Incorporating Species-Conditional Co-Occurrence When Selecting Indicator Species to Monitor Restoration after Mangrove Removal from the Siangshan Wetland, Taiwan

Abstract

This paper presents an approach for incorporating species-conditional co-occurrence into models used for the selection of marine indicator species. Mangrove invasion within the Siangshan Wetland in Hsinchu, Taiwan, has changed the original structures and functions of habitats for benthic organisms. The Hsinchu City Government ran a large-scale mangrove removal project from October 2015 to March 2016 to restore the wetland. From October 2015 to September 2016, we investigated the biological effects of mangrove removal on benthic crabs and their adjacent habitats. Density, number of species, Shannon–Weaver index (H′) and Palou’s evenness index (J′) were calculated and compared between mangrove and non-mangrove regions. The results showed that values for these attributes in the non-mangrove regions were higher than those of the mangrove regions. After mangrove removal, species returned to their original habitats and the related density increased significantly. Using conditional co-occurrence algorithms, we identified five indicator species (Mictyris brevidactylus, Macrophthalmus banzai, Uca arcuata, Uca lacteal and Uca borealis) with high co-occurrence probabilities, whose population responses provided direct evidence of the benefits of mangrove removal for wetland restoration. The results indicate that mangrove removal is an appropriate habitat rehabilitation strategy for benthic organisms, and that the chosen indicator species may provide valuable ecological information for coastal managers seeking to control the spread of mangroves.

Developing a Model to Select Indicator Species Based on Individual Species’ Contributions to Biodiversity

“…On the other hand, a previous study [36] used the same material to select ISs by using the conditional probability of species co-occurrence. When a species exists in a habitat with other species, the status of these species is most likely to represent other species living in the same community [36]. Finally, the indicator species were selected, including M. brevidactylus, M. banzai, U. arcuata, U. lacteal, U. borealis, and H. formosensis.…”

“…The expansion of mangrove lands changed the state of the habitat substrate, and U. formosensis, for example, was forced to move outwards or even disappeared. When mangroves are removed, the habitat gradually returns to its original state [36]. At this time, species that disappeared in the past gradually return and increase in abundance.…”

“…Integrating the ideas of previous studies [35,36], more complex conditions will be presented to select ISs. Another approach, trait-based metrics, can also be used to select ISs, where assemblages respond to a gradient of environmental degradation.…”

“…Vegetation changes were monitored for good management practices for the sustainable use of natural rangeland resources. Several quantitative models have been developed to select IS, including principal component analysis [33], context-sensitive joint species distribution model (JSDM) [34], probability of species occurrence combined with environmental factors [35], maximum conditional co-occurrence [36], and threshold indicator taxa analysis (TITAN) [37]. However, few studies have quantitatively selected ISs from completely assembled species data, especially for benthic organisms.…”

A network-based methodology to reconstruct biodiversity based on interactions with indicator species

“…The statistical methods used to determine IS are developing rapidly and was used as an informing management decision tool (Fleming et al, 2020;Chu et al, 2021). Thus, a method based on the null models of species co-occurrence classification to identify sets of species that may have similar and distinct response patterns to their environment has been developed by Azeria et al (2009) and Terrigeol et al (2022).…”