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Multi-purpose crops as maize, rice, soybean, and wheat are key in the debate concerning food, land, water and energy security and sustainability. While strong evidence exists on the effects of climate variability on the production of these crops, so far multifaceted attributes of droughts—magnitude, frequency, duration, and timing—have been tackled mainly separately, for a limited part of the cropping season, or over small regions. Here, a more comprehensive assessment is provided on how droughts with their complex patterns—given by their compound attributes—are consistently related to negative impacts on crop yield on a global scale. Magnitude and frequency of both climate and yield variability are jointly analysed from 1981 to 2016 considering multiscale droughts, i.e., dry conditions occurring with different durations and timings along the whole farming season, through two analogous and standardized indicators enabling comparison among crops, countries, and years. Mainly winter wheat and then spring wheat, soybean and the main maize’s season reveal high susceptibility of yield under more complex drought patterns than previously assessed. The second maize’s season and rice present less marked and more uncertain results, respectively. Overall, southern and eastern Europe, the Americas and sub-Saharan Africa presents multi-crop susceptibility, with eastern Europe, Middle East and Central Asia appearing critical regions for the most vulnerable crop, which is wheat. Finally, yield losses for wheat and soybean clearly worsen when moving from moderate to extreme multiscale droughts.

This study presents a new global gridded dataset of bioclimatic indicators at 0.5° by 0.5° resolution for historical and future conditions. The dataset, called CMCC-BioClimInd, provides a set of 35 bioclimatic indices, expressed as mean values over each time interval, derived from post-processing both climate reanalysis for historical period (1960–1999) and an ensemble of 11 bias corrected CMIP5 simulations under two greenhouse gas concentration scenarios for future climate projections along two periods (2040–2079 and 2060–2099). This new dataset complements the availability of spatialized bioclimatic information, crucial aspect in many ecological and environmental wide scale applications and for several disciplines, including forestry, biodiversity conservation, plant and landscape ecology. The data of individual indicators are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format. Measurement(s) bioclimatic indicators Technology Type(s) Geographic Information System • computational modeling technique Factor Type(s) temporal interval • geographic location Sample Characteristic - Environment climate system Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12927443

Tropical forests are undergoing land use change in many regions of the world, including the African continent. Human populations living close to forest margins fragmented and disturbed by deforestation may be particularly exposed to zoonotic infections because of the higher likelihood for humans to be in contact with disease reservoirs. Quantitative analysis of the nexus between deforestation and the emergence of Ebola virus disease (EVD), however, is still missing. Here we use land cover change data in conjunction with EVD outbreak records to investigate the association between recent (2004–2014) outbreaks in West and Central Africa, and patterns of land use change in the region. We show how in these EVD outbreaks the index cases in humans (i.e. spillover from wildlife reservoirs) occurred mostly in hotspots of forest fragmentation.

Climate change has profound implications for global ecosystems, particularly in mountainous regions where species distribution and composition are highly sensitive to changing environmental conditions. Understanding the potential impacts of climate change on native forest species is crucial for effective conservation and management strategies. Despite numerous studies on climate change impacts, there remains a need to investigate the future dynamics of climate suitability for key native forest species, especially in specific mountainous sections. This study aims to address this knowledge gap by examining the potential shifts in altitudinal range and suitability for forest species in Italy's mountainous regions. By using species distribution models, through MaxEnt we show the divergent impacts among species and scenarios, with most species experiencing a contraction in their altitudinal range of suitability whereas others show the potential to extend beyond the current tree line. The Northern and North-Eastern Apennines exhibit the greatest and most widespread impacts on all species, emphasizing their vulnerability. Our findings highlight the complex and dynamic nature of climate change impacts on forest species in Italy. While most species are projected to experience a contraction in their altitudinal range, the European larch in the Alpine region and the Turkey oak in the Apennines show potential gains and could play significant roles in maintaining wooded populations. The tree line is generally expected to shift upward, impacting the European beech—a keystone species in the Italian mountain environment—negatively in the Alpine arc and Northern Apennines, while showing good future suitability above 1,500 meters in the Central and Southern Apennines. Instead, the Maritime pine emerges as a promising candidate for the future of the Southern Apennines. The projected impacts on mountain biodiversity, particularly in terms of forest population composition, suggest the need for comprehensive conservation and management strategies. The study emphasizes the importance of using high-resolution climate data and considering multiple factors and scenarios when assessing species vulnerability. The findings have implications at the local, regional, and national levels, emphasizing the need for continued efforts in producing reliable datasets and forecasts to inform targeted conservation efforts and adaptive management strategies in the face of climate change.

