Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning

The global land-water-climate nexus of drought-tolerant succulent plants for bioenergy in abandoned croplands and arid marginal lands

Bioenergy is considered among the main mitigation strategies to meet a green-growth development paradigm of 1.5 °C. However, climate change has been dramatically restructuring agriculture and damaging crops, threatening the achievement of global food security and bioenergy goals. Studies have shown that succulent plants through their water efficient and highly temperature-drought-tolerant Crassulacean Acid Metabolism (CAM) pathway, could be a key opportunity to meet future energy demands under global change scenarios. However, specific bioenergy potentials under alternative management conditions, irrigation, and land availability that minimize resource conflicts with food production, biodiversity transgressions, or water withdrawals in water scarce regions, remain unclear. Here, we estimate under a bottom-up approach the global bioenergy potentials of four relevant CAM species on recently abandoned croplands and arid marginal lands, unravelling the interdependencies between land availability, water requirements and climatic conditions at the global scale. We identify a potential of 16-36 Ej yr-1 (27-62% of current bioenergy demand) in abandoned croplands depending on local and management factors. Rainfed CAM potentials (16 Ej yr-1) are comparable with the potentials of bioenergy crops such as miscanthus (19 Ej yr-1) under similar environmental conditions. However, the water requirement of CAM species to reach this potential is 24-30% of what is expected for C3 and C4 bioenergy crops. Additionally, we identify a carbon sequestration potential via plant growth of -3.09 Pg C yr-1 between 1960 and 2020, which reveals an underestimated opportunity in arid marginal lands. We highlight the remarkable contribution of the CAM pathway as a nature-based solution under global change scenarios.

The internal decay of wood is driven by the interplay between foraging Magellanic woodpeckers and environmental conditions

Although woodpeckers are known to forage in decaying trees, their contribution to internal wood decay is not well known. In this sense, non-destructive techniques for structural wood degradation provide an opportunity to quantitatively assess the role of woodpeckers in tree decay. We used sonic tomography to test that the trunks of living trees pecked by Magellanic woodpeckers show pronounced decay, which accelerates under environmental conditions favorable to wood-decaying fungi. The internal decomposition of wood and its decay rate were measured over four years on 156 living southern beech (Nothofagus) trees belonging to four dominant species of southern temperate forests in northern Patagonia. Half of these live trees had woodpecker feeding holes, while the rest served as controls. The percentage of decayed wood, although not severely decayed, increased in sections with the presence of woodpecker holes, but was also influenced by temperatures and biophysical variables such as elevation and topography. The trunk sections with woodpecker holes and exposed to intensive foraging showed accelerated inter-annual decay. Woodpecker foraging activity interacted with vegetation characteristics, resulting in accelerated wood decay in forest sites with an open canopy and exposed to water stress. Thus, sonic tomography provided evidence of a close relationship between woodpeckers and internal wood decomposition, suggesting a positive feedback mechanism regulated by forest disturbance. The approach used here can be extended to gain insight into the influence of woodpeckers on tree decay and mortality in regions experiencing severe drought and forest degradation, such as northern Patagonia.

A multilevel dataset of landform mapping and geomorphologic descriptors for the Loess Plateau of China

The Loess Plateau is a region of importance in geomorphologic research because of its typical loess layers and intense surface erosion. Analysing the landforms on the Loess Plateau is helpful for understanding changes in the surface environment. However, geomorphologic data with high resolution are lacking for the Loess Plateau, which limits the progress of geomorphologic studies at finer scales. This study provides the first 30 m resolution landform classification and characteristics dataset for the Loess Plateau (LPL30). Considering morphological characteristics, dominant dynamics, and material conditions, the landforms on the Loess Plateau were categorized into three levels with 28 landform types. Moreover, we calculated 6 metrics to quantify the spatial distribution and category composition of the landforms in 16 analytical units with different scales and shapes, resulting in 96 geomorphologic descriptors. This dataset provides fundamental data for the study of landform formation processes and evolution mechanisms at various scales and can be employed as a geomorphological gradient to support ecology and environmental research.

Seasonal Groundwater Level Dynamics in Unconfined Aquifers across the United States

Groundwater hydrographs contain a rich set of information on the dynamics of aquifer systems and the processes and properties that influence them. While the importance of seasonal cycles in hydrologic and environmental state variables is widely recognized there has yet to be a comprehensive analysis of the seasonal dynamics of groundwater across the United States. Here we use time series of groundwater level measurements from 997 wells from the National Groundwater Monitoring Network to identify and describe groundwater seasonal cycles in unconfined aquifers across the United States. We use functional data analysis to obtain a functional form fit for each site and apply an unsupervised clustering algorithm to identify a set of five distinct seasonal cycles regimes. Each seasonal cycle regime has a distinctive shape and distinct timing of its annual minimum and maximum water level. There are clear spatial patterns in the occurrence of each seasonal cycle regime, with the relative occurrence of each regime strongly influenced by the geologic setting (aquifer system), climate, and topography. Our findings provide a comprehensive characterization of groundwater seasonal cycles across much of the United States and present both a methodology and results useful for assessing and understanding unconfined groundwater systems.

The biogeography of the Amazonian tree flora

We describe the geographical variation in tree species composition across Amazonian forests and show how environmental conditions are associated with species turnover. Our analyses are based on 2023 forest inventory plots (1 ha) that provide abundance data for a total of 5188 tree species. Within-plot species composition reflected both local environmental conditions (especially soil nutrients and hydrology) and geographical regions. A broader-scale view of species turnover was obtained by interpolating the relative tree species abundances over Amazonia into 47,441 0.1-degree grid cells. Two main dimensions of spatial change in tree species composition were identified. The first was a gradient between western Amazonia at the Andean forelands (with young geology and relatively nutrient-rich soils) and central-eastern Amazonia associated with the Guiana and Brazilian Shields (with more ancient geology and poor soils). The second gradient was between the wet forests of the northwest and the drier forests in southern Amazonia. Isolines linking cells of similar composition crossed major Amazonian rivers, suggesting that tree species distributions are not limited by rivers. Even though some areas of relatively sharp species turnover were identified, mostly the tree species composition changed gradually over large extents, which does not support delimiting clear discrete biogeographic regions within Amazonia.

