Mean Annual Temperature Research Articles

Landscape and paleoenvironmental change in stream valleys of the Central Great Plains, North America, during Marine Isotope Stage 3 (ca. 59–27 ka)

Recent studies of late Quaternary landscape evolution in stream valleys throughout the central Great Plains of North America have identified thick packages of MIS 3-age alluvium and colluvium beneath terraces, alluvial fans, and footslopes. Here, we review and build on these studies to highlight the geomorphic history of river systems in the Central Plains during MIS 3 and reconstruct the paleoclimatic conditions and vegetation communities that existed in the region at that time.A suite of 50 radiocarbon and optically stimulated luminescence ages indicate that late MIS 3 was a period of widespread landscape stability and soil development in the Central Plains. In general, river systems were actively aggrading during early MIS 3, but aggradation slowed after ca. 40 ka, allowing soils to develop until ca. 30–27 ka. In some river valleys, a second pulse of aggradation occurred shortly after that time followed by a period of landscape stability and pedogenesis during early MIS 2 (ca. 24–19 ka). Evidence for widespread landscape stability in stream valleys implies that high magnitude flooding was minimal. Atmospheric circulation patterns that favor these conditions include zonal airflow that inhibits incursions of moist, maritime air from the Gulf of Mexico.Paleoenvironmental conditions during late MIS 3 were reconstructed using the stable carbon, oxygen, and hydrogen isotopic composition of pedogenic phyllosilicates, carbonates, and organic matter. Estimated paleotemperatures ranged from 6 to 9 °C ± 3 °C and are approximately 4–7 °C lower than modern mean annual temperature. The estimated δ18O values of soil water from phyllosilicates range from −10.6‰ to −9.6‰. These estimates are lower (18O-depleted) by about 1.5–3‰ relative to modern meteoric δ18O values and we estimate that the δ18O of paleoprecipitation was likely 18O-depleted by up to 8.5‰ relative to modern values. Our estimated δ18O values for soil waters in the Central Plains during late MIS 3 can be explained by climatic conditions characterized by 1) cooler temperatures, 2) precipitation being sourced primarily from Pacific/Arctic air masses, 3) seasonal changes in precipitation patterns (i.e., predominantly winter precipitation), or 4) a combination of the above. δ13C values indicate the presence of a notable proportion of C4 vegetation in the Central Plains during late MIS 3. We suggest that C4 productivity was likely favored at that time by a low atmospheric CO2 environment that had sufficiently warm temperatures and effective precipitation during the growing season.

How can dry tropical forests respond to climate change? Predictions for key Non-Timber Forest Product species show different trends in India.

The present study provides an assessment of the distribution of key Non-Timber Forest Product species in India, namely Aegle marmelos (L.) Correa, Buchanania lanzan Spreng., Madhuca longifolia (J. Koenig ex L.) J. F. Macbr., Phyllanthus emblica L. and Terminalia bellirica (Gaertn.) Roxb. The suitable habitat was analyzed under current climate scenarios and subsequently, the future distribution (2050s and 2070s) was mapped under RCP 2.6 and 8.5 scenarios, along with the past distribution (mid-Holocene, ~ 6000calyear BP) using the MaxEnt species distribution model. The distribution of all species is primarily driven by key bioclimatic factors, including annual precipitation (Bio_12), mean annual temperature (Bio_1), isothermality (Bio_3) and precipitation of the coldest quarter (Bio_19). The results indicate that the present distribution of these species is mainly centred in the Western Ghats regions, Central Highlands, North-eastern India and Siwalik hills. The current study suggests that under the future climate change, the suitable habitat for A. marmelos and T. bellirica is expected to increase while for B. lanzan, M. longifolia and P. emblica, it is projected to decline. A. marmelos and T. bellirica are anticipated to exhibit resilience to future climate changes and are expected to be minimally affected, while B. lanzan, M. longifolia and P. emblica are highly sensitive to high temperature and alteration in rainfall pattern expected under future climate changes. The projections of habitat suitability areas can be used as a valuable foundation for developing conservation and restoration strategies aimed at alleviating the climate change impacts on NTFP species.

