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\begin{document}

\title{Unveiling the Suitable Habitats and Conservation Gaps of Tridacna maxima
in the Indo-Pacific Core Area Based on Species Distribution Model}



\author[1]{Shenghao Liu}%
\author[1]{Tingting Li}%
\author[1]{Bailin Cong}%
\author[1]{Leyu Yang}%
\author[2]{Zhaohui Zhang}%
\author[1]{Linlin Zhao}%
\affil[1]{First Institute of Oceanography Ministry of Natural Resources}%
\affil[2]{First Institute of Oceanography}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{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 of T. 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 of T. maxima, with suitable habitats predominantly found in
shallow coastal waters rather than deep sea areas. Furthermore, we
modeled potential distribution areas for T. 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.%
\end{abstract}%



\sloppy


\textbf{Unveiling the Suitable Habitats and Conservation Gaps of
\emph{Tridacna maxima} in the Indo-Pacific Core Area Based on Species
Distribution Model}

\textbf{Shenghao Liu\textsuperscript{1,2,3}, Tingting
Li\textsuperscript{1}, Bailin Cong\textsuperscript{1,3}, Leyu
Yang\textsuperscript{1,3}, Zhaohui Zhang\textsuperscript{1,2}, Linlin
Zhao\textsuperscript{1,2,3}*}

\textsuperscript{1}Key Laboratory of Marine Ecology and Environment
Science, First Institute of Oceanography, Ministry of Natural Resources,
Qingdao, 266061, China; \textsuperscript{2} Marine Ecology and
Environmental Science Laboratory, Pilot National Laboratory for Marine
Science and Technology, Qingdao 266237, China; \textsuperscript{3}
School of Advanced Manufacturing, Fuzhou University, Jinjiang 362200,
China

*Correspondence: Linlin Zhao, Email: zhaolinlin@fio.org.cn

\textbf{Abstract}

\emph{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 of\emph{T. 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 of\emph{T.maxima} , with suitable habitats
predominantly found in shallow coastal waters rather than deep sea
areas. Furthermore, we modeled potential distribution areas for\emph{T.
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 \emph{T. maxima}
is 1,519,764.73 km\textsuperscript{2}, 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.

\textbf{Keywords:}Giant clams, \emph{Tridacna maxima,}Species
distribution models, Habitat suitability, Potential distribution area,
Marine protected areas

\textbf{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 \emph{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). \emph{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 \emph{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 for\emph{Tridacna} . It is estimated that
approximately 66\% of the energy source for \emph{Tridacna} comes from
the photosynthetic activity of zooxanthellae (Klumpp et al., 1992;
Norton et al., 1992). Over the past two decades,
\emph{Tridacna}populations have suffered substantial damage due to human
activities and global environmental changes, leading to critical
endangerment for most species (Andr\selectlanguage{ngerman}éfouët et al., 2013; Cabaitan et al.,
2008; Neo et al., 2015). \emph{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 \emph{Tridacna} species under
future climate conditions and ongoing human activities.

Based on geographical and genetic variations, the \emph{T.
maxima}population 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.

\textbf{Materials and Methods}

\textbf{2.1 Study area and species occurrence data collection}

\emph{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\textasciitilde{}140°E, 11°S\textasciitilde{}15°N, based on the known
distribution ranges of the two populations. We obtained occurrence
records of \emph{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).

\textbf{2.2 Environmental variables acquisition and filtering}

The spatial distribution of species can be significantly influenced by
the surrounding habitat environment (Zhao et al., 2023). In this study,
we obtained raster data of variable projections from the comprehensive
dataset Bio-ORACLE (http://www.bio-oracle.org) and Global Marine
Environment Datasets (http://gmed.aucklan-d.ac.nz), considering their
biological relevance and data availability. To assess collinearity among
candidate predictor variables, scatter collinearity analysis, Pearson's
correlation coefficients, and the variation inflation factor (VIF) were
employed (Zuur et al., 2010). Variables with correlation values below
0.8 and VIF values below 10 were retained. Nine predictors were
considered based on the collinearity analysis, including mean current
velocity, mean salinity, mean temperature, temperature range, mean
dissolved oxygen, light at bottom, phytoplankton, water depth, and land
distance (Table 1, Fig. 2).

