Future Climate Research Articles

Potential of constructing all‐encompassing soil–plant‐atmosphere‐continuum stations and datasets from meteorological, flux, soil moisture station networks and plant‐relevant observations

AbstractThe establishment of SPAC (soil–plant‐atmosphere continuum) stations is essential for comprehensive monitoring of land‐atmosphere interactions and ecological and hydrological processes. This paper addresses the critical limitations of existing observation networks, which often rely on single‐aspect observations, resulting in insufficient data for a holistic understanding of SPAC dynamics. Specifically, SPAC stations provide critical multi‐variable observations that enhance process‐based model calibration and physical constraints and improve the empirical basis of data‐driven models. Advanced technologies such as machine learning and remote sensing are proposed to transform current weather and soil moisture stations into quasi‐SPAC sites capable of estimating carbon and water flux data. Additionally, the strategic placement of new SPAC sites in regions projected to be sensitive to future climate change and climate risks, as indicated by models such as CMIP6, is recommended. Furthermore, promoting comprehensive observational systems like Europe's Integrated Carbon Observation System (ICOS) in other regions, establishing a unified management framework and coordinating the upgrading of existing global observation networks are essential steps. Ultimately, the proposed enhancements will advance global ecological and hydrological studies, providing a more integrated and accurate understanding of the SPAC system and its responses to climate variability.

Relationship Between Evolutionary Diversity and Aboveground Biomass During 150 Years of Natural Vegetation Regeneration in Temperate China.

While the link between plant species diversity and biomass has been well-studied, the impact of evolutionary diversity on community biomass across long timescales or ongoing change remains a subject of debate. We elucidated the association between evolutionary diversity and community aboveground biomass (AGB) using an ideal experimental system with over 150-year history of natural vegetation regeneration. Higher phylogenetic diversity facilitated the sampling effect under the influence of environmental filtering, and caused an increase in AGB. Phylogenetic structure varied from aggregation to dispersion during the later period of vegetation recovery (70-150 years), which was correlated with increases in niche complementarity and increasing AGB. Woody plant evolutionary diversity was used as a key to predict the relationship between vegetation recovery and AGB, with a total explanatory power of ~84.7%. Mixed forests composed of evergreen conifers and deciduous broadleaf forests had higher carbon sequestration potential than that of pure forests, which is advantageous for increasing top-stage AGB. This research expands our knowledge of the causes and effects of biodiversity and ecosystem function dynamics over time and space, which is important for accurately predicting future climate change effects.

Evaluating variation of respiration:photosynthesis ratio in sagebrush species: Implications for carbon flux modeling

AbstractPlant respiration and photosynthesis are the two main processes influencing carbon (C) flux balance at leaf‐to‐ecosystem scales. The ratio of respiration to photosynthesis (R:A) or carbon use efficiency (CUE) is considered an important trait for determining global carbon storage in the near future. One school of thought assumes that R:A is constant in terrestrial productivity models, irrespective of biomass, climate, and species. Others believe it is variable, although within a limited range. Semiarid systems dominated by woody vegetation, such as sagebrush steppe, have been recognized as potentially important C sinks on regional to global scales in the context of future climate scenarios. Therefore, there is a critical need to study R:A over different organizational scales (i.e., at the leaf, whole plant, and ecosystem scales) to use this approach for future C flux predictions under climate change scenarios. The objective of this study was to compare leaf‐, shrub‐, and ecosystem‐scale R:A among three sagebrush (Artemisia spp.) communities, and to determine how R:A varies throughout the growing season (i.e., early, mid‐, and late summer) among these communities. We measured photosynthesis and respiration monthly in three sagebrush communities spanning a 685‐m elevation gradient at the Reynolds Creek Experimental Watershed and Critical Zone Observatory in southwestern Idaho. Consistent with our expectations, we found large seasonal variations in R and A at all scales, but with differences in A among the three sagebrush communities significant only at the leaf scale. The R:A ratio was not significantly different among the three species at all organizational scales. However, the R:A ratio did vary among months at the leaf level and there was a statistical interaction between species and month at both leaf and shrub levels. Our study indicates that the R:A ratio is generally conservative, although not tightly constrained (range: 0.12–0.77) among three sagebrush species. Therefore, approaches that assume conservative R:A ratios in terrestrial productivity models need to be considered carefully to evaluate the impact of projected climatic changes on future C cycling in shrub‐dominated rangeland ecosystems.

