Genetic signatures of divergent selection in European beech (Fagus sylvatica L.) are associated with the variation in temperature and precipitation across its distribution range

High genetic variation and extensive gene flow may help forest trees with adapting to ongoing climate change, yet the genetic bases underlying their adaptive potential remain largely unknown. We investigated range-wide patterns of potentially adaptive genetic variation in 64 populations of European beech (Fagus sylvatica L.) using 270 SNPs from 139 candidate genes involved either in phenology or in stress responses. We inferred neutral genetic structure and processes (drift and gene flow) and performed differentiation outlier analyses and gene-environment association (GEA) analyses to detect signatures of divergent selection. Beech range-wide genetic structure was consistent with the species' previously identified postglacial expansion scenario and recolonization routes. Populations showed high diversity and low differentiation along the major expansion routes. A total of 52 loci were found to be putatively under selection and 15 of them turned up in multiple GEA analyses. Temperature and precipitation related variables were equally represented in significant genotype-climate associations. Signatures of divergent selection were detected in the same proportion for stress response and phenology-related genes. The range-wide adaptive genetic structure of beech appears highly integrated, suggesting a balanced contribution of phenology and stress-related genes to local adaptation, and of temperature and precipitation regimes to genetic clines. Our results imply a best-case scenario for the maintenance of high genetic diversity during range shifts in beech (and putatively other forest trees) with a combination of gene flow maintaining within-population neutral diversity and selection maintaining between-population adaptive differentiation.

Biogeoinformatics for the management of Farm Animal Genetic Resources (FAnGR)

Solange Catherine Gaillard

In the context of both severe selection in farm animals and potential effects of climate change, it is crucial to implement a sustainable management of the breeding practice, supported by a judicious use of geographic information technologies. Based on this observation, this thesis advocates the use of biogeoinformatics (the combined use of biology, geographic information and informatics) to cope with the challenges encountered by the livestock sector. Indeed, although biogeoinformatics can provide key insights for FAnGR (Farm Animal Genetic Resources) management, the variety and complexity of tasks involved hinders a wider usage of this type of analyses. The thesis shows how novel dedicated tools are likely to facilitate the adoption of biogeoinformatics by animals scientists and by stakeholders in the livestock sector, while investigating three main challenges related to FAnGR management, namely i) erosion of genetic diversity, ii) effects of climate change on the breeding activity, and iii) pressure on typical cultural breeding practices such as high alpine grazing. On this basis, the thesis is organised around three axes:

Preserving locally adapted breeds In order to prevent the erosion of genetic diversity, locally adapted breeds should be monitored to prevent their extinction. To this end, we developed the open source GENMON WebGIS platform, able to monitor FAnGR and to evaluate the degree of endangerment of livestock breeds. The system integrates various sources of information that are linked with the help of geographic information: pedigree and introgression, geographical concentration of animals, cryo-conservation and sustainability of breeding activities. The score can be visualised on a map and allows a fast and regional identification of breeds in danger.
Preserving locally adapted genetic variations Considering the pace at which genetic diversity is being eroded, it has become urgent to identify and then preserve important genetic variations linked to locally adapted phenotypes. In this context, the recent SamÎ²ada software was designed to search for signatures of local adaptation through the study of genomeâenvironment association. However, preâ and postprocessing of data for this analysis can be labourâintensive, and, we therefore developed the R.SamBada R package providing a pipeline for landscape genomic analyses. Based on SamÎ²ada, it spans from the retrieval of environmental conditions at sampling locations to gene annotation using the Ensembl genome browser. As a result, it grants access to biogeoinformatic analyses to researchers with no skills in geography.
Preserving a traditional farming technique suited for local breeds The preservation of locally adapted breeds is also strongly linked with the conservation of traditional farming techniques, which in Switzerland, include the grazing of high alpine pastures during summer. One major effect of this transhumance on cows is that milk production declines due to food shortage and climatic stress. Here, we developed a new mathematical model to fit a lactation curve for mountain-pastured cows, and tested the influence of environmental, physiological, and morphological factors on the production using five million monthly milk records from Braunvieh cows. When compared to physiological factors, environmental variables show a limited impact on milk production at alpine pastures, precipitation in spring being the most important.