Bio-ORACLE provides future environmental projections for two time
periods (2040-2025, 2090-2100) using three atmospheric-ocean general
circulation models (AOGCMs: CCSM4, HadGEM2-ES and MIROC5) under four
Representative Concentration Pathways (RCPs) emission scenarios: RCP
2.6, RCP 4.5, RCP 6.0 and RCP8.5 (Assis et al., 2018). To reduce
uncertainty in environmental projections, we averaged the predictions
from the three AOGCMs to represent future climate conditions. RCP 2.6
indicates an optimistic emission level with well-controlled greenhouse
gas concentration, while RCP 4.5 and RCP 6.0 represent moderate emission
levels. RCP8.5 represents a pessimistic scenario without controlled
emissions (Moss et al., 2010). For comparability, we used two RCPs (RCP
2.6 and RCP 8.5) to predict future distributions for the 2050s
(2040--2050) and 2100s (2090--2100). Future environmental factors,
including current velocity, mean salinity, mean temperature, temperature
range, are downloaded from Bio-ORACLE, while the other factors were
assumed to remain unchanged in the future (Zhang et al., 2021).

\textbf{2.3 Model construction and assessment}

The modeling program used in this study is SDM based on the biomod2
package in the R platform (Thuiller et al., 2020). The package includes
ten modeling algorithms: maximum entropy (Maxent), random forest (RF),
surface extent envelope (SRE), multiple adaptive regression splines
(MARS), artificial neural network (Moss et al.), flexible discriminant
analysis (FDA), classification tree analysis (CTA), generalized boosting
model (GBM), generalized linear model (GLM), and generalized additive
model (GAM). Species distribution modeling and prediction rely on
existing/pseudo-missing records and current environmental data. Due to
limited actual sampling point data for most species, pseudo-distribution
data is used as a substitute to overcome this limitation (Barbet-Massin
et al., 2012). The pseudo-absences function in the R package MOPA is
employed to randomly simulate an equal number of pseudo-absent records
and compare them with the conditions of actual presence points, thereby
improving the model's predictive performance (Guisan et al., 2017). To
evaluate the accuracy of the model predictions, a five-fold
cross-validation method with 10 replicates was used (Fu et al., 2021).
In this methodology, 80\% of the dataset is randomly selected for model
calibration and testing, while the remaining 20\% is reserved for
assessing model predictions.

Two model evaluation indices embedded in biomod2, namely true skill
statistics (TSS) and the area under the receiver operating
characteristic curve (AUC), are calculated to estimate predictive
accuracy. Given the potential diversity in outcomes generated by the ten
models, an ensemble projecting approach is adopted to reduce uncertainty
and enhance reliability (Buisson et al., 2010; Morato et al., 2020). To
improve the accuracy of ensemble models, only models with an AUC greater
than 0.8 and a TSS greater than 0.7 are retained, indicating high
predicted accuracy and low uncertainty (Allouche et al., 2010; Mei et
al., 2017).

Species-level and population-level modeling analyses are conducted,
drawing species response curves for each environmental variable using
the generated SDMs to visualize the variation in species occurrence
probability along the environmental gradient. The significance of each
predictor is estimated using a randomly permuted method (Guisan et al.,
2017). The potential distributions of the entire species and the two
populations (EIOS and WPI) under present and future climate scenarios
(2050s, 2100s) for RCP 2.6 and RCP 8.5, respectively, were predicted
using the ensemble models at both species and population levels.
Continuous habitat suitability maps are created based on the direct
outputs from the ensemble models. Using automatically derived thresholds
that maximize the TSS values of the ensemble model, we translated the
continuous prediction into binary values for a clearer comprehension of
habitat appropriateness (Liu et al., 2013).

\textbf{2.4 Niche differentiation comparison}

The ecological niche space occupied by a population, which depends on
environmental variables influencing population occurrence, can be
estimated using the n-dimensional ecological hypervolume (Lê et al.,
2008). Using the R package hypervolume, we then estimated the
four-dimensional hypervolumes for the EIOS and WPI populations based on
the selected principal components. The size of the population's niche
space can be quantified by the values of hypervolume, a unitless
measure. We used the R package BAT to calculate the niche
differentiation between the EIOS and WPI populations. Total niche
differentiation (\selectlanguage{greek}β\selectlanguage{english}Total) represents the degree of overlap or separation
between two hypervolumes. It can be further divided into two parts:
niche contraction/expansion, which indicates the net variations in how
much space each hypervolume encompasses, and niche shift, which
represents the replacement of the space between the hypervolumes
inhabited by two populations (Carvalho \& Cardoso, 2020; Mammola \&
Cardoso, 2020).