Predictive landslide susceptibility modeling in the southeastern hilly region of Bangladesh: application of machine learning algorithms in Khagrachari district

Landslides pose a severe threat to people, buildings, and infrastructure. The rugged terrain of the Chattogram Hill Tract region in southeastern Bangladesh frequently experiences landslides, particularly during rainy seasons. This study provides a comparative analysis of innovative machine learning (ML) algorithms used for the purpose of landslide susceptibility (LS) mapping for the Khagrachari district of Bangladesh. The dataset for this study comprises 15 landslide conditioning factors and 127 landslide inventory points. The landslide inventory points included 71 landslide and 56 non-landslide points. Then, the data were split randomly into training data (70%) and testing data (30%). Three ML algorithms, namely random forest (RF), boosted regression trees (BRT), and k-nearest neighbor (KNN), were utilized to evaluate the LS zone. The models were validated using the area under the curve (AUC), overall accuracy, precision, and recall. Based on the AUC value, the BRT model demonstrated the highest performance with a value of 0.95, while the AUC values for RF and KNN were 0.91 and 0.86, respectively. Besides, overall accuracy, precision, and recall values (0.82, 0.81, and 0.86) also indicated BRT as the most effective model. The results showed that maximum rainfall and elevation were the most influential factors for both BRT and RF models. This research provides valuable insight into understanding the LS areas in Khagrachari, aiding in informed decision-making regarding landslide-related concerns in the region, and can be applied to the broader scale to develop effective planning and mitigation strategies for comprehensive disaster management and natural hazard response.

Groundwater-dependent ecosystem map exposes global dryland protection needs

Groundwater is the most ubiquitous source of liquid freshwater globally, yet its role in supporting diverse ecosystems is rarely acknowledged1,2. However, the location and extent of groundwater-dependent ecosystems (GDEs) are unknown in many geographies, and protection measures are lacking1,3. Here, we map GDEs at high-resolution (roughly 30 m) and find them present on more than one-third of global drylands analysed, including important global biodiversity hotspots4. GDEs are more extensive and contiguous in landscapes dominated by pastoralism with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to water and land use practices. Nevertheless, 53% of GDEs exist within regions showing declining groundwater trends, which highlights the urgent need to protect GDEs from the threat of groundwater depletion. However, we found that only 21% of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems. Furthermore, we examine the linkage of GDEs with cultural and socio-economic factors in the Greater Sahel region, where GDEs play an essential role in supporting biodiversity and rural livelihoods, to explore other means for protection of GDEs in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them.

Multi-Tier Land Use and Land Cover Mapping Framework and Its Application in Urbanization Analysis in Three African Countries

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa’s anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous “developed” or “built-up” area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses.

Evaluating ecosystem protection and fragmentation of the world's major mountain regions

Conserving mountains is important for protecting biodiversity because they have high beta diversity and endemicity, facilitate species movement, and provide numerous ecosystem benefits for people. Mountains are often thought to have lower levels of human modification and contain more protected area than surrounding lowlands. To examine this, we compared biogeographic attributes of the largest, contiguous, mountainous region on each continent. In each region, we generated detailed ecosystems based on Köppen-Geiger climate regions, ecoregions, and detailed landforms. We quantified anthropogenic fragmentation of these ecosystems based on human modification classes of large wild areas, shared lands, and cities and farms. Human modification for half the mountainous regions approached the global average, and fragmentation reduced the ecological integrity of mountain ecosystems up to 40%. Only one-third of the major mountainous regions currently meet the Kunming-Montreal Global Biodiversity Framework target of 30% coverage for all protected areas; furthermore, the vast majority of ecosystem types present in mountains were underrepresented in protected areas. By measuring ecological integrity and human-caused fragmentation with a detailed representation of mountain ecosystems, our approach facilitates tracking progress toward achieving conservation goals and better informs mountain conservation.

A case for stronger integration of physical landscape processes in conservation science and practice

I argue that the dynamic nature of contemporary, landscape-shaping (geomorphic) processes deserves more consideration in conservation science and practice. In an analysis of a sample of fundamental terms related to geomorphology and area-based conservation in the Web of Science, I found that the terms co-occurred in <2% of the analyzed entries (titles, abstracts, and keywords) from 2000 to 2020. This result is indicative of the rather peripheral attention that, more broadly, landscape-shaping processes seem to receive in the conservation literature. Among conservation scientists and practitioners, landforms that define the physical structure of habitat are often perceived as largely static, whereas the consideration of their dynamic adjustments to geomorphic processes is often limited to extreme events. I use examples derived from river-floodplain environments to illustrate strong, multifaceted, and reciprocal interactions between biota and various erosional and depositional processes. These ubiquitous interdependencies clearly demonstrate that geomorphic processes are an integral part of ecosystem dynamics at time scales relevant for conservation. Crucially, erosional and depositional processes modulate many environmental impacts of past and current anthropogenic activities. I conclude that the absence of a more explicit and widespread consideration of geomorphic processes in conservation science and practice is surprising and detrimental to their effectiveness. I call for bolstered efforts among the conservation and geoscience communities to better integrate landscape dynamics within the field of conservation. The rise of the ecosystem-based and social-ecological systems approaches to conservation and the growth of interdisciplinary geoscience branches (e.g., biogeomorphology, ecohydraulics, and geoconservation) will facilitate such an integration.

Physiography, foraging mobility, and the first peopling of Sahul

The route and speed of migration into Sahul by Homo sapiens remain a major research question in archaeology. Here, we introduce an approach which models the impact of the physical environment on human mobility by combining time-evolving landscapes with Lévy walk foraging patterns, this latter accounting for a combination of short-distance steps and occasional longer moves that hunter-gatherers likely utilised for efficient exploration of new environments. Our results suggest a wave of dispersal radiating across Sahul following riverine corridors and coastlines. Estimated migration speeds, based on archaeological sites and predicted travelled distances, fall within previously reported range from Sahul and other regions. From our mechanistic movement simulations, we then analyse the likelihood of archaeological sites and highlight areas in Australia that hold archaeological potential. Our approach complements existing methods and provides interesting perspectives on the Pleistocene archaeology of Sahul that could be applied to other regions around the world.

Abiotic factors impact on oak forest decline in Lorestan Province, Western Iran

The Zagros oak forests in Iran are facing a concerning decline due to prolonged and severe drought conditions over several decades, compounded by the simultaneous impact of temperature on oak populations. This study in oak woodlands of central Zagros forests in Lorestan province analyzed abiotic factors such as climate properties, topographic features, land use, and soil properties from 1958 to 2022. We found that higher elevation areas with steeper slopes and diverse topography show significant potential for enhancing oak tree resilience in the face of climate change. Additionally, traditional land use practices like livestock keeping and dryland farming contribute to a widespread decline in oak populations. Preserving forest biodiversity and ensuring ecological sustainability requires immediate attention. Implementing effective land-use management strategies, such as protecting and regulating human-forest interaction, and considering meteorological factors to address this issue is crucial. Collaborative efforts from stakeholders, policymakers, and local communities are essential to oppose destructive suburban sprawl and other developments. Sustainable forestry practices should be implemented to improve the living standards of local communities that rely on forests and traditional livestock keeping, offer forestry-related jobs, and ensure social security. Such efforts are necessary to promote conservation awareness and sustainable practices, safeguarding this unique and vital ecosystem for future generations.