Host genetics shapes Pinus radiata phenotypic plasticity under drought and is linked with root-associated soil microbiome shifts

Under current and future climate scenarios, identifying drought-resistant tree species, tree genotypes, and beneficial interactions between trees and their root-associated soil microbiomes is becoming more imperative for maintaining tree health and sustaining increasingly vulnerable forests. We designed a genotype x soil x watering x time glasshouse experiment using Pinus radiata as a model tree to assess the magnitude of the effect of host genotype and root-associated soil microbiome on the phenotype response (functional traits, metabolome, nutrients) under drought. We identified the shikimate pathway as a critical metabolic pathway for Pinus radiata drought resistance, with the shikimic acid intermediate being one of the strongest drought signals, besides downstream metabolites such as flavonoids and phenylpropanoids. Overall, we found that the host genotype diversity was a key actor in the observed phenotype response of P. radiata to drought. In contrast, the microbiome was attributed a minor supporting role. Contrary to our hypothesis, dry soils could not support drought-sensitive genotypes under drought stress. Instead, the drought-resistant genotype was able to leverage locally adaptive bacteria to match local selective drought pressures at the expense of tree growth. This highlights the significance of finding specific combinations of tree genotype and mutualistic microbial communities that would thrive under future environmental pressures.

Modeling the Current and Future Distribution of Indianthus virgatus (Roxb.) Suksathan & Borchs.: AMonotypic Plant Endemic to the Western Ghats-Sri Lanka Biodiversity Hotspot.

Species distribution modeling (SDM) is an essential tool in ecology and conservation for predicting species distributions based on species presence/absence data and environmental variables. The present study aimed to understand the distribution pattern and habitat suitability of Indianthus virgatus under current and future climate change scenarios (2050 and 2070) using MaxEnt (3.4.4) and Wallace Ecological Modeling (v2.1.2) tools. The study also intended to identify key environmental predictors of I. virgatus' distribution. Species occurrence data were collected from various sources, including herbarium (online and physical), field surveys, and online databases, yielding 105 unique locations in the Western Ghats (WG) of India and Sri Lanka. We used 19 bioclimatic variables and elevation data sourced from WorldClim for modeling. The MaxEnt and Wallace models showed excellent performance in predicting the distribution of I. virgatus, with area under the curve values of 0.958 (± 0.002) and 0.93, respectively. In MaxEnt modeling, Temperature Seasonality (bio4) was the most significant environmental parameter, followed by the Precipitation of the Coldest Quarter (bio19). In contrast, the Annual Mean Temperature (bio1), Temperature Seasonality (bio4), and Annual Precipitation (bio12) were among the key contributors in Wallace EcoMod. Both the models predicted relatively lesser areas in the species' distribution range as highly suitable habitats (HSH) in India and Sri Lanka. We found divergent trends in predicting I. virgatus distributions using MaxEnt and Wallace EcoMod, particularly for future projections. Nevertheless, both models predicted significant habitat loss under future climate change scenarios, especially under RCP85, with varying degrees of suitability across India and Sri Lanka. Overall, our findings on expected habitat loss under future climate change scenarios highlight the importance of conserving I. virgatus, which has already been declared critically endangered (CR) in Sri Lanka.

DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models With 3D Diffusion Models

AbstractEarth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low‐cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low‐cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio‐temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity.