Acceleration of phosphorus weathering under warm climates.

The release of phosphorous (P) via chemical weathering is a vital process that regulates the global cycling of numerous key elements and shapes the size of the Earth's biosphere. It has long been postulated that global climate should theoretically play a prominent role in governing P weathering rates. Yet, there is currently a lack of direct evidence for this relationship based on empirical data at the global scale. Here, using a compilation of temperature and P content data of global surface soils (0 to 30 cm), we demonstrate that P release does enhance at high mean annual surface temperatures. We propose that this amplification of nutrient supply with warming is a critical component of Earth's natural thermostat, and that this relationship likely caused expanded oceanic anoxia during past climate warming events. The potential acceleration of phosphorus loss from soils due to anthropogenic climate warming may pose threats to agricultural production, terrestrial and marine ecosystems, and alter marine redox landscapes.

Long-term changes in tropospheric temperature in India: Insights from radiosonde measurements, reanalysis data and CMIP6 model projections

The continuous rise in greenhouse gases (GHGs) in the atmosphere is one of the major factors responsible for the global warming and enhanced tropospheric temperature. In this context, we examine the long-term spatiotemporal changes in the tropospheric temperature (1000–100 hPa) in India using radiosonde measurements and the reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), Modern-Era Retrospective analysis for Research and Application Version 2 (MERRA2) and National Center for Environmental Prediction (NCEP) for the period 1980–2020. We also investigate the future projections of tropospheric temperature in the Shared Socio-economic Pathways (SSPs) 2–4.5 and 5–8.5 by using the Coupled Model Intercomparison Project 6 (CMIP6) results for the period 2015–2100 over India. Our analysis shows a significant positive trend (0.01–0.04 °C/yr) in the annual mean temperature in the upper troposphere (500–100 hPa) in India during the period 1980–2020. The warming trends are also noticeable in the middle and lower troposphere of India. Among the regions, the northeast, north central and northwest India exhibit significant warming of about 0.01–0.06 °C/yr in the upper troposphere (500–100 hPa). On the other hand, most regions show a decline in temperature at the tropopause (100 hPa), which is stronger in the interior peninsular, north central and east coast of India, at about −0.05 ± 0.01°C/yr. All reanalysis data show consistent warming and cooling trends in the respective regions as observed from the radiosondes, albeit slight differences in their values. The future high emission scenario of SSP5–8.5 shows a warming trend of about 0.04–0.08 °C/yr at 1000–200 hPa by the end of the century, which is highest at 250 hPa. The increasing trend in tropospheric temperature is a serious concern, which calls for adaptation and mitigation measures to alleviate the impacts of this accelerated warming in India.

Biomass carbon stock and allocation of planted and natural forests in the Loess Plateau of China

Establishing planted forests (PF) by afforestation and naturally regenerating forests (NF) are important measures of enhance carbon (C) sequestration in terrestrial ecosystems. However, the difference of biomass C stocks and allocation between NF and PF and their determinants in water-limited areas remain unclear. To address this gap, we conducted a synthesis of above-ground biomass C (AGBC), below-ground biomass C (BGBC) stocks and root to shoot ratios (R:S) of PF and NF in the Chinese Loess Plateau. The changes in biomass C stock and R:S along a climate gradient were investigated, and the relationships between R:S and soil properties were also quantified. We found that PF exhibited lower AGBC and BGBC stocks compared to NF, but the average R:S in PF was significantly higher than that of NF (0.48 ± 0.27 vs. 0.30 ± 0.11, p < 0.05). Turning points existed in biomass C allocation along climatic gradient for both PF and NF. More biomass C was allocated to AGBC in PF and NF when mean annual precipitation exceeded 494 mm and 588 mm, respectively. Higher mean annual temperature promoted larger C allocations to BGBC in PF compared to NF. Soil properties had a stronger effect on the biomass C allocation patterns after afforestation, but to a lesser extent on NF. This study indicates that afforestation promotes a larger allocation of biomass C into the below-ground compared to naturally regenerated forests, and using the default R:S estimates may result in a severe underestimation of below-ground C stocks in water-limited areas. The biomass C stocks and allocation are collectively determined by climate conditions and soil properties, and accumulated below-ground biomass C stock induced by dryland afforestation may be an important C sink at a broad geographic scale.

