A new global dataset of bioclimatic indicators

Diversity and ecological niche model of malaria vector and non-vector mosquito species in Covè, Ouinhi, and Zangnanado, Southern Benin

The present study aimed to assess mosquito species diversity, distribution, and ecological preferences in the Covè, Ouinhi, and Zangnanado communes, Southern Benin. Such information is critical to understand mosquito bio-ecology and to focus control efforts in high-risk areas for vector-borne diseases. Mosquito collections occurred quarterly in 60 clusters between June 2020 and April 2021, using human landing catches. In addition to the seasonal mosquito abundance, Shannon's diversity, Simpson, and Pielou's equitability indices were also evaluated to assess mosquito diversity. Ecological niche models were developed with MaxEnt using environmental variables to assess species distribution. Overall, mosquito density was higher in the wet season than in the dry season in all communes. A significantly higher Shannon's diversity index was also observed in the wet season than in the dry seasons in all communes (p < 0.05). Habitat suitability of An. gambiae s.s., An. coluzzii, Cx. quinquefasciatus and Ma. africana was highly influenced by slope, isothermality, site aspect, elevation, and precipitation seasonality in both wet and dry seasons. Overall, depending on the season, the ecological preferences of the four main mosquito species were variable across study communes. This emphasizes the impact of environmental conditions on mosquito species distribution. Moreover, mosquito populations were found to be more diverse in the wet season compared to the dry season.

Modeling the species occurrence probability and response of climate change on Himalayan Somalata plant under different Shared Socioeconomic Pathways

In this study, the current distribution probability of Ephedra gerardiana (Somalata), a medicinally potent species of the Himalayas, was assessed, and its spatial distribution change was forecasted until the year 2100 under three Shared Socioeconomic Pathways. Here, we used the maximum entropy model (MaxEnt) on 274 spatially filtered occurrence data points accessed from GBIF and other publications, and 19 bioclimatic variables were used as predictors against the probability assessment. The area under the curve, Continuous Boyce Index, True Skill Statistics, and kappa values were used to evaluate and validate the model. It was observed that the SSP5-8.5, a fossil fuel-fed scenario, saw a maximum habitat decline for E. gerardiana driving its niche towards higher altitudes. Nepal Himalayas witnessed a maximum decline in suitable habitat for the species, whereas it gained area in Bhutan. In India, regions of Himachal Pradesh, Uttarakhand, Jammu and Kashmir, and Sikkim saw a maximum negative response to climate change by the year 2100. Mean annual temperature, isothermality, diurnal temperature range, and precipitation seasonality are the most influential variables isolated by the model that contribute in defining the species' habitat. The results provide evidence of the effects of climate change on the distribution of endemic species in the study area under different scenarios of emissions and anthropogenic coupling. Certainly, the area of consideration encompasses several protected areas, which will become more vulnerable to increased variability of climate, and regulating their boundaries might become a necessary step to conserve the regions' biodiversity in the future.

Modeling arsenic in European topsoils with a coupled semiparametric (GAMLSS-RF) model for censored data

Arsenic (As) is a versatile heavy metalloid trace element extensively used in industrial applications. As is carcinogen, poses health risks through both inhalation and ingestion, and is associated with an increased risk of liver, kidney, lung, and bladder tumors. In the agricultural context, the repeated application of arsenical products leads to elevated soil concentrations, which are also affected by environmental and management variables. Since exposure to As poses risks, effective assessment tools to support environmental and health policies are needed. However, the most comprehensive soil As data available, the Land Use/Cover Area frame statistical Survey (LUCAS) database, contains severe limitations due to high detection limits. Although within International Organization for Standardization standards, the detection limits preclude the adoption of standard methodologies for data analysis. The present work focused on developing a new method to model As contamination in European soils using LUCAS soil samples. We introduce the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, we produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). We found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection.

Habitat suitability of Opuntia ficus-indica (L.) MILL. (CACTACEAE): a comparative temporal evaluation using diverse bio-climatic earth system models and ensemble machine learning approach

A comprehensive evaluation of the habitat suitability across the India was conducted for the introduced species Opuntia ficus-indica. This assessment utilized a newly developed model called BioClimInd, takes into account five Earth System Models (ESMs). These ESMs consider two different emission scenarios known as Representative Concentration Pathways (RCP), specifically RCP 4.5 and RCP 8.5. Additionally, the assessment considered two future time frames: 2040-2079 (60) and 2060-2099 (80). Current study provided the threshold limit of different climatic variables in annual, quarter and monthly time slots like temperature annual range (26-30 °C), mean temperature of the driest quarter (25-28 °C); mean temperature of the coldest month (22-25 °C); minimum temperature of coldest month (13-17 °C); precipitation of the wettest month (250-500 mm); potential evapotranspiration Thronthwaite (1740-1800 mm). Predictive climatic habitat suitability posits that the introduction of this exotic species is deemed unsuitable in the Northern as well as the entirety of the cooler eastern areas of the country. The states of Rajasthan and Gujarat exhibit the highest degree of habitat suitability for this particular species. Niche hypervolumes and climatic variables affecting fundamental and realized niches were also assessed. This study proposes using multi-climatic exploration to evaluate habitats for introduced species to reduce modeling uncertainties.