The Streamflow Response to Multi‐Day Warm Anomaly Events: Sensitivity to Future Warming and Spatiotemporal Variability by Event Magnitude

AbstractPersistent warm temperature anomalies can drive streamflow in regions where snow and glacier melt are important constituents of streamflow. However, the spatiotemporal variability of the streamflow response depends on both the magnitude of the forcing temperature anomalies and the nature of the underlying hydrological system. Here we ask: when, where, and for what magnitude of temperature anomalies will the streamflow response change most rapidly under warming? We use observed streamflow and temperature for 868 basins across Canada to quantify the streamflow response during warm temperature anomalies and how such responses vary in space, time, and by anomaly magnitude. We first identify two temporal modes of the streamflow response, one in autumn and one in spring, the relative strength of which varies by climate. We then use sinusoidal approximations of seasonal temperature cycles to characterize the sensitivity of such modes to changes in annual temperature. At individual basins, we find that relative to moderate warm events, the streamflow response to more extreme warm events is more sensitive to changes in mean annual temperatures, and this sensitivity is greatest in the coastal, southern, and central regions of Canada. Our results have implications for how the hydrological impacts of extreme events, such as heatwaves, will change in space and time under future climate change.

Assessment of suitable region of Asparagus cochinchinensis (Lour.) Merr. under different climatic conditions in China by the MaxEnt model and HPLC analysis.

Asparagus cochinchinensis is a member of the Asparagaceae family whose medicinal part is the dried root tuber. The distribution of A. cochinchinensis and its secondary metabolites are closely associated with environmental factors, such as climate and soil properties. By establishing and optimizing a maximum entropy model, we analyzed and predicted the distribution pattern and migration direction of suitable habitats for A. cochinchinensis and determined the main environmental factors affecting the accumulation of secondary metabolites. Under current climatic conditions, the area of suitable habitats for A. cochinchinensis (208.38 × 104 km2) accounts for 21.71% of the land area of China, and the areas of lowly, moderately, and highly suitable areas were 64.15 × 104 km2, 113.66 × 104 km2, and 30.57 × 104 km2, respectively. Under future climate scenarios, the total area of suitable habitats hardly changes. The area of highly suitable habitats significantly decreases under the SSP1-RCP2.6 scenario (to 83.22% of the current value) and the SSP3-RCP7 scenario (to 48.94% of the current value), but eventually increases to 112.86% of the current value under the SSP5-RCP8.5 scenario, which indicates that A. cochinchinensis might adapt better to a high-carbon-emissions scenario. Under different climate scenarios, low-impact areas mainly occur in southern China and will correspond 92.07% of the current suitable area. Highly suitable habitats primarily occur in the southeastern Sichuan Basin, northern Guangxi, eastern Guizhou, and western Hunan. HPLC analysis showed that the content of protodioscin (0.373%) and protogracillin (0.044%) in S2 was the highest. The total saponins contents of S1 and S2 were the highest, which were 35.6586 and 33.1262 mg/g, respectively. The total polysaccharide content of S9 was the highest (16.9467%). The total contents of saponins and polysaccharides in A. cochinchinensis were significantly, but oppositely, correlated with temperature, precipitation, and other factors. This study has identified environmental factors affecting the growth and quality of A. cochinchinensis, which has guiding significance for resource conservation and site selection for large-scale cultivation.

Enhanced photorespiratory and TCA pathways by elevated CO2 to manage ammonium nutrition in tomato leaves