Assessment of climate change on river streamflow under different representative concentration pathways

Climate change and excessive greenhouse gas emissions profoundly impact hydrological cycles, particularly in arid and semi-arid regions, necessitating assessments of their effects on water resource management, agriculture, soil fertility, nutrient transport, hydropower generation, and flood risk. This study investigates climate change repercussions on streamflow in the Zarrineh River Basin, Iran, across three decadal intervals (2020–2029, 2055–2064, and 2090–2099) aiming to develop effective adaptation and mitigation strategies. Four General Circulation Models (GCMs), chosen based on distinct Representative Concentration Pathways (RCPs) determined by the annual mean temperature gradient, are employed. These models generate daily maximum (Tmax) and minimum (Tmin) temperatures along with precipitation data. Subsequently, these variables are integrated into the Soil and Water Assessment Tool (SWAT) model to analyze river flow alterations for each decadal timeframe. Comparison between future projections and observed climate data reveals a gradual decline in precipitation and Tmax, coupled with a substantial increase in Tmin. The average precipitation diminishes from 0.77 mm in the period 1985–1994 to a range of 0.42–0.28 mm in 2090–2099. The simulated flow at the basin outlet highlights that the GCM with the highest annual mean temperature gradient yields the lowest streamflow, while conversely, the model with the lowest gradient generates the highest. Consequently, streamflow experiences a decline from 52 m3/s in 1985–1994 to a range of 41–20 m3/s in 2090–2099.

Subsurface urban heat island in the city of Ekaterinburg

Research subject. The subsurface thermal field in the city of Ekaterinburg (subsurface urban heat island). Aim. To determine criteria for the anomaly of mean annual subsurface temperatures in Ekaterinburg; to identify patterns of spatial distribution of underground temperatures; to quantify the main factors forming an urban heat island and changes in the heat content of rocks using mathematical modeling. Materials and methods. The main experimental data were obtained during the annual cycle of geothermal studies in observational boreholes of Ekaterinburg (22 boreholes) and surrounding areas (10 boreholes in Degtyarskiy, Verkh-Sysertskiy, Gagarskiy districts). Statistical analysis and mathematical modeling describing the impact of climate, local temperature anomalies of ground surface, and groundwater filtration to the underground thermal field were used when interpreting the obtained data. Results. At a depth of 20 m, the mean annual temperatures being less than 5°C and more than 6°C should be considered as anomalous. The maximum intensity of the urban heat island in Ekaterinburg is confined to densely built-up central areas of the city. The highest temperatures (&gt;10°C) at a depth of 20 m are observed in boreholes located near buildings or directly therein. Here, a rapid decrease in temperature with depth is typical. Moderate anomalies from 6°C to 10°C are observed far from buildings. Remoteness from the central regions apparently plays a more important role in the formation of temperature anomalies than the type of urban surfaces (asphalt, concrete, lawns). Background temperatures (less than 6°C) were recorded in boreholes located outside the Ring Road. An analysis of patterns in the attenuation of annual temperature variations with depth allowed an area with intense vertical filtration (up to 24 m/year) to be identified near the City Pond. The most significant changes in heat content in the range of 10–50 m are associated with heat leakage from the basements of buildings, equaling to (23–46) × 107 J/m2. However, this heat is only hundredths of a percent of the total energy consumption spent on heating. Conclusions. The subsurface urban heat island of a large Russian city has been characterized for the first time. The results obtained can be used when developing a strategy for megacities in changing climate conditions.

