Do traits of plant species predict the efficacy of species distribution models for finding new occurrences?

Forecasting species' responses to climate change using space-for-time substitution

To anticipate species' responses to climate change, ecologists have largely relied on the space-for-time-substitution (SFTS) approach. However, the hypothesis and its underlying assumptions have been poorly tested. Here, we detail how the efficacy of using the SFTS approach to predict future locations will depend on species' traits, the ecological context, and whether the species is declining or introduced. We argue that the SFTS approach will be least predictive in the contexts where we most need it to be: forecasting the expansion of the range of introduced species and the recovery of threatened species. We highlight how evaluating the underlying assumptions, along with improved methods, will rapidly advance our understanding of the applicability of the SFTS approach, particularly in the context of modelling the distribution of species.

Small but visible: Predicting rare bryophyte distribution and richness patterns using remote sensing-based ensembles of small models

In Canadian boreal forests, bryophytes represent an essential component of biodiversity and play a significant role in ecosystem functioning. Despite their ecological importance and sensitivity to disturbances, bryophytes are overlooked in conservation strategies due to knowledge gaps on their distribution, which is known as the Wallacean shortfall. Rare species deserve priority attention in conservation as they are at a high risk of extinction. This study aims to elaborate predictive models of rare bryophyte species in Canadian boreal forests using remote sensing-derived predictors in an Ensemble of Small Models (ESMs) framework. We hypothesize that high ESMs-based prediction accuracy can be achieved for rare bryophyte species despite their low number of occurrences. We also assess if there is a spatial correspondence between rare and overall bryophyte richness patterns. The study area is located in western Quebec and covers 72,292 km2. We selected 52 bryophyte species with <30 occurrences from a presence-only database (214 species, 389 plots in total). ESMs were built from Random Forest and Maxent techniques using remote sensing-derived predictors related to topography and vegetation. Lee's L statistic was used to assess and map the spatial relationship between rare and overall bryophyte richness patterns. ESMs yielded poor to excellent prediction accuracy (AUC > 0.5) for 73% of the modeled species, with AUC values > 0.8 for 19 species, which confirmed our hypothesis. In fact, ESMs provided better predictions for the rarest bryophytes. Likewise, our study revealed a spatial concordance between rare and overall bryophyte richness patterns in different regions of the study area, which have important implications for conservation planning. This study demonstrates the potential of remote sensing for assessing and making predictions on inconspicuous and rare species across the landscape and lays the basis for the eventual inclusion of bryophytes into sustainable development planning.