Shenghao Liu1,2,3, Tingting Li1, Bailin Cong1,3, Leyu Yang1,3, Zhaohui Zhang1,2, Linlin Zhao1,2,3*
1Key Laboratory of Marine Ecology and Environment Science, First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China; 2 Marine Ecology and Environmental Science Laboratory, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China; 3 School of Advanced Manufacturing, Fuzhou University, Jinjiang 362200, China
*Correspondence: Linlin Zhao, Email: zhaolinlin@fio.org.cn
Abstract
Tridacna maxima (T. maxima)are widely distributed in shallow areas near coral reefs and hold significant commercial value as a food source and for marine tourism. However, it has been extensively harvested and depleted in many regions, leading to it being listed as endangered species by the International Union for Conservation of Nature (IUCN). While marine protected areas (MPAs) are considered effective conservation tools, it remains uncertain whether existing MPAs adequately protect these vulnerable giant clams. Here, we employed a Species Distribution Models (SDMs) approach, combining occurrence records ofT. maxima with environmental variables, to predict their distribution and capture spatiotemporal changes. The findings revealed the importance of land distance and light at bottom in determining the distribution ofT.maxima , with suitable habitats predominantly found in shallow coastal waters rather than deep sea areas. Furthermore, we modeled potential distribution areas forT. maxima in 2050 and 2100 under different climate change scenarios, highlighting varying impacts on suitable habitats across different model predictions. To evaluate current conservation gaps, we conducted an analysis by overlaying suitable areas with existing protected areas. The results showed that the potential distribution area of T. maxima is 1,519,764.73 km2, accounting for only 16.10% of the total protected areas. It became evident that the existing protected areas are insufficiently large or well-connected, suggesting their ineffectiveness in safeguarding giant clams. Therefore, management efforts should focus on establishing a network of MPAs along the coastlines of West Pacific-Indonesia, matching the dispersal capability of giant clams. These findings provide valuable insights for the conservation of endangered giant clams, offering a scientific foundation for designing MPA networks in the Indo-Pacific region.
Keywords:Giant clams, Tridacna maxima,Species distribution models, Habitat suitability, Potential distribution area, Marine protected areas
Introduction
Geographical environmental factors exert constraints on the growth, development, and geographic migration of species. The flourishing and reproductive success of marine organisms hinge upon their reliance on distinct ecological niches. Unfortunately, climate change and human activities have triggered a series of alterations within the marine environment, including elevated water temperatures, diminished primary productivity, ocean acidification, and hypoxia (Cheung et al., 2013; Lumpkin et al., 2020; Monllor-Hurtado et al., 2017). These alterations have the potential to surpass physiological and ecological thresholds, leading to habitat loss and even the extinction of numerous species (Duncan et al., 2023; Penn & Deutsch, 2022). In comparison to terrestrial communities, marine communities are more susceptible to environmental changes induced by climate change (Sorte et al., 2010a; Sorte et al., 2010b). As a species occupies a specific ecological niche, modifications in the attached environmental conditions can disrupt the distribution of that species (Faleiro et al., 2018; Fu et al., 2021). Given these circumstances, comprehending the impact of future climate change on species distribution is vital for effective species conservation.
Giant clams, belonging to the Tridacna genus, hold significant importance as coral reef inhabitants in the Indian and Pacific Oceans. Their massive colored shells and vibrant mantle tissue make them easily recognizable (Huelsken et al., 2013). Tridacna species are crucial for coral reef ecosystems, serving as ecologically valuable reef-builders with protective functions. They provide habitat, breeding grounds, and shelter for other reef organisms, thus playing a crucial role in marine environments, particularly coral reef ecosystems. The feeding mechanism of Tridacna is one of its unique biological characteristics. It involves symbiotic zooxanthellae living in its mantle tissue, utilizing inorganic nutrients from seawater through photosynthesis for growth and respiration (Jantzen et al., 2008; Lucas, 2014; Todd et al., 2009). This symbiosis holds both ecological and morphological significance forTridacna . It is estimated that approximately 66% of the energy source for Tridacna comes from the photosynthetic activity of zooxanthellae (Klumpp et al., 1992; Norton et al., 1992). Over the past two decades, Tridacnapopulations have suffered substantial damage due to human activities and global environmental changes, leading to critical endangerment for most species (Andréfouët et al., 2013; Cabaitan et al., 2008; Neo et al., 2015). T. maxima , a giant clam species, has been classified as an endangered species in the ”China Red List,” listed under Appendix II of CITES, categorized as a species of least concern in the IUCN Red List, and listed as a Class II protected wild animal in the ”National Key Protected Wildlife List”. It exhibits wide distribution in the western Indian Ocean and the Red Sea, spanning from the Indo-Malayan archipelago to the Socotra archipelago in the central Pacific (Andréfouët et al., 2014; Gilbert et al., 2007).
