Mode of seed dispersal is an important trait for understanding geographical distributions, population dynamics, and community composition of plant species. However, data of dispersal modes are scarce for Chinese plant species. Previous studies have shown that growth form, plant height, fruit type, and seed mass have strong correlations with seed dispersal modes, thus predictions using modelling could be an alternative to gain this information. We collected information on growth forms, plant height, fruit types, seed mass, and dispersal modes from 360 kinds of Chinese angiosperm plants, and built a neural network model (NNET), decision tree (TREE), Fisher linear discriminant model (LDA), and support vector machine model (SVM) to predict seed syndromes from these four traits. For each model, an 80% sample (288 species) was randomly drawn from dataset as the training sample, with remaining 20% of data was used as a test sample. Results showed that all four models achieved rather good predictions, and the average total correctness rate for the NNET, TREE, LDA, and SVM was 78.90%, 77.09%, 77.81%, 78.14%, respectively. The neural network model had the highest correctness rates for different dispersal modes, i.e., zoochory (81.32%), autochory (74.90%), and anemochory (81.45%). This paper establishes the basis for the prediction of seed dispersal modes.
Received: 17 January 2017
Published: 20 September 2017