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).