Taxonomic affinity, habitat and seed mass strongly predict seed desiccation response: A boosted regression trees analysis based on 17 539 species

Abstract

Background and Aims: Seed desiccation response plays an important role in plant regeneration ecology, and has significant implications for species conservation. The majority of seed plants produce desiccation-tolerant (orthodox) seeds, whilst comparatively few produce desiccation-sensitive (recalcitrant) seeds that are unable to survive dehydration, and which cannot be conserved in traditional seed banks. This study develops a set of models to predict seed desiccation response in unstudied species. Methods: Taxonomy, trait, location and climate data were compiled to form a global data set of 17 539 species. Three boosted regression trees models were then developed to predict species' seed desiccation responses based on habitat and trait information for the species, and the seed desiccation responses of close relatives (either members of the same genus, family or order, depending on the model). Ten-fold cross-validation was used to test model predictive success. The utility of the models was then demonstrated by predicting seed desiccation response for two floras: Ecuador, and Britain and Ireland. Key Results: The three models had varying success rates for identifying the desiccation-sensitive species: 89 % for the genus-level model, 79 % for the family-level model and 60 % for the order-level model. The most important predictor variables were the seed desiccation responses of a species' relatives, seed mass and annual precipitation. It is predicted that 10 % of seed plants from Ecuador and 1.2 % of those from Britain and Ireland produce desiccation-sensitive seeds. Due to data availability, prediction accuracy is likely to be higher for the British and Irish flora, where it is estimated that a desiccation-sensitive species had a 96.7 % chance of being correctly identified, compared with 80.8 % in the Ecuador flora. Conclusions: These models can utilize existing data to predict species' likely seed desiccation responses, providing a gap-filling tool for global studies of plant traits, as well as critical decision-making support for plant conservation activities.