Spatial stratified heterogeneity analysis of field scale permafrost in Northeast China based on optimal parameters-based geographical detector

Affected by global warming, the permafrost in Northeast China (NEC) has been continuously degrading in recent years. Many researchers have focused on the spatial and temporal distribution characteristics of permafrost in NEC, however, few studies have delved into the field scale. In this study, based on the Optimal Parameters-based Geographical Detector (OPGD) model and Receiver Operating Characteristic (ROC) test, the spatial stratified heterogeneity of permafrost distribution and the indicating performance of environmental variables on permafrost in NEC at the field scale were analyzed. Permafrost spatial distribution data were obtained from the Engineering Geological Investigation Reports (EGIR) of six highways located in NEC and a total of 19 environmental variables related to heat transfer, vegetation, soil, topography, moisture, and ecology were selected. The H-factors (variables with the highest contribution in factor detector results and interaction detector results): slope position (γ), surface frost number (SFN), elevation (DEM), topographic diversity (TD), and annual snow cover days (ASCD) were found to be the major contributors to the distribution of permafrost at the field scale. Among them, γ has the highest contribution and is a special explanatory variable for permafrost. In most cases, interaction can improve the impact of variables, especially the interaction between H-factors. The risk of permafrost decreases with the increase of TD, RN, and SBD, and increases with the increase of SFN. The performance of SFN to indicate permafrost distribution was found to be the best among all variables (AUC = 0.7063). There is spatial heterogeneity in the distribution of permafrost on highways in different spatial locations. This study summarized the numerical and spatial location between permafrost and different environmental variables at the field scale, and many results were found to be informative for environmental studies and engineering construction in NEC.

Understanding how environmental degradation, microclimate, and management shape honey production across different spatial scales

Although the abundance, survival, and pollination performance of honeybees are sensitive to changes in habitat and climate conditions, the processes by which these effects are transmitted to honey production and interact with beekeeping management are not completely understood. Climate change, habitat degradation, and beekeeping management affect honey yields, and may also interact among themselves resulting in indirect effects across spatial scales. We conducted a 2-year, multi-scale study on Chiloe Island (northern Patagonia), where we evaluated the most relevant environmental and management drivers of honey produced by stationary beekeepers. We found that the effects of microclimate, habitat, and management variables changed with the spatial scale. Among the environmental variables, minimum temperature, and cover of the invasive shrub, gorse (Ulex europaeus) had the strongest detrimental impacts on honey production at spatial scales finer than 4 km. Specialized beekeepers who adopted conventional beekeeping and had more mother colonies were more productive. Mean and minimum temperatures interacted with the percentage of mother colonies, urban cover, and beekeeping income. The gorse cover increased by the combination of high temperatures and the expansion of urban lands, while landscape attributes, such as Eucalyptus plantation cover, influenced beekeeping management. Results suggest that higher temperatures change the available forage or cause thermal stress to honeybees, while invasive shrubs are indicators of degraded habitats. Climate change and habitat degradation are two interrelated environmental phenomena whose effects on beekeeping can be mitigated through adaptive management and habitat restoration.

Conventional Fossil Fuel Extraction, Associated Biogeochemical Processes, and Topography Influence Methane Groundwater Concentrations in Appalachia

The production of fossil fuels, including oil, gas, and coal, retains a dominant share in US energy production and serves as a major anthropogenic source of methane, a greenhouse gas with a high warming potential. In addition to directly emitting methane into the air, fossil fuel production can release methane into groundwater, and that methane may eventually reach the atmosphere. In this study, we collected 311 water samples from an unconventional oil and gas (UOG) production region in Pennsylvania and an oil and gas (O&G) and coal production region across Ohio and West Virginia. Methane concentration was negatively correlated to distance to the nearest O&G well in the second region, but such a correlation was shown to be driven by topography as a confounding variable. Furthermore, sulfate concentration was negatively correlated with methane concentration and with distance to coal mining in the second region, and these correlations were robust even when considering topography. We hypothesized that coal mining enriched sulfate in groundwater, which in turn inhibited methanogenesis and enhanced microbial methane oxidation. Thus, this study highlights the complex interplay of multiple factors in shaping groundwater methane concentrations, including biogeochemical conversion, topography, and conventional fossil extraction.

Deeper topsoils enhance ecosystem productivity and climate resilience in arid regions, but not in humid regions

Understanding the controlling mechanisms of soil properties on ecosystem productivity is essential for sustaining productivity and increasing resilience under a changing climate. Here we investigate the control of topsoil depth (e.g., A horizons) on long-term ecosystem productivity. We used nationwide observations (n = 2401) of topsoil depth and multiple scaled datasets of gross primary productivity (GPP) for five ecosystems (cropland, forest, grassland, pasture, shrubland) over 36 years (1986-2021) across the conterminous USA. The relationship between topsoil depth and GPP is primarily associated with water availability, which is particularly significant in arid regions under grassland, shrubland, and cropland (r = .37, .32, .15, respectively, p < .0001). For every 10 cm increase in topsoil depth, the GPP increased by 114 to 128 g C m-2  year-1 in arid regions (r = .33 and .45, p < .0001). Paired comparison of relatively shallow and deep topsoils while holding other variables (climate, vegetation, parent material, soil type) constant showed that the positive control of topsoil depth on GPP occurred primarily in cropland (0.73, confidence interval of 0.57-0.84) and shrubland (0.75, confidence interval of 0.40-0.94). The GPP difference between deep and shallow topsoils was small and not statistically significant. Despite the positive control of topsoil depth on productivity in arid regions, its contribution (coefficients: .09-.33) was similar to that of heat (coefficients: .06-.39) but less than that of water (coefficients: .07-.87). The resilience of ecosystem productivity to climate extremes varied in different ecosystems and climatic regions. Deeper topsoils increased stability and decreased the variability of GPP under climate extremes in most ecosystems, especially in shrubland and grassland. The conservation of topsoil in arid regions and improvements of soil depth representation and moisture-retention mechanisms are critical for carbon-sequestration ecosystem services under a changing climate. These findings and relationships should also be included in Earth system models.