\textbf{2.5 Identifying the conservation gaps}

For subsequent analysis, the binary maps were utilized to calculate the
extent of suitable habitat and retrieve information on all marine
protected areas within the Indo-Pacific core area, Data online from the
world's protected areas (http://www.protectedplanet.net, WDPA) in June
2023. ArcGIS was employed to conduct an overlay analysis by combining
the distribution data of these protected areas with the potential
distribution range of \emph{T. maxima} . This analysis aimed to assess
current protection gaps and identify areas lacking protection for
the\emph{T. maxima} species in the Indo-Pacific core area. Furthermore,
to evaluate the effectiveness of the existing protected area system
for\emph{T. maxima} in the Indo-Pacific core area, we also calculated
the ratio between the species' distribution area within the established
protected areas and its potential distribution area. This ratio serves
as a measure to determine the level of effective protection provided to
the species.

\textbf{Results}

\textbf{3.1 Niche differentiation among the two populations}

To minimize the dimensionality of the niche space, we initially
conducted a principal component analysis (PCA) on environmental
variables. The top four principal components were retained as they
collectively explained 81.7\% of the total variance (Fig. 3A). The EIOS
(1992.44) had a larger four-dimensional hypervolume than the WPI
(1588.38). There was a modest level of niche differentiation between the
two populations (\selectlanguage{greek}β\selectlanguage{english}Total = 0.465), primarily driven by niche shift
(0.180), which accounted for 49.32\% of the observed difference, and
niche contraction/expansion (0.184), which accounted for 50.41\% of the
difference. Analyzing the niche centroids for the two population
comparisons revealed that PCA1 played a crucial role in explaining the
distinct niches of the EIOS and WPI populations (Fig. 3B). This
difference was mainly attributed to variations in mean temperature, mean
dissolved oxygen, phytoplankton abundance, and mean salinity (Fig. 3C).

\textbf{3.2 Model training fitting and environmental variable
assessment}

To improve model performance, a 10-replicate five-fold cross-validation
procedure was employed. During simulation, 20\% of the known species
distribution data was used for model validation, while the remaining
80\% was used for training. The predictive performance of the 10
modeling algorithms varied, as indicated by different TSS and AUC
values. The modeling technique with the highest predictive performance,
determined by TSS and AUC values, was selected. Eight out of the ten
single models (excluding MAXENT and SRE) were chosen to create weighted
ensemble models at both the species and population levels (Fig. 4A, 3B).
Overall, the ensemble models demonstrated good accuracy for most
species, with AUC values above 0.80 and TSS values above 0.70. The high
AUC and TSS values across all three ensemble models indicated strong
predictive performance (Table 2).

The habitat suitability models developed in this study incorporated nine
different predictors, each contributing differently to the modeled
species (Fig. 4C). In general, land distance and light at bottom emerged
as the most important predictors for \emph{T. maxima} , contrasting with
ocean depth, current velocity, dissolved oxygen, salinity, mean
temperature, and phytoplankton (Fig. 4C). The species-level model
revealed that the distribution of \emph{T. maxima} was primarily
influenced by land distance and light at bottom. The model predicted
that \emph{T. maxima} tends to prefer environments with land distances
between 0 and 30 km (Fig. 5A1) and light at bottom between 5 and 45
(Fig. 5A2). At the population level, land distance and mean temperature
were the most significant predictors for the distribution of EIOS, while
light at bottom and land distance played key roles in predicting the
distribution of WPI. For the EIOS population, occurrence probability
decreased with increasing distance from land and was highest when the
mean temperature ranged from 10 to 30 \selectlanguage{ngerman}°C (Fig. 5B1, 4B2). As for the WPI
population, preferred habitats exhibited light at bottom above 20 and
were located near the coast within approximately 100 m (Fig. 5C1, 4C2).

