Aim To assess the effect of local adaptation and phenotypic plasticity on the potential distribution of species under future climate changes. Trees may be adapted to specific climatic conditions; however, species range predictions have classically been assessed by species distribution models (SDMs) that do not account for intra-specific genetic variability and phenotypic plasticity, because SDMs rely on the assumption that species respond homogeneously to climate change across their range, i.e. a species is equally adapted throughout its range, and all species are equally plastic. These assumptions could cause SDMs to exaggerate or underestimate species at risk under future climate change.Location The Iberian Peninsula.Methods Species distributions are predicted by integrating experimental data and modelling techniques. We incorporate plasticity and local adaptation into a SDM by calibrating models of tree survivorship with adaptive traits in provenance trials. Phenotypic plasticity was incorporated by calibrating our model with a climatic index that provides a measure of the differences between sites and provenances. ResultsWe present a new modelling approach that is easy to implement and makes use of existing tree provenance trials to predict species distribution models under global warming. Our results indicate that the incorporation of intrapopulation genetic diversity and phenotypic plasticity in SDMs significantly altered their outcome. In comparing species range predictions, the decrease in area occupancy under global warming conditions is smaller when considering our survivaladaptation model than that predicted by a 'classical SDM' calibrated with presenceabsence data. These differences in survivorship are due to both local adaptation and plasticity. Differences due to the use of experimental data in the model calibration are also expressed in our results: we incorporate a null model that uses survival data from all provenances together. This model always predicts less reduction in area occupancy for both species than the SDM calibrated with presence-absence. Main conclusionsWe reaffirm the importance of considering adaptive traits when predicting species distributions and avoiding the use of occurrence data as a predictive variable. In light of these recommendations, we advise that existing predictions of future species distributions and their component populations must be reconsidered.

Summary Improving our understanding of species ranges under rapid climate change requires application of our knowledge of the tolerance and adaptive capacity of populations to changing environmental conditions. Here, we describe an emerging modelling approach, ΔTraitSDM, which attempts to achieve this by explaining species distribution ranges based on phenotypic plasticity and local adaptation of fitness‐related traits measured across large geographical gradients. The collection of intraspecific trait data measured in common gardens spanning broad environmental clines has promoted the development of these new models – first in trees but now rapidly expanding to other organisms. We review, explain and harmonize the main findings from this new generation of models that, by including trait variation over geographical scales, are able to provide new insights into future species ranges. Overall, ΔTraitSDM predictions generally deliver a less alarming message than previous models of species distribution under new climates, indicating that phenotypic plasticity should help, to a considerable degree, some plant populations to persist under climate change. The development of ΔTraitSDMs offers a new perspective to analyse intraspecific variation in single and multiple traits, with the rationale that trait (co)variation and consequently fitness can significantly change across geographical gradients and new climates.

Question: Will the predicted climate changes affect species distribution in the Iberian Peninsula? Location: Iberian Peninsula (Spain and Portugal). Methods: We modelled current and future tree distributions as a function of climate, using a computational framework that made use of one machine learning technique, the random forest (RF) algorithm. This algorithm provided good predictions of the current distribution of each species, as shown by the area under the corresponding receiver operating characteristics (ROC) curves. Species turnover, richness and the change in distributions over time to 2080 under four Intergovernmental panel on climate change (IPCC) scenarios were calculated using the species map outputs. Results and Conclusions: The results show a notable reduction in the potential distribution of the studied species under all the IPCC scenarios, particularly so for mountain conifer species such as Pinus sylvestris, P. uncinata and Abies alba. Temperate species, especially Fagus sylvatica and Quercus petraea, were also predicted to suffer a reduction in their range; also sub‐mediterranean species, especially Q. pyrenaica, were predicted to undergo notable decline. In contrast, typically Mediterranean species appeared to be generally more capable of migration, and are therefore likely to be less affected.

