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Efforts to understand the mechanisms explaining the relationship between abiotic stress tolerance and range size and filling have hitherto yielded contradictory results. Unlike previous studies that have focused on single stress factors, we here examine the extent to which range size and filling can be explained by tolerance of multiple abiotic stressors (cold, shade, drought and waterlogging). As range metrics, we used range size and filling (the ratio between actual and potential range) for 331 European and North American temperate woody plant species. Stress tolerance strategies were expressed as a multivariate axis reflecting a cold/waterlogging-drought tolerance trade-off. We used mixed models to evaluate the relationship between range size/filling and this multivariate stress tolerance axis, using latitude as a covariate, and phylogeny and plant functional type as random effects. Range size and stress tolerance were negatively correlated, mostly independently of latitude and continent. Thus, cold/wet-tolerant species had the largest range sizes and cold-sensitive/drought-tolerant species the smallest. In contrast, range filling mostly depended on latitude. Our results show that abiotic stress tolerance can explain interspecific differences in range size, and to a lesser extent range filling, which sets up predictions for range size variation in plants that go beyond latitude.

Predicting the occurrence probability of species is intrinsically dependent on the quality of the training dataset and, in particular, on the sample prevalence (i.e., the ratio between presences and absences). Whenever the number of presences and absences is not equal within the training dataset, the predictions deviate towards higher values as the sample prevalence increases and vice versa. As a result, probability models of species occurrence with different sample prevalence cannot be directly compared. The favourability concept was introduced to amend this limitation. Indeed, the favourability – i.e., the variation in the probability of occurrence regardless the sample prevalence – could reduce the degree of uncertainty when comparing species distributions despite different sample prevalences. To test this hypothesis, we simulated 50 virtual species and compared the predictive performance of four probability-based and favourability-based Species Distribution Models (GLM, GAM, RF, BRT) under a set of different prevalence values and sampling strategies (i.e, random and stratified sampling). Favourability-based models performed slightly better than probability-based models in predicting the species distribution over geographic space, confirming also their capability to reduce the variability of the predictions across different degrees of sample prevalence.

Predicting the occurrence probability of species is intrinsically dependent on the quality of the training dataset and, in particular, on the sample prevalence (i.e., the ratio between presences and absences). Whenever the number of presences and absences is not equal within the training dataset, the predictions deviate towards higher values as the sample prevalence increases and vice versa. As a result, probability models of species occurrence with different sample prevalence cannot be directly compared. The favourability concept was introduced to amend this limitation. Indeed, the favourability – i.e., the variation in the probability of occurrence regardless the sample prevalence – could reduce the degree of uncertainty when comparing species distributions despite different sample prevalences. To test this hypothesis, we simulated 50 virtual species and compared the predictive performance of four probability-based and favourability-based Species Distribution Models (GLM, GAM, RF, BRT) under a set of different prevalence values and sampling strategies (i.e, random and stratified sampling). Favourability-based models performed slightly better than probability-based models in predicting the species distribution over geographic space, confirming also their capability to reduce the variability of the predictions across different degrees of sample prevalence.

[Aim]: Assessing how different sampling strategies affect the accuracy and precision of species response curves estimated by parametric species distribution models., [Major Taxa Studied]: Virtual plant species., [Location]: Abruzzo (Italy)., [Time Period]: Timeless (simulated data)., [Methods]: We simulated the occurrence of two virtual species with different ecology (generalist vs specialist) and distribution extent. We sampled their occurrence following different sampling strategies: random, stratified, systematic, topographic, uniform within the environmental space (hereafter, uniform) and close to roads. For each sampling design and species, we ran 500 simulations at increasing sampling efforts (total: 42,000 replicates). For each replicate, we fitted a binomial generalised linear model, extracted model coefficients for precipitation and temperature, and compared them with true coefficients from the known species' equation. We evaluated the quality of the estimated response curves by computing bias, variance and root mean squared error (RMSE). Additionally, we (i) assessed the impact of missing covariates on the performance of the sampling approaches and (ii) evaluated the effect of incompletely sampling the environmental space on the uniform approach., [Results]: For the generalist species, we found the lowest RMSE when uniformly sampling the environmental space, while sampling occurrence data close to roads provided the worst performance. For the specialist species, all sampling designs showed comparable outcomes. Excluding important predictors similarly affected all sampling strategies. Sampling limited portions of the environmental space reduced the performance of the uniform approach, regardless of the portion surveyed., [Main Conclusions]: Our results suggest that a proper estimate of the species response curve can be obtained when the choice of the sampling strategy is guided by the species' ecology. Overall, uniformly sampling the environmental space seems more efficient for species with wide environmental tolerances. The advantage of seeking the most appropriate sampling strategy vanishes when modelling species with narrow realised niches.

With the aim to identify future challenges and opportunities in vegetation science, we brought together a group of 22 early career vegetation scientists from diverse backgrounds to perform a horizon scan. In this contribution, we present a selection of 15 topics that were ranked by participants as the most emergent and impactful for vegetation science in the face of global change. We highlight methodological tools that we expect will play a critical role in resolving emerging issues by providing ways to unveil new aspects of plant community dynamics and structure. These tools include next generation sequencing, plant spectral imaging, process-based species distribution models, resurveying studies and permanent plots. Further, we stress the need to integrate long-term monitoring, the study of novel ecosystems, below-ground traits, pollination interactions and global networks of near-surface microclimate data at fine spatio-temporal resolutions to fully understand and predict the impacts of climate change on vegetation dynamics. We also emphasize the need to integrate traditional forms of knowledge and a diversity of stakeholders into research, teaching, management and policy-making to advance the field of vegetation science. The conclusions reached by this horizon scan naturally reflect the background, expertise and interests of a representative pool of early career vegetation scientists, which should serve as basis for future developments in the field.

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package—rasterdiv—to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.

Mapping the distribution of invasive species under current and future climate conditions is crucial to implement sustainable and effective conservation strategies. Several studies showed how invasive species may benefit from climate change fostering their invasion rate and, consequently, affecting the native species community. In the Canary Islands and on Tenerife in particular, previous research mostly focused on climate change impacts on the native communities, whereas less attention has been paid on alien species distribution under climate change scenarios. In this study, we modelled the habitat distribution of Pennisetum setaceum, one of the most invasive alien species on Tenerife. In addition, we described the species’ potential distribution shift in the light of two climate change scenarios (RCP2.6, RCP8.5), highlighting the areas that should be prioritized during management and eradication programs. P. setaceum’s suitable areas are located in the coastal area, with higher habitat suitability near cities and below 800 m asl. In both future climate change scenarios, the geographic distribution of P. setaceum suitable areas is characterized by an elevational shift, which is more pronounced in the RCP8.5 scenario. Despite being drought resistant, water supply is crucial for the species’ seed germination, thus supporting future species’ shift to higher elevation and in the north–north–west part of the island, where it could benefit from the combined effect of orographic precipitations and humidity carried by trade winds.