\textbf{3.3 Habitat suitability in scenarios of the current and future
climatic conditions}

The modeling analyses were conducted at both the species level (species
model) and population level (EIOS model and WPI model). Under present
conditions, potential habitat for \emph{T. maxima} exhibited higher
suitability indices in the Indo-Pacific core area. In terms of
distribution ranges, the predictions from the species-level and
population-level models frequently demonstrated good agreement (Fig.
6A). \emph{T. maxima} 's preferred habitats occurred primarily in
shallow coastal waters, with little of it in deep ocean regions.
Notably, the species-level prediction showed the largest suitable
habitat area (1,519,764.73 km\textsuperscript{2}), while the two
population-level predictions indicated the areas of 1,326,478.08
km\textsuperscript{2}and 1,204,511.84 km\textsuperscript{2},
respectively. Despite limited ecological niche differentiation, certain
variations in the projected results were identified between two
populations that were predicted by EIOS model and WPI model (Fig. 6B and
C). Moreover, distinct differences are observed in certain regions. For
instance, in the South China Sea, the species-level model predicts the
smallest suitable habitat area for\emph{T. maxima} , whereas the WPI
population-level model predicts the largest area. In the Strait of
Malacca, the EIOS model predicts a larger suitable zone compared to the
species-level and WPI models.

The extent of habitat change depends on the scenario of climate change.
Under the pessimistic scenario of uncontrolled greenhouse gas emissions
(RCP 8.5), significant changes in the suitable range are projected
(Table 3, Fig. 7). The species-level and population-level models predict
different impacts of climate change on potential suitable habitats. The
species-level model presents a more pessimistic outcome with greater
loss of potential suitable habitats for \emph{T. maxima} in the Nansha
Islands, Strait of Malacca, and Java Sea regions under RCP 8.5
scenarios. The EIOS model predicted moderate loss of suitable areas
for\emph{T. maxima} among the Indo-Pacific core area, while the WPI
model forecasted substantial loss of suitable areas in shallow waters
surrounding the Philippine Islands, Sumatra Island, and Java Island.
Unlike the species-level and WPI models, the EIOS model predicted a gain
(approximately 2.43\%) of spotted suitable habitats in coastal waters of
the Nansha Islands, Mindoro Island, Eastern and Southern Indonesia under
RCP 8.5 scenarios.

\textbf{3.4 Identifying the conservation gaps in the Indo-Pacific core
area}

The distribution of potential suitable habitats was predicted by SDMs.
Areas far from the coast were identified as less critical for
conservation, while the most important areas tended to be coastal. From
the distribution of existing protected areas (Fig. 8), it can be seen
that they are scattered in different directions and present a
''point-like'' pattern, greatly weakening their protective role for
habitat. According to the gap analysis, the potential distribution area
of \emph{T. maxima} within natural protected areas is 244730.58
km\textsuperscript{2}, with a protection ratio of only 16.10\%, which
does not match the status of the study area as a biodiversity center.
Most of the potential areas for the \emph{T. maxima} have not yet been
protected, indicating an urgent need to expand and optimize the
protected area system in the Indo-Pacific core region, moving from a
''point-like'' pattern towards a ''grid-like'' one, to comprehensively
improve the effectiveness of species protection.

\textbf{Discussion}

\textbf{4.1 Ecological niche differentiation in SDMs}

The use of species distribution models (SDMs) in biodiversity
assessments primarily focuses on estimating habitat suitability at the
species level (Collart et al., 2021; Elith et al., 2010; Elith \&
Leathwick, 2009). These species-level SDMs assume ''ecological niche
conservatism'' and overlook within-species phylogenetic or functional
heterogeneity. However, recent studies have highlighted the importance
of considering population-level differences in sustainable management
systems, as they reveal variations in climate predictor responses within
species (Benito Garzón et al., 2019; Collart et al., 2021; Nielsen et
al., 2021; Song \& Li, 2023). Using the Japanese crayfish
(\emph{Cambaroides japonicus} ) as an example, researchers demonstrated
that by accounting for local adaptations in distinct populations, SDMs
with resolutions below the species level can more reliably forecast
changes in biodiversity (Zhang et al., 2021).

Our findings indicate intermediate niche differences (0.465) between the
EIOS and WPI lineages of \emph{T. maxima} , suggesting the need for
population-level SDMs for this species (Fig. 1-2). While all our models
exhibited strong predictive performance and good model transferability
(Table 2), the species-level model projected larger range contractions
due to climate change compared to the lineage-level models (Table 3).
Incorporating possible local adaptations, population-level SDMs yield
different predictions regarding the effects of climate change.
Therefore, our distribution predictions were less pessimistic when using
lineage-level models, aligning with earlier research suggesting that
intraspecific variation may mitigate the impact of climate change on
species distribution (Chardon et al., 2020; Razgour et al., 2019).