Plants grown under exclusive ammonium (NH4+) nutrition have high carbon (C) demand to sustain proper nitrogen (N) assimilation and energy required for plant growth, generally impaired compared to nitrate (NO3-) nutrition. Thereby, the increment of the atmospheric carbon dioxide (CO2) concentration, in the context of climate change, will potentially allow plants to better face ammonium nutrition. In this work, tomato (Solanum lycopersicum L.) plants were grown under ammonium or nitrate nutrition in conditions of ambient (aCO2, 400 ppm) or elevated CO2 (eCO2, 800 ppm) atmosphere. Elevated CO2 increased photosynthesis rate and tomato shoot growth regardless of the N source. In the case of NH4+-fed leaves the positive effect of elevated CO2 occurred despite of the high tissue NH4+ accumulation. Under eCO2 ammonium nutrition triggered, among others, the modulation of genes related to C provision pathways (including carbonic anhydrase and glyoxylate cycle), antioxidant response and cell membranes protection. The enhanced photosynthate production at eCO2 facilitated C skeleton provision through the TCA cycle and anaplerotic pathways to promote amino acid synthesis. Moreover, photorespiratory activity was stimulated by eCO2 and contributed to yield serine as additional sink for NH4+ excess. Overall, these changes denote a connection between the respiratory and the photorespiratory pathways linked to ammonium nutrition. This metabolic strategy may allow crops to grow efficiently using ammonium as fertilizer in a future climate change scenario, while mitigating N losses.

Latitudinal gradients of biodiversity and ecosystem services in protected and non-protected oak forest areas can inform climate smart conservation

Adaptive governance of areas set aside for future protection of biodiversity, sustainable production, and recreation requires knowledge about whether and how effects of area protection are modulated by climate change and redistribution of species. To investigate this, we compare biodiversity of plants (assessed using vegetation plots) and arthropods (collected with Malaise traps, analyzed using metabarcoding) and productivity (tree growth, determined using dendrochronology) in protected and non-protected oak (Quercus spp.) forests along a latitudinal gradient (55.6 °N – 60.8 °N) in Sweden. We also compare historical, recent and projected future climate in the region. In contrast to established global latitudinal diversity gradients, species richness of plants and arthropods increased northwards, possibly reflecting recent climate-induced community redistributions, but neither was higher in protected than in non-protected areas, nor associated with contemporary ground temperature. Species composition of arthropods also did not differ between protected and non-protected areas. Arthropod biomass increased with latitude, suggesting that the magnitude of cascading effects mediated via their roles as pollinators, herbivores, and prey for other trophic levels, varies geographically and will change with a moving climate. Annual growth rate of oaks (an ecosystem service in the form of biomass increase and carbon sequestration) was independent of latitude and did not differ between protected and non-protected areas. Our findings question the efficacy of contemporary designation and management of protected oak forests, and emphasize that development and implementation of modified climate smart conservation strategies is needed to safeguard ecosystem functioning, biodiversity, and recreational values of protected forest areas against future challenges.

Exploring Global Data Sets to Detect Changes in Soil Microbial Carbon and Nitrogen Over Three Decades

AbstractUnderstanding the temporal dynamics of soil microbial biomass is crucial for assessing soil ecosystem functions and services, yet these dynamics are globally uncertain. Here, we compiled a data set of soil microbial biomass carbon (MBC) and nitrogen (MBN) from 1493 studies between 1988 and 2019 to elucidate their temporal trends and potential drivers. Results showed that global MBC and MBN significantly decreased by 0.033 Mg C ha−1 yr−1 and 0.007 Mg N ha−1 yr−1 at 0–30 cm soil depth, between 1988 and 2019, respectively, which might be primarily attributed to the warming of the climate, the increase in global precipitation, and reduction of soil organic carbon (SOC) stock. The rate of decline in MBC and MBN showed a non‐linear trend: following a decline from 1988 to 1999, it slowed down until 2014, likely due to the global warming hiatus. Afterward, the pace of decline increased again from 2015 to 2019. Boreal biomes experienced the largest decrease in soil microbial biomass with the reduction rate of MBC being 4.3 times higher than in temperate biomes, showing a higher sensitivity in boreal biomes to climate change. Grassland ecosystems also exhibited greater reductions, possibly driven by their degradation. These findings shed valuable insights on the long‐term dynamics of soil microbial biomass on a global scale over the last three decades. Furthermore, this study underscores the importance of preserving soil microbial biomass as a key strategy to mitigate the adverse effects of future climate change, thereby sustaining ecosystem health and resilience.

