings such as seasonality, habitat type, land use and human population density. Each extinction correlate exhibited ranges of values that were especially associated with risk, and the importance of different risk factors was not geographically uniform across the globe. We also found that about 10% of mammals not currently recognized as at-risk have biological traits and occur in environments that predispose them towards extinction. Southeast Asia had the most actually and potentially threatened species, underscoring the urgent need for conservation in this region. Additionally, nearly 40% of currently threatened species were predicted to experience rapid climate change at 0.5 km/year or more. Biological and environmental correlates of mammalian extinction risk exhibit distinct statistical and geographic distributions. These results provide insight into species-level patterns and processes underlying geographic variation in extinction risk. They also offer guidance for future conservation research focused on specific geographic regions, or evaluating the degree to which species-level patterns mirror spatial variation in the pressures faced by populations within the ranges of individual species. The added impacts from climate change may increase the susceptibility of at-risk species to extinction and expand the regions where mammals are most vulnerable globally.

Assessing wildfire exposure in the Wildland-Urban Interface area of the mountains of central Argentina

Wildfires are a major threat to people and property in Wildland Urban Interface (WUI) communities worldwide, but while the patterns of the WUI in North America, Europe and Oceania have been studied before, this is not the case in Latin America. Our goals were to a) map WUI areas in central Argentina, and b) assess wildfire exposure for WUI communities in relation to historic fires, with special emphasis on large fires and estimated burn probability based on an empirical model. We mapped the WUI in the mountains of central Argentina (810,000 ha), after digitizing the location of 276,700 buildings and deriving vegetation maps from satellite imagery. The areas where houses and wildland vegetation intermingle were classified as Intermix WUI (housing density > 6.17 hu/km² and wildland vegetation cover > 50%), and the areas where wildland vegetation abuts settlements were classified as Interface WUI (housing density > 6.17 hu/km², wildland vegetation cover < 50%, but within 600 m of a vegetated patch larger than 5 km²). We generated burn probability maps based on historical fire data from 1999 to 2011; as well as from an empirical model of fire frequency. WUI areas occupied 15% of our study area and contained 144,000 buildings (52%). Most WUI area was Intermix WUI, but most WUI buildings were in the Interface WUI. Our findings suggest that central Argentina has a WUI fire problem. WUI areas included most of the buildings exposed to wildfires and most of the buildings located in areas of higher burn probability. Our findings can help focus fire management activities in areas of higher risk, and ultimately provide support for landscape management and planning aimed at reducing wildfire risk in WUI communities.

The effect of protected areas on forest disturbance in the Carpathian Mountains 1985-2010

Protected areas are a cornerstone for forest protection, but they are not always effective during times of socioeconomic and institutional crises. The Carpathian Mountains in Eastern Europe are an ecologically outstanding region, with widespread seminatural and old-growth forest. Since 1990, Carpathian countries (Czech Republic, Hungary, Poland, Romania, Slovakia, and Ukraine) have experienced economic hardship and institutional changes, including the breakdown of socialism, European Union accession, and a rapid expansion of protected areas. The question is how protected-area effectiveness has varied during these times across the Carpathians given these changes. We analyzed a satellite-based data set of forest disturbance (i.e., forest loss due to harvesting or natural disturbances) from 1985 to 2010 and used matching statistics and a fixed-effects estimator to quantify the effect of protection on forest disturbance. Protected areas in the Czech Republic, Slovakia, and the Ukraine had significantly less deforestation inside protected areas than outside in some periods; the likelihood of disturbance was reduced by 1-5%. The effectiveness of protection increased over time in these countries, whereas the opposite was true in Romania. Older protected areas were most effective in Romania and Hungary, but newer protected areas were more effective in Czech Republic, and Poland. Strict protection (International Union for Conservation of Nature [IUCN] protection category Ia-II) was not more effective than landscape-level protection (IUCN III-VI). We suggest that the strength of institutions, the differences in forest privatization, forest management, prior distribution of protected areas, and when countries joined the European Union may provide explanations for the strikingly heterogeneous effectiveness patterns among countries. Our results highlight how different the effects of protected areas can be at broad scales, indicating that the effectiveness of protected areas is transitory over time and space and suggesting that generalizations about the effectiveness of protected areas can be misleading.