Refuge-yeah or refuge-nah? Predicting locations of forest resistance and recruitment in a fiery world

Climate warming, land use change, and altered fire regimes are driving ecological transformations that can have critical effects on Earth's biota. Fire refugia-locations that are burned less frequently or severely than their surroundings-may act as sites of relative stability during this period of rapid change by being resistant to fire and supporting post-fire recovery in adjacent areas. Because of their value to forest ecosystem persistence, there is an urgent need to anticipate where refugia are most likely to be found and where they align with environmental conditions that support post-fire tree recruitment. Using biophysical predictors and patterns of burn severity from 1180 recent fire events, we mapped the locations of potential fire refugia across upland conifer forests in the southwestern United States (US) (99,428 km2 of forest area), a region that is highly vulnerable to fire-driven transformation. We found that low pre-fire forest cover, flat slopes or topographic concavities, moderate weather conditions, spring-season burning, and areas affected by low- to moderate-severity fire within the previous 15 years were most commonly associated with refugia. Based on current (i.e., 2021) conditions, we predicted that 67.6% and 18.1% of conifer forests in our study area would contain refugia under moderate and extreme fire weather, respectively. However, potential refugia were 36.4% (moderate weather) and 31.2% (extreme weather) more common across forests that experienced recent fires, supporting the increased use of prescribed and resource objective fires during moderate weather conditions to promote fire-resistant landscapes. When overlaid with models of tree recruitment, 23.2% (moderate weather) and 6.4% (extreme weather) of forests were classified as refugia with a high potential to support post-fire recruitment in the surrounding landscape. These locations may be disproportionately valuable for ecosystem sustainability, providing habitat for fire-sensitive species and maintaining forest persistence in an increasingly fire-prone world.

Landscape dynamics and the Phanerozoic diversification of the biosphere

The long-term diversification of the biosphere responds to changes in the physical environment. Yet, over the continents, the nearly monotonic expansion of life started later in the early part of the Phanerozoic eon1 than the expansion in the marine realm, where instead the number of genera waxed and waned over time2. A comprehensive evaluation of the changes in the geodynamic and climatic forcing fails to provide a unified theory for the long-term pattern of evolution of life on Earth. Here we couple climate and plate tectonics models to numerically reconstruct the evolution of the Earth's landscape over the entire Phanerozoic eon, which we then compare to palaeo-diversity datasets from marine animal and land plant genera. Our results indicate that biodiversity is strongly reliant on landscape dynamics, which at all times determine the carrying capacity of both the continental domain and the oceanic domain. In the oceans, diversity closely adjusted to the riverine sedimentary flux that provides nutrients for primary production. On land, plant expansion was hampered by poor edaphic conditions until widespread endorheic basins resurfaced continents with a sedimentary cover that facilitated the development of soil-dependent rooted flora, and the increasing variety of the landscape additionally promoted their development.

Mapping potentially groundwater-dependent vegetation in the Mediterranean biome using global geodata targeting site conditions and vegetation characteristics

Groundwater-dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat for species, and sustaining human livelihoods. However, climate and land-use change are threatening GDV, highlighting the need for harmonised, global mapping of the distribution and extent of GDV. This need is particularly crucial in vulnerable biodiversity hotspots such as the Mediterranean biome. This study presents a novel multicriteria index to identify areas in the Mediterranean biome that provide suitable environmental conditions to support potentially groundwater-dependent vegetation (pGDV) where vegetation behaviour is also indicative of groundwater use. Global datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks have been combined for the first time in an easy-to-use index. Layer weightings from Analytical Hierarchy Process and Random Forest showed limited applicability on biome scale, but an unweighted overlay of eleven thematic layers produced plausible results. The final pGDV map indicates that 31 % of the natural vegetation in the Mediterranean biome likely depend on groundwater. Moreover, moderate to good agreement was found compared to actual GDV locations in Campania, Italy (91 % with at least moderate potential) and California, USA (87 % with at least moderate potential). The results provide valuable information for identifying regions with a substantial presence of pGDV in the Mediterranean biome and can be used for decision making, e.g. to prioritise field surveys and high-resolution remote sensing for GDV mapping. It can therefore support effective groundwater resource management and the conservation of biodiversity hotspots.

Fragmentation effects on an endangered species across a gradient from the interior to edge of its range

Understanding how habitat fragmentation affects individual species is complicated by challenges associated with quantifying species-specific habitat and spatial variability in fragmentation effects within a species' range. We aggregated a 29-year breeding survey data set for the endangered marbled murrelet (Brachyramphus marmoratus) from >42,000 forest sites throughout the Pacific Northwest (Oregon, Washington, and northern California) of the United States. We built a species distribution model (SDM) in which occupied sites were linked with Landsat imagery to quantify murrelet-specific habitat and then used occupancy models to test the hypotheses that fragmentation negatively affects murrelet breeding distribution and that these effects are amplified with distance from the marine foraging habitat toward the edge of the species' nesting range. Murrelet habitat declined in the Pacific Northwest by 20% since 1988, whereas the proportion of habitat comprising edges increased by 17%, indicating increased fragmentation. Furthermore, fragmentation of murrelet habitat at landscape scales (within 2 km of survey stations) negatively affected occupancy of potential breeding sites, and these effects were amplified near the range edge. On the coast, the odds of occupancy decreased by 37% (95% confidence interval [CI] -54 to 12) for each 10% increase in edge habitat (i.e., fragmentation), but at the range edge (88 km inland) these odds decreased by 99% (95% CI 98 to 99). Conversely, odds of murrelet occupancy increased by 31% (95% CI 14 to 52) for each 10% increase in local edge habitat (within 100 m of survey stations). Avoidance of fragmentation at broad scales but use of locally fragmented habitat with reduced quality may help explain the lack of murrelet population recovery. Further, our results emphasize that fragmentation effects can be nuanced, scale dependent, and geographically variable. Awareness of these nuances is critical for developing landscape-level conservation strategies for species experiencing broad-scale habitat loss and fragmentation.