Machine learning-based climate zoning and asphalt selection for pavement infrastructure under changing climate: A focused study of Ningxia, China

Climate change poses significant challenges to the durability and performance of asphalt pavements. This study presents a comprehensive analysis of climatic factors in Ningxia, China, to establish a robust climate zoning framework for asphalt pavements. Utilizing machine learning techniques, specifically the Fuzzy C-means (FCM) algorithm, three distinct climate zones within Ningxia were divided considering climatic features such as maximum temperature, minimum temperature, average temperature, maximum temperature difference, cumulative precipitation, and cumulative radiation. Based on the historical climate data and LTPP model, five asphalt PG zones were classified in Ningxia Province. Besides, six climate sub-zones, which integrated the asphalt PG zones into climate zones, provided a more refined strategy for the asphalt selection. The study also projected future climate scenarios using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset to assess the impact of climate change on asphalt selection in Ningxia. The significant changes in pavement temperature indicated the necessity to adapt asphalt pavement designs to future climate scenarios. Overall, this research contributed to the construction of more climate-resilient pavement infrastructures and provided an analysis framework for other regions facing similar climate-induced challenges.

The effect of experimental warming on reproductive performance and parental care in the burying beetle Nicrophorus nepalensis.

Rising temperatures can adversely affect parental care and reproductive performance across a range of taxa. However, the warming impact is contingent upon understanding how temperature affects the spectrum of parental behaviours and their interplay. Here, we assessed how temperature affects parental care and reproductive success in the burying beetle, Nicrophorus nepalensis, which exhibits complex parental care behaviours. We exposed breeding pairs of N. nepalensis, to three temperature regimes (18°C, 20°C and 22°C) and assessed changes in parental care, and the subsequent development and growth of their offspring. Our findings show that 22°C disrupts carcass nest building by the parents and results in smaller clutches. Moreover, no eggs successfully hatched in the 22°C treatment. A milder increase to 20°C did not affect the hatching rate but resulted in smaller broods and lighter offspring, even when considering brood size, suggesting a change in post-hatching care quality. Our research suggests that warming may weakly affect parental care but has strong detrimental effects on offspring performance. These findings highlight the necessity of investigating the effect of ambient temperature across a diversity of traits and behaviours and across a range of life-history stages to fully assess species vulnerability in the face of future climate change.

A Novel Approach for Identifying and Assessing MOR‐Based CMIP6 Model for Hydrological Analysis in an Ungauged Watershed

ABSTRACTThe identification of the onset and retreat dates of the monsoon season is a crucial and intricate phenomenon, given its annual spatiotemporal variability. The monsoon season contributes significantly to rainfall, replenishing water sources and hydrological systems but causes hydrological extremes, especially for the high‐altitude watersheds in Southeast Asia. Global Circulation Model (GCM)‐Coupled Model Intercomparison Project Phase 6 (CMIP6)‐based rainfall and temperature data are helpful for adequately representing present and future climate scenarios. However, the usability of uncorrected GCM‐CMIP6 datasets needs to be assessed regionally. This study focuses on identifying the best‐suited GCM‐CMIP6 based on the monsoon onset (MO) and retreat (MR) dates along with other climatological temporal parameters. A numerical definition for MO and MR has been formulated to find the best‐suited GCM‐CMIP6 (i.e., CMCC‐ESM2). In this context, runoff simulation is carried out using the meteorological inputs of the monsoon onset‐retreat (MOR)‐based best‐suited GCM to evaluate its usability. A multi‐model simulation approach has been carried out for runoff estimation based on observed datasets to find a better‐suited hydrological model. The proposed overall methodology is tested in a hydrological extreme‐prone ungauged watershed (i.e., Ranikhola). CMCC‐ESM2 and SSP2‐4.5 has been identified as best‐suited SSP based on statistical evolution (R2 [0.693], NSE [0.662] and RSR [0.581]) for future daily runoff prediction. Future hydrological analysis shows that the average monsoon peak runoff magnitude will increase from the calibrated period (2015–2020) by 18.01% in the coming years (2021–2049).

