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Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum (LGM) from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ashFraxinus pennsylvanicausing 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross‐validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic‐genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially‐explicit model. Approximate Bayesian computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross‐validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data. 

Abstract The spatiotemporal genetic variation at early plant life stages may substantially affect the natural recolonization of human-altered areas, which is crucial to understand plant and habitat conservation. In animal-dispersed plants, dispersers’ behavior may critically drive the distribution of genetic variation. Here, we examine how genetic rarity is spatially and temporally structured in seedlings of a keystone pioneer palm ( Chamaerops humilis ) and how the variation of genetic rarity could ultimately affect plant recruitment. We intensively monitored the seed rain mediated by two medium-sized carnivores during two consecutive seasons in a Mediterranean human-altered area. We genotyped 143 out of 309 detected seedlings using 12 microsatellite markers. We found that seedlings emerging from carnivore-dispersed seeds showed moderate to high levels of genetic diversity and no evidence of inbreeding. We found inflated kinship among seedlings that emerged from seeds within a single carnivore fecal sample, but a dilution of such FSGS at larger spatial scales (e.g. latrine). Seedlings showed a significant genetic sub-structure and the sibling relationships varied depending on the spatial scale. Rare genotypes arrived slightly later throughout the dispersal season and tended to be spatially isolated. However, genetic rarity was not a significant predictor by itself which indicates that, at least, its influence on seedling survival was smaller than other spatiotemporal factors. Our results suggest strong C. humilis resilience to genetic bottlenecks due to human disturbances. We highlight the study of plant-animal interactions from a genetic perspective since it provides crucial information for plant conservation and the recovery of genetic plant resilience. 

Abstract Evolutionary change begins at the population scale. Therefore, understanding adaptive variation requires the identification of the factors maintaining and shaping standing genetic variation at the within‐population level. Spatial and temporal environmental heterogeneity represent ecological drivers of within‐population genetic variation, determining the evolutionary trajectory of populations along with random processes. Here, we focused on the effects of spatiotemporal heterogeneity on quantitative and molecular variation in a natural population of the annual plant Arabidopsis thaliana . We sampled 1093 individuals from a Spanish A. thaliana population across an area of 7.4 ha for 10 years (2012–2021). Based on a sample of 279 maternal lines, we estimated spatiotemporal variation in life‐history traits and fitness from a common garden experiment. We genotyped 884 individuals with nuclear microsatellites to estimate spatiotemporal variation in genetic diversity. We assessed spatial patterns by estimating spatial autocorrelation of traits and fine‐scale genetic structure. We analysed the relationships between phenotypic variation, geographical location and genetic relatedness, as well as the effects of environmental suitability and genetic rarity on phenotypic variation. The common garden experiment indicated that there was more temporal than spatial variation in life‐history traits and fitness. Despite the differences among years, genetic distance in ecologically relevant traits (e.g. flowering time) tended to be positively correlated to genetic distance among maternal lines, while isolation by distance was less important. Genetic diversity exhibited significant spatial structure at short distances, which were consistent among years. Finally, genetic rarity, and not environmental suitability, accounted for genetic variation in life‐history traits. Synthesis . Our study highlighted the importance of repeated sampling to detect the large amount of genetic diversity at the quantitative and molecular levels that a single A. thaliana population can harbour. Overall, population genetic attributes estimated from our long‐term monitoring scheme (genetic relatedness and genetic rarity), rather than biological (dispersal) or ecological (vegetation types and environmental suitability) factors, emerged as the most important drivers of within‐population structure of phenotypic variation in A. thaliana . 

Abstract Population differentiation is a pervasive process in nature. At present, evolutionary studies on plant population differentiation address key questions by undertaking joint ecological and genetic approaches and employing a combination of molecular and experimental means. In this special issue, we gathered a collection of papers dealing with various ecological and genetic aspects of population differentiation in plants. In particular, this special issue encompasses eight research articles and two reviews covering a wide array of worldwide environments, plant functional types, genetic and genomic approaches, and common garden experiments to quantify molecular and/or quantitative trait differentiation in plant populations. Overall, this special issue stresses the validity of traditional evolutionary studies focused on plant populations, whilst emphasizing the integration of classical biological disciplines and state-of-the-art molecular techniques into a unique toolkit for evolutionary plant research. 

