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In recent years, the popularity of rock-climbing has grown tremendously, setting an increasing pressure on cliff habitats. Climbing may be particularly harmful in the Mediterranean biome due to its appropriate environmental conditions for climbing. A few studies have identified the effect of climbing on plant diversity at a small-scale (namely locally or even just in specific climbing areas). However, no studies exist assessing the potential risk of rock-climbing on a broad-scale (e.g., regional or national). The study aims to identify the priority locations and priority cliff plant species in Spain to focus future study efforts. Spain was selected because it is a plant biodiversity hotspot, with a great diversity of endemic and endangered species, and one of the most popular destinations for climbers. We used a geographic information system-based approach to model the spatial concurrence among Spanish climbing areas (and climbing intensity), natural protected areas (NPAs), and distribution of threatened cliff plants (and their IUCN threat category). We found that 53.5% of climbing areas in Spain are located within a NPA, most of them falling into NPAs of medium protection level. We mapped 151 threatened cliff plants, identifying four medium priority Mediterranean locations and eight priority species in which future research efforts should be focused. High-priority study locations are absent in Spain according to our spatial modeling. For the first time on a national scale, this study identifies areas in which climbing represents a potential threat for cliff habitats and threatened plants. These findings contribute to designing field studies on the effects of rock-climbing on Mediterranean cliffs, laying the groundwork for a sustainable, yet challenging, balance between the protection of these unique habitats and rock-climbing.
Climate forecasts show that in many regions the temporal distribution of precipitation events will become less predictable. Root traits may play key roles in dealing with changes in precipitation predictability, but their functional plastic responses, including transgenerational processes, are scarcely known. We investigated root trait plasticity of Papaver rhoeas with respect to higher versus lower intra-seasonal and inter-seasonal precipitation predictability (i.e., the degree of temporal autocorrelation among precipitation events) during a four-year outdoor multi-generation experiment. We first tested how the simulated predictability regimes affected intra-generational plasticity of root traits and allocation strategies of the ancestors, and investigated the selective forces acting on them. Second, we exposed three descendant generations to the same predictability regime experienced by their mothers or to a different one. We then investigated whether high inter-generational predictability causes root trait differentiation, whether transgenerational root plasticity existed and whether it was affected by the different predictability treatments. We found that the number of secondary roots, root biomass and root allocation strategies of ancestors were affected by changes in precipitation predictability, in line with intra-generational plasticity. Lower predictability induced a root response, possibly reflecting a fast-acquisitive strategy that increases water absorbance from shallow soil layers. Ancestors’ root traits were generally under selection, and the predictability treatments did neither affect the strength nor the direction of selection. Transgenerational effects were detected in root biomass and root weight ratio (RWR). In presence of lower predictability, descendants significantly reduced RWR compared to ancestors, leading to an increase in performance. This points to a change in root allocation in order to maintain or increase the descendants’ fitness. Moreover, transgenerational plasticity existed in maximum rooting depth and root biomass, and the less predictable treatment promoted the lowest coefficient of variation among descendants’ treatments in five out of six root traits. This shows that the level of maternal predictability determines the variation in the descendants’ responses, and suggests that lower phenotypic plasticity evolves in less predictable environments. Overall, our findings show that roots are functional plastic traits that rapidly respond to differences in precipitation predictability, and that the plasticity and adaptation of root traits may crucially determine how climate change will affect plants.
The change in allele frequencies within a population over time represents a fundamental process of evolution. By monitoring allele frequencies, we can analyze the effects of natural selection and genetic drift on populations. To efficiently track time-resolved genetic change, large experimental or wild populations can be sequenced as pools of individuals sampled over time using high-throughput genome sequencing (called the Evolve & Resequence approach, E&R). Here, we present a set of experiments using hundreds of natural genotypes of the model plant Arabidopsis thaliana to showcase the power of this approach to study rapid evolution at large scale. First, we validate that sequencing DNA directly extracted from pools of flowers from multiple plants -- organs that are relatively consistent in size and easy to sample -- produces comparable results to other, more expensive state-of-the-art approaches such as sampling and sequencing of individual leaves. Sequencing pools of flowers from 25-50 individuals at ∼40X coverage recovers genome-wide frequencies in diverse populations with accuracy r > 0.95. Secondly, to enable analyses of evolutionary adaptation using E&R approaches of plants in highly replicated environments, we provide open source tools that streamline sequencing data curation and calculate various population genetic statistics two orders of magnitude faster than current software. To directly demonstrate the usefulness of our method, we conducted a two-year outdoor evolution experiment with A. thaliana to show signals of rapid evolution in multiple genomic regions. We demonstrate how these laboratory and computational Pool-seq-based methods can be scaled to study hundreds of populations across many climates.