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

A Distinct Role for Aerosol and GHG Forcing in Historical CMIP6 Evapotranspiration Trends

AbstractEvapotranspiration (ET) is crucial for the global water balance, plant growth, and freshwater availability. It connects the surface water balance with surface energy fluxes, making its accurate representation vital for climate projections. However, global climate models (GCMs) struggle with ET representation due to resolution limitations and simplified depictions of soil, plant, and atmosphere interactions. Simulated future changes in ET are uncertain, and the role of driving processes remain unclear. Here, we explore the utility of a simple and interpretable method to disentangle these varying drivers. We investigate the sensitivity of JJA ET to different atmospheric variables through simple linear models predicting ET from atmospheric variables only. The model consistently yields good results across GCMs or forcing scenarios. We find that GCMs have shown strong decreases and subsequent increases in ET over the historical period, related to changes in net surface radiation. For future climate projections, decreases in water availability compete with higher available surface radiation, making future projections uncertain. Single forcing GCM realizations show that historical ET trends in densely populated regions have been more influenced by aerosol emissions than greenhouse gases. Finally, we investigate which atmospheric variables explain most short‐term (year‐to‐year) and long‐term (decadal) changes. While water availability may be the most important driver of short‐term variability, for certain regions, radiation trends dominate long‐term forcing. This paper leverages a simple approach to provide a comprehensive and understandable view into recent and future changes in ET, reconciling the evidence provided by more complex case studies.

Interaction Between Climate Change Scenarios and Biological Invasion Reveals Complex Cascading Effects in Freshwater Ecosystems.

Climate change often facilitates biological invasions, leading to potential interactive impacts of these global drivers on freshwater ecosystems. Although climatic mitigation efforts may reduce the magnitude of these interactive impacts, we are still missing experimental evidence for such effects under multiple climate change scenarios within a multi-trophic framework. To address this knowledge gap, we experimentally compared the independent and interactive effects of two climate change scenarios (mitigation and business-as-usual) and biological invasion on the biomass of major freshwater trophic groups (phytoplankton, zooplankton, periphyton, macroinvertebrates, and a native macrophyte) and the decomposition rate of allochthonous material. Among the independent effects, we found that the business-as-usual climate treatment resulted in lower native macrophyte biomass and higher periphyton biomass compared to the climatic baseline and mitigation treatments. This indicates the potential of climate change to alter the relative dominance of different freshwater producers and demonstrates that climate mitigation efforts can counteract these effects. Biological invasion alone increased the biomass of chironomids, a dominant macroinvertebrate group in tropical freshwater ecosystems, demonstrating a compensatory effect on climate change. Climate change and biological invasion interactively reduced the decomposition rate of allochthonous detritus, likely mediated by the feeding preference of abundant chironomids for periphytic algae associated with the presence of non-native macrophytes. We concluded that (i) climatic mitigation can maintain climate baseline conditions in freshwater ecosystems, and (ii) the interactive effects between future climate scenarios and biological invasion are related to complex cascading interactions among trophic groups on ecosystem processes.

Soil erosion assessment using SWAT, in relation withLand use, agricultural practices, and future climate change in a semi-arid catchment in Tunisia