Connecting the multiple dimensions of global soil fungal diversity

How the multiple facets of soil fungal diversity vary worldwide remains virtually unknown, hindering the management of this essential species-rich group. By sequencing high-resolution DNA markers in over 4000 topsoil samples from natural and human-altered ecosystems across all continents, we illustrate the distributions and drivers of different levels of taxonomic and phylogenetic diversity of fungi and their ecological groups. We show the impact of precipitation and temperature interactions on local fungal species richness (alpha diversity) across different climates. Our findings reveal how temperature drives fungal compositional turnover (beta diversity) and phylogenetic diversity, linking them with regional species richness (gamma diversity). We integrate fungi into the principles of global biodiversity distribution and present detailed maps for biodiversity conservation and modeling of global ecological processes.

Demystifying global climate models for use in the life sciences

For each assessment cycle of the Intergovernmental Panel on Climate Change (IPCC), researchers in the life sciences are called upon to provide evidence to policymakers planning for a changing future. This research increasingly relies on highly technical and complex outputs from climate models. The strengths and weaknesses of these data may not be fully appreciated beyond the climate modelling community; therefore, uninformed use of raw or preprocessed climate data could lead to overconfident or spurious conclusions. We provide an accessible introduction to climate model outputs that is intended to empower the life science community to robustly address questions about human and natural systems in a changing world.

Comparing the Performance of CMCC-BioClimInd and WorldClim Datasets in Predicting Global Invasive Plant Distributions

Species distribution modeling (SDM) has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of these variables may affect the performance of SDM. This investigation elucidates a new bioclimate variable dataset (i.e., CMCC-BioClimInd) for its use in SDM. The predictive performance of SDM that includes WorldClim and CMCC-BioClimInd was evaluated by AUC and omission rate and the explanatory power of both datasets was assessed by the jackknife method. Furthermore, the ODMAP protocol was used to record CMCC-BioClimInd to ensure reproducibility. The results indicated that CMCC-BioClimInd effectively simulates invasive plant species' distribution. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, it was inferred that the modified and simplified continentality and Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical, and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. This method has great potential to improve the efficiency of species distribution modeling, thereby providing a new perspective for risk assessment and management of global invasive plant species.

Bioclimatic drivers of forage growth and cover in alpine rangelands

Context

Climate change and human activities have significant impacts on the Qinghai–Tibetan Plateau; the alpine ecosystem in this region has been degraded. A decline in forage yield reduces the livestock carrying capacity, but an unmitigated increase may lead to overfeeding and damage to vegetation. These changes have eventually led to grassland degradation and a series of ecological problems. Therefore, it is essential to examine bioclimatic factors that affect forage growth in grasslands.

Objective

To identify bioclimatic factors associated with forage growth and coverage in the Qinghai–Tibetan Plateau.

Methods

We examined how forage growth and coverage are affected by 35 bioclimatic indicators published in a global database (CMCC-BioClimInd).

Results and conclusions

We comprehensively considered the relationship between 35 indicators and forage yield and coverage and found that the combination of temperature and precipitation indicators had a very high correlation with yield and coverage. When we evaluated the relationship between each index and forage yield, forage yield was found to be significantly correlated with 16 bioclimatic indices. Forage yield was positively correlated with yearly positive precipitation (R2 = 0.49, p &lt; 0.05), annual precipitation (R2 = 0.48, p &lt; 0.05), and precipitation of driest quarter (R2 = 0.47, p &lt; 0.05), and negatively correlated with temperature seasonality (R2 = 0.52, p &lt; 0.05), precipitation seasonality (R2 = 0.39, p &lt; 0.05), and simplified continentality index (R2 = 0.48). Forage coverage was significantly correlated with 15 bioclimatic indicators. It showed positive correlations with precipitation of driest quarter (R2 = 0.36, p &lt; 0.05), precipitation of driest month (R2 = 0.33, p &lt; 0.05), and annual precipitation (R2 = 0.31, p &lt; 0.05), and negative correlations with temperature seasonality (R2 = 0.415, p &lt; 0.05), annual temperature range, precipitation seasonality, and simplified continentality index (R2 = 0.37, p &lt; 0.05).

