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Simple Summary After domestication in specific regions, livestock followed human migrations and colonized the whole world. During this population expansion, human and natural selection, together with demographic events, molded the livestock genome leading to local breeds and populations able to produce milk, meat, wool and tractive power in many different agro-climatic conditions. The climate is changing, with temperatures and the frequency of extreme climatic events increasing, which affects livestock welfare and production efficiency, particularly of the highly productive breeds. Genomics is now able to explore the DNA of local breeds adapted to extreme environments in search of genes carrying signatures of selection for adaptation. This review summarizes methods used to accomplish this task, giving examples of results achieved and perspectives for future breeding. Abstract 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.

In developing countries, the waterborne transmission of hepatitis E virus (HEV), caused by HEV genotypes 1 (HEV-1) and 2 (HEV-2), leads to the onset of large recurrent outbreaks. HEV infections are of particular concern among pregnant women, due to very high mortality rates (up to 70%). Unfortunately, good understanding of the factors that trigger the occurrence of HEV epidemics is currently lacking; therefore, anticipating the onset of an outbreak is yet not possible. In order to map the geographical regions at higher risk of HEV epidemics and the conditions most favorable for the transmission of the virus, we compiled a dataset of HEV waterborne outbreaks and used it to obtain models of geographical suitability for HEV across the planet. The main three variables that best predict the geographical distribution of HEV outbreaks at global scale are population density, annual potential evapotranspiration and precipitation seasonality. At a regional scale, the temporal occurrence of HEV outbreaks in the Ganges watershed is negatively correlated with the discharge of the river (r = −0.77). Combined, our findings suggest that ultimately, population density and water balance are main parameters influencing the occurrence of HEV-1 and HEV-2 outbreaks. This study expands the current understanding of the combination of factors shaping the biogeography and seasonality of waterborne viral pathogens such as HEV-1 and HEV-2, and contributes to developing novel concepts for the prediction and control of human waterborne viruses in the near future.

Occurrence of bacteria belonging to the order Chlamydiales was investigated for the first time in common toad ( Bufo bufo ) tadpole populations collected from 41 ponds in the Geneva metropolitan area, Switzerland. A Chlamydiales -specific real-time PCR was used to detect and amplify the Chlamydiales 16S ribosomal RNA-encoding gene from the tails of 375 tadpoles. We found the studied amphibian populations to host Chlamydia -like organisms (CLOs) attributable to the genera Similichlamydia, Neochlamydia, Protochlamydia and Parachlamydia (all belonging to the family Parachlamydiaceae ), Simkania (family Simkaniaceae ) and Estrella (family Criblamydiaceae ); additionally, DNA from the genus Thermoanaerobacter (family Thermoanaerobacteriaceae ) was detected. Global autocorrelation analysis did not reveal a spatial structure in the observed CLOs occurrence rates, and association tests involving land cover characteristics did not evidence any clear effect on CLOs occurrence rates in B. bufo. Although preliminary, these results suggest a random and ubiquitous distribution of CLOs in the environment, which would support the biogeographical expectation 'everything is everywhere' for the concerned microorganisms.

Given the challenges related to climate change and dependency from fossil fuels, modification of the energy systems infrastructure to increase the share of renewable energy is a priority in urban energy planning. The high heating density in cities makes it more economically competitive to deploy district heating (DH), which is essential for large-scale integration of renewable energy sources. Combining georeferenced data with district heating design methods allows to improve the quality of the system design. However, increasing the spatial resolution can lead to intractable model sizes. This paper presents a methodology to spatially assess the integration of DH networks in urban energy systems. Given georeferenced data of buildings, resource availability and road networks, the methodology allows the identification of promising sites for DH deployment. First, an Integer Linear Programming (ILP) model divides the urban system into spatial clusters (of buildings). Graph theory and routing methods are then used to optimally design the DH configuration in each cluster considering the road network in the routing algorithm. A Mixed-Integer Linear Programming (MILP) model is formulated in order to economically evaluate the DH integration over the whole urban area. The proposed methodology is applied to an example case study, evaluating the use of geothermal energy (deep aquifer) for direct heat supply. The results of the optimization show the interest of deploying geothermal DH in some of the clusters. The profitability of DH integration is strongly affected by the spatial density of the heating demand.