\textbf{4.2 Effects of environment factors on the distribution
of\emph{T. maxima}}

Land distance and light at bottom were important variables of habitat
suitability for \emph{T. maxima}(Fig. 5), consistent with studies on
species distributions of\emph{Acropora tenuis} in the Great Barrier Reef
(Strahl et al., 2019) and \emph{T. maxima} in the Central Red Sea
(Rossbach et al., 2019). The most unique biological feature of \emph{T.
maxima} is its nutritional relationship with\emph{Zooxanthellae} ,
whereby the majority of their required nutrients and energy are obtained
through photosynthesis (Lucas, 2014; Yonge, 1975). This symbiotic
relationship has significant ecological and morphological significance
for \emph{T. maxima} . On the one hand, the distribution of \emph{T.
maxima} is limited by their symbiotic dinoflagellates, which require
sufficient light for photosynthesis. Land distance is generally shallow
with high transparency and ample sunshine, meeting the basic conditions
for maintaining the symbiotic relationship. In addition, \emph{T.
maxima}attach themselves to coral reefs or live freely. Their habitat is
mainly in the low tide zone near coral reefs in tropical waters or in
shallower reefs, making them an important component of coral reef
ecosystems. Giant clams support overall reef biodiversity and
functionality, making them flagship taxa for coral reef conservation
efforts (Killam et al., 2023; Lee et al., 2022). A significant decline
in the number of \emph{T. maxima} in a particular area indicates damage
to the coral reef ecosystem. Conversely, a relatively stable population
size and species composition of \emph{T. maxima} indicate good health of
the coral reef ecosystem (Dewiyanti et al., 2021). Interestingly, the
distribution range of coral reefs highly coincides with that of \emph{T.
maxima} , as shown in the Allen Coral Atlas (Lyons et al., 2020),
further explaining why \emph{T. maxima} are found at a close land
distance and verifying the accuracy of species distribution models in
predicting their distribution. We speculate that coevolution of these
species in this shared environment has led to similar response patterns
among associated species in the face of climate change (Chen et al.,
2023). This also provides an ideal system for studying how each species
in \emph{Tridacninae} , corals, and\emph{Zooxanthellae} utilizes local
climate adaptation, dispersal, and other strategies to mitigate the
risks of climate change in the future. In addition, light directly or
indirectly affects\emph{Tridacninae} physiological activities, such as
growth and energy metabolism (Ip et al., 2006). Studies have shown that
the oxygen production rate in \emph{T. crocea}gradually increases with
rising light intensity within an experimental range. Light intensity
significantly impacts the metabolic synthesis of\emph{T. crocea} and
other symbiotic dinoflagellates, promoting their photosynthetic activity
and growth (Liu et al., 2018; Liu et al., 2021).

\textbf{4.3 Giant clam protection and adaptative management}

SDMs establish relationships between species occurrence records and
environmental factors using species coordinates and environmental data,
enabling the prediction of suitable habitats and distribution patterns
(Guisan et al., 2017; Hällfors et al., 2016). As field surveys have
limited coverage, SDM predictions play a crucial role as supplements in
marine biodiversity conservation efforts. In recent years, numerous
studies have utilized SDMs to identify distribution characteristics of
diverse marine taxonomic groups, providing a vital foundation for the
development of scientifically sound conservation plans (Hu et al., 2022;
Wang et al., 2023). Notably, there is increasing momentum under the
Post-2020 Global Biodiversity Framework to raise protection targets to
30\% of the ocean by 2030 (Brito-Morales et al., 2022). Our study
revealed that the potential distribution area of \emph{T. maxima} is
1,519,764.73 km\textsuperscript{2}, while only 244,730.58
km\textsuperscript{2} falls within protected areas, constituting 16.10\%
of the total (Fig. 8). These findings contradict the Indo-Pacific
region's reputation as the world's richest in terms of marine species.
Although a certain number of marine protected areas have been
established here (with a total area of approximately 459,711.65
km\textsuperscript{2}), they inadequately cover the suggested priority
conservation areas identified in this study due to their scattered
distributions. The existing protected areas are not sufficiently large
or well-connected, suggesting that they may not be effective for
protecting giant clams. Therefore, management efforts should include
establishing a MPA network along the coastlines of West
Pacific-Indonesia to match the dispersal capability of giant clams.