Species Distribution Models (SDMs) are currently valuable tools for predicting potential species distribution (Anibaba et al., 2022; Capinha et al., 2011; Guisan et al., 2017). The underlying principle of SDMs involves using existing species distribution data and environmental variables to establish ecological requirements based on the species’ niche. This approach explores the non-random relationship between environmental characteristics in known distribution areas and potential distribution areas (Araújo et al., 2019). It allows for the prediction of current and future species distribution under varying climatic conditions (Booth et al., 2014; Guisan & Thuiller, 2005). However, most contemporary studies only use SDMs at the species level, neglecting intraspecific species variation (Zhang et al., 2021). Local adaptation and intraspecific variation can influence how a species responds to environmental changes (Li et al., 2022). Therefore, species-level SDMs often overpredict a species’ future distribution (Hu et al., 2021; Pack et al., 2022). Consequently, incorporating intraspecific genetic differences into SDMs can result in better and more accurate predictions, providing valuable information for marine biodiversity conservation efforts (Hu et al., 2021).
The convergence zone between the Indian and Pacific Oceans, a tropical region, represents a biogeographic hotspot characterized by exceptionally high species diversity in shallow marine ecosystems. This hotspot is predominantly centered around the Indo-Malay Archipelago (Hoeksema, 2007; Nuryanto & Kochzius, 2009). The Indo-Pacific core region provides unprecedented opportunities for scientific investigations into the origin, maintenance, and conservation of biodiversity. Literature has documented significant levels of biodiversity in the Central Indian Ocean-Pacific, Western Indian Ocean, and Central Pacific regions across various dimensions, designating them as priority areas for conservation (Fan et al., 2023). Current research efforts in this area predominantly focus on unraveling the ecological and evolutionary processes that shape marine biodiversity. However, human activities, notably overfishing and pollution, have contributed to the loss of marine biodiversity (Halpern et al., 2008). In response, marine protected areas (MPAs) have been established to preserve the marine environment and its biodiversity (Sala & Giakoumi, 2018). MPAs have proven effective as area-based conservation techniques for protecting marine biodiversity (Grorud-Colvert et al., 2021). Despite these efforts, few studies have assessed the effectiveness of existing protected areas in protecting threatened Tridacna species under future climate conditions and ongoing human activities.
Based on geographical and genetic variations, the T. maximapopulation in the Indo-Pacific core region is primarily divided into two evolutionary lineages: East Indian Ocean-South China Sea (EIOS) and West Pacific-Indonesia (WPI) (Hui et al., 2016; Nuryanto & Kochzius, 2009). Although the degree of distribution overlap and genetic exchange between populations remains uncertain, these two populations have inhabited distinct ecological environments throughout their extensive evolutionary history, potentially leading to local adaptations. This study aims to assess the distribution patterns of species richness between these two populations and identify priority conservation areas as well as conservation gaps of the current protected areas network. Our study will provide scientific support for the Post-2020 Global Biodiversity Framework and aid in the development of comprehensive conservation plans for the marine biodiversity of the Indo-Pacific core region.
Materials and Methods
2.1 Study area and species occurrence data collection
T. maxima is mainly distributed in the Indian Ocean and the Western Pacific. The Indo-West Pacific region, centered around the Indo-Malay Archipelago, exhibits the highest species diversity in shallow waters of the ocean. Our study focuses on a limited area of 90~140°E, 11°S~15°N, based on the known distribution ranges of the two populations. We obtained occurrence records of T. maxima (27091 records) from online public databases such as the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/), iNaturalist (https://www.inaturalist.org/), and the Ocean Biogeographic Information System (OBIS, https://obis.org/). To minimize sampling bias and avoid the representation of conditions in densely sampled areas, we employed the R software package spThin for spatial refinement of the distribution data. Each 5×5 arc-minute grid was assigned one occurrence point, resulting in a spatial resolution of 9.2×9.2 km, consistent with the environmental predictors’ resolution. After data cleansing, we retrieved 213 records within our study area. According to the genetic population structure (Hui et al., 2016), the EIOS clade was assigned 113 occurrence records, and the WPI clade had 100 occurrence records (Fig. 1).