Significance

We identified bioclimatic indicators that affect forage growth in the northeastern Qinghai–Tibetan Plateau, and explored the physiological and ecological mechanisms underlying forage growth. Our results provide a scientific basis for future forage management, early determination of livestock carrying capacity, rational management of animal husbandry practices, and ecological protection and restoration efforts.

Thermal bioclimatic indicators over Southeast Asia: present status and future projection using CMIP6

Mapping potential changes in bioclimatic characteristics are critical for planning mitigation goals and climate change adaptation. Assessment of such changes is particularly important for Southeast Asia (SEA) - home to global largest ecological diversity. Twenty-three global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were used in this study to evaluate changes in 11 thermal bioclimatic indicators over SEA for two shared socioeconomic pathways (SSPs), 2-4.5 and 5-8.5. Spatial changes in the ensemble mean, 5th, and 95th percentile of each indicator for near (2020-2059) and far (2060-2099) periods were examined in order to understand temporal changes and associated uncertainty. The results indicated large spatial heterogeneity and temporal variability in projected changes of bioclimatic indicators. A higher change was projected for mainland SEA in the far future and less in maritime region during the near future. At the same time, uncertainty in the projected bioclimatic indices was higher for mainland than maritime SEA. Analysis of mean multi-model ensemble revealed a change in mean temperature ranged from - 0.71 to 3.23 °C in near and from 0.00 to 4.07 °C in far futures. The diurnal temperature range was projected to reduce over most of SEA (ranging from - 1.1 to - 2.0 °C), while isothermality is likely to decrease from - 1.1 to - 4.6%. A decrease in isothermality along with narrowing of seasonality indicated a possible shift in climate, particularly in the north of mainland SEA. Maximum temperature in the warmest month/quarter was projected to increase a little more than the coldest month/quarter and the mean temperature in the driest month to increase more than the wettest month. This would cause an increase in the annual temperature range in the future.

Worldwide Selection Footprints for Drought and Heat in Bread Wheat (Triticum aestivum L.)

Genome–environment Associations (GEA) or Environmental Genome-Wide Association scans (EnvGWAS) have been poorly applied for studying the genomics of adaptive traits in bread wheat landraces (Triticum aestivum L.). We analyzed 990 landraces and seven climatic variables (mean temperature, maximum temperature, precipitation, precipitation seasonality, heat index of mean temperature, heat index of maximum temperature, and drought index) in GEA using the FarmCPU approach with GAPIT. Historical temperature and precipitation values were obtained as monthly averages from 1970 to 2000. Based on 26,064 high-quality SNP loci, landraces were classified into ten subpopulations exhibiting high genetic differentiation. The GEA identified 59 SNPs and nearly 89 protein-encoding genes involved in the response processes to abiotic stress. Genes related to biosynthesis and signaling are mainly mediated by auxins, abscisic acid (ABA), ethylene (ET), salicylic acid (SA), and jasmonates (JA), which are known to operate together in modulation responses to heat stress and drought in plants. In addition, we identified some proteins associated with the response and tolerance to stress by high temperatures, water deficit, and cell wall functions. The results provide candidate regions for selection aimed to improve drought and heat tolerance in bread wheat and provide insights into the genetic mechanisms involved in adaptation to extreme environments.

FutureStreams, a global dataset of future streamflow and water temperature

There is growing evidence that climate change impacts ecosystems and socio-economic activities in freshwater environments. Consistent global data of projected streamflow and water temperature are key to global impact assessments, but such a dataset is currently lacking. Here we present FutureStreams, the first global dataset of projected future streamflow and water temperature for multiple climate scenarios (up to 2099) gridded at a 5 arcminute spatial resolution (~10 km at the equator), including recent past data (1976–2005) for comparison. We generated the data using global hydrological and water temperature models (PCR-GLOBWB, DynWat) forced with climate data from five general circulation models. We included four representative concentration pathways to cover multiple future greenhouse gas emission trajectories and associated changes in climate. Our dataset includes weekly streamflow and water temperature for each year as well as a set of derived indicators that are particularly relevant from an ecological perspective. FutureStreams provides a crucial starting point for large-scale assessments of the implications of changes in streamflow and water temperature for society and freshwater ecosystems.

