TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Hydroclimatological Patterns and Limnological Characteristics of Unique Wetland Systems on the Argentine High Andean Plateau

High-elevation wetlands in South America are not well described despite their high sensitivity to human impact and unique biodiversity. We describe the hydroclimatological and limnological characteristics of 21 wetlands on the High Andean Plateau of Argentina, synthesizing information gathered over ten years (2010–2020). We collected physical-chemical, phytoplankton, and zooplankton data and counted flamingos in each wetland. We also conducted an extensive analysis of climatic patterns and hydrological responses since 1985. These wetlands are shallow, with a wide range of salinity (from fresh to brine), mostly alkaline, and are dominated by carbonate and gypsum deposits and sodium-chloride waters. They tend to have high nutrient concentrations. Plankton shows a low species richness and moderate to high dominance of taxa. Flamingos are highly dependent on the presence of Bacillariophyta, which appears to be positively linked to silica and soluble reactive phosphorus availability. Climatic conditions show a strong region-wide increase in average air temperature since the mid-1980s and a decrease in precipitation between 1985–1999 and 2000–2020. These high-elevation wetlands are fundamentally sensitive systems; therefore, having baseline information becomes imperative to understanding the impact of climatic changes and other human perturbations. This work attempts to advance the body of scientific knowledge of these unique wetland systems.

Climate-induced outbreaks in high-elevation pines are driven primarily by immigration of bark beetles from historical hosts

Warming temperatures are allowing native insect herbivores to expand into regions that previously exceeded their thermal tolerance, encounter new host species, and pose significant threats to native communities. However, the dynamics of these expansions remain poorly understood, particularly in the extent to which outbreaks remain reliant on emigration from historical hosts or are driven by local reproduction within novel hosts in the expanded range. We tested these non-mutually exclusive hypotheses using spatially explicit data on mountain pine beetle (Dendroctonus ponderosae), which historically undergoes intermittent outbreaks in low-elevation lodgepole pine (Pinus contorta), but is now causing severe mortality in a high-elevation endangered species, whitebark pine (Pinus albicaulis). We compiled data from 2000 to 2019 across British Columbia, Canada, at 1-km² resolution, and analyzed spatiotemporal patterns of beetle infestations, lodgepole pine distributions, expansion into habitats dominated by whitebark pine, and the likelihood of future outbreaks in all pine communities under simulated conditions. Overall, we found strong support for the hypothesis of emigration from the historical host species continuing to be a major driver of outbreaks in the more recently accessed host. First, beetle population pressure was consistently the best predictor of infestation severity in both lodgepole and whitebark pine, and appeared to be mostly unidirectional from lodgepole to whitebark pine. Second, infestations in lodgepole pine were of a longer duration than those in whitebark pine, which appeared too brief to sustain transitions from endemic to eruptive dynamics. Furthermore, resource depletion appears to drive emigration from lodgepole pine, whereas in whitebark pine drought appears to favor establishment of immigrants although bioclimatic factors and stand structure preclude self-sustaining outbreaks. Finally, we project that most pine in British Columbia will be at risk in the event of a new major outbreak. We describe implications for conserving and protecting whitebark pine and to other climate-driven range expansions.

Accelerated increase in vegetation carbon sequestration in China after 2010: A turning point resulting from climate and human interaction

China has increased its vegetation coverage and enhanced its terrestrial carbon sink through ecological restoration since the end of the 20th century. However, the temporal variation in vegetation carbon sequestration remains unclear, and the relative effects of climate change and ecological restoration efforts are under debate. By integrating remote sensing and machine learning with a modelling approach, we explored the biological and physical pathways by which both climate change and human activities (e.g., ecological restoration, cropland expansion, and urbanization) have altered Chinese terrestrial ecosystem structures and functions, including vegetation cover, surface heat fluxes, water flux, and vegetation carbon sequestration (defined by gross and net primary production, GPP and NPP). Our study indicated that during 2001-2018, GPP in China increased significantly at a rate of 49.1-53.1 TgC/yr² , and the climatic and anthropogenic contributions to GPP gains were comparable (48%-56% and 44%-52%, respectively). Spatially, afforestation was the dominant mechanism behind forest cover expansions in the farming-pastoral ecotone in northern China, on the Loess Plateau and in the southwest karst region, whereas climate change promoted vegetation cover in most parts of southeastern China. At the same time, the increasing trend in NPP (22.4-24.9 TgC/yr² ) during 2001-2018 was highly attributed to human activities (71%-81%), particularly in southern, eastern, and northeastern China. Both GPP and NPP showed accelerated increases after 2010 because the anthropogenic NPP gains during 2001-2010 were generally offset by the climate-induced NPP losses in southern China. However, after 2010, the climatic influence reversed, thus highlighting the vegetation carbon sequestration that occurs with ecological restoration.

The Identification and Classification of Arid Zones through Multicriteria Evaluation and Geographic Information Systems—Case Study: Arid Regions of Northwest Mexico

Arid and semiarid regions are geographic units that cover approximately 43% of the earth’s surface worldwide, and conditions of extreme drought and reduced vegetation cover predominate in these regions. In Mexico, arid and semiarid ecosystems cover more than half of the territory, with desertification, mainly caused by anthropogenic activities and climatic events, as the main problem in these regions. The present research aimed to assess, identify, and classify arid and semiarid zones by employing a methodology based on multicriteria evaluation analysis (MCA) using the weighted linear combination (WLC) technique and geographic information systems (GIS) in the hydrological administrative regions (HARs) of the North Pacific, Northwest, and Baja California Peninsula, located in Northwest Mexico. Data related to aridity, desertification, degradation, and drought were investigated, and the main factors involved in the aridity process, such as surface temperature, soil humidity, precipitation, slopes, orientations, the normalized difference vegetation index (NDVI), and evapotranspiration, were obtained. For the standardization of factors, a fuzzy inference system was used. The weight of each factor was then determined with the analytical hierarchy process (AHP). To delimit arid regions, the classification of arid zones proposed by the United Nations Environment Program (UNEP) was used, and the result was an aridity suitability map. To validate the results, the sensitivity analysis method was applied. Quantitative and geospatial aridity indicators were obtained at the administrative hydrological level and by state. The main results indicated that semiarid and dry subhumid zones predominated, representing 40% and 43% of the surface of the study area, respectively, while arid regions represented 17%, and humid regions represented less than 1%. In addition, of the states for which 100% of the surface lay in the study area, it was observed that Baja California and Baja California Sur had the largest arid and semiarid zones, while subhumid regions predominated in Sonora and Sinaloa.

Estimation of air temperature and the mountain-mass effect in the Yellow River Basin using multi-source data

Quantitative studies of the multiple factors influencing the mountain-mass effect, which causes higher temperatures in mountainous than non-mountainous regions, remain insufficient. This study estimated the air temperature in the Yellow River Basin, which spans three different elevation ranges, using multi-source data to address the uneven distribution of regional meteorological stations. The differences in mountain-mass effect for different geomorphic regions at the same altitude were then compared. The Manner-Kendall nonparametric test was used to analyse time series changes in temperature. Moreover, we employed the geographically weighted regression (GWR) model, with MODIS land-surface and air-temperature data, station-based meteorological data, vertical temperature gradients corresponding to the 2000-2015 period, and elevation data, to estimate the correlation between monthly mean surface temperature and air temperature in the Yellow River Basin. The following major results were obtained. (1) The GWR method and ground station-based observations enhanced the accuracy of air-temperature estimates with an error of only ± 0.74°C. (2) The estimated annual variations in the spatial distributions of 12-month average temperatures showed that the upper Tibetan Plateau is characterised by low annual air temperatures with a narrow spatial distribution, whereas north-eastern areas upstream of the Inner Mongolia Plateau are characterised by higher air temperatures. Changes in the average monthly air temperature were also high in the middle and lower reaches, with a narrow spatial distribution. (3) Considering the seasonal variation in the temperature lapse rate, the mountain-mass effect in the Yellow River Basin was very high. In the middle of each season, the variation of air temperature at a given altitude over the Tibetan Plateau was higher than that over the Loess Plateau and Jinji Mountain. The results of this study reveal the unique temperature characteristics of the Yellow River Basin according to its geomorphology. Furthermore, this research contributes to quantifying mountain-mass effects.