Enhancing lucerne (Medicago sativa) yield and nutritional quality: a meta-analysis of fertilization types and environmental factors in China.

Lucerne (Medicago sativa), is a cornerstone of China's livestock industry, however, due to the backward agronomic strategies and technology, lucerne in China faces cultivation challenges that result in lower productivity and quality than global standards. Therefore, we undertook a meta-analysis to evaluate the impact of five distinct fertilization types on lucerne yield and nutritional quality in various locations in China. The fertilizer practices included manure application, combined mineral fertilizer and manure application (FM), biological fertilizer application, unbalanced application of two or more mineral fertilizer types, and balanced mineral fertilizer application. Furthermore, we investigate influential factors of yield and quality of lucerne under fertilization, including climatic variables (mean annual precipitation, mean annual temperature), initial soil properties (soil organic carbon; total nitrogen, pH), and agronomic factors (seeding rate, harvest frequency, and lucerne stand age). Our study analyzed 53 published papers to discern the most beneficial fertilizer for enhancing lucerne yield and nutritional quality. The results showed that the fertilizer practices, on average, significantly improved yield by 31.72% and crude protein content by 11.29%, with FM emerging as the most effective, this is because mineral fertilizers provide available nutrients for lucerne, manure provides essential organic matter for microorganisms and improve soil properties. In addition, the fertilizer practices significantly reduced neutral and acid detergent fiber contents by 6.28% and 8.50%, respectively, while increasing ash content and relative feeding value. Furthermore, climatic variables, soil properties, and planting system factors such as sowing date and harvest frequency significantly affected yield and nutritional quality. The practical implications of our results emphasize the need for balanced and strategic fertilizer application to optimize lucerne production and highlight the potential to adjust cultivation practices according to environmental conditions. Balanced and strategic fertilizer application can simultaneously improve soil properties, enhance soil carbon sequestration, and reduce the emission of greenhouse gases from the soil, which is a vital measure for realizing sustainable agricultural development.

Species pool and soil properties in mangrove habitats influence the species-immigration process of diazotrophic communities across southern China.

Microbial immigration is an ecological process in natural environments; however, the ecological trade-off mechanisms that govern the balance between species extinction and migration are still lacking. In this study, we investigated the mechanisms underlying the migration of diazotrophic communities from soil to leaves across six natural mangrove habitats in southern China. The results showed that the diazotrophic alpha and beta diversity exhibited significant regional and locational variations. The diazotrophic species pool gradually increased from the leaves to nonrhizosphere soil at each site, exhibiting a vertical distribution pattern. Mantel test analyses suggested that climate factors, particularly mean annual temperature, significantly influenced the structure of the diazotrophic community. The diazotrophic community assembly was mainly governed by dispersal limitation in soil and root samples, whereas dispersal limitation and ecological drift were dominant in leaves. Partial least squares path modeling revealed that the species pool and soil properties, particularly the oxidation-reduction potential and pH, were closely linked to the species-immigration ratio of diazotrophic communities. Our study provides novel insights for understanding the ecological trait diversity patterns and spread pathways of functional microbial communities between below- and aboveground habitats in natural ecosystems.IMPORTANCEEnvironmental selection plays key roles in microbial transmission. In this study, we have provided a comprehensive framework to elucidate the driving patterns of the ecological trade-offs in diazotrophic communities across large-scale mangrove habitats. Our research revealed that Bradyrhizobium japonicum, Marinobacterium lutimaris, and Agrobacterium tumefaciens were more abundant in root-associated soil than in leaves by internal and external pathways. The nonrhizospheric and rhizospheric soil samples harbored the most core amplicon sequence variants, indicating that these dominant diazotrophs could adapt to broader ecological niches. Correlation analysis indicated that the diversities of the diazotrophic community were regulated by biotic and abiotic factors. Furthermore, this study found a lower species immigration ratio in the soil than in the leaves. Both species pool and soil properties regulate the species-immigration mechanisms of the diazotrophic community. These results suggest that substantial species immigration is a widespread ecological process, leading to alterations in local community diversity across diverse host environments.