COULD WHITE-NOSE SYNDROME MANIFEST DIFFERENTLY IN MYOTIS LUCIFUGUS IN WESTERN VERSUS EASTERN REGIONS OF NORTH AMERICA? A REVIEW OF FACTORS

White-nose syndrome (WNS) has notably affected the abundance of Myotis lucifugus (little brown myotis) in North America. Thus far, substantial mortality has been restricted to the eastern part of the continent where the cause of WNS, the invasive fungus Pseudogymnoascus destructans, has infected bats since 2006. To date, the state of Washington is the only area in the Western US or Canada (the Rocky Mountains and further west in North America) with confirmed cases of WNS in bats, and there the disease has spread more slowly than it did in Eastern North America. Here, we review differences between M. lucifugus in western and eastern parts of the continent that may affect transmission, spread, and severity of WNS in the West and highlight important gaps in knowledge. We explore the hypothesis that western M. lucifugus may respond differently to WNS on the basis of different hibernation strategies, habitat use, and greater genetic structure. To document the effect of WNS on M. lucifugus in the West most effectively, we recommend focusing on maternity roosts for strategic disease surveillance and monitoring abundance. We further recommend continuing the challenging work of identifying hibernation and swarming sites to better understand the microclimates, microbial communities, and role in disease transmission of these sites, as well as the ecology and hibernation physiology of bats in noncavernous hibernacula.

Spatial predictions of tree density and tree height across Mexico forests using ensemble learning and forest inventory data

The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r 2 = .35 [.12, .51] (mean [min, max]) than for tree density r 2 = .23 [.05, .42]. The best predictive performance when mapping tree height was for broadleaf and coniferous-broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for analytical tools that help us exploit the full potential of the Mexican forest inventory datasets.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BACKGROUND

Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.

METHODS

A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.

RESULTS

The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.

CONCLUSIONS

This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

Satellite monitoring of terrestrial plastic waste

Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at wide geographic scale, finding waste sites in twelve countries across Southeast Asia. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located within 200 m of a waterway. Numerous sites sit directly on riverbanks, with high risk of ocean leakage.

Integrating presence-only and occupancy data to model habitat use for the northernmost population of jaguars

Species distribution models (SDMs) have become an essential tool for the management and conservation of imperiled species. However, many at-risk species are rare and characterized by limited data on their spatial distribution and habitat relationships. This has led to the development of SDMs that integrate multiple types and sources of data to leverage more information and provide improved predictions of habitat associations. We developed a novel integrated species distribution model to predict habitat suitability for jaguars (Panthera onca) in the border region between northern Mexico and the southwestern USA. Our model combined presence-only and occupancy data to identify key environmental correlates, and we used model results to develop a probability of use map. We adopted a logistic regression modeling framework, which we found to be more straightforward and less computationally intensive to fit than Poisson point process-based models. Model results suggested that high terrain ruggedness and the presence of riparian vegetation were most strongly related to habitat use by jaguars in our study region. Our best model, on average, predicted that there is currently 25,463 km² of usable habitat in our study region. The United States portion of the study region, which makes up 38.6% of the total area, contained 40.6% of the total usable habitat. Even though there have been few detections of jaguars in the southwestern USA in recent decades, our results suggest that protection of currently suitable habitats, along with increased conservation efforts, could significantly contribute to the recovery of jaguars in the USA.

A Global 250-m Downscaled NDVI Product from 1982 to 2018

Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (Pearson’s R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson’s R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments.

Informing conservation decisions to target private lands of highest ecological value and risk of loss

Natural habitats on private lands are potentially important components of national biodiversity conservation strategies, yet they are being rapidly lost to development. Conservation easements and other means of protecting these habitats have expanded in use and will be most effective if they target private lands of highest biodiversity value and risk of loss. We developed a Biodiversity Conservation Priority Index (BCPI) based on ecological value and risk of habitat loss for remaining areas of natural vegetation cover (NVC) in the northwestern United States and addressed two questions: (1) Which remaining NVC on private lands is the highest priority for biodiversity conservation based on ecological value and risk of development? And (2) are conservation easements in NVC placed preferentially in locations of high biodiversity conservation priority? Drawing on the concept of ecological integrity, we integrated five metrics of ecological structure, function, and composition to quantify ecological value of NVC. These included net primary productivity, species richness, ecosystem type representation, imperiled species range rarity, and connectivity among "Greater Wildland Ecosystems." Risk of habitat loss was derived from analysis of biophysical and sociodemographic predictors of NVC loss. Ecological value and risk of loss were combined into the BCPI. We then analyzed spatial patterns of BCPI to identify the NVC highest in biodiversity conservation priority and examined the relationship between BCPI and conservation easement status. We found that BCPI varied spatially across the study area and was highest in western and southern portions of the study area. High BCPI was associated with suburban and rural development, roads, urban proximity, valley bottom landforms, and low intensity of current development. Existing conservation easements were distributed more towards lower BCPI values than unprotected NVC at both the study area and region scales. The BCPI can be used to better inform land use decision making at local, regional, and potentially national scales in order to better achieve biodiversity goals.

SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models

Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, and less extensive local calibration and validation. Thorough calibration and validation are difficult because the quantity of observations needed for such scales do not exist or is inaccessible to modelers. We present the Stream Analysis for Bias Estimation and Reduction (SABER) method for bias correction targeting large models. SABER is intended for model consumers to apply to a subset of a larger domain at gauged and ungauged locations and address issues with data size and availability. SABER extends frequency-matching postprocessing techniques using flow duration curves (FDC) at gauged subbasins to be applied at ungauged subbasins using clustering and spatial analysis. SABER uses a “scalar” FDC (SFDC), a ratio of simulated to observed FDC, to characterize biases spatially, temporally, and for varying exceedance probabilities to make corrections at ungauged subbasins. Biased flows at ungauged locations are corrected with the scalar values from the SFDC. Corrected flows are refined to fit a Gumbel Type 1 distribution. We present the theory, procedure, and validation study in Colombia. SABER reduces biases and improves composite metrics, including Nash Sutcliffe and Kling Gupta Efficiency. Recommendations for future work and a discussion of limitations are provided.

Informing wind energy development: Land cover and topography predict occupancy for Arizona bats

Wind energy is a growing source of renewable energy with a 3-fold increase in use globally over the last decade. However, wind turbines cause bat mortality, especially for migratory species. The southwest United States has high bat species diversity and is an important area for migratory species, although little is known about their seasonal distribution. To examine potential risk to bats in areas proposed for wind energy development, we characterized bat occupancy spatially and temporally across northern Arizona, identifying use during summer when bats are reproductively active and fall during the migratory season. Our objectives were to determine occupancy of migratory species and species of greatest conservation need and develop a probability of occupancy map for species to identify areas of potential conflict with wind energy development. We selected 92 sites in 10 clusters with potential for development and used acoustic detectors to sample bats in the summer and fall of 2016 and 2017 for 6 nights per site per year. We predicted response of migratory bat species and species of special concern to 9 landscape variables using Program MARK. During summer, higher densities of forest on the landscape resulted in a higher probability of occupancy of migratory species such as hoary bats (Lasiurus cinereus), silver-haired bats (Lasionycteris noctivagans), big free-tailed bats (Nyctinomops macrotis), and species of conservation need such as spotted bats (Euderma maculatum). During the fall, higher concentration of valleys on the landscape predicted occupancy of hoary bats, big free-tailed bats, and spotted bats. High bat occupancy in the fall was also associated with higher elevation and close proximity to forests. We recommend that wind turbines be placed in open, flat grasslands away from forested landscapes and concentrations of valleys or other topographic variation.

Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion

High-elevation trees cannot always reach the thermal treeline, the potential upper range limit set by growing-season temperature. But delineation of the realized upper range limit of trees and quantification of the drivers, which lead to trees being absent from the treeline, is lacking. Here, we used 30 m resolution satellite tree-cover data, validated by more than 0.7 million visual interpretations from Google Earth images, to map the realized range limit of trees along the Himalaya which harbours one of the world's richest alpine endemic flora. The realized range limit of trees is ~800 m higher in the eastern Himalaya than in the western and central Himalaya. Trees had reached their thermal treeline positions in more than 80% of the cases over eastern Himalaya but are absent from the treeline position in western and central Himalaya, due to anthropogenic disturbance and/or premonsoon drought. By combining projections of the deviation of trees from the treeline position due to regional environmental stresses with warming-induced treeline shift, we predict that trees will migrate upslope by ~140 m by the end of the twenty-first century in the eastern Himalaya. This shift will cause the endemic flora to lose at least ~20% of its current habitats, highlighting the necessity to reassess the effectiveness of current conservation networks and policies over the Himalaya.

Quantitative analysis of vegetation restoration and potential driving factors in a typical subalpine region of the Eastern Tibet Plateau

Vegetation restoration is an essential approach to re-establish the ecological balance in subalpine areas. Changes in vegetation cover represent, to some extent, vegetation growth trends and are the consequence of a complex of different natural factors and human activities. Microtopography influences vegetation growth by affecting the amount of heat and moisture reaching the ground, a role that is more pronounced in subalpine areas. However, little research is concerned with the characteristics and dynamics of vegetation restoration in different microtopography types. The respective importance of the factors driving vegetation changes in subalpine areas is also not clear yet. We used linear regression and the Hurst exponent to analyze the trends in vegetation restoration and sustainability in different microtopography types since 2000, based on Fractional Vegetation Cover (FVC) and identified potential driving factors of vegetation change and their importance by using Geographical Detector. The results show that: (1) The FVC in the region under study has shown an up-trend since 2000, and the rate of increase is 0.26/year (P = 0.028). It would be going from improvement to degradation, continuous decrease or continuous significant decrease in 47.48% of the region, in the future. (2) The mean FVC is in the following order: lower slope (cool), lower slope, lower slope (warm), valley, upper slope (warm), upper slope, valley (narrow), upper slope (cool), cliff, mountain/divide, peak/ridge (warm), peak/ridge, peak/ridge (cool). The lower slope is the microtopographic type with the best vegetation cover, and ridge peak is the most difficult to be afforested. (3) The main factors affecting vegetation restoration in subalpine areas are aspect, microtopographic type, and soil taxonomy great groups. The interaction between multiple factors has a much stronger effect on vegetation cover than single factors, with the effect of temperatures and aspects having the most significant impact on the vegetation cover changes. Natural factors have a greater impact on vegetation restoration than human factors in the study area. The results of this research can contribute a better understanding of the influence of different drivers on the change of vegetation cover, and provide appropriate references and recommendations for vegetation restoration and sustainable development in typical logging areas in subalpine areas.

Verifying Experimental Wet Bulb Globe Temperature Hindcasts Across the United States

Hot and humid heat exposures challenge the health of outdoor workers engaged in occupations such as construction, agriculture, first response, manufacturing, military, or resource extraction. Therefore, government institutes developed guidelines to prevent heat-related illnesses and death during high heat exposures. The guidelines use Wet Bulb Globe Temperature (WBGT), which integrates temperature, humidity, solar radiation, and wind speed. However, occupational heat exposure guidelines cannot be readily applied to outdoor work places due to limited WBGT validation studies. In recent years, institutions have started providing experimental WBGT forecasts. These experimental products are continually being refined and have been minimally validated with ground-based observations. This study evaluated a modified WBGT hindcast using the historical National Digital Forecast Database and the European Centre for Medium-Range Weather Forecasts Reanalysis v5. We verified the hindcasts with hourly WBGT estimated from ground-based weather observations. After controlling for geographic attributes and temporal trends, the average difference between the hindcast and in situ data varied from -0.64°C to 1.46°C for different Köppen-Geiger climate regions, and the average differences are reliable for decision making. However, the results showed statistically significant variances according to geographical features such as aspect, coastal proximity, land use, topographic position index, and Köppen-Geiger climate categories. The largest absolute difference was observed in the arid desert climates (1.46: 95% CI: 1.45, 1.47), including some parts of Nevada, Arizona, Colorado, and New Mexico. This research investigates geographic factors associated with systematic WBGT differences and points toward ways future forecasts may be statistically adjusted to improve accuracy.

"No-regrets" pathways for navigating climate change: planning for connectivity with land use, topography, and climate

As both plant and animal species shift their ranges in response to a changing climate, maintaining connectivity between present habitat and suitable habitat in the future will become increasingly important to ensure lasting protection for biodiversity. Because the temporal period commensurate with planning for mid-century change is multi-generational for most species, connectivity designed to facilitate climate adaptation requires pathways with 'stepping-stones' between current and future habitat. These areas should have habitats suitable not only for dispersal, but for all aspects of species lifecycles. We integrated present-day land use, topographic diversity, and projections of shifting climate regimes into a single connectivity modeling approach to identify pathways for mid-century shifts in species ranges. Using Omniscape we identified climate linkages, or areas important for climate change-driven movement, as the areas with more current flow than would be expected in the absence of climate considerations. This approach identified connectivity potential between natural lands in the present climate and natural lands with future analogous climate following topo-climatically diverse routes. We then translated the model output into a strategic framework to improve interpretation and to facilitate a more direct connection with conservation action. Across modified landscapes, pathways important to climate-driven movement were highly coincident with the last remaining present-day linkages, reinforcing their importance. Across unfragmented lands, the presence of climate-adapted pathways helped inform the prioritization of conservation actions in areas where multiple connectivity options still exist. Many climate linkages follow major watercourses along elevational gradients, highlighting the importance of protecting or managing for these natural linear pathways that provide movement routes for climate adaptation. By integrating enduring landscape features with climate projections and present-day land uses, our approach reveals "no-regrets" pathways to plan for a connected landscape in an uncertain future.