Time-Varying Effects of Meteorological Variables on Malaria Epidemiology in the Context of Interrupted Control Efforts in the Amazon Rainforest, 2000-2017

Successful malaria control interventions, mostly based on the training of health workers, distribution of insecticide-treated nets, and spraying, decrease malaria incidence; however, when these interventions are interrupted, a resurgence may occur. In the Peruvian Amazon, after discontinuing the control activities implemented by the PAMAFRO project (2006–2010)-a Global Fund-sponsored project for the strengthening of malaria control and surveillance in multiple countries in Latin America– malaria cases re-emerged dramatically. In parallel, meteorological factors determine the conditions suitable for the development, reproduction, and survival of mosquito vectors and parasites. This study hypothesized that interruption of malaria interventions may have modified the meteorological-malaria relationships over time (i.e., temporal changes in the dose-response between meteorological variables and malaria incidence). In this panel data analysis, we assessed the extent that relationships between meteorological variables and malaria changed temporally using data of monthly malaria incidence due to Plasmodium vivax or P. falciparum in Loreto, Peru (2000–2017). Generalized additive models were used to explore how the effects of meteorological variables changed in magnitude before, during, and after the PAMAFRO intervention. We found that once the PAMAFRO intervention had been interrupted, the estimated effects (dose-response) of meteorological variables on incidence rates decreased for both malaria parasite species. However, these fitted effect estimates did not reach their baseline levels (before the PAMAFRO period); variations of time-varying slopes between 0.45 and 2.07 times were observed after the PAMAFRO intervention. We also reported significant heterogeneity in the geographical distributions of malaria, parasite species, and meteorological variables. High malaria transmission occurred consistently in the northwestern provinces of Loreto Department. Since the end of the PAMAFRO period, a higher effect of precipitation and actual evapotranspiration was described on P. falciparum compared to P. vivax. The effect of temperature on malaria was greater over a shorter time (1-month lag or less), compared with precipitation and actual evapotranspiration (12-month lag). These findings demonstrate the importance of sustained malaria control efforts since interruption may enhance the links between meteorological factors and malaria. Our results also emphasize the importance of considering the time-varying effect of meteorological factors on malaria incidence to tailor control interventions, especially to better manage the current and future climate change crisis.

Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

Benchmarking of drought and climate indices for agricultural drought monitoring in Argentina

Site-specific studies are required to identify suitable drought indices (DIs) for assessing and predicting drought-related impacts. This study presents a benchmark of eight DIs and 19 large-scale climate indices (CIs) to monitor agricultural drought in Argentina. First, the link between the CIs and DIs was investigated at the departmental-administrative level and at different temporal scales. Then, the effectiveness of the DIs in explaining the variability of crop yields, understood as impacts of agricultural droughts, was evaluated using statistical regression models. Soybeans were used as the reference crop. Additionally, the performances of DIs and CIs in explaining the variability of crop yields were compared. The CIs located in the Pacific Ocean (El Niño 3.4 and El Niño 4) were found to have the best correlations with the DIs (R values up to 0.49). These relationships were stronger with longer temporal aggregations and during the wet and hot seasons (summer), showing a significant role in the triggering of droughts in Argentina. The DIs that best corelated with CIs were those that included temperature in their calculations (STCI, SVHI, and SPEI). The impacts of droughts on soybean production were better explained using DIs than with CIs (up to 89% vs 8% of variability explained) as predictors of the statistical models. SVHI-6 and SPEI-6, depending on the area of interest, were, during the phenological period of crop growth (summer), the most effective DIs in explaining annual variations in soybean yields. The results may be of interest in water resource management, drought risk management, and the Argentinean soybean production sector. Furthermore, they provide a foundation for future studies aimed at forecasting agricultural droughts and their impacts.

Datasets of drought and flood impact on croplands in Southeast Asia from 1980 to 2019

Data on droughts and floods and their impacts on croplands and production are important for policy makers and the scientific community. This dataset was developed to provide data of the impacts of droughts and floods on agriculture in the Monsoon Climate Region and Equatorial Climate Region of Southeast Asia during the crop growing seasons over a 40-year period between 1980 and 2019. The data were generated using the TerraClimate global high-resolution gridded Palmer Drought Severity Index (PDSI) datasets in Google Earth Engine along with a set of algorithms. Datasets were available on 47,192 grid points of a 10 × 10 km resolution containing PDSI, their latitude longitude between 1980 and 2019 with five years interval, monthly temporal PDSI data, cropland drought and flood intensity data between 1980 and 2019.

Drought Assessment in the São Francisco River Basin Using Satellite-Based and Ground-Based Indices

The São Francisco River Basin (SFRB) plays a key role for the agricultural and hydropower sectors in Northeast Brazil (NEB). Historically, in the low part of the SFRB, people have to cope with strong periods of drought. However, there are incipient signs of increasing drought conditions in the upper and middle parts of the SFRB, where its main reservoirs (i.e., Três Marias, Sobradinho, and Luiz Gonzaga) and croplands are located. Therefore, the assessment of the impacts of extreme drought events in the SFRB is of vital importance to develop appropriate drought mitigation strategies. These events are characterized by widespread and persistent dry conditions with long-term impacts on water resources and rain-fed agriculture. The purpose of this study is to provide a comprehensive evaluation of extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and soil moisture over different time windows between 1980 and 2020. The Standardized Precipitation-Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI) at 3- and 12-month time scales derived from ground data were used as benchmark drought indices. The self-calibrating Palmer Drought Severity Index (scPDSI) and the Soil Moisture and Ocean Salinity-based Soil Water Deficit Index (SWDIS) were used to assess the agricultural drought. The Water Storage Deficit Index (WSDI) and the Groundwater Drought Index (GGDI) both derived from the Gravity Recovery and Climate Experiment (GRACE) were used to assess the hydrological drought. The SWDISa and WSDI showed the best performance in assessing agricultural and hydrological droughts across the whole SFRB. A drying trend at an annual time scale in the middle and south regions of the SFRB was evidenced. An expansion of the area under drought conditions was observed only during the southern hemisphere winter months (i.e., JJA). A marked depletion of groundwater levels concurrent with an increase in soil moisture content was observed during the most severe drought conditions, indicating an intensification of groundwater abstraction for irrigation. These results could be useful to guide social, economic, and water resource policy decision-making processes.