Spatiotemporal analysis of equine infectious anemia and prediction of risk areas in Europe

Equine Infectious Anemia (EIA) is a vector-borne persistent viral infection in equine animals. The EIA is characterized by recurrent fever, thrombocytopenia, depression, anemia, rapid weight loss, and lower body edema. Control of EIA is achieved through the elimination or isolation of infected animals, resulting in significant economic losses. In recent years, many countries in Europe have experienced outbreaks of EIA, which could potentially develop into a new wave of epidemic and pose a significant threat to the healthy development of the equine industry. This study utilized spatiotemporal analysis techniques and ecological niche modeling to investigate the spatiotemporal distribution characteristics of historical EIA outbreaks and predict risk areas for EIA occurrence in Europe. Spatiotemporal analysis results indicate that from 2005 to 2023, the EIA outbreaks in Europe exhibit five significant spatiotemporal clusters, with hotspots concentrated in southeastern France and northwestern Italy. Ecological niche modeling reveals that western, central, and southern Europe are high-risk areas for EIA outbreaks. Annual mean temperature, annual precipitation, and horse density are important variables that influence the occurrence of EIA. The results of this study can provide decision-makers with valuable insights, helping with EIA monitoring and resource allocation.

Spatiotemporal interpretable mapping framework for soil heavy metals

Mapping the spatiotemporal distribution of soil heavy metals is a prerequisite for soil pollution prevention and control. However, current mapping methods make it difficult to balance robustness and interpretability. In this study, we proposed a spatiotemporal interpretable mapping framework for soil heavy metals and conducted a case study of soil cadmium (Cd) in cropland of the Poyang Lake region in China. The results showed that the average soil Cd concentration increased significantly (p < 0.01) from 0.13 mg/kg in 1983 to 0.19 mg/kg in 2010. Soil Cd in 1983 was mainly controlled by human activities and topography, whereas it was jointly driven by human activities, topography, and climate in 2010. In addition, the framework revealed the spatial distribution of soil Cd and their uncertainties at a 12.5-m resolution in 1983 (R2 = 0.94) and 2010 (R2 = 0.97). The top three covariates in 1983 were nighttime-light, PM10, and land surface temperature (day), whereas those in 2010 were mean annual minimum temperature, gross domestic product, and industrial enterprises, with Shapley additive explanation (SHAP) values of 0.44, 0.42, and 0.27, and 0.31, 0.11, and 0.09 mg/kg, respectively. By integrating machine learning, variable and model selection, and the SHAP module, the proposed framework achieves a balance between the robustness and interpretability in mapping the spatiotemporal distribution of soil Cd, going beyond the limitations of the classical statistical “smoothing effect” and the artificial intelligence “black box.” Our study emphasizes the importance of considering time, space, models, variables, and mechanisms in digital soil mapping, which will enable more accurate and scientific predictions of soil contaminants across time and space.

Geospatial insights into Alphonso mango cultivation: a comprehensive land suitability study in the coastal belt of Maharashtra, India.

Land suitability assessment is integral to the advancement of precision agriculture. This inquiry is focused on identifying optimal regions for cultivating Alphonso mango in the coastal belt of Maharashtra, spanning across Palghar, Raigad, Thane, Ratnagiri, and Sindhudurg districts. Employing a GIS-based Analytic Hierarchy Process (AHP) methodology, 10 crucial parameters have been considered, encompassing climatic, physical, and chemical soil characteristics: cation exchange capacity, organic carbon, slope, rainfall, soil pH, soil texture, mean annual soil temperature, base saturation, soil drainage, and soil depth. Weights are assigned to these parameters based on expert opinions and existing literature to determine their significance in developing a soil suitability map. The study reveals distinct land suitability zones for Alphonso mango cultivation. The land suitability map designates 25.78% of the study area as highly suitable, while 9.18% is considered unsuitable for Alphonso mango cultivation. To validate the study, the Receiver Operating Characteristic (ROC) curve has been employed, indicating an 83% approval rate for the reliability and performance of the soil suitability. The results categorise soil suitability classes, providing valuable insights for farmers and agricultural planners to make informed decisions regarding Alphonso mango cultivation in similar geoenvironmental regions.