EPFL2020

Species conservation in the face of global environmental changes: surface-based modelling of geo-environmental data to identify vulnerable populations

Estelle Rochat

Human activities are resulting in many land-use changes, particularly due to urbanisation and intensification of agricultural practices. Because of these changes, in addition to climate change, many species are facing habitat degradation. In order to avoid extinction under these conditions, they can either move to more favourable areas or adapt to their new environment. To develop appropriate conservation measures, it is essential to identify vulnerable populations that may not be able to disperse or adapt. In this context, modelling tools can be used to predict the potential impact of environmental changes on species and populations. However, to date, few approaches take into account the degree of exposure, the possibility of dispersal and the potential for adaptation. In this thesis, we present modelling approaches based on geo-environmental data to integrate these three elements. First, we use ecological niche models to estimate the distribution of suitable habitats for a given species as a function of environmental conditions. We propose an improvement of commonly used models by developing an approach to integrate spatio-temporal variability of environmental predictors. In addition, we develop a nested model to predict the distribution of vector-borne pathogens. This model can be used to identify populations that may be threatened by an increasing presence of pathogens in their habitat. We use it to model the evolution of the distribution of Ixodes ricinus ticks and their Chlamydiales bacterial pathogen. We then use landscape graphs to analyse the connectivity between habitats and estimate the possibilities for threatened species to move to more favourable areas. Connectivity is also essential for maintaining gene flow and genetic diversity, which is necessary for greater adaptive capacity. We propose here an approach combining landscape graphs, simulations and empirical genetic data to identify the impact of reduced connectivity on population persistence and genetic diversity. We use it to identify butterfly populations that are threatened by increasing fragmentation in an urban landscape. Finally, we develop the concept of "Spatial Areas of Genotypes Probability" (SPAG) to better analyse the adaptive potential of populations. SPAGs make it possible to model the probability of finding locally adapted genetic variants in a given territory, as well as to identify vulnerable populations lacking in genetic variants that would favour adaptation to future climate conditions. We use it to highlight populations of Moroccan and European goats that are poorly adapted to the climatic conditions predicted under a climate change scenario for 2070 (strong variations in precipitation or increased drought). To conclude, we show how the three modelling approaches presented can be combined and integrated into a more general conservation framework to identify vulnerable populations facing high exposure to environmental changes, low dispersal possibilities and reduced adaptive capacity.

EPFL2020

Population structure and signals of local adaptation in Eugenia uniflora (Myrtaceae), a widely distributed species in the Atlantic Forest

Elia Vajana

Evolutionary diversity in species can arise in many ways, including local adaptation. Despite the global importance of tropical forest ecosystems, few studies have explored patterns of local adaptation in tropical tree species. We investigated population genetic structure and adaptive genetic diversity in Eugenia uniflora, a widely distributed tree species across the Atlantic Forest Domain (AFD). We sampled E. uniflora in distinct environments from the AFD and genotyped 523 single-nucleotide polymorphisms (SNPs) using a candidate gene approach. We studied neutral population structure and used two outlier methods based on population differentiation (Bayescan and LEA) and one gene-environment association analysis (LFMM) to detect putative SNPs under divergent selection. We detected spatial population structuring between the northern and southern range of the species distribution and higher genetic diversity in southern populations from the riparian forest. We found 18 candidate loci potentially involved in local adaptation, among which LEA and LFMM both detected seven loci. Overall, precipitation-related variables were more represented in statistically significant genotype-climate associations (c. 60%). Our study provides a first insight into the distribution of adaptive genetic variation in E. uniflora, highlighting how tropical tree species may adapt over time and across the AFD.

OXFORD UNIV PRESS2022

Biogeoinformatics for the management of Farm Animal Genetic Resources (FAnGR)

Solange Catherine Gaillard

Preserving locally adapted breeds In order to prevent the erosion of genetic diversity, locally adapted breeds should be monitored to prevent their extinction. To this end, we developed the open source GENMON WebGIS platform, able to monitor FAnGR and to evaluate the degree of endangerment of livestock breeds. The system integrates various sources of information that are linked with the help of geographic information: pedigree and introgression, geographical concentration of animals, cryo-conservation and sustainability of breeding activities. The score can be visualised on a map and allows a fast and regional identification of breeds in danger.
Preserving locally adapted genetic variations Considering the pace at which genetic diversity is being eroded, it has become urgent to identify and then preserve important genetic variations linked to locally adapted phenotypes. In this context, the recent SamÎ²ada software was designed to search for signatures of local adaptation through the study of genomeâenvironment association. However, preâ and postprocessing of data for this analysis can be labourâintensive, and, we therefore developed the R.SamBada R package providing a pipeline for landscape genomic analyses. Based on SamÎ²ada, it spans from the retrieval of environmental conditions at sampling locations to gene annotation using the Ensembl genome browser. As a result, it grants access to biogeoinformatic analyses to researchers with no skills in geography.
Preserving a traditional farming technique suited for local breeds The preservation of locally adapted breeds is also strongly linked with the conservation of traditional farming techniques, which in Switzerland, include the grazing of high alpine pastures during summer. One major effect of this transhumance on cows is that milk production declines due to food shortage and climatic stress. Here, we developed a new mathematical model to fit a lactation curve for mountain-pastured cows, and tested the influence of environmental, physiological, and morphological factors on the production using five million monthly milk records from Braunvieh cows. When compared to physiological factors, environmental variables show a limited impact on milk production at alpine pastures, precipitation in spring being the most important.

EPFL2020

Species conservation in the face of global environmental changes: surface-based modelling of geo-environmental data to identify vulnerable populations

Estelle Rochat

EPFL2020