Measurement(s)	Water temperature • Streamflow
Technology Type(s)	water temperature model • hydrological model
Factor Type(s)	climate
Sample Characteristic - Environment	stream
Sample Characteristic - Location	Global

Modeling Current and Future Distribution of Epicauta Dejean (Meloinae, Epicautini) under Changing Climate Conditions in America

Epicauta Dejean is one of the largest genera within Meloidae, with approximately 400 species identified to date. In this work, I applied the maximum entropy algorithm (Maxent) to predict the current and future distribution of this genus in America. A total of 12,130 points and 19 bioclimatic variables were used to model its potential distribution area under current and future climate scenarios. Maxent showed high prediction performance, and 7 out of the 19 variables used were found to be the most influential on the current and future distribution of Epicauta. It also allowed to predict the distribution of Epicauta in geographical areas where different bioclimatic criteria are combined. These areas belong to several provinces of the Nearctic, Neotropical regions and the Mexican and South American transition zones. Maxent also revealed that in North America, the current and future potential distribution of Epicauta is located within 38°N 97°W, while in South America, it is further south, within 25°S 60°W. According to this, it can be concluded that its greatest diversity is circumscribed to temperate and semi-arid regions, and that the tropical habitats of middle America have apparently served as effective barriers to faunal exchange since the intercontinental connection that occurred four million years ago until now. The findings from the present study provide a theoretical basis to better understand the distribution patterns of Epicauta spp. under changing climate conditions.

Habitat Characteristics of Magnolia Based on Spatial Analysis: Landscape Protection to Conserve Endemic and Endangered Magnolia sulawesiana Brambach, Noot., and Culmsee

Based on habitat preferences, in this study, we investigated the spatial distribution of the Magnolia genus in the northern part of Sulawesi. Habitat characteristics, especially temperature, precipitation, and topography, were determined using spatial analysis. The temperature and precipitation datasets were obtained from WorldClim BIO Variables V1, and topographical data were obtained from the Google Earth Engine. Data collection began in 2008–2009 and was completed in 2019–2020. In total, we analyzed 786 waypoints. The genus distribution was then predicted based on the most suitable habitat characteristics and mapped spatially. This study confirmed that Magnolia spp. distribution is affected by the annual temperature range, precipitation seasonality, and elevation. We discovered endemic and endangered species, Magnolia sulawesiana Brambach, Noot., and Culmsee, that were previously distributed exclusively in the central part of Sulawesi. Five waypoints of the endemic species were found in the conservation area of the Gunung Ambang Nature Reserve and on the border of Bogani Nani Wartabone Nation Park. In general, M. sulawesiana is distributed at higher elevations than other Magnolia species. This study provides a scientific basis for forest officers to develop in-situ and ex-situ conservation strategies and landscape protection measures to maintain the sustainable use of the genus, especially the sustainability of endemic species.

Lower COVID-19 Incidence in Low-Continentality West-Coast Areas of Europe

In March 2020, the first known cases of COVID-19 occurred in Europe. Subsequently, the pandemic developed a seasonal pattern. The incidence of COVID-19 comprises spatial heterogeneity and seasonal variations, with lower and/or shorter peaks resulting in lower total incidence and higher and/or longer peaks resulting higher total incidence. The reason behind this phenomena is still unclear. Unraveling factors that explain why certain places have higher versus lower total COVID-19 incidence can help health decision makers understand and plan for future waves of the pandemic. We test whether differences in the total incidence of COVID-19 within five European countries (Norway, Sweden, Germany, Italy, and Spain), correlate with two environmental factors: the Köppen-Geiger climate zones and the Continentality Index, while statistically controlling for crowding. Our results show that during the first 16 months of the pandemic (March 2020 to July 2021), climate zones with larger annual differences in temperature and annually distributed precipitation show a higher total incidence than climate zones with smaller differences in temperature and dry seasons. This coincides with lower continentality values. Total incidence increases with continentality, up to a Continentality Index value of 19, where a peak is reached in the semicontinental zone. Low continentality (high oceanic influence) appears to be a strong suppressing factor for COVID-19 spread. The incidence in our study area is lowest at open low continentality west coast areas.

Climatic niche lability but growth form conservatism in the African woody flora

Climatic niche evolution during the diversification of tropical plants has received little attention in Africa. To address this, we characterised the climatic niche of >4000 tropical African woody species, distinguishing two broad bioclimatic groups (forest vs. savanna) and six subgroups. We quantified niche conservatism versus lability at the genus level and for higher clades, using a molecular phylogeny of >800 genera. Although niche stasis at speciation is prevalent, numerous clades individually cover vast climatic spaces suggesting a general ease in transcending ecological limits, especially across bioclimatic subgroups. The forest biome was the main source of diversity, providing many lineages to savanna, but reverse shifts also occurred. We identified clades that diversified in savanna after shifts from forest. The forest-savanna transition was not consistently associated with a growth form change, though we found evolutionarily labile clades whose presence in forest or savanna is associated respectively with climbing or shrubby species diversification.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the T_min in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02172-8.

The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.

Is New Always Better? Frontiers in Global Climate Datasets for Modeling Treeline Species in the Himalayas

Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.