One advantageous strategy for the restoration of human‐disturbed landscapes is the use of ecologically “important” species such as nurse plants. We propose a field‐based approach to measure the functional importance of nurse species (i.e. their relative facilitative effects on other plant species) and to identify which species will yield more efficient revegetation programs regarding their abundance. We identified 30 nurse‐beneficiary spatial associations, with the functional importance varying largely among four nurse species and three human‐disturbed areas. A Mediterranean endemic palm was the most important nurse species, thus showing its potential key role in revegetation programs by promoting spatial associations with late‐successional plant species. We encourage restorers to use nurse species with a disproportionate (regarding their relative abundance) impact on ecosystems to save additional resources. 

Abstract Background Disentangling the drivers of genetic differentiation is one of the cornerstones in evolution. This is because genetic diversity, and the way in which it is partitioned within and among populations across space, is an important asset for the ability of populations to adapt and persist in changing environments. We tested three major hypotheses accounting for genetic differentiation—isolation-by-distance (IBD), isolation-by-environment (IBE) and isolation-by-resistance (IBR)—in the annual plant Arabidopsis thaliana across the Iberian Peninsula, the region with the largest genomic diversity. To that end, we sampled, genotyped with genome-wide SNPs, and analyzed 1772 individuals from 278 populations distributed across the Iberian Peninsula. Results IBD, and to a lesser extent IBE, were the most important drivers of genetic differentiation in A. thaliana . In other words, dispersal limitation, genetic drift, and to a lesser extent local adaptation to environmental gradients, accounted for the within- and among-population distribution of genetic diversity. Analyses applied to the four Iberian genetic clusters, which represent the joint outcome of the long demographic and adaptive history of the species in the region, showed similar results except for one cluster, in which IBR (a function of landscape heterogeneity) was the most important driver of genetic differentiation. Using spatial hierarchical Bayesian models, we found that precipitation seasonality and topsoil pH chiefly accounted for the geographic distribution of genetic diversity in Iberian A. thaliana . Conclusions Overall, the interplay between the influence of precipitation seasonality on genetic diversity and the effect of restricted dispersal and genetic drift on genetic differentiation emerges as the major forces underlying the evolutionary trajectory of Iberian A. thaliana . 

Abstract AimBiogeographers have used three primary data types to examine shifts in tree ranges in response to past climate change: fossil pollen, genetic data and contemporary occurrences. Although recent efforts have explored formal integration of these types of data, we have limited understanding of how integration affects estimates of range shift rates and their uncertainty. We compared estimates of biotic velocity (i.e. rate of species' range shifts) using each data type independently to estimates obtained using integrated models. LocationEastern North America. TaxonFraxinus pennsylvanicaMarshall (green ash). MethodsUsing fossil pollen, genomic data and modern occurrence data, we estimated biotic velocities directly from 24 species distribution models (SDMs) and 200 pollen surfaces created with a novel Bayesian spatio‐temporal model. We compared biotic velocity from these analyses to estimates based on coupled demographic‐coalescent simulations and Approximate Bayesian Computation that combined fossil pollen and SDMs with population genomic data collected across theF. pennsylvanicarange. ResultsPatterns and magnitude of biotic velocity over time varied by the method used to estimate past range dynamics. Estimates based on fossil pollen yielded the highest rates of range movement. Overall, integrating genetic data with other data types in our simulation‐based framework reduced apparent uncertainty in biotic velocity estimates and resulted in greater similarity in estimates between SDM‐ and pollen‐integrated analyses. Main ConclusionsBy reducing uncertainty in our assessments of range shifts, integration of data types improves our understanding of the past distribution of species. Based on these results, we propose further steps to reach the integration of these three lines of biogeographical evidence into a unified analytical framework.