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Data from: Application of a trait‐based species screening framework for vegetation restoration in a tropical coral island of China

Cite this dataset

Liu, Nan et al. (2020). Data from: Application of a trait‐based species screening framework for vegetation restoration in a tropical coral island of China [Dataset]. Dryad.


1. Selecting suitable species for vegetation restoration presents a notable challenge for land managers and scientists. Recently developed trait‐based approaches may be an effective means of overcoming this challenge. However, we lack a trait‐based species screening model that can be used to select potential species for restoration of degraded ecosystems.

2. Here, we developed a species screening model based on quantitative trait‐based theory and a maximum entropy algorithm. The objective was to select more species that have comparable restoration abilities to the target species that have high survival rates for vegetation restoration based on species’ functional traits. Thus, species diversity will be improved to facilitate restoration. We also developed a software platform that can be used to implement the model. We then applied our model and software platform to select species for restoration efforts in a tropical coral island which is part of Hainan Island, China.

3. As a prerequisite, we started with three target species which have high potential for restoring the island. Likewise, 66 non‐native species were selected as the potential species pool. For each species, we identified and measured 28 traits that are strongly associated with harsh environments. Harsh environments are those with drought stress, high temperatures, intensive UV radiation, lack of real soil and nutrients, and high salinity and alkalinity. Then, our software platform was used to run the species selection model. Finally, 12 out of 66 species being identified as suitable species for restoration.

4. We transplanted seedlings of all 66 species to the island to monitor seedlings survival. We found that the 12 species identified from our model had high survival rates, which ranged from 86% to 91%. In contrast, the mean survival rate for species not identified from our model was less than 40%. These results suggest that our species screening procedure was appropriate for selecting candidate species for use in vegetation restoration.

5. We show that by using species natural history information, as well as functional trait data, candidate species for restoration efforts can be successfully identified in a timely manner. Importantly, our proposed method is faster and less costly than more commonly used ‘trial-and-error’ method. The most time-consuming aspect of our approach is the need to measure the functional traits of target and potential species. Ultimately, we provide a protocol for using functional traits to quickly select a large number of suitable species for restoring degraded ecosystems. We expect that this work will be important for future vegetation restoration efforts in tropical islands, and perhaps other ecosystems as well. We also expect that our model will help prevent the invasive species and promote specific ecosystem functions.


National Natural Science Foundation of China, Award: 419,050,943,177,046,000,000,000

Hainan University, Award: KYQD (ZR) 1876

Chinese Academy of Sciences, Award: XDA13020500,XDA13010302

Youth Innovation Promotion Association, Award: 20,163,112,019,339