Projected rising temperatures and vapour pressure deficit threaten Arabica coffee production Tanzania's burgeoning coffee region: Empirical insight from Mbinga district, Tanzania

Tanzania is emerging as a key coffee producer, but climate change poses a serious threat to coffee production in the country. While extensive literature addresses climatic variables’ trends and variability in coffee-growing regions globally, elevation, a crucial factor in coffee cultivation, is often overlooked. This paper aims to fill this knowledge gap by examining the future trends and variability of climatic variables identified in previous studies being crucial for Arabica coffee (rainfall, temperature, and vapor pressure Deficit (VPD)) during different phenological stages of Arabica coffee growth across three elevation zones (lowland, midland, and highland). We study the Mbinga district, now a major Arabica-producing area and the forefront of hopes for expanding production in Tanzania. We used a Multi-Model Ensemble (MME) of two Global Circulation Models (GCMs) to estimate the future trends and variability of rainfall, temperature, and VPD under SSP2-4.5 and SSP5-8.5 scenarios from 2023 to 2064. We analyzed these data using non-parametric trend analysis (Mann-Kendal and Sen's slope test estimation) and standardized anomalies. Our results suggest that both temperatures (minimum and maximum) and VPD during critical flowering and growing seasons are projected to increase significantly over the next 42 years across all elevation zones under both emission scenarios. The mean annual temperatures across elevation zones in Mbinga are projected to surpass the optimal temperature envelope (23 °C) for producing quality Arabica, challenging the notion that the highlands might serve as a refuge for future production. The mean annual VPD in the lowlands are projected to surpass the optimal envelope (0.82 kPa) for Arabica productivity from 2023 to 2064 under both emission scenarios, while midlands are projected to surpass the optimal VPD envelope from mid-2040s onwards. In constrast, the highlands are projected to stay within the optimal VPD envelope for Arabica productivity from 2023 to 2064 under both emission scenarios. Nevertheless, as highlands are projected to get more warmer in the future because of rising temperatures, this hope might not be feasible in the post-2064. To secure Mbinga's future in global coffee value chains, the Tanzanian government and stakeholders must support adaptation measures, enhancing smallholders’ resilience against climate-induced disruptions.

In the Qaidam Basin, Soil Nutrients Directly or Indirectly Affect Desert Ecosystem Stability under Drought Stress through Plant Nutrients.

The low nutrient content of soil in desert ecosystems results in unique physiological and ecological characteristics of plants under long-term water and nutrient stress, which is the basis for the productivity and stability maintenance of the desert ecosystem. However, the relationship between the soil and the plant nutrient elements in the desert ecosystem and its mechanism for maintaining ecosystem stability is still unclear. In this study, 35 sampling sites were established in an area with typical desert vegetation in the Qaidam Basin, based on a drought gradient. A total of 90 soil samples and 100 plant samples were collected, and the soil's physico-chemical properties, as well as the nutrient elements in the plant leaves, were measured. Regression analysis, redundancy analysis (RDA), the Theil-Sen Median and Mann-Kendall methods, the structural equation model (SEM), and other methods were employed to analyze the distribution characteristics of the soil and plant nutrient elements along the drought gradient and the relationship between the soil and leaf nutrient elements and its impact on ecosystem stability. The results provided the following conclusions: Compared with the nutrient elements in plant leaves, the soil's nutrient elements had a more obvious regularity of distribution along the drought gradient. A strong correlation was observed between the soil and leaf nutrient elements, with soil organic carbon and alkali-hydrolyzed nitrogen identified as important factors influencing the leaf nutrient content. The SEM showed that the soil's organic carbon had a positive effect on ecosystem stability by influencing the leaf carbon, while the soil's available phosphorus and the mean annual temperature had a direct positive effect on stability, and the soil's total nitrogen had a negative effect on stability. In general, the soil nutrient content was high in areas with a low mean annual temperature and high precipitation, and the ecosystem stability in the area distribution of typical desert vegetation in the Qaidam Basin was low. These findings reveal that soil nutrients affect the stability of desert ecosystems directly or indirectly through plant nutrients in the Qaidam Basin, which is crucial for maintaining the stability of desert ecosystems with the background of climate change.