SARS-CoV-2 spreads via close contact during daily activities, forming clusters of cases mainly in households and workplaces. A crucial challenge to contain the spread lies in the early detection of these outbreak clusters, and the localisation and isolation of infected people. Modern geospatial tools leveraging the precise location of residence of patients with COVID-19 are key digital instruments that have great potential from a prevention perspective. These methods, by allowing early identification of clusters and monitoring of local spread of disease across space and time, can support strategies that dynamically inform epidemiologists and decision makers, to ultimately enable targeted interventions at a local scale.

In a context of rapid global change, one of the key components for the survival of species is their genetic evolutionary potential for adaptation. Many methods have been developed to identify genetic variants underpinning adaptation to climate, but few tools were made available to integrate this knowledge into conservation management. We present here the SPatial Areas of Genotype probability (SPAG), a method to transpose the results of genotype-environment association studies into an evolutionary potential spatial prediction framework. We define a univariate model predicting the spatial distribution of a single-locus adaptive genotype and three multivariate models allowing the integration of several adaptive loci in a composite genotype. Unlike existing methods, SPAGs provide (a) a flexible approach to combine loci under different types of intergenic relationships and (b) a cross-validation framework to assess the pertinence of evolutionary potential predictions. SPAGs can be integrated with climate change projections to forecast the future spatial distribution of genotypes. The analysis of the mismatch between current and future SPAGs ("genomic offset") makes it possible to identify vulnerable populations potentially lacking the adaptive genotypes necessary for future survival. We tested the SPAG approach on a simulated population and applied it to characterize the evolutionary potential of 161 Moroccan goats to bioclimatic conditions. We identified seven regions of the Moroccan goat genome strongly associated with the precipitation seasonality and used the SPAG approach to predict the evolutionary potential. We then forecasted the shift in SPAGs under a strong climate change scenario and uncovered the goat populations likely to be threatened in future conditions. The SPAG methodology is an efficient and flexible tool to characterize the evolutionary potential across a landscape and to transpose evolutionary information into conservation frameworks.

Magnesium (Mg) is an essential element for plant growth and human health. Its availability and spatial distribution in soils depends on a wide variety of intrinsic and extrinsic factors. Understanding how Mg availability changes in space and time is crucial for preventing potential deficiencies. In 1987, a soil-monitoring network (FRIBO) was launched in the canton of Fribourg, Switzerland. It was based on 250 sites distributed evenly throughout the canton so as to include a large variety of soil types (Cambisols, Gleysols, Rendzinas, Regosols, Lithosols, Luvisols and Fluvisols) under three different land use types (croplands, permanent grasslands and mountain pastures). The aim of this research was to characterize the spatial and temporal variation of total and available forms of Mg in the agricultural soils of the canton of Fribourg and to discuss potential implications for Mg fertilization management. Total Mg concentration (MgT) averaged 5.5 g kg−1, with small differences between land use types. Spatial distribution of MgT showed higher values on the southern part of the study area, mostly on Rendzinas and Cambisols. Average concentrations of available Mg forms were significantly different according to extraction methods, with water extraction (MgH2O) having the lowest value (14.2 mg kg−1) followed by calcium extraction (MgCaCl2, 109.4 mg kg−1) and ammonium acetate + EDTA extraction (MgAAE, 148.7 mg kg−1). On average, permanent grasslands had significantly higher values for all Mg forms compared to croplands and mountain pastures, a result further corroborated by the analysis of spatial distribution. Intrinsic factors, such as soil type and terrain attributes, appeared to have a major influence on total Mg content, whereas available Mg forms depended mostly on extrinsic factors, such as land use type. Temporal analysis of soil available Mg forms revealed an overall increase between 1987 and 2016, especially after a land use change from croplands to permanent grasslands. In the light of the essential role of Mg for plant growth and development, as well as its critical role in animal health, the status of Mg should continue to be monitored in the FRIBO network and plant analysis should be implemented.

International audience; This communication presents the last development on the Symbology Encoding and the Web Processing Services standards, implemented in the OrbisGIS platform. Use cases are discussed during a practical session.