Trade and Deforestation Predict Rat Lungworm Disease, an Invasive-Driven Zoonosis, at Global and Regional Scales

The introduction of non-native species and deforestation are both important drivers of environmental change that can also facilitate the geographic spread of zoonotic pathogens and increase disease risk in humans. With ongoing trends in globalization and land-use conversions, introduced species and deforestation are ever more likely to pose threats to human health. Here, we used rat lungworm disease, an emerging zoonotic disease caused by Angiostrongylus cantonensis and maintained by invasive rats and snails, to explore how these two forms of environmental change can impact zoonotic disease risk. We used logistic regressions to examine the role of global trade in the introduction of A. cantonensis at a country level and used model estimates to predict the probability of introduction as a function of trade. We then used hurdle-based regression models to examine the association between deforestation and rat lungworm disease in two regions where A. cantonensis is already established: Hawaii and Thailand. At the global scale, we found the trade of horticultural products to be an important driver in the spread of A. cantonensis and that the majority of countries at high risk of future A. cantonensis introduction are islands. At country scales, we found deforestation to increase the per-capita risk of A. cantonensis exposure in Hawaii and Thailand. Our study provides a preliminary view of the associations between species introductions, deforestation, and risk of A. cantonensis exposure in people. Better understanding how these two widespread and overlapping forms of environmental change affect human health can inform international biosecurity protocols, invasive species management, and land-use policies.

Snowmelt and Snow Sublimation in the Indus Basin

The Indus basin is considered as the one with the highest dependence on snowmelt runoff in High Mountain Asia. The recent High Mountain Asia snow reanalysis enables us to go beyond previous studies by evaluating both snowmelt and snow sublimation at the basin scale. Over 2000–2016, basin-average snowmelt was 101 ± 11 Gt.a−1 (121 ± 13 mm.a−1), which represents about 25–30% of basin-average annual precipitation. Snow sublimation accounts for 11% of the mean annual snow ablation, but with a large spatial variability across the basin.

Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models

Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R²) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario.

CO2, nitrogen deposition and a discontinuous climate response drive water use efficiency in global forests

Reduced stomatal conductance is a common plant response to rising atmospheric CO₂ and increases water use efficiency (W). At the leaf-scale, W depends on water and nitrogen availability in addition to atmospheric CO₂. In hydroclimate models W is a key driver of rainfall, droughts, and streamflow extremes. We used global climate data to derive Aridity Indices (AI) for forests over the period 1965-2015 and synthesised those with data for nitrogen deposition and W derived from stable isotopes in tree rings. AI and atmospheric CO₂ account for most of the variance in W of trees across the globe, while cumulative nitrogen deposition has a significant effect only in regions without strong legacies of atmospheric pollution. The relation of aridity and W displays a clear discontinuity. W and AI are strongly related below a threshold value of AI ≈ 1 but are not related where AI > 1. Tree ring data emphasise that effective demarcation of water-limited from non-water-limited behaviour of stomata is critical to improving hydrological models that operate at regional to global scales.

Rapidly declining surface and terrestrial water resources in Central Asia driven by socio-economic and climatic changes

A systematic understanding of the dynamics of surface water resources and terrestrial water storage (TWS) is extremely important for human survival in Central Asia (CA) and maintaining the balance of regional ecosystems. Although several remote sensing products have been used to map surface water, the spatial resolution of some of them (hundreds of meters) is not sufficient to identify small surface water bodies, with monitoring data only being available for a few years or less. Thus, long-term continuous monitoring of surface water dynamics has not yet been achieved. To address this, we used all available Landsat images and the adjacent-years interpolation method to describe the dynamics of surface water in CA with a 30-m spatial resolution during 1990-2019. Subsequently, based on the multiple stepwise regression model, the climatic changes and human activity drivers affecting the surface water were systematically assessed. The permanent surface water areas (PSWA) of downstream countries with water scarcity decreased over time. The PSWA of Kazakhstan continues to decline at a maximum rate of 1189 km²/a. Additionally, human activities represented by population and reservoir areas are the dominant drivers affecting surface water resources in CA. The relationship between TWS and PSWA in CA and the constraints on social and economic development provided by the available water resources are discussed. The findings demonstrate that more than one-third of the croplands in CA are suffering from declining SWAs and TWS. The water crisis in CA has intensified, and the spatial mismatch between water and land resources is expected to remain one of the biggest challenges for future social and economic development in CA. Our dataset and findings provide high-precision surface water dynamics data that could be valuable for mitigating the water crisis in CA and provide a current scientific reference for achieving the United Nations' Sustainable Development Goals.

A prion-like protein regulator of seed germination undergoes hydration-dependent phase separation

Many organisms evolved strategies to survive desiccation. Plant seeds protect dehydrated embryos from various stressors and can lay dormant for millennia. Hydration is the key trigger to initiate germination, but the mechanism by which seeds sense water remains unresolved. We identified an uncharacterized Arabidopsis thaliana prion-like protein we named FLOE1, which phase separates upon hydration and allows the embryo to sense water stress. We demonstrate that biophysical states of FLOE1 condensates modulate its biological function in vivo in suppressing seed germination under unfavorable environments. We find intragenic, intraspecific, and interspecific natural variation in FLOE1 expression and phase separation and show that intragenic variation is associated with adaptive germination strategies in natural populations. This combination of molecular, organismal, and ecological studies uncovers FLOE1 as a tunable environmental sensor with direct implications for the design of drought-resistant crops, in the face of climate change.

Responses of Withania frutescens (L.) Pauquy (Solanaceae) Growing in the Mediterranean Area to Changes in the Environmental Conditions: An Approach of Adaptation

Background

Flora growth is always influenced by the environmental conditions that surround the plant.

The Objective of the Study

The present research work was undertaken to study the response of Withania frutescens (L.) Pauquy to changes in the environmental conditions by investigating topographical, climatological, morphological, histological, and phytochemical aspects influencing the plant’s growth.

Materials and Methods

The topography of the study area was described using the Digital Terrain Model (DTM). The bioclimatic study was carried out using a climatological database (TerraClimate) used for an old period 1960–1970 (OP) and a new period 2000–2019 (NP). The histological study on stems, leaves, and roots was investigated according to protocols as described in the present study. The phytochemical analysis was assessed using a gas chromatograph coupled to a mass spectrophotometer after silylation.

Results

The topographical study showed that the selected stations varied in terms of topographical characteristics. The average annual precipitation recorded for the study area was 624 mm/year, and the thermal amplitude was around 34.64°C. The morphometrical study revealed that the plant had changes in length and width from one zone to another. The histometrical study of organs showed changes in tissue structure over time and location. The phytochemical content in the studied plants was also found to vary over time and location.

Conclusion

Based on the obtained results, we could confirm that W. frutescens has developed alternate strategies to mitigate increasingly harsh environmental conditions.

Tracking the impacts of El Niño drought and fire in human-modified Amazonian forests

With humanity facing an unprecedented climate crisis, the conservation of tropical forests has never been so important - their vast terrestrial carbon stocks can be turned into emissions by climatic and human disturbances. However, the duration of these effects is poorly understood, and it is unclear whether impacts are amplified in forests with a history of previous human disturbance. Here, we focus on the Amazonian epicenter of the 2015-16 El Niño, a region that encompasses 1.2% of the Brazilian Amazon. We quantify, at high temporal resolution, the impacts of an extreme El Niño (EN) drought and extensive forest fires on plant mortality and carbon loss in undisturbed and human-modified forests. Mortality remained higher than pre-El Niño levels for 36 mo in EN-drought-affected forests and for 30 mo in EN-fire-affected forests. In EN-fire-affected forests, human disturbance significantly increased plant mortality. Our investigation of the ecological and physiological predictors of tree mortality showed that trees with lower wood density, bark thickness and leaf nitrogen content, as well as those that experienced greater fire intensity, were more vulnerable. Across the region, the 2015-16 El Niño led to the death of an estimated 2.5 ± 0.3 billion stems, resulting in emissions of 495 ± 94 Tg CO2 Three years after the El Niño, plant growth and recruitment had offset only 37% of emissions. Our results show that limiting forest disturbance will not only help maintain carbon stocks, but will also maximize the resistance of Amazonian forests if fires do occur.

