Community structure of benthic macroinvertebrates in native and introduced mangroves of northern Beibu Gulf, China: Implication for restoring mangrove ecosystems

Introduced mangroves are widely used to restore mangrove ecosystems in South China. Results of potential impacts on indicative benthic macroinvertebrates are divergent. We explored the community structure of benthic macroinvertebrates in the mangrove ecosystem of northern Beibu Gulf, China across four habitats: native Avicennia marina mangrove, introduced Laguncularia racemosa mangrove, native-introduced mixed mangrove, and unvegetated intertidal flat. Based on the Hill number, community structure was estimated from the dimensions of estimated species richness, diversity, evenness, and species composition similarity. Benthic macroinvertebrates in the unvegetated flat significantly differed from the other three assemblages in mangroves; introduced L. racemosa mangrove had relatively distinct benthic macroinvertebrate assemblage from the native A. marina and the mixed mangroves, with lower species richness and similarity but higher diversity and evenness. Considering the lack of unanimous conclusion of potential impact on benthic macroinvertebrates under complex species interactions, native mangroves should be of top priority in ecosystem restoration.

Can tree species diversity be assessed with Landsat data in a temperate forest?

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015.