1. Genomic signatures of climate-driven (mal)adaptation in an iconic conifer, the English Yew (Taxus baccata L.)Thomas Francisco, Santiago C. González-Martínez, Maria Mayol, Elia Vajana, Miquel Riba, Marjana Westergren, Stephen Cavers, Sara Pinosio, Francesca Bagnoli, Maurizio Marchi, Filipos Aravanopoulos, 2025, complete scientific database of research data Abstract: This dataset consists of a Variant Call Format (VCF) file containing genomic data from Taxus baccata (European yew). The dataset includes 11,374 single nucleotide polymorphisms (SNPs) identified across 490 individual trees sampled from across the European range of the species. This dataset was used to carry out a study investigating patterns of local adaptation and to assessing the risk of climate maladaptation using genomic offset approaches. English (2025-05-28)
Keywords: climate change , genomic offset , genotype- environment association , local adaptation , Taxus baccata
Published in DiRROS: 03.11.2025; Views: 318; Downloads: 88
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2. Genomic signatures of climate-driven (mal)adaptation in an iconic conifer, the English Yew (Taxus baccata L.)Thomas Francisco, Maria Mayol, Elia Vajana, Miquel Riba, Marjana Westergren, Stephen Cavers, Sara Pinosio, Francesca Bagnoli, Maurizio Marchi, Filipos Aravanopoulos, 2025, original scientific article Abstract: The risk of climate maladaptation is increasing for numerous species, including trees. Developing robust methods to assess population maladaptation remains a critical challenge. Genomic offset approaches aim to predict climate maladaptation by characterizing the genomic changes required for populations to maintain their fitness under changing climates. In this study, we assessed the risk of climate maladaptation in European populations of English yew (Taxus baccata), a long-lived tree with a patchy distribution across Europe, the Atlas Mountains, and the Near East, where many populations are small or threatened. We found evidence suggesting local climate adaptation by analyzing 8616 SNPs in 475 trees from 29 European T. baccata populations, with climate explaining 18.1% of genetic variance and 100 unlinked climate-associated loci identified via genotype-environment association (GEA). Then, we evaluated the deviation of populations from the overall gene-climate association to assess variability in local adaptation or different adaptation trajectories across populations and found the highest deviations in low latitude populations. Moreover, we predicted genomic offsets and successfully validated these predictions using phenotypic traits assessed in plants from 26 populations grown in a comparative experiment. Finally, we integrated information from current local adaptation, genomic offset, historical genetic differentiation, and effective migration rates to show that Mediterranean and high-elevation T. baccata populations face higher vulnerability to climate change than low-elevation Atlantic and continental populations. Our study demonstrates the practical use of the genomic offset framework in conservation genetics, offers insights for its further development, and highlights the need for a population-centered approach that incorporates additional statistics and data sources to credibly assess climate vulnerability in wild plant populations.
Keywords: climate change , genomic offset , genotype- environment association , local adaptation , Taxus baccata
Published in DiRROS: 10.10.2025; Views: 422; Downloads: 183
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3. Marginality indices for biodiversity conservation in forest treesNicolas Picard, Maurizio Marchi, Maria Jesus Serra-Varela, Marjana Westergren, Stephen Cavers, Eduardo Notivol, Andrea Piotti, Paraskevi Alizoti, Michele Bozzano, Santiago C. González-Martínez, 2022, original scientific article Abstract: Marginal and peripheral populations are important for biodiversity conservation. Their original situation in a species’ geographic and ecological space often confers them genetic diversity and traits of high adaptive value. Yet theoretical hypotheses related to marginality are difficult to test because of confounding factors that influence marginality, namely environment, geography, and history. There is an urgent need to develop metrics to disentangle these confounding factors. We designed nine quantitative indices of marginality and peripherality that define where margins lie within species distributions, from a geographical, an environmental and a historical perspective. Using the distribution maps of eight European forest tree species, we assessed whether these indices were idiosyncratic or whether they conveyed redundant information. Using a database on marginal and peripheral populations based on expert knowledge, we assessed the capacity of the indices to predict the marginality status of a population. There was no consistent pattern of correlation between indices across species, confirming that the indices conveyed different information related to the specific geometry of the species distributions. Contrasting with this heterogeneity of correlation patterns across species, the relative importance of the indices to predict the marginality status of populations was consistent across species. However, there was still a significant country effect in the marginality status, showing a variation in expert opinion of marginality vis-á-vis the species distribution. The marginality indices that we developed are entirely based on distribution maps and can be used for any species. They pave the way for testing hypotheses related to marginality and peripherality, with important implications in quantitative ecology, genetics, and biodiversity conservation.
Keywords: centre-periphery hypothesis, environmental indices, geographical indices, migration indices, marginal populations, peripheral populations, in situ genetic conservation
Published in DiRROS: 07.04.2025; Views: 630; Downloads: 376
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4. Evaluating WorldClim version 1 (1961-1990) as the baseline for sustainable use of forest and environmental resources in a changing climateMaurizio Marchi, Iztok Sinjur, Michele Bozzano, Marjana Westergren, 2019, original scientific article Abstract: WorldClim version 1 is a high-resolution, global climate gridded dataset covering 1961-1990; a ˝normal˝ climate. It has been widely used for ecological studies thanks to its free availability and global coverage. This study aims to evaluate the quality of WorldClim data by quantifying any discrepancies by comparison with an independent dataset of measured temperature and precipitation records across Europe. BIO1 (mean annual temperature, MAT) and BIO12 (mean total annual precipitation, MAP) were used as proxies to evaluate the spatial accuracy of the WorldClim grids. While good representativeness was detected for MAT, the study demonstrated a bias with respect to MAP. The average difference between WorldClim predictions and climate observations was around +0.2 °C for MAT and -48.7 mm for MAP, with large variability. The regression analysis revealed a good correlation and adequate proportion of explained variance for MAT (adjusted R2 = 0.856) but results for MAP were poor, with just 64% of the variance explained (adjusted R2 = 0.642). Moreover no spatial structure was found across Europe, nor any statistical relationship with elevation, latitude, or longitude, the environmental predictors used to generate climate surfaces. A detectable spatial autocorrelation was only detectable for the two most thoroughly sampled countries (Germany and Sweden). Although further adjustments might be evaluated by means of geostatistical methods (i.e., kriging), the huge environmental variability of the European environment deeply stressed the WorldClim database. Overall, these results show the importance of an adequate spatial structure of meteorological stations as fundamental to improve the reliability of climate surfaces and derived products of the research (i.e., statistical models, future projections).
Keywords: spatial analysis, spatial interpolation, geostatistics, ecological mathematics
Published in DiRROS: 20.02.2020; Views: 3247; Downloads: 1820
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