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Future disruptions to fire activity will threaten ecosystems and human well-being throughout the world, yet there are few fire projections at global scales and almost none from a broad range of global climate models (GCMs). Here we integrate global fire datasets and environmental covariates to build spatial statistical models of fire probability at a 0.5° resolution and examine environmental controls on fire activity. Fire models are driven by climate norms from 16 GCMs (A2 emissions scenario) to assess the magnitude and direction of change over two time periods, 2010-2039 and 2070-2099. From the ensemble results, we identify areas of consensus for increases or decreases in fire activity, as well as areas where GCMs disagree. Although certain biomes are sensitive to constraints on biomass productivity and others to atmospheric conditions promoting combustion, substantial and rapid shifts are projected for future fire activity across vast portions of the globe. In the near term, the most consistent increases in fire activity occur in biomes with already somewhat warm climates; decreases are less pronounced and concentrated primarily in a few tropical and subtropical biomes. However, models do not agree on the direction of near-term changes across more than 50% of terrestrial lands, highlighting major uncertainties in the next few decades. By the end of the century, the magnitude and the agreement in direction of change are projected to increase substantially. Most far-term model agreement on increasing fire probabilities (∼62%) occurs at mid- to high-latitudes, while agreement on decreasing probabilities (∼20%) is mainly in the tropics. Although our global models demonstrate that long-term environmental norms are very successful at capturing chronic fire probability patterns, future work is necessary to assess how much more explanatory power would be added through interannual variation in climate variables. This study provides a first examination of global disruptions to fire activity using an empirically based statistical framework and a multi-model ensemble of GCM projections, an important step toward assessing fire-related vulnerabilities to humans and the ecosystems upon which they depend.

Mountain streams provide important habitats for many species, but their faunas are especially vulnerable to climate change because of ectothermic physiologies and movements that are constrained to linear networks that are easily fragmented. Effectively conserving biodiversity in these systems requires accurate downscaling of climatic trends to local habitat conditions, but downscaling is difficult in complex terrains given diverse microclimates and mediation of stream heat budgets by local conditions. We compiled a stream temperature database ( n = 780) for a 2500-km river network in central Idaho to assess possible trends in summer temperatures and thermal habitat for two native salmonid species from 1993 to 2006. New spatial statistical models that account for network topology were parameterized with these data and explained 93% and 86% of the variation in mean stream temperatures and maximas, respectively. During our study period, basin average mean stream temperatures increased by 0.38°C (0.27°C/decade), and maximas increased by 0.48°C (0.34°C/decade), primarily due to long-term (30-50 year) trends in air temperatures and stream flows. Radiation increases from wildfires accounted for 9% of basin-scale temperature increases, despite burning 14% of the basin. Within wildfire perimeters, however, stream temperature increases were 2-3 times greater than basin averages, and radiation gains accounted for 50% of warming. Thermal habitat for rainbow trout ( Oncorhynchus mykiss ) was minimally affected by temperature increases, except for small shifts towards higher elevations. Bull trout ( Salvelinus confluentus ), in contrast, were estimated to have lost 11-20% (8-16%/decade) of the headwater stream lengths that were cold enough for spawning and early juvenile rearing, with the largest losses occurring in the coldest habitats. Our results suggest that a warming climate has begun to affect thermal conditions in streams and that impacts to biota will be specific to both species and context. Where species are at risk, conservation actions should be guided based on considerations of restoration opportunity and future climatic effects. To refine predictions based on thermal effects, more work is needed to understand mechanisms associated with biological responses, climate effects on other habitat features, and habitat configurations that confer population resilience.