A luminescence-derived cryptostratigraphy from the Lake Suigetsu sedimentary profile, Japan: 45,000–30,200 IntCal20 yr BP

The luminescence characteristics of sediments are affected by a variety of environmental factors, reflecting both local and broader regional influences. If seeking to apply stimulated luminescence as a ‘pure’ dating technique, variability in these external variables needs to be controlled for, involving, inter alia, lengthy pretreatment procedures and complex dose rate corrections. However, in so doing, a lot of potentially valuable palaeoenvironmental information is lost.Instead, in the present study, we explicitly analysed raw, non-pretreated sediment that preserves this wealth of contributory environmental influence. Using a SUERC portable luminescence (POSL) reader, we performed rapid profiling across a 14,800 year interval of the annually laminated (varved) Lake Suigetsu sedimentary profile, central Japan (i.e., 45,000 to 30,200 IntCal20 yr BP), producing 303 contiguous measurements with a mean sampling resolution of 49 years. To further inform our understanding of this dataset, additional follow-up laboratory dosing was performed to provide sensitivity estimates.The ‘cryptostratigraphy’ (‘hidden stratigraphy’) revealed by our data includes the identification of a step-change in luminescence parameters circa 39,200 IntCal20 yr BP, which we attribute to a major earthquake that resulted in re-routing of inflow to the lake. Further variability in the derived luminescence signals is compared with supporting high resolution x-ray fluorescence (μXRF) data and palynological data from Lake Suigetsu. A correlation between the luminescence profile (both net infra-red-stimulated and net blue light-stimulated signals) and mean annual temperature is revealed, mediated through subtle differences in sediment characteristics under warmer or cooler climatic conditions.

Analyzing Spatio-Temporal Dynamics of Grassland Resilience and Influencing Factors in the West Songnen Plain, China, for Eco-Restoration.

Grassland plays an indispensable role in the stability and development of terrestrial ecosystems. Quantitatively assessing grassland resilience is of great significance for conducting research on grassland ecosystems. However, the quantitative measurement of resilience is difficult, and research on the spatio-temporal variation of grassland resilience remains incomplete. Utilizing the Global Land Surface Satellite (GLASS) leaf area index (LAI) product derived from MODIS remote sensing data, along with land cover and meteorological data, this paper constructed the grassland resilience index (GRI) in the west Songnen Plain, China, a typical region with salt and alkali soils. This paper analyzed the spatio-temporal changes of the GRI and explored the contribution of climate factors, human activities, and geographical factors to the GRI. The results revealed that from 2000 to 2021, the GRI in the study area ranged from 0.1 to 0.22, with a multi-year average of 0.14. The average GRI exhibited a pattern of high-value aggregations in the north and low-value distributions in the south. Trend analysis indicated that areas with an improved GRI accounted for 59.09% of the total grassland area, but there were still some areas with serious degradation. From 2000 to 2015, the latitude and mean annual temperature (MAT) were principal factors to control the distribution of the GRI. In 2020, the mean annual precipitation (MAP) and MAT played important roles in the distribution of the GRI. From 2000 to 2021, the influence of human activities was consistently less significant compared to geographical location and climate variables.

Trees have overlapping potential niches that extend beyond their realized niches.