Human and climate drivers of global biomass burning variability

Biomass burning is one of the most critical factors impacting vegetation and atmospheric trends, with important societal implications, particularly when extreme weather conditions occur. Trends and factors of burned area (BA) have been analysed at regional and global scales, but little effort has been dedicated to study the interannual variability. This paper aimed to better understand factors explaining this variation, under the assumption that the more human control of fires the more frequently they occur, as burnings will be less dependent of weather cycles. Interannual variability of BA was estimated from the coefficient of variation of the annual BA (BA_CV) estimated from satellite data at 250 m, covering the period from 2001 to 2018. These data and the explanatory variables were resampled at 0.25-degree resolution for global analysis. Relations between this variable and explanatory factors, including human and climate drivers, were estimated using Random Forest (RF) and generalized additive models (GAM). BA_CV was negatively related to BA_Mean, implying that areas with higher average BA have lower variability as well. Interannual BA variability decreased when maximum temperature (TMAX) and actual and potential evapotranspiration (AET, PET) increased, cropland and livestock density increased and the human development index (HDI) values decreased. GAM models indicated interesting links with AET, PET and precipitation, with negative relation with BA_CV for the lower ranges and positive for the higher ones, the former indicating fuel limitations of fire activity, and the latter climate constrains. For the global RF model, TMAX, AET and HDI were the main drivers of interannual variability. As originally hypothesised, BA_CV was more dependent on human factors (HDI) in those areas with medium to large BA occurrence, particularly in tropical Africa and Central Asia, while climatic factors were more important in boreal regions, but also in the tropical regions of Australia and South America.

Global patterns and drivers of rainfall partitioning by trees and shrubs

Spatiotemporal redistribution of incident rainfall in vegetated ecosystems results from the partitioning by plants into intercepted, stemflow, and throughfall fractions. However, variation in patterns and drivers of rainfall partitioning across global biomes remains poorly understood, which limited the ability of climate models to improve the predictions of biome hydrological cycle under global climate change scenario. Here, we synthesized and analyzed the partitioning of incident rainfall into interception, stemflow, and throughfall by trees and shrubs at the global scale using 2430 observations from 236 independent publications. We found that (1) globally, median levels of relative interception, stemflow, and throughfall accounted for 21.8%, 3.2%, and 73.0% of total incident rainfall, respectively; (2) rainfall partitioning varied among different biomes, due to variation in plant composition, canopy structure, and macroclimate; (3) relative stemflow tended to be driven by plant traits, such as crown height:width ratio, basal area, and height, while relative interception and throughfall tended to be driven by plant traits as well as meteorological variables. Our global assessment of patterns and drivers of rainfall partitioning underpins the role of meteorological factors and plant traits in biome-specific ecohydrological cycles. We suggest to include these factors in climate models to improve the predictions of local hydrological cycles and associated biodiversity and function responses to changing climate conditions.

Landscape epidemiology of Batrachochytrium salamandrivorans: reconciling data limitations and conservation urgency

Starting in 2010, rapid fire salamander (Salamandra salamandra) population declines in northwestern Europe heralded the emergence of Batrachochytrium salamandrivorans (Bsal), a salamander-pathogenic chytrid fungus. Bsal poses an imminent threat to global salamander diversity owing to its wide host range, high pathogenicity, and long-term persistence in ecosystems. While there is a pressing need to develop further research and conservation actions, data limitations inherent to recent pathogen emergence obscure necessary insights into Bsal disease ecology. Here, we use a hierarchical modeling framework to describe Bsal landscape epidemiology of outbreak sites in light of these methodological challenges. Using model selection and machine learning, we find that Bsal presence is associated with humid and relatively cool, stable climates. Outbreaks are generally located in areas characterized by low landscape heterogeneity and low steepness of slope. We further find an association between Bsal presence and high trail density, suggesting that human-mediated spread may increase risk for spillover between populations. We then use distribution modeling to show that favorable conditions occur in lowlands influenced by the North Sea, where increased survey effort is needed to determine how Bsal impacts local newt populations, but also in hill- and mountain ranges in northeastern France and the lower half of Germany. Finally, connectivity analyses suggest that these hill- and mountain ranges may act as stepping stones for further spread southward. Our results provide initial insight into regional environmental conditions underlying Bsal epizootics, present updated invasibility predictions for northwestern Europe, and lead us to discuss a wide variety of potential survey and research actions needed to advance future conservation and mitigation efforts.

A hidden deadly venomous insect: First eco-epidemiological assessment and risk mapping of lonomism in Argentina

BACKGROUND

Envenomation by the South American Lonomia saturniid caterpillars, named lonomism, constitutes an emerging and somewhat neglected public health issue in Argentina and neighboring countries. Considering that there is an intricate relationship between environment and human health in such cases, this study aimed to analyze the eco-epidemiological profile of 40 accidents and 33 occurrences of Lonomia spp. in Misiones (Argentina) between January 2014 and May 2020.

METHODOLOGY/PRINCIPAL FINDINGS

We described the eco-epidemiological variables and characterized the abiotic scenario of such cases. Additionally, we obtained a density map that shows the punctual intensity of Lonomia records throughout Misiones. Most of the accidents occurred in the Department of Guaraní and involved male victims younger than 20 years old. The accidental/occasional occurrence of Lonomia spp. (considering both adult and caterpillar stages together) was significantly higher in the rural area, whereas only adult specimens were found in urban areas. We determined that the presence of this insect in Misiones is positively related to higher temperatures and solar radiation, and larger precipitation and evapotranspiration throughout the year.

CONCLUSION/SIGNIFICANCE

This study represents an initial step towards the global understanding of lonomism as a public health problem in Argentina. It provides a map of the risk level for this envenomation in Misiones, which could help authorities address public health policy efforts to implement sustainable strategies for prevention and response to this threat in Northeastern Argentina and neighboring regions.

Greening drylands despite warming consistent with carbon dioxide fertilization effect

The rising atmospheric CO₂ concentration leads to a CO₂ fertilization effect on plants-that is, increased photosynthetic uptake of CO₂ by leaves and enhanced water-use efficiency (WUE). Yet, the resulting net impact of CO₂ fertilization on plant growth and soil moisture (SM) savings at large scale is poorly understood. Drylands provide a natural experimental setting to detect the CO₂ fertilization effect on plant growth since foliage amount, plant water-use and photosynthesis are all tightly coupled in water-limited ecosystems. A long-term change in the response of leaf area index (LAI, a measure of foliage amount) to changes in SM is likely to stem from changing water demand of primary productivity in water-limited ecosystems and is a proxy for changes in WUE. Using 34-year satellite observations of LAI and SM over tropical and subtropical drylands, we identify that a 1% increment in SM leads to 0.15% (±0.008, 95% confidence interval) and 0.51% (±0.01, 95% confidence interval) increments in LAI during 1982-1998 and 1999-2015, respectively. The increasing response of LAI to SM has contributed 7.2% (±3.0%, 95% confidence interval) to total dryland greening during 1999-2015 compared to 1982-1998. The increasing response of LAI to SM is consistent with the CO₂ fertilization effect on WUE in water-limited ecosystems, indicating that a given amount of SM has sustained greater amounts of photosynthetic foliage over time. The LAI responses to changes in SM from seven dynamic global vegetation models are not always consistent with observations, highlighting the need for improved process knowledge of terrestrial ecosystem responses to rising atmospheric CO₂ concentration.