To better coordinate the relationship between grain production (GP) and economic development (ED) in the Huang-Huai-Hai (HHH) region and promote the coordinated growth of grain and economy, this study first established a multi-factor index system of GP and ED and then built a gray coupling coordination (GCC) model based on gray incidence analysis (GIA) method. Finally, the spatial statistical models and principal component analysis (PCA) were combined to reveal the spatiotemporal correlation mechanism of coupling degree (CD), and different coupling coordination types were classified. The results show that: (1) The correlations between the HHH GP system and ED system are all above 0.6, and the correlations are strong. Output efficiency has the strongest supporting effect on ED, and growth potential has the weakest coercive effect on GP. (2) The CD of GP and ED in HHH showed a spatial differentiation pattern of “high in the South, low in the Midwest.” The center of gravity for the CD shows a gradual shift from the southwest to the northeast during 2010–2019. The CD between HHH GP and ED is Moran′sI>0, and different types of coupling regions show a strong spatial dependence. (3) Most cities in HHH are in the break-in stage of GP and ED. The antagonistic type and the coordinated type are clustered in the east, while the low-level coupling type is mainly distributed in the north and southeast.

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

The provision of ecosystem services (ESs) is driven by land use and biophysical conditions and is thus intrinsically linked to space. Large-scale ES models, developed to inform policy makers on ES drivers, do not usually consider spatial autocorrelation that could be inherent to the distribution of these ESs or to the modeling process. The objective of this study is to estimate the drivers of ecosystem services in France using statistical models and show how taking into account spatial autocorrelation improves the predictive quality of these models. We study six regulating ESs (habitat quality index, water retention index, topsoil organic matter, carbon storage, soil erosion control, and nitrogen oxide deposition velocity) and three provisioning ESs (crop production, grazing livestock density, and timber removal). For each of these ESs, we estimated and compared five spatial statistical models to investigate the best specification (using statistical tests and goodness-of-fit metrics). Our results show that (1) taking into account spatial autocorrelation improves the predictive accuracy of all ES models (Δ R 2 ranging from 0.13 to 0.58); (2) land use and biophysical variables (weather and soil texture) are significant drivers of most ESs; (3) forest was the most balanced land use for provision of a diversity of ESs compared to other land uses (agriculture, pasture, urban, and others); (4) Urban area is the worst land use for provision of most ESs. Our findings imply that further studies need to consider spatial autocorrelation of ESs in land use change and optimization scenario simulations.

This study investigated the possible contributing factors to the digital gender divide and the spatial spillover effects of submarine communication cables on neighboring areas. Using spatial statistical models and global data, this study examined the Internet penetration rate and the digital gender divide in 186 countries. Data sources included open source data, social media, ITU and UNCTAD databases, and the World Bank. The study estimated the average male and female Internet users in 186 countries using Facebook API. The number of submarine communication cables between countries was used as the basis for constructing the spatial weights matrix. The countries with the highest Internet penetration rate were mostly in Europe. Those with the lowest Internet penetration rate were in Africa, Central Asia, and West Asia. Thirteen of the 15 countries with the worst Internet penetration rate were located in the African region. A significant digital gender divide arose in the developing and the least-developed countries. We argue that connectivity is key to the reduction of the digital gender divide and to ensuring that more women have access to the Internet. The study ascertains that the Internet penetration rate is related to the digital gender divide in individual countries and has a spatial spillover impact in surrounding nations connected by submarine communication cables. The study demonstrates that submarine cables serve as important infrastructure for closing the digital gender divide and deserve increased analytical attention. This study looked into why there is a difference in how men and women use the internet, and also how undersea communication cables can affect nearby areas. We used models and data from around the world to study how much the internet is used and how it’s used differently by men and women in 186 countries. We gathered information from various sources like social media, ITU, UNCTAD, and the World Bank. We used Facebook data to estimate the number of male and female internet users in each country. The number of undersea communication cables between countries helped them see how connected they were. The countries in Europe had the most internet users, while countries in Africa, Central Asia, and West Asia had the fewest. Especially in Africa, there were many countries with very low internet usage. We found a significant difference between men and women in how they use the internet in developing and less-developed countries. The researchers suggest that better internet access is important to reduce this gap and make sure more women can use the internet. We also found that the internet usage in one country can affect neighboring countries connected by undersea cables. These cables play an essential role in bridging the digital gender gap and should be given more attention when analyzing internet usage patterns.

The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.