Conservation hotspots for marine turtle nesting in the United States based on coastal development

Coastal areas provide nesting habitat for marine turtles that is critical for the persistence of their populations. However, many coastal areas are highly affected by coastal development, which affects the reproductive success of marine turtles. Knowing the extent to which nesting areas are exposed to these threats is essential to guide management initiatives. This information is particularly important for coastal areas with both high nesting density and dense human development, a combination that is common in the United States. We assessed the extent to which nesting areas of the loggerhead (Caretta caretta), the green (Chelonia mydas), the Kemp's ridley (Lepidochelys kempii), and leatherback turtles (Dermochelys coriacea) in the continental United States are exposed to coastal development and identified conservation hotspots that currently have high reproductive importance and either face high exposure to coastal development (needing intervention), or have low exposure to coastal development, and are good candidates for continued and future protection. Night-time light, housing, and population density were used as proxies for coastal development and human disturbance. About 81.6% of nesting areas were exposed to housing and human population, and 97.8% were exposed to light pollution. Further, most (>65%) of the very high- and high-density nesting areas for each species/subpopulation, except for the Kemp's ridley, were exposed to coastal development. Forty-nine nesting sites were selected as conservation hotspots; of those high-density nesting sites, 49% were sites with no/low exposure to coastal development and the other 51% were exposed to high-density coastal development. Conservation strategies need to account for ~66.8% of all marine turtle nesting areas being on private land and for nesting sites being exposed to large numbers of seasonal residents.

Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data

Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous USA and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated area-under-the-curve AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species' potential distributions may have already shifted due recent climate change. However, long-term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short-term climate variability are not available, long-term climate information is a sufficient predictor of species distributions in many cases. However, short-term climate variability data may provide information not captured with long-term climate data for use in SDMs.

Wetland loss due to land use change in the Lower Paraná River Delta, Argentina

Wetland loss is a global concern because wetlands are highly diverse ecosystems that provide important goods and services, thus threatening both biodiversity and human well-being. The Paraná River Delta is one of the largest and most important wetland ecosystems of South America, undergoing expanding cattle and forestry activities with widespread water control practices. To understand the patterns and drivers of land cover change in the Lower Paraná River Delta, we quantified land cover changes and modeled associated factors. We developed land cover maps using Landsat images from 1999 and 2013 and identified main land cover changes. We quantified the influence of different socioeconomic (distance to roads, population centers and human activity centers), land management (area within polders, cattle density and years since last fire), biophysical variables (landscape unit, elevation, soil productivity, distance to rivers) and variables related to extreme system dynamics (flooding and fires) on freshwater marsh conversion with Boosted Regression Trees. We found that one third of the freshwater marshes of the Lower Delta (163,000ha) were replaced by pastures (70%) and forestry (18%) in only 14years. Ranching practices (represented by cattle density, area within polders and distance to roads) were the most important factors responsible for freshwater marsh conversion to pasture. These rapid and widespread losses of freshwater marshes have potentially large negative consequences for biodiversity and ecosystem services. A strategy for sustainable wetland management will benefit from careful analysis of dominant land uses and related management practices, to develop an urgently needed land use policy for the Lower Delta.

Factors related to building loss due to wildfires in the conterminous United States

Wildfire is globally an important ecological disturbance affecting biochemical cycles and vegetation composition, but also puts people and their homes at risk. Suppressing wildfires has detrimental ecological effects and can promote larger and more intense wildfires when fuels accumulate, which increases the threat to buildings in the wildland-urban interface (WUI). Yet, when wildfires occur, typically only a small proportion of the buildings within the fire perimeter are lost, and the question is what determines which buildings burn. Our goal was to examine which factors are related to building loss when a wildfire occurs throughout the United States. We were particularly interested in the relative roles of vegetation, topography, and the spatial arrangement of buildings, and how their respective roles vary among ecoregions. We analyzed all fires that occurred within the conterminous United States from 2000 to 2010 and digitized which buildings were lost and which survived according to Google Earth historical imagery. We modeled the occurrence as well as the percentage of buildings lost within clusters using logistic and linear regression. Overall, variables related to topography and the spatial arrangement of buildings were more frequently present in the best 20 regression models than vegetation-related variables. In other words, specific locations in the landscape have a higher fire risk, and certain development patterns can exacerbate that risk. Fire policies and prevention efforts focused on vegetation management are important, but insufficient to solve current wildfire problems. Furthermore, the factors associated with building loss varied considerably among ecoregions suggesting that fire policy applied uniformly across the United States will not work equally well in all regions and that efforts to adapt communities to wildfires must be regionally tailored.