Observed increasing water constraint on vegetation growth over the last three decades

Despite the growing interest in predicting global and regional trends in vegetation productivity in response to a changing climate, changes in water constraint on vegetation productivity (i.e., water limitations on vegetation growth) remain poorly understood. Here we conduct a comprehensive evaluation of changes in water constraint on vegetation growth in the extratropical Northern Hemisphere between 1982 and 2015. We document a significant increase in vegetation water constraint over this period. Remarkably divergent trends were found with vegetation water deficit areas significantly expanding, and water surplus areas significantly shrinking. The increase in water constraints associated with water deficit was also consistent with a decreasing response time to water scarcity, suggesting a stronger susceptibility of vegetation to drought. We also observed shortened water surplus period for water surplus areas, suggesting a shortened exposure to water surplus associated with humid conditions. These observed changes were found to be attributable to trends in temperature, solar radiation, precipitation, and atmospheric CO₂. Our findings highlight the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate.

Jiao et al. conducted a comprehensive evaluation of changes in water constraint on vegetation growth in the extratropical Northern Hemisphere between 1982 and 2015. They document a significant increase in vegetation water constraint over the last three decades.

Low-density aspen seedling establishment is widespread following recent wildfires in the western United States

Sexual regeneration is increasingly recognized as an important regeneration pathway for aspen in the western United States, a region previously thought to be too dry for seedling establishment except for during unusually wet periods. Because of this historical assumption, information on aspen seedling establishment and factors influencing its occurrence is limited and frequently anecdotal. We conducted a systematic field survey of 15 recent fire footprints that burned in 2018 in the western United States to quantify how common aspen seedling establishment is following fire and to identify factors associated with establishment. We found aspen seedling establishment in 12 of 15 (80%) of fire footprints surveyed, although densities were mostly low. Establishment probability was positively associated with mean annual precipitation and negatively associated with seed-source distance and the density of asexual aspen regeneration. Our results suggest that aspen seedling establishment may be a widespread, if often low-density, feature in postdisturbance areas. Even in low numbers, aspen seedlings may play a disproportionately large role in aspen regeneration ecology, providing adaptive capacity and facilitating local range expansion.

Recent Changes in Hydroclimatic Patterns over Medium Niger River Basins at the Origin of the 2020 Flood in Niamey (Niger)

Niamey, the capital of Niger, is particularly prone to floods, since it is on the banks of the Niger River, which in its middle basin has two flood peaks: one in summer (the red flood) and one in winter (the black flood). In 2020, the Niger River in Niamey reached its all-time highest levels following an abundant rainy season. On the other hand, the floods in Niamey have been particularly frequent in the last decade, a symptom of a change in hydroclimatic behaviour already observed since the end of the great droughts of the 1970s and 1980s and which is identified with the name of Sahelian Paradox. This study, starting from the analysis of the 2020 flood and from the update of the rating curve of the Niamey hydrometric station, analyses the rainfall–runoff relationship on the Sahelian basins of the Medium Niger River Basin (MNRB) that are at the origin of the local flood. The comparative analysis of runoffs, annual maximum flows (AMAX) and runoff coefficients with various rainfall indices calculated on gridded datasets allowed to hydroclimatically characterise the last decade as a different period from the wet one before the drought, the dry one and the post-drought one. Compared to the last one, the current period is characterised by a sustained increase in hydrological indicators (AMAX +27%) consistent with the increase in both the accumulation of precipitation (+11%) and the number (+51%) and magnitude (+54%) of extreme events in the MNRB. Furthermore, a greater concentration of rainfall and extremes (+78%) in August contributes to reinforcing the red flood’s positive anomalies (+2.23 st.dev in 2020). The study indicates that under these conditions the frequency of extreme hydrological events in Niamey will tend to increase further also because of the concurrence of drivers such as river-bed silting and levee effects. Consequently, the study concludes with the need for a comprehensive flood-risk assessment on the Niamey city that considers both recent hydroclimatic trends and urbanisation dynamics in flood zones hence defining the most appropriate risk-reduction strategies.

Drivers of Organic Carbon Stocks in Different LULC History and Along Soil Depth for a 30 Years Image Time Series

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.

Malar stripe size and prominence in peregrine falcons vary positively with solar radiation: support for the solar glare hypothesis

Many falcons (Falco spp.) exhibit a distinct dark plumage patch below the eye, termed the malar stripe. This stripe is hypothesized to reduce the amount of solar glare reflected into the eyes while foraging, thereby increasing hunting efficiency in bright conditions. Here, we use a novel, global-scale correlative approach to test this 'solar glare hypothesis' in peregrine falcons (Falco peregrinus), the most widespread falcon species, using web-sourced photographs from across the species' global range. We found that the size and prominence of the malar stripe were positively associated with average annual solar radiation, but not with other environmental variables, such as temperature and rainfall. Our results provide the first published evidence for the hypothesis that this plumage feature functions to reduce the amount of solar glare reflected into the falcon's eyes, thereby improving the ability to pinpoint and target agile prey in bright conditions.

Global patterns of forest autotrophic carbon fluxes

Carbon (C) fixation, allocation, and metabolism by trees set the basis for energy and material flows in forest ecosystems and define their interactions with Earth's changing climate. However, while many studies have considered variation in productivity with latitude and climate, we lack a cohesive synthesis on how forest carbon fluxes vary globally with respect to climate and one another. Here, we draw upon 1,319 records from the Global Forest Carbon Database, representing all major forest types and the nine most significant autotrophic carbon fluxes, to comprehensively review how annual C cycling in mature, undisturbed forests varies with latitude and climate on a global scale. Across all flux variables analyzed, rates of C cycling decreased continuously with absolute latitude-a finding that confirms multiple previous studies and contradicts the idea that net primary productivity of temperate forests rivals that of tropical forests. C flux variables generally displayed similar trends across latitude and multiple climate variables, with no differences in allocation detected at this global scale. Temperature variables in general, and mean annual temperature or temperature seasonality in particular, were the best single predictors of C flux, explaining 19%-71% of variation in the C fluxes analyzed. The effects of temperature were modified by moisture availability, with C flux reduced under hot and dry conditions and sometimes under very high precipitation. Annual C fluxes increased with growing season length and were also influenced by growing season climate. These findings clarify how forest C flux varies with latitude and climate on a global scale. In an era when forests will play a critical yet uncertain role in shaping Earth's rapidly changing climate, our synthesis provides a foundation for understanding global patterns in forest C cycling.

Understanding and predicting forest mortality in the western United States using long-term forest inventory data and modeled hydraulic damage

Global warming is expected to exacerbate the duration and intensity of droughts in the western United States, which may lead to increased tree mortality. A prevailing proximal mechanism of drought-induced tree mortality is hydraulic damage, but predicting tree mortality from hydraulic theory and climate data still remains a major scientific challenge. We used forest inventory data and a plant hydraulic model (HM) to address three questions: can we capture regional patterns of drought-induced tree mortality with HM-predicted damage thresholds; do HM metrics improve predictions of mortality across broad spatial areas; and what are the dominant controls of forest mortality when considering stand characteristics, climate metrics, and simulated hydraulic stress? We found that the amount of variance explained by models predicting mortality was limited (R² median = 0.10, R² range: 0.00-0.52). HM outputs, including hydraulic damage and carbon assimilation diagnostics, moderately improve mortality prediction across the western US compared with models using stand and climate predictors alone. Among factors considered, metrics of stand density and tree size tended to be some of the most critical factors explaining mortality, probably highlighting the important roles of structural overshoot, stand development, and biotic agent host selection and outbreaks in mortality patterns.

Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis

Plant phenology depends largely on temperature, but temperature alone cannot explain the Northern Hemisphere shifts in the start of the growing season (SOS). The spatio–temporal distribution of SOS sensitivity to climate variability has also changed in recent years. We applied the partial least squares regression (PLSR) method to construct a standardized SOS sensitivity evaluation index and analyzed the combined effects of air temperature (Tem), water balance (Wbi), radiation (Srad), and previous year’s phenology on SOS. The spatial and temporal distributions of SOS sensitivity to Northern Hemisphere climate change from 1982 to 2014 were analyzed using time windows of 33 and 15 years; the dominant biological and environmental drivers were also assessed. The results showed that the combined sensitivity of SOS to climate change (SCom) is most influenced by preseason temperature sensitivity. However, because of the asymmetric response of SOS to daytime/night temperature (Tmax/Tmin) and non-negligible moderating of Wbi and Srad on SOS, SCom was more effective in expressing the effect of climate change on SOS than any single climatic factor. Vegetation cover (or type) was the dominant factor influencing the spatial pattern of SOS sensitivity, followed by spring temperature (Tmin > Tmax), and the weakest was water balance. Forests had the highest SCom absolute values. A significant decrease in the sensitivity of some vegetation (22.2%) led to a decreasing trend in sensitivity in the Northern Hemisphere. Although temperature remains the main climatic factor driving temporal changes in SCom, the temperature effects were asymmetric between spring and winter (Tems/Temw). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. Vegetation adaptation has a greater influence on the temporal variability of SOS sensitivity relative to each climatic factor (Tems, Temw, Wbi, Srad). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. This study provides a basis for vegetation phenology sensitivity assessment and prediction.

A Review of Climate Change Impacts on the USA-Mexico Transboundary Santa Cruz River Basin

In the parched Upper Santa Cruz River Basin (USCRB), a binational USA–Mexico basin, the water resources depend on rainfall-triggered infrequent flow events in ephemeral channels to recharge its storage-limited aquifers. In-situ data from the basin highlight a year-round warming trend since the 1980s and a concerning decline in average precipitation (streamflow) from 1955–2000 to 2001–2020 by 50% (87.6%) and 17% (63%) during the winter and summer, respectively. Binational sustainable management of the basins water resources requires a careful consideration of prospective climatic changes. In this article we review relevant studies with climate projections for the mid-21st century of four weather systems that affect the region’s precipitation. First, the North American Monsoon (NAM) weather system accounts for ~60% of the region’s annual rainfall. The total NAM precipitation is projected to decline while heavy rainfall events are expected to intensify. Second, the frequency of the pacific cold fronts, the region’s prevalent source of winter precipitation, is projected to decline. Third, the frequency and intensity of future atmospheric rivers, a weather system that brings winter rainfall to the region, are projected to increase. Fourth, the frequency and intensity of large eastern pacific tropical cyclones (TC) are expected to increase. On rare occasions, remnants of TC make their way to the USCRB to cause storms with considerable impact on the region’s water resources. In contrast to the high confidence projections for the warming trend to persist throughout the mid-21st century, the precipitation projections of these four weather systems affecting the region encompass large uncertainties and studies have often reported contradicting trends. An added source of uncertainty is that the USCRB is located at the periphery of the four rain-bearing weather systems and small mesoscale changes in these weather systems may have accentuated impacts on their edges. Despite the high uncertainty in the projections of future precipitation, the early 21st century drying trend and the projected mid-21st century decline in precipitation events serve as a pressing call for planning and actions to attain sustainable water resources management that reliably satisfies future demands.

Climate-driven range shifts reduce persistence of competitors in a perennial plant community

Forecasting the impacts of climate change on species persistence in diverse natural communities requires a way to account for indirect effects mediated through species interactions. In particular, we expect species to experience major changes in competition as they track favorable climates. Here, we combine experimental data with a recently developed theoretical framework based on coexistence theory to measure the impact of climate-driven range shifts on alpine plant persistence under climate change. We transplanted three co-dominant alpine perennial species to five elevations, creating a maximum of 5°C increase in average growing-season temperature. We statistically modeled species' demographic rates in response to the environment and interpolated species' intrinsic ranges-the environmental mapping of reproduction in the absence of competition. We used low-density population growth rates-species' initial rate of invasion into an established community-as a metric of persistence. Further analysis of low-density growth rates (LGRs) allowed us to parse the direct impacts of climate change from indirect impacts mediated by shifting competition. Our models predict qualitatively different range shifts for each species based on the climate conditions under which growth rates are maximized and where they are zero. Overall, climate change is predicted to increase the intrinsic (competition free) growth rates of all species, as warmer and wetter conditions increase the favorability of alpine habitat. However, these benefits are entirely negated by increased competition arising from greater overlap between competitors in their intrinsic ranges. Species were highly dispersal limited, which can prevent species from tracking shifting intrinsic ranges by reducing population spread rates. Yet dispersal limitation also promoted species' persistence because it promotes persistence mechanisms. Our study demonstrates the complex pathways by which climate change impacts species' persistence by altering their competitive environment, and highlights how a persistence framework based on LGRs can help disentangle impacts.

Drivers to dust emissions over dust belt from 1980 to 2018 and their variation in two global warming phases

Dust storms are one of the major disasters in arid and semi-arid regions. Understanding the impact factors is crucial for early warning and disaster mitigation. Many factors have been affecting the spatiotemporal patterns of dust storms. However, the relative importance of those factors to dust emissions in recent 40 years over the whole dust belt has not been well documented. This study explored the relative importance of those factors to the interannual variation in dust emissions over the whole dust belt. The difference in the primary contributors over two global warming phases was compared to investigate the association of dust emission trend with global warming. The results indicated that the wind regimes, such as the nocturnal low-level jet, were key factors to the wintertime dust emissions over the Sahel. The springtime dust storms related to cold air and cyclones primarily occurred in the southern coast of the Mediterranean and northwestern China. The cold high and heat low were typical mechanisms for the summertime dust emissions, which frequently formed in western North Africa, the Middle East, and northwestern Indian subcontinent. Whereas the land cover and drought conditions play significant roles in the relatively wetter regions, i.e., the southern coast of the Mediterranean, the Ustyurt Plateau, the northwest coast of Indian Ocean, the Thar desert and the Taklimakan desert. The wintertime global warming coincided well with the decreasing trend of dust emissions over the dust sources inland with a more significant effect seen in Asia. The positive anomalies of summertime dust emissions were primarily found over the dust sources in the low-lying coastal areas on the foot of high mountains. Understanding the relative importance of those drivers to dust emission trends and their variation under different warming periods can improve the prediction of dust storm evolutions and mitigate their impacts under future climate change.