The Mutis-Timau Forest Complex, one of the remaining mountainous tropical forest areas in Timor Island, eastern Indonesia that covers an area of 31,984 ha, tends to decrease gradually. Efforts to secure mountain forest functions and counteract the negative impact of declining forest areas are often constrained by data uncertainty on factors contributing to deforestation. For this reason, this study attempts to develop models of deforestation and predict future deforestation in the Mutis-Timau Forest Complex. We constructed models of deforestation that describe the relationship between deforestation and factors contributing to deforestation using spatial statistical models. In this model, we used the deforestation data for the 1987–2017 period obtained from a previous study as dependent variables and the potential causes of deforestation generated from Geographic Information System spatial analysis as independent variables. Using the probability of deforestation derived from the model, we predicted future deforestation under two different scenarios, namely, business-as-usual (as the reference scenario) and reducing emission from deforestation and forest degradation. Our findings showed that a positive relationship exists between probability of deforestation, distance to the settlement, and population density variables, whereas a negative relationship exists between likelihood of deforestation, elevation, slope, distance to the road, distance to the savanna, and forest management unit variables. During the 2017–2030 period, under the business-as-usual scenario, the Mutis-Timau Forest Complex will lose 1327.65 ha in forest area with an annual deforestation rate of 0.54%. Meanwhile, under the reducing emission from deforestation and forest degradation scenario, the overall forest loss was estimated to be 1237.11 ha with an annual deforestation rate of 0.50%. The predicted area of avoided deforestation in 2017–2030 under the reducing emission from deforestation and forest degradation scenario was 90.54 ha. Such data and information are important for the Mutis-Timau Forest Complex authority in prioritizing actions for combating deforestation and designing appropriate forest-related policies and supporting data for reducing emission from deforestation and forest degradation programme or other incentive schemes in reducing deforestation.

Mastering the coupling and coordination relationship and driving mechanism of urbanization and ecosystem service value (ESV) is of great significance to ecological protection and regional sustainable development. In this paper, the coupling coordination model, geographic detector and GWR model are used to analyze the spatio-temporal coupling interaction between urbanization and ESV and the spatial differentiation characteristics of influencing factors from 1995 to 2018. The results of the study are as follows: (1) During the study period, cities in the Yellow River Basin experienced accelerated urban expansion, and the ESV of forests, water and wetlands increased, which offset the reduction in ESV due to the expansion of construction land and farmland and grassland. (2) The degree of coupling and coordination between the two gradually improved, but the overall situation showed a low-level coupling and coordination process. Mild coupling coordination gradually increased, reaching an increase of 38.10%; severe imbalance types tended to disappear, decreasing by 52.38%, and coupling subtypes developed from lagging urbanization to ESV backward types. The high-value areas of the coupling coordination degree are distributed in the high-value areas of ESV in the north of the upper reaches, and the low-value areas are distributed in the cities of Henan and Shandong with high urbanization levels in the downstream and most resource-based cities in the middle reaches. (3) In addition, the spatial intensity of the effect of each dominant factor on the degree of coupling coordination is different. Economic growth, technological development, environmental regulations and the proportion of forest land have positive and belt-shaped alienation characteristics for the coupling and coordination of the two, and infrastructure and temperature show negative driving characteristics. Therefore, the coupling and coordination relationship between ESV and urbanization should be clarified to help future urban planning. On the basis of determining the regional environmental carrying capacity and the adjustment direction of the rational planning of land resources, the impact of urban barriers formed by administrative boundaries and natural geographical conditions on the development of urban agglomerations should be broken to achieve the overall high-quality and coordinated development of the basin.

This study examines the relationship between air temperature and urban environment indices, mainly focusing on sky view factor (SVF) in Seoul, Korea. We use air temperature data observed from 295 automatic weather stations (AWS) during the day and night in Seoul. We conduct a spatial regression analysis to capture the effect of spatial autocorrelation in our data and identify changes in the effects of SVF on air temperature, while conducting the regression model for each dataset according to the floor area ratio (FAR). The findings of our study indicate that SVF negatively affects air temperature during both day and night when other effects are controlled through spatial regression models. Moreover, we address the environmental indices associated with day-time and night-time air temperatures and identify the changing effects of SVF on air temperature according to the areal floor area ratio of the analysis datasets. This study contributes to the literature on the relationship between SVF and air temperature in high-density cities and suggests policy implications for improving urban thermal environments with regard to urban design and planning.