分类亲缘关系、生境和种子质量强烈预测种子脱水响应：基于 17539 个物种的提升回归树分析。
Taxonomic affinity, habitat and seed mass strongly predict seed desiccation response: a boosted regression trees analysis based on 17 539 species.
机构信息
Royal Botanic Gardens, Kew, Wakehurst Place, West Sussex, UK.
出版信息
Ann Bot. 2018 Jan 25;121(1):71-83. doi: 10.1093/aob/mcx128.
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.
背景与目的
种子干燥响应在植物再生生态学中起着重要作用，对物种保护具有重要意义。大多数种子植物产生耐干燥（正统）的种子，而相对较少的植物产生不耐干燥（顽固）的种子，这些种子不能在传统的种子库中保存。本研究开发了一套模型来预测未研究物种的种子干燥响应。
方法
收集了分类学、特征、位置和气候数据，形成了一个包含 17539 个物种的全球数据集。然后，根据物种的生境和特征信息，以及近缘种（取决于模型，是同一属、科或目的成员）的种子干燥响应，开发了三个增强回归树模型来预测物种的种子干燥响应。使用十折交叉验证来测试模型的预测成功率。然后通过预测厄瓜多尔和英国爱尔兰两个植物群的种子干燥响应来证明模型的实用性。
结果
这三个模型对识别敏感干燥的物种的成功率不同：属级模型为 89%，科级模型为 79%，目级模型为 60%。最重要的预测变量是物种亲缘关系的种子干燥响应、种子质量和年降水量。预测厄瓜多尔 10%的种子植物和英国爱尔兰 1.2%的种子植物产生敏感干燥的种子。由于数据的可用性，英国和爱尔兰植物群的预测准确性可能更高，在这些植物群中，预测敏感干燥的物种的正确识别率估计为 96.7%，而在厄瓜多尔植物群中为 80.8%。
结论
这些模型可以利用现有数据来预测物种可能的种子干燥响应，为植物特征的全球研究提供一个填补空白的工具，并为植物保护活动提供关键的决策支持。
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