Widespread decline in winds delayed autumn foliar senescence over high latitudes

The high northern latitudes (>50°) experienced a pronounced surface stilling (i.e., decline in winds) with climate change. As a drying factor, the influences of changes in winds on the date of autumn foliar senescence (DFS) remain largely unknown and are potentially important as a mechanism explaining the interannual variability of autumn phenology. Using 183,448 phenological observations at 2,405 sites, long-term site-scale water vapor and carbon dioxide flux measurements, and 34 y of satellite greenness data, here we show that the decline in winds is significantly associated with extended DFS and could have a relative importance comparable with temperature and precipitation effects in contributing to the DFS trends. We further demonstrate that decline in winds reduces evapotranspiration, which results in less soil water losses and consequently more favorable growth conditions in late autumn. In addition, declining winds also lead to less leaf abscission damage which could delay leaf senescence and to a decreased cooling effect and therefore less frost damage. Our results are potentially useful for carbon flux modeling because an improved algorithm based on these findings projected overall widespread earlier DFS than currently expected by the end of this century, contributing potentially to a positive feedback to climate.

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on susceptibility modeling for the two hazards together have been conducted for this region, and the intersection and the uncertainty of the two hazards are rarely assessed. Thus, the intersection of landslide and wildfire susceptibility and the spatial uncertainty of the susceptibility maps were studied in this paper. Reliable landslide and wildfire susceptibility maps are necessary for disaster management and land use planning. This work used three advanced ensemble machine learning algorithms: RF (Random Forest), GBDT (Gradient Boosting Decision Tree) and AdaBoost (Adaptive Boosting) to assess the landslide and wildfire susceptibility for SEA. A geo-database was established with 2759 landslide locations, 1633 wildfire locations and 18 predictor variables in total. The performances of the models were assessed using the overall classification accuracy (ACC), Precision, the area under the ROC (receiver operating curve) (AUC) and confusion matrix values. The results showed RF performs superior in both landslide (ACC = 0.81, Precision = 0.78 and AUC= 0.89) and wildfire (ACC= 0.83, Precision = 0.83 and AUC = 0.91) susceptibility modeling, followed by GBDT and AdaBoost. The overall superiority of RF over other models indicates that it is potentially an efficient model for landslide and wildfire susceptibility mapping. The landslide and wildfire susceptibility were obtained using the RF model. This paper also conducted an overlay analysis of the two hazards. The uncertainty of the susceptibility was further assessed using the coefficient of variation (CV). Additionally, the distance to roads is relatively important in both landslide and wildfire susceptibility, which is the most important in landslides and the second most important in wildfires. The result of this paper is useful for mastering the whole situation of hazard susceptibility and proves that RF is a robust model in the hazard susceptibility assessment in SEA.

Topsoil organic matter build-up in glacier forelands around the world

Since the last glacial maximum, soil formation related to ice-cover shrinkage has been one major sink of carbon accumulating as soil organic matter (SOM), a phenomenon accelerated by the ongoing global warming. In recently deglacierized forelands, processes of SOM accumulation, including those that control carbon and nitrogen sequestration rates and biogeochemical stability of newly sequestered carbon, remain poorly understood. Here, we investigate the build-up of SOM during the initial stages (up to 410 years) of topsoil development in 10 glacier forelands distributed on four continents. We test whether the net accumulation of SOM on glacier forelands (i) depends on the time since deglacierization and local climatic conditions (temperature and precipitation); (ii) is accompanied by a decrease in its stability and (iii) is mostly due to an increasing contribution of organic matter from plant origin. We measured total SOM concentration (carbon, nitrogen), its relative hydrogen/oxygen enrichment, stable isotopic (¹³ C, ¹⁵ N) and carbon functional groups (C-H, C=O, C=C) compositions, and its distribution in carbon pools of different thermal stability. We show that SOM content increases with time and is faster on forelands experiencing warmer climates. The build-up of SOM pools shows consistent trends across the studied soil chronosequences. During the first decades of soil development, the low amount of SOM is dominated by a thermally stable carbon pool with a small and highly thermolabile pool. The stability of SOM decreases with soil age at all sites, indicating that SOM storage is dominated by the accumulation of labile SOM during the first centuries of soil development, and suggesting plant carbon inputs to soil (SOM depleted in nitrogen, enriched in hydrogen and in aromatic carbon). Our findings highlight the potential vulnerability of SOM stocks from proglacial areas to decomposition and suggest that their durability largely depends on the relative contribution of carbon inputs from plants.

A regional suitable conditions index to forecast the impact of climate change on dengue vectorial capacity

BACKGROUND

The mosquitoes Aedes aegypti and Ae. albopictus are the primary vectors of dengue virus, and their geographic distributions are predicted to expand further with economic development, and in response to climate change. We aimed to estimate the impact of future climate change on dengue transmission through the development of a Suitable Conditions Index (SCI), based on climatic variables known to support vectorial capacity. We calculated the SCI based on various climate change scenarios for six countries in the Asia-Pacific region (Australia, China, Indonesia, The Philippines, Thailand and Vietnam).

METHODS

Monthly raster climate data (temperature and precipitation) were collected for the period January 2005 to December 2018 along with projected climate estimates for the years 2030, 2050 and 2070 using Representative Concentration Pathway (RCP) 4·5, 6·0 and 8·5 emissions scenarios. We defined suitable temperature ranges for dengue transmission of between 17·05-34·61 °C for Ae. aegypti and 15·84-31·51 °C for Ae. albopictus and then developed a historical and predicted SCI based on weather variability to measure the expected geographic limits of dengue vectorial capacity. Historical and projected SCI values were compared through difference maps for the six countries.

FINDINGS

Comparing different emission scenarios across all countries, we found that most South East Asian countries showed either a stable pattern of high suitability, or a potential decline in suitability for both vectors from 2030 to 2070, with a declining pattern particularly evident for Ae. albopictus. Temperate areas of both China and Australia showed a less stable pattern, with both moderate increases and decreases in suitability for each vector in different regions between 2030 and 2070.

INTERPRETATION

The SCI will be a useful index for forecasting potential dengue risk distributions in response to climate change, and independently of the effects of human activity. When considered alongside additional correlates of infection such as human population density and socioeconomic development indicators, the SCI could be used to develop an early warning system for dengue transmission.

Global drought monitoring with big geospatial datasets using Google Earth Engine

Drought or dryness occurs due to the accumulative effect of certain climatological and hydrological variables over a certain period. Droughts are studied through numerically computed simple or compound indices. Vegetation condition index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the temperature condition index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and hydrological drought and for that purpose, the index, soil moisture condition index (SMCI), is computed. The deviation of precipitation from normal is a major cause for meteorological droughts and for that purpose, precipitation condition index (PCI) is computed. The years when the indices escalated the dryness situation to severe and extreme are pointed out in this research. Furthermore, an interactive dashboard is generated in the Google Earth Engine (GEE) for users to compute the said indices using country boundary, time period, and ecological mask of their choice: Agriculture Drought Monitoring. Apart from global results, three case studies of droughts (2002 in Australia, 2013 in Brazil, and 2019 in Thailand) computed via the dashboard are discussed in detail in this research.

Impact of land cover and landfills on the breeding effect and nest occupancy of the white stork in Poland

Food wastes are among the factors with the greatest effects on animal populations. The white stork is among bird species that clearly profit from feeding at landfills, at least in Western Europe and North Africa. However, the rate and the consequences of this feeding are still unknown in the Central-Eastern European population, which differs from the western population not only in terms of migration routes but also in the greater availability of suitable natural breeding habitats due to less intensified agriculture. The aim of the study was to describe the use of landfills and its consequences in terms of probability of nest occupation and breeding effects in different regions of Poland. Although the most important factors influencing nest-site selection and breeding effect are still habitat quality and weather conditions, distance to landfills is important in selection of nest sites. White storks use landfills most intensively late in the breeding season, independently of the density of breeding pairs. The results suggest that the use of landfills is not currently essential in the Central-Eastern European population of the white stork, does not affect breeding effect, and may be more frequent in non-breeders. However, this phenomenon is still developing and requires continuous monitoring.