Visitation Rate Analysis of Geoheritage Features from Earth Science Education Perspective Using Automated Landform Classification and Crowdsourcing: A Geoeducation Capacity Map of the Auckland Volcanic Field, New Zealand

The increase in geoheritage studies has secured recognition globally regarding the importance of abiotic natural features. Prominent in geoheritage screening practices follows a multicriteria assessment framework; however, the complexity of interest in values often causes decision making to overlook geoeducation, one of the primary facets of geosystem services. Auckland volcanic field in New Zealand stretches through the whole area of metropolitan Auckland, which helps preserve volcanic cones and their cultural heritage around its central business district (CBD). They are important sites for developing tourist activities. Geoeducation is becoming a significant factor for tourists and others visiting geomorphological features, but it cannot be achieved without sound planning. This paper investigates the use of big data (FlickR), Geopreservation Inventory, and Geographic Information System for identifying geoeducation capacity of tourist attractions. Through landform classification using the Topographic Position Index and integrated with geological and the inventory data, the underpromoted important geoeducation sites can be mapped and added to the spatial database Auckland Council uses for urban planning. The use of the Geoeducation Capacity Map can help resolve conflicts between the multiple objectives that a bicultural, metropolitan city council need to tackle in the planning of upgrading open spaces while battling of growing demand for land.

Testing a Generalizable Machine Learning Workflow for Aquatic Invasive Species on Rainbow Trout (Oncorhynchus mykiss) in Northwest Montana

Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.

A 1-km hourly air-temperature model for 13 northeastern U.S. states using remotely sensed and ground-based measurements

BACKGROUND

Accurate and precise estimates of ambient air temperatures that can capture fine-scale within-day variability are necessary for studies of air temperature and health.

METHOD

We developed statistical models to predict temperature at each hour in each cell of a 927-m square grid across the Northeast and Mid-Atlantic United States from 2003 to 2019, across ~4000 meteorological stations from the Integrated Mesonet, using inputs such as elevation, an inverse-distance-weighted interpolation of temperature, and satellite-based vegetation and land surface temperature. We used a rigorous spatial cross-validation scheme and spatially weighted the errors to estimate how well model predictions would generalize to new cell-days. We assess the within-county association of temperature and social vulnerability in a heat wave as an example application.

RESULTS

We found that a model based on the XGBoost machine-learning algorithm was fast and accurate, obtaining weighted root mean square errors (RMSEs) around 1.6 K, compared to standard deviations around 11.0 K. We found similar accuracy when validating our model on an external dataset from Weather Underground. Assessing predictions from the North American Land Data Assimilation System-2 (NLDAS-2), another hourly model, in the same way, we found it was much less accurate, with RMSEs around 2.5 K. This is likely due to the NLDAS-2 model's coarser spatial resolution, and the dynamic variability of temperature within its grid cells. Finally, we demonstrated the health relevance of our model by showing that our temperature estimates were associated with social vulnerability across the region during a heat wave, whereas the NLDAS-2 showed a much weaker association.

CONCLUSION

Our high spatiotemporal resolution air temperature model provides a strong contribution for future health studies in this region.

Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa

Estimation and monitoring of soil organic carbon (SOC) stocks is important for maintaining soil productivity and meeting climate change mitigation targets. Current global SOC maps do not provide enough detail for landscape-scale decision making, and do not allow for tracking carbon sequestration or loss over time. Using an optical satellite-driven machine learning workflow, we mapped SOC stocks (topsoil; 0 to 30 cm) under natural vegetation (86% of land area) over South Africa at 30 m spatial resolution between 1984 and 2019. We estimate a total topsoil SOC stock of 5.6 Pg C with a median SOC density of 6 kg C m^-2 (IQR: interquartile range 2.9 kg C m^-2). Over 35 years, predicted SOC underwent a net increase of 0.3% (relative to long-term mean) with the greatest net increases (1.7%) and decreases (-0.6%) occurring in the Grassland and Nama Karoo biomes, respectively. At the landscape scale, SOC changes of up to 25% were evident in some locations, as evidenced from fence-line contrasts, and were likely due to local management effects (e.g. woody encroachment associated with increased SOC and overgrazing associated with decreased SOC). Our SOC mapping approach exhibited lower uncertainty (R² = 0.64; RMSE = 2.5 kg C m^-2) and less bias compared to previous low-resolution (250-1000 m) national SOC mapping efforts (average R² = 0.24; RMSE = 3.7 kg C m^-2). Our trend map remains an estimate, pending repeated measures of soil samples in the same location (time-series); a global priority for tracking SOC changes. While high resolution SOC maps can inform land management decisions aimed at climate mitigation (natural climate solutions), potential increases in SOC are likely limited by local climate and soils. It is also important that climate mitigation efforts such as planting trees balance trade-offs between carbon, biodiversity and overall ecosystem function.

Looking at the past to infer into the future: Thermal traits track environmental change in Liolaemidae

The diversity of habitats generated by the Andes uplift resulted a mosaic of heterogeneous environments in South America for species to evolve a variety of ecological and physiological specializations. Species in the lizard family Liolaemidae occupy a myriad of habitats in the Andes. Here, we analyze the tempo and mode of evolution in the thermal biology of liolaemids. We assessed whether there is evidence of local adaptation (lability) or conservatism (stasis) in thermal traits. We tested the hypothesis that abiotic factors (e.g., geography, climate) rather than intrinsic factors (egg-laying [oviparous] or live-bearing [viviparous], substrate affinity) explain variation in field active body temperature (T_b ), preferred temperature (T_p ), hours of restriction of activity, and potential hours of activity. Although most traits exhibited high phylogenetic signal, we found variation in thermal biology was shaped by geography, climate, and ecological diversity. Ancestral character reconstruction showed shifts in T_b tracked environmental change in the past ∼20,000 years. Thermal preference is 3°C higher than T_b , yet exhibited a lower rate of evolution than T_b and air temperature. Viviparous Liolaemus have lower T_b s than oviparous species, whereas T_p is high for both modes of reproduction, a key difference that results in a thermal buffer for viviparous species to cope with global warming. The rapid increase in environmental temperatures expected in the next 50-80 years in combination with anthropogenic loss of habitats are projected to cause extirpations and extinctions in oviparous species.

Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms

The ubiquitous nature of satellite data has led to an explosion of studies on the surface urban heat island (SUHI). Relatively few have simultaneously used air temperature measurements to compare SUHI with the canopy UHI (CUHI), which is more relevant to public health. Using crowdsourced citizen weather stations (>50,000) and satellite data over Europe, we estimate the CUHI and SUHI intensity in 342 urban clusters during the 2019 heat wave. Satellites produce a sixfold overestimate of UHI relative to station measurements (mean SUHI 1.45°C; CUHI 0.26°C), with SUHI exceeding CUHI in 96% of cities during daytime and in 80% at night. Using empirical evidence, we confirm the control of aerodynamic roughness on UHI intensity, but find evaporative cooling to have a stronger overall impact during this time period. Our results support urban greening as an effective UHI mitigation strategy and caution against relying on satellite data for urban heat risk assessments.

Global progress in incorporating climate adaptation into land protection for biodiversity since Aichi targets

Climate adaptation strategies are being developed and implemented to protect biodiversity from the impacts of climate change. A well-established strategy involves the identification and addition of new areas for conservation, and most countries agreed in 2010 to expand the global protected area (PA) network to 17% by 2020 (Aichi Biodiversity Target 11). Although great efforts to expand the global PA network have been made, the potential of newly established PAs to conserve biodiversity under future climate change remains unclear at the global scale. Here, we conducted the first global-extent, country-level assessment of the contribution of PA network expansion toward three key land prioritization approaches for biodiversity persistence under climate change: protecting climate refugia, protecting abiotic diversity, and increasing connectivity. These approaches avoid uncertainties of biodiversity predictions under climate change as well as the issue of undescribed species. We found that 51% of the countries created new PAs in locations with lower mean climate velocity (representing better climate refugia) and 58% added PAs in areas with higher mean abiotic diversity compared to the available, non-human-dominated lands not chosen for protection. However, connectivity among PAs declined in 53% of the countries, indicating that many new PAs were located far from existing PAs. Lastly, we identified potential improvements for climate adaptation, showing that 94% of the countries have the opportunity to improve in executing one or more approaches to conserve biodiversity. Most countries (60%) were associated with multiple opportunities, highlighting the need for integrative strategies that target multiple land protection approaches. Our results demonstrate that a global improvement in the protection of climate refugia, abiotic diversity, and connectivity of reserves is needed to complement land protection informed by existing and projected species distributions. Our study also provides a framework for countries to prioritize land protection for climate adaptation using publicly available data.

Mapping Land Use/Cover Dynamics of the Yellow River Basin from 1986 to 2018 Supported by Google Earth Engine

Changes in the land use/cover alter the Earth system processes and affect the provision of ecosystem services, posing a challenge to achieve sustainable development. In the past few decades, the Yellow River (YR) basin faced enormous social and environmental sustainability challenges associated with environmental degradation, soil erosion, vegetation restoration, and economic development, which makes it important to understand the long-term land use/cover dynamics of this region. Here, using three decades of Landsat imagery (17,080 images) and incorporating physiography data, we developed an effective annual land use/cover mapping framework and provided a set of 90 m resolution continuous annual land use/cover maps of the YR basin from 1986 to 2018 based on the Google Earth Engine and the Classification and Regression Trees algorithm. The independent random sampling validations based on the field surveys (640 points) and Google Earth (3456 points) indicated that the overall accuracy of these maps is 78.3% and 80.0%, respectively. The analysis of the land system of the YR basin showed that this region presents complex temporal and spatial changes, and the main change patterns include no change or little change, cropland loss and urban expansion, grassland restoration, increase in orchard and terrace, and increase in forest during the entire study period. The major land use/cover change has occurred in the transitions from forests, grasslands, and croplands to the class of orchard and terrace (19.8% of all change area), which not only increase the greenness but also raised the income, suggesting that YR progress towards sustainable development goals for livelihood security, economic growth, and ecological protection. Based on these data and analysis, we can further understand the role of the land system in the mutual feedback between society and the environment, and provide support for ecological conservation, high-quality development, and the formulation of sustainable management policies in this basin, highlighting the importance of continuous land use/cover information for understanding the interactions between the human and natural systems.

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

Plant biomes demonstrate that landscape resilience today is the lowest it has been since end-Pleistocene megafaunal extinctions

Resilient landscapes have helped maintain terrestrial biodiversity during periods of climatic and environmental change. Identifying the tempo and mode of landscape transitions and the drivers of landscape resilience is critical to maintaining natural systems and preserving biodiversity given today's rapid climate and land use changes. However, resilient landscapes are difficult to recognize on short time scales, as perturbations are challenging to quantify and ecosystem transitions are rare. Here we analyze two components of North American landscape resilience over 20,000 years: residence time and recovery time. To evaluate landscape dynamics, we use plant biomes, preserved in the fossil pollen record, to examine how long a biome type persists at a given site (residence time) and how long it takes for the biome at that site to reestablish following a transition (recovery time). Biomes have a median residence time of only 230-460 years. Only 64% of biomes recover their original biome type, but recovery time is 140-290 years. Temperatures changing faster than 0.5°C per 500 years result in much reduced residence times. Following a transition, biodiverse biomes reestablish more quickly. Landscape resilience varies through time. Notably, short residence times and long recovery times directly preceded the end-Pleistocene megafauna extinction, resulting in regional destabilization, and combining with more proximal human impacts to deliver a one-two punch to megafauna species. Our work indicates that landscapes today are once again exhibiting low resilience, foreboding potential extinctions to come. Conservation strategies focused on improving both landscape and ecosystem resilience by increasing local connectivity and targeting regions with high richness and diverse landforms can mitigate these extinction risks.

Quantifying Climate-Wise Connectivity across a Topographically Diverse Landscape

Climate-wise connectivity is essential to provide species access to suitable habitats in the future, yet we lack a consistent means of quantifying climate adaptation benefits of habitat linkages. Species range shifts to cooler climates have been widely observed, suggesting we should protect pathways providing access to cooler locations. However, in topographically diverse regions, the effects of elevation, seasonality, and proximity to large water bodies are complex drivers of biologically relevant temperature gradients. Here, we identify potential terrestrial and riparian linkages and their cooling benefit using mid-century summer and winter temperature extremes for interior coastal ranges in Northern California. It is rare for the same area to possess both terrestrial and riparian connectivity value. Our analysis reveals distinct differences in the magnitude and orientation of cooling benefits between the summer maximum and winter minimum temperatures provided by the linkages we delineated for the area. The cooling benefits for both linkage types were maximized to the west during summer, but upslope and to the northeast during winter. The approach we employ here provides an improved method to prioritize climate-wise connectivity and promote landscape resilience for topographically diverse regions.