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Aim To better understand and more realistically predict future species distribution ranges, it is critical to account for local adaptation and phenotypic plasticity in populations' responses to climate. This is challenging because local adaptation and phenotypic plasticity are trait‐dependent and traits covary along climatic gradients, with differential consequences for fitness. Our aim is to quantify local adaptation and phenotypic plasticity of vertical and radial growth, leaf flushing and survival across the range of Fagus sylvatica and to estimate the contribution of each trait to explaining the species' occurrence. Location Europe. Time period 1995–2014; 2070. Major taxa studied Fagus sylvatica L. Methods We used vertical and radial growth, flushing phenology and mortality of F. sylvatica L. recorded in the BeechCOSTe52 database (>150,000 trees). Firstly, we performed linear mixed‐effect models that related trait variation and covariation to local adaptation (related to the planted populations' climatic origin) and phenotypic plasticity (accounting for the climate of the plantation), and we made spatial predictions under current and representative concentration pathway (RCP 8.5) climates. Secondly, we combined spatial trait predictions in a linear model to explain the occurrence of the species. Results The contribution of plasticity to intraspecific trait variation is always higher than that of local adaptation, suggesting that the species is less sensitive to climate change than expected; different traits constrain beech's distribution in different parts of its range: the northernmost edge is mainly delimited by flushing phenology (mostly driven by photoperiod and temperature), the southern edge by mortality (mainly driven by intolerance to drought), and the eastern edge is characterized by decreasing radial growth (mainly shaped by precipitation‐related variables in our model); considering trait covariation improved single‐trait predictions. Main conclusions Population responses to climate across large geographical gradients are dependent on trait × environment interactions, indicating that each trait responds differently depending on the local environment.

This paper reports a bioclimatic envelope model study of the potential distribution of 19 tree species in the Iberian Peninsula during the Last Glacial Maximum (LGM; 21 000 yr BP) and the Mid-Holocene (6000 yr BP). Current patterns of tree species richness and distributions are believed to have been strongly influenced by the climate during these periods. The modelling employed novel machine learning techniques, and its accuracy was evaluated using a threshold-independent method. Two atmospheric general circulation models, UGAMP and ECHAM3 (generated by the Palaeoclimate Modelling Intercomparison Project, PMIP), were used to provide climate scenarios under which the distributions of the 19 tree species were modelled. The results obtained for these scenarios were assessed by agreement measure analysis; they differed significantly for the LGM, but were more similar for the Mid-Holocene. The results for the LGM support the inferred importance of pines in the Iberian Peninsula at this time, and the presence of evergreen Quercus in the south. Important differences in the altitude at which the modelled species grew were also predicted. During the LGM, some normally higher mountain species potentially became reestablished in the foothills of the Pyrenees. The warm Mid-Holocene climate is clearly reflected in the predicted expansion of broad-leaved forests during this period, including the colonization of the northern part of the Iberian Peninsula by evergreen Quercus species.

How populations of long‐living species respond to climate change depends on phenotypic plasticity and local adaptation processes. Marginal populations are expected to have lags in adaptation (i.e. differences between the climatic optimum that maximizes population fitness and the local climate) because they receive pre‐adapted alleles from core populations preventing them from reaching a local optimum in their climatically marginal habitat. Yet, whether adaptation lags in marginal populations are a common feature across phylogenetically and ecologically different species and how lags can change with climate change remain unexplored. To test for range‐wide patterns of phenotypic variation and adaptation lags of populations to climate, we (a) built model ensembles of tree height accounting for the climate of population origin and the climate of the site for 706 populations monitored in 97 common garden experiments covering the range of six European forest tree species; (b) estimated populations' adaptation lags as the differences between the climatic optimum that maximizes tree height and the climate of the origin of each population; (c) identified adaptation lag patterns for populations coming from the warm/dry and cold/wet margins and from the distribution core of each species range. We found that (a) phenotypic variation is driven by either temperature or precipitation; (b) adaptation lags are consistently higher in climatic margin populations (cold/warm, dry/wet) than in core populations; (c) predictions for future warmer climates suggest adaptation lags would decrease in cold margin populations, slightly increasing tree height, while adaptation lags would increase in core and warm margin populations, sharply decreasing tree height. Our results suggest that warm margin populations are the most vulnerable to climate change, but understanding how these populations can cope with future climates depend on whether other fitness‐related traits could show similar adaptation lag patterns.

We present BeechCOSTe52; a database of European beech (Fagus sylvatica) phenotypic measurements for several traits related to fitness measured in genetic trials planted across Europe. The dataset was compiled and harmonized during the COST-Action E52 (2006–2010), and subsequently cross-validated to ensure consistency of measurement data among trials and provenances. Phenotypic traits (height, diameter at breast height, basal diameter, mortality, phenology of spring bud burst and autumn–leaf discoloration) were recorded in 38 trial sites where 217 provenances covering the entire distribution of European beech were established in two consecutive series (1993/95 and 1996/98). The recorded data refer to 862,095 measurements of the same trees aged from 2 to 15 years old over multiple years. This dataset captures the considerable genetic and phenotypic intra-specific variation present in European beech and should be of interest to researchers from several disciplines including quantitative genetics, ecology, biogeography, macroecology, adaptive management of forests and bioeconomy.