Abiotic Environment Predicts Micro- but Not Macroevolutionary Patterns of Flower Color in Monkeyflowers (Phrymaceae)

Anthocyanin pigments are responsible for many of the vivid pink, purple, red, and blue flower colors across angiosperms and frequently vary within and between closely related species. While anthocyanins are well known to influence pollinator attraction, they are also associated with tolerance to abiotic stressors such as extreme temperatures, reduced precipitation, and ultraviolet radiation. Using a comparative approach, we tested whether abiotic variables predict floral anthocyanin in monkeyflowers (Phrymaceae) across western North America. Within two polymorphic species, we found that abiotic variables predicted flower color across their geographic ranges. In Erythranthe discolor, the frequency of pink flowered (anthocyanin producing) individuals was greater in populations with reduced precipitation. In Diplacus mephiticus, the frequency of pink flowered individuals was greater at higher elevations that had reduced precipitation and lower temperatures but less ultraviolet radiation. At the macroevolutionary scale, across two parallel radiations of North American monkeyflowers, species with floral anthocyanins (pink, purple, or red corollas) occupied areas with reduced precipitation in Erythranthe but not Diplacus. However, after accounting for phylogenetic relatedness, we found no evidence for the joint evolution of flower color and environmental affinity in either clade. We conclude that although abiotic stressors may play a role in the evolution of flower color within polymorphic species, we found no evidence that these processes lead to macroevolutionary patterns across monkeyflowers.

Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change

Tropical secondary forests sequester carbon up to 20 times faster than old-growth forests. This rate does not capture spatial regrowth patterns due to environmental and disturbance drivers. Here we quantify the influence of such drivers on the rate and spatial patterns of regrowth in the Brazilian Amazon using satellite data. Carbon sequestration rates of young secondary forests (<20 years) in the west are ~60% higher (3.0 ± 1.0 Mg C ha^-1 yr^-1) compared to those in the east (1.3 ± 0.3 Mg C ha^-1 yr^-1). Disturbances reduce regrowth rates by 8-55%. The 2017 secondary forest carbon stock, of 294 Tg C, could be 8% higher by avoiding fires and repeated deforestation. Maintaining the 2017 secondary forest area has the potential to accumulate ~19.0 Tg C yr^-1 until 2030, contributing ~5.5% to Brazil's 2030 net emissions reduction target. Implementing legal mechanisms to protect and expand secondary forests whilst supporting old-growth conservation is, therefore, key to realising their potential as a nature-based climate solution.

Synthesizing a Regional Territorial Evapotranspiration Dataset for Northern China

As a vital role in the processes of the energy balance and hydrological cycles, actual evapotranspiration (ET) is relevant to many agricultural, ecological and water resource management studies. The available global or regional ET products provide ET estimations with various temporal ranges, spatial resolutions and calculation methods (algorithms, inputs and parameterization, etc.), leading to varying degrees of introduced uncertainty. Northern China is the main agriculturally productive region supporting the whole country; thus, understanding the spatial and temporal changes in ET is essential to ensure water resource and food security. We developed a synthesis ET dataset for Northern China at a 1000 m spatial resolution, with a monthly temporal resolution covering a period ranging from 1982 to 2017, using an in-depth assessment of several ET products. Specifically, assessments were performed using in situ measured ET from eddy covariance (EC) observation towers at the site-pixel scale over interannual months under the conditions of different land cover types, climatic zones and elevation levels to select the most optimally performing ET products to be used in the synthesized ET dataset. Eight indicators under 21 conditions were involved in the assessment sheet, while the statistics of the different ET product occurrences and corresponding ratios were analyzed to select the best-performing ET products to build the synthesis ET dataset using the weighted mean method. The weights were determined by the Taylor skill score (TSS), calculated with ET products and EC ET observation data. Based on the assessment results, the Penman–Monteith–Leuning (PML_v2), ETWatch and Operational Simplified Surface Energy Balance (SSEBop) datasets were selected for implementation in the synthesis ET dataset from 2003 to 2017, while Global Land Evaporation Amsterdam Model (GLEAM) v3.3a, complementary relationship (CR) ET, and Numerical Terradynamic Simulation Group (NTSG) datasets were chosen for the synthesis ET dataset from 1982 to 2002. The weighted mean synthesized results from 2003 to 2017 performed well when compared to the in situ measured EC ET values produced under all of the above conditions, while the synthesized results from 1982 to 2002 performed well through the water balance method in Heihe River Basin. These results can provide more stable ET estimations for Northern China, which can contribute to relevant agricultural, ecological and hydrological studies.

The role of short-term changes in cognitive capacity on economic expenditure among Kenyan agro-pastoralists

The increased exposure of pastoralist communities in East Africa to climatic shocks has focused attention on the resilience of these communities. Although many social scientists directly or indirectly infer versions of homo-economic agents, increasing evidence in development behavioral economics, indicates that such assumptions may be misplaced. Despite on-going advances in the science concerning the effects of stress on dynamic changes in short-term cognitive capacity, there remains limited understanding of the effects of changes in cognitive capacity on economic decision making. The present research empirically evaluates the drivers of short-term changes in cognitive capacity-cognitive ability and heuristic use-and its effect on crop and livestock expenditure among predominantly poor Kenyan agro-pastoralists. Three rounds of cognition and survey data from Samburu, Kenya is analysed. The primary data was collected at the end of the 2015-16 East African drought and covers an 11-month period between October 2016 and September 2017. Dynamic panel estimation, employing maximum likelihood, is used on balanced and unbalanced data. Results indicate that fluid intelligence and heuristic use, along with literacy and stressors, affect crop expenditure. Perceptions of scarcity, relative to prior expectations, are also identified as an important determinant of short-term changes in cognitive ability. These results underscore the importance of better understanding the effects of short-term changes in cognitive capacity on economic expenditure among the poor.

Climate Change and Local Host Availability Drive the Northern Range Boundary in the Rapid Expansion of a Specialist Insect Herbivore, Papilio cresphontes

Species distributions, abundance, and interactions have always been influenced by human activity and are currently experiencing rapid change. Biodiversity benchmark surveys traditionally require intense human labor inputs to find, identify, and record organisms limiting the rate and impact of scientific enquiry and discovery. Recent emergence and advancement of monitoring technologies have improved biodiversity data collection to a scale and scope previously unimaginable. Community science web platforms, smartphone applications, and technology assisted identification have expedited the speed and enhanced the volume of observational data all while providing open access to these data worldwide. How to integrate and leverage the data into valuable information on how species are changing in space and time requires new best practices in computational and analytical approaches. Here we integrate data from three community science repositories to explore how a specialist herbivore distribution changes in relation to host plant distributions and other environmental factors. We generate a series of temporally explicit species distribution models to generate range predictions for a specialist insect herbivore (Papilio cresphontes) and three predominant host-plant species. We find that this insect species has experienced rapid northern range expansion, likely due to a combination of the range of its larval host plants and climate changes in winter. This case study shows rapid data collection through large scale community science endeavors can be leveraged through thoughtful data integration and transparent analytic pipelines to inform how environmental change impacts where species are and their interactions for a more cost effective method of biodiversity benchmarking.