Tree species appear to prefer distinct climatic conditions, but the true nature of these preferences is obscured by species interactions and dispersal, which limit species' ranges. We quantified realized and potential thermal niches of 188 North American tree species to conduct a continental-scale test of the architecture of niches. We found strong and consistent evidence that species occurring at thermal extremes occupy less than three-quarters of their potential niches, and species' potential niches overlap at a mean annual temperature of ~12°C. These results clarify the breadth of thermal tolerances of temperate tree species and support the centrifugal organization of thermal niches. Accounting for the nonrealized components of ecological niches will advance theory and prediction in global change ecology.

Divergence in spatial patterns of leaf stoichiometry between native and non-native plants across coastal wetlands

The spatial pattern of leaf stoichiometry is critical in predicting plant palatability and ecosystem productivity and nutrient cycling rates and thus is a major focus of community ecological research. Coastal wetlands as vital blue carbon ecosystems, with high possibility to be vulnerable to plant invasion, studies focused on stoichiometry and its pattern are important to unveil the elements cycling process. However, previous studies have mainly focused on stoichiometry in terrestrial ecosystems, there are few studies conducted on coastal wetland ecosystems, especially the studies that compare leaf stoichiometry between native and non-native plants in coastal wetlands. In this study, we compared the latitudinal patterns of leaf nutrient contents and their stoichiometric ratios between native and non-native plant species across coastal wetland ecosystems and investigated whether leaf stoichiometric patterns were driven by climatic factors. We used a compiled global data set of 954 records to conduct a systematic meta-analysis. The results showed that there were significant differences in latitudinal patterns of leaf carbon (C) and nitrogen (N) contents and C:N ratio between native and non-native species, as well as significant differences in leaf C, N, and phosphorus (P) contents. For native species, we found significant latitudinal patterns in leaf C, N, and P contents and C:N and C:P ratios, whereas for non-native species, we found significant latitudinal patterns in leaf N content and C:P and N:P ratios. Mean annual temperature of the data collection site was a significant predictor of leaf stoichiometry of native plants but only of leaf N content and C:P ratio of non-native plants. Thus, we demonstrated spatial heterogeneity in leaf stoichiometries between native and non-native plants in coastal wetlands, indicating that such differences should be emphasized in future biogeochemical models and plant-herbivore interaction studies owing to the important role of wetland plants in global C, N, and P cycles. Our findings increase understanding of plant-related nutrient and elements cycling in coastal wetlands, as well as improve predictions of plant growth rates and vegetation productivity across large scales under plant invasion scenarios.

The influence of proxy selection on global annual mean temperature reconstructions during the Common Era

The influence of proxy selection on global annual mean temperature reconstructions during the Common Era

Climatic conditions and landscape diversity predict plant–bee interactions and pollen deposition in bee‐pollinated plants

Climate change, landscape homogenization, and the decline of beneficial insects threaten pollination services to wild plants and crops. Understanding how pollination potential (i.e. the capacity of ecosystems to support pollination of plants) is affected by climate change and landscape homogenization is fundamental for our ability to predict how such anthropogenic stressors affect plant biodiversity. Models of pollinator potential are improved when based on pairwise plant–pollinator interactions and pollinator's plant preferences. However, whether the sum of predicted pairwise interactions with a plant within a habitat (a proxy for pollination potential) relates to pollen deposition on flowering plants has not yet been investigated. We sampled plant–bee interactions in 68 Scandinavian plant communities in landscapes of varying land‐cover heterogeneity along a latitudinal temperature gradient of 4–8°C, and estimated pollen deposition as the number of pollen grains on flowers of the bee‐pollinated plants Lotus corniculatus and Vicia cracca. We show that plant–bee interactions, and the pollination potential for these bee‐pollinated plants increase with landscape diversity, annual mean temperature, and plant abundance, and decrease with distances to sand‐dominated soils. Furthermore, the pollen deposition in flowers increased with the predicted pollination potential, which was driven by landscape diversity and plant abundance. Our study illustrates that the pollination potential, and thus pollen deposition, for wild plants can be mapped based on spatial models of plant–bee interactions that incorporate pollinator‐specific plant preferences. Maps of pollination potential can be used to guide conservation and restoration planning.