Evaluation of downscaled, gridded climate data for the conterminous United States

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates? (2) Are there significant regional differences in accuracy among data sets? (3) How accurate are their mean values compared with extremes? (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

Prioritizing land management efforts at a landscape scale: a case study using prescribed fire in Wisconsin

One challenge in the effort to conserve biodiversity is identifying where to prioritize resources for active land management. Cost-benefit analyses have been used successfully as a conservation tool to identify sites that provide the greatest conservation benefit per unit cost. Our goal was to apply cost-benefit analysis to the question of how to prioritize land management efforts, in our case the application of prescribed fire to natural landscapes in Wisconsin, USA. We quantified and mapped frequently burned communities and prioritized management units based on a suite of indices that captured ecological benefits, management effort, and the feasibility of successful long-term management actions. Data for these indices came from LANDFIRE, Wisconsin's Wildlife Action Plan, and a nationwide wildland-urban interface assessment. We found that the majority of frequently burned vegetation types occurred in the southern portion of the state. However, the highest priority areas for applying prescribed fire occurred in the central, northwest, and northeast portion of the state where frequently burned vegetation patches were larger and where identified areas of high biological importance area occurred. Although our focus was on the use of prescribed fire in Wisconsin, our methods can be adapted to prioritize other land management activities. Such prioritization is necessary to achieve the greatest possible benefits from limited funding for land management actions, and our results show that it is feasible at scales that are relevant for land management decisions.

The pace of past climate change vs. potential bird distributions and land use in the United States

Climate change may drastically alter patterns of species distributions and richness, but predicting future species patterns in occurrence is challenging. Significant shifts in distributions have already been observed, and understanding these recent changes can improve our understanding of potential future changes. We assessed how past climate change affected potential breeding distributions for landbird species in the conterminous United States. We quantified the bioclimatic velocity of potential breeding distributions, that is, the pace and direction of change for each species' suitable climate space over the past 60 years. We found that potential breeding distributions for landbirds have shifted substantially with an average velocity of 1.27 km yr(-1) , about double the pace of prior distribution shift estimates across terrestrial systems globally (0.61 km yr(-1) ). The direction of shifts was not uniform. The majority of species' distributions shifted west, northwest, and north. Multidirectional shifts suggest that changes in climate conditions beyond mean temperature were influencing distributional changes. Indeed, precipitation variables that were proxies for extreme conditions were important variables across all models. There were winners and losers in terms of the area of distributions; many species experienced contractions along west and east distribution edges, and expansions along northern distribution edges. Changes were also reflected in the potential species richness, with some regions potentially gaining species (Midwest, East) and other areas potentially losing species (Southwest). However, the degree to which changes in potential breeding distributions are manifested in actual species richness depends on landcover. Areas that have become increasingly suitable for breeding birds due to changing climate are often those attractive to humans for agriculture and development. This suggests that many areas might have supported more breeding bird species had the landscape not been altered. Our study illustrates that climate change is not only a future threat, but something birds are already experiencing.

The rise of novelty in ecosystems

Rapid and ongoing change creates novelty in ecosystems everywhere, both when comparing contemporary systems to their historical baselines, and predicted future systems to the present. However, the level of novelty varies greatly among places. Here we propose a formal and quantifiable definition of abiotic and biotic novelty in ecosystems, map abiotic novelty globally, and discuss the implications of novelty for the science of ecology and for biodiversity conservation. We define novelty as the degree of dissimilarity of a system, measured in one or more dimensions relative to a reference baseline, usually defined as either the present or a time window in the past. In this conceptualization, novelty varies in degree, it is multidimensional, can be measured, and requires a temporal and spatial reference. This definition moves beyond prior categorical definitions of novel ecosystems, and does not include human agency, self-perpetuation, or irreversibility as criteria. Our global assessment of novelty was based on abiotic factors (temperature, precipitation, and nitrogen deposition) plus human population, and shows that there are already large areas with high novelty today relative to the early 20th century, and that there will even be more such areas by 2050. Interestingly, the places that are most novel are often not the places where absolute changes are largest; highlighting that novelty is inherently different from change. For the ecological sciences, highly novel ecosystems present new opportunities to test ecological theories, but also challenge the predictive ability of ecological models and their validation. For biodiversity conservation, increasing novelty presents some opportunities, but largely challenges. Conservation action is necessary along the entire continuum of novelty, by redoubling efforts to protect areas where novelty is low, identifying conservation opportunities where novelty is high, developing flexible yet strong regulations and policies, and establishing long-term experiments to test management approaches. Meeting the challenge of novelty will require advances in the science of ecology, and new and creative. conservation approaches.