Abstract Soil erosion is a severe environmental concern arising from intensive agricultural uses, land degradation, and anthropogenic activities. This problem threatens agricultural productivity and sustainable development, particularly in emerging countries. Therefore, evaluating soil erosion is essential in conservation, planning, and management on a watershed or basin scale. This study aims to assess the erosion of soil loss in the El Gouazine Watershed, central Tunisia, using the Soil and water assessment tool (SWAT). We define the impact of soil and water conservation management implementation combined with climate change scenario. We identified the spatial distribution of erosion rates based on soil properties, topography, and land use. The observed specific erosion rate of the watershed is estimated at 1.6 t.ha−1.yr−1, whereas according to the SWAT model, the average soil loss rate is 1.4 t.ha−1.yr−1. Furthermore, the obtained results highlight importance of the slope factor in affecting the severity of the soil loss rates in the El Gouazine watershed. It was also demonstrated that it’s urgent to prioritize other measures such as contour cropping or conservation agriculture, to enhance and strengthen the soils’ resistance against the detachment due to discharge water. In this context, this research found that these techniques decrease considerably the soil loss by 22% for the strip cropping, 33% for the No-tillage, and 72% for the bench terracing. Moreover, these farming techniques, contribute at the same time to the amelioration of the water balance by reducing the evapotranspiration and enhancing the soil water storage. To go further in this study a soil erosion forecast using the worst-case scenario for climate change RCP 8.5 was conducted. an overview of the future soil erosion patterns is obtained. We noticed then a decrease of the average annual soil loss rate until 2050 and then a prominent increase from 2051 to 2100.

Climate Futures for Lizards and Snakes in Western North America May Result in New Species Management Issues.

We assessed changes in fundamental climate-niche space for lizard and snake species in western North America under modeled climate scenarios to inform natural resource managers of possible shifts in species distributions. We generated eight distribution models for each of 130 snake and lizard species in western North America under six time-by-climate scenarios. We combined the highest-performing models per species into a single ensemble model for each scenario. Maps were generated from the ensemble models to depict climate-niche space for each species and scenario. Patterns of species richness based on climate suitability and niche shifts were calculated from the projections at the scale of the entire study area and individual states and provinces, from Canada to Mexico. Squamate species' climate-niche space for the recent-time climate scenario and published known ranges were highly correlated (r = 0.81). Overall, reptile climate-niche space was projected to move northward in the future. Sixty-eight percent of species were projected to expand their current climate-niche space rather than to shift, contract, or remain stable. Only 8.5% of species were projected to lose climate-niche space in the future, and these species primarily occurred in Mexico and the southwestern U.S. We found few species were projected to lose all suitable climate-niche space at the state or province level, although species were often predicted to occupy novel areas, such as at higher elevations. Most squamate species were projected to increase their climate-niche space in future climate scenarios. As climate niches move northward, species are predicted to cross administrative borders, resulting in novel conservation issues for local landowners and natural resource agencies. However, information on species dispersal abilities, landscape connectivity, biophysical tolerances, and habitat suitability is needed to contextualize predictions relative to realized future niche expansions.

Eutrophication promotes resource use efficiency and toxin production of Microcystis in a future climate warming scenario

Addressing the risks of cyanobacterial blooms and toxin production under ongoing and accelerating eutrophication and climate warming is crucial for both water ecosystem services and human health. Therefore, we here explored the interactive effects of eutrophication and warming on freshwater ecosystems, focusing on Microcystis and its cyanotoxin production. We employed a large-scale mesocosm system simulating future climate warming scenarios in concert with varying degrees of nutrient enrichment. We explored the full range of identified cyanobacterial toxins and cyanotoxin-producing genes under different experimental conditions and assessed the effects of both eutrophication and warming on both phytoplankton community structure (algal densities, community stability) and function (resource use efficiency, RUE). We show here that eutrophication increases the RUE of Microcystis and promotes an increase in toxin-producing genes, leading to a substantial increase in the dominance of Microcystis. This increase correlates with enhanced cyanotoxin production, a trend exacerbated under the influence of future climate warming, suggesting interactions between eutrophication and climate warming on Microcystis ecology and cyanotoxin dynamics. Hence, heatwaves and eutrophication lead the phytoplankton community to be dominated by a minority of algal species with higher toxic capacity. In a broader context, our study underscores the urgent need for holistic management strategies, addressing both nutrient control and climate mitigation, to effectively manage the escalating ecological risks associated with cyanobacterial dominance and toxin production.