Forest conservation strategies and plans can be unsuccessful if the new habitat conditions determined by climate change are not considered. Our work aims at investigating the likelihood of future suitability, distribution and diversity for some common European forest species under the projected changes in climate, focusing on Southern Europe. We combine an Ensemble Platform for Species Distribution Models (SDMs) to five Global Circulation Models (GCMs) driven by two Representative Concentration Pathways (RCPs), to produce maps of future climate‐driven habitat suitability for ten categories of forest species and two time horizons. For each forest category and time horizon, ten maps of future distribution (5 GCMs by 2 RCPs) are thus combined in a single suitability map supplied with information about the “likelihood” adopting the IPCC terminology based on consensus among projections. Then, the statistical significance of spatially aggregated changes in forest composition at local and regional level is analyzed. Finally, we discuss the importance, among SDMs, that environmental predictors seem to have in influencing forest distribution. Future impacts of climate change appear to be diversified across forest categories. A strong change in forest regional distribution and local diversity is projected to take place, as some forest categories will find more suitable conditions in previously unsuitable locations, while for other categories the same new conditions will become less suited. A decrease in species diversity is projected in most of the area, with Alpine region showing the potentiality to become a refuge for species migration. Future likelihood suitability map for Abies: 2070 Scenario.

This paper introduces VHR-PRO_IT (Very High-Resolution PROjections for ITaly), an open access hourly climate projection with a resolution of ≃2.2 km (i.e., Convection Permitting Scale) up to 2050, covering the Italian peninsula and some neighbouring areas. VHR-PRO_IT is produced within the Highlander project ( https://highlanderproject.eu/ ) by dynamically downscaling the Italy8km-CM climate projection (spatial resolution ≃8 km; output frequency = 6 h; driven CMIP5 GCM = CMCC-CM) with the Regional Climate Model COSMO-CLM under the IPCC RCP4.5 and RCP8.5 scenarios. It covers the 60-year period 1989–2050. VHR-PRO_IT is intended for research purposes in the field of climate studies. For example, it may be included in the ongoing activities to clarify the added value of running climate simulation at the convection-permitting scale.

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.

A new multiscale Standardized Precipitation Evapotranspiration Index (SPEI) dataset is provided for a reference period (1960–1999) and two future time horizons (2040–2079) and (2060–2099). The historical forcing is based on combined climate observations and reanalysis (WATer and global CHange Forcing Dataset), and the future projections are fed by the Fast Track experiment of the Inter-Sectoral Impact Model Intercomparison Project under representative concentration pathways (RCPs) 4.5 and 8.5 and by an additional Earth system model (CMCC-CESM) forced by RCP 8.5. To calculate the potential evapotranspiration (PET) input to the SPEI, the Hargreaves–Samani and Thornthwaite equations were adopted. This ensemble considers uncertainty due to different climate models, development pathways, and input formulations. The SPEI is provided for accumulation periods of potential moisture deficit from 1 to 18 months starting in each month of the year, with a focus on the within-period variability, excluding long-term warming effects on PET. In addition to supporting drought analyses, this dataset is also useful for assessing wetter-than-normal conditions spanning one or more months. The SPEI was calculated using the SPEIbase package.

The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate‐smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate‐driven processes, and state‐of‐the‐art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications. The project HIGHLANDER, led by Cineca, aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. It relies on high quality informationon different components of the landscape, with a focus on climate, and state‐of‐the‐art computing infrastructures. In this article, we describe the HIGHLANDER Data Portal, a platform that maximizes the impact of HIGHLANDER results by providing access to data products and services.

GOWARE (transnational Guide toward an Optimal WAter REgime) represents a Decision Support Tool (DST) developed to support the implementation of innovative Best Management Practices (BMPs) for drinking water protection and flood/drought risk mitigation. The tool is one of the main outputs of the PROLINE-CE Project, an EU project funded within the Interreg Central Europe (CE) Programme (2014–2020). The aim of this paper is illustrating the design and the methodological approaches proposed for the operative development of the tool. Furthermore, the paper provides the results of a number of tests carried out to evaluate the understandability of the analysis’s processes and assessing the stakeholders’ acceptance. Specifically, GOWARE-DST has been developed for supporting single users or groups of users in the decision-making process. The tool has been provided with a catalogue of 92 BMPs to handle water issues in different land use contexts. The selection of practices suitable for addressing the specific user’s requirements is supported by the Analytic Hierarchy Process, a method that allows filtering a subset of BMPs by accounting for the relative importance that the user assigns to each characterizing criterion. GOWARE-DST represents an innovative tool for supporting users at different levels of planning (operational and strategic) by promoting sustainable land and water management and defining long-term governance activities.