It has been demonstrated that under future climate scenarios, global
marine species distributions are shifting at rates of tens to hundreds
of kilometers per decade (Stuart-Smith et al., 2017). In the present
study, the projected future potential distribution area of \emph{T.
maxima} is 1,285,800.49 km\textsuperscript{2}, with 208,080.73
km\textsuperscript{2} falling within protected areas, accounting for
16.18\% (Fig. 8). Considering the impact of climate change on habitats,
it is crucial to assess whether existing protected areas can still
fulfill their original conservation functions. Additionally, management
efforts should address the effects of future climate change on habitats
in order to maintain population connectivity and adaptability in
separated regions. This requires constructing appropriate models to
understand the distribution patterns of numerous threatened species
under climate change, identifying species richness hotspots in the
Indo-Pacific core region, and evaluating the effectiveness of individual
and networked marine protected areas. Such endeavors hold significant
importance in formulating adaptive and forward-looking conservation
policies.

\textbf{4.4 Model performance and verification}

Habitat suitability modeling techniques have inherent limitations, but
we have taken several measures to ensure the accuracy of our research
findings. We employed ensemble modeling to reduce uncertainties
associated with SDMs. However, it is important to acknowledge that SDM
modeling algorithms still have their own limitations. Previous studies
have emphasized the presence of uncertainties in SDMs, which can be
minimized through a combination modeling approach (Guisan et al., 2017;
Thuiller et al., 2019; Zhang et al., 2019). By integrating multiple
models, we can address variable importance and capture habitat changes
resulting from different factors, thereby advancing the study of
species' spatial distribution more efficiently and accurately (Fig.
3-4). Furthermore, during model validation, techniques for validating
presence/absence models are well-developed and offer higher
interpretability of prediction results (Palialexis et al., 2011).
Therefore, this study utilized presence/absence data for habitat
analysis. Moreover, existing evidence suggests that \emph{T.
maxima}exhibits two evolutionary branches within our study area's
spatial scale (Nuryanto \& Kochzius 2009; Hui et al. 2016). However, a
more detailed classification would likely lead to more accurate
predictions. Future research can explore this hypothesis by conducting
further genetic studies on \emph{T. maxima} .

\textbf{Conclusion}

Post-2020, biodiversity conservation has become a shared concern among
all parties to the Convention on Biological Diversity. Giant clams hold
significant economic and ecological importance as coral reef species. In
the present study, we employed SDMs with a dataset comprising 213
occurrence records and nine environmental variables to assess the
potential distribution of \emph{T. maxima} , a giant clam species, in
the Indo-Pacific core region. We identified that land distance and light
intensity are the dominate factors influencing the distribution
of\emph{T. maxima} . Our analysis encompassed both current and future
climate scenarios. Our study revealed that the potential distribution
area of T. maxima is 1,519,764.73 km2, constituting 16.10\% of the total
protected areas. Additionally, through an overlay analysis, we evaluated
the alignment between potential suitable areas and existing protected
areas, enabling us to identify gaps in conservation efforts. Our
findings provide insight into the spatial distribution patterns
of\emph{T. maxima} , offering scientific guidance for effective
conservation management and recommendations for the establishment of
future protected areas.

\textbf{Data availability statement}

The raw data used in this study are available through the following
links: 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/).
Bio-ORACLE (https://bio-oracle.org/downloads-to-email.php) and Global
Marine Environment Datasets (GMED,
https://gmed.auckland.ac.nz/download.html). If the manuscript is
accepted, then the code for the analysis will be available at
datadryad.org.

\textbf{Author contributions}

Shenghao Liu: Conceptualization; Formal analysis; Funding acquisition;
Writing -- original draft. Tingting Li: Data curation; Formal analysis;
Methodology; Visualization. Bailin Cong: Data curation; Formal analysis;
Methodology. Leyu Yang: Data curation; Formal analysis. Zhaohui Zhang:
Conceptualization; Supervision; Project administration. Linlin Zhao:
Conceptualization; Funding acquisition; Writing -- review \& editing.

\textbf{Acknowledgements}

This research was supported by National Key Research and Development
Program of China, grant/award number: 2022YFF0802204; the China-ASEAN
Blue Partnership Construction Program, grant/award number: WJ0923011.

\textbf{Conflict of Interest}

The authors declare that they have no conflicts of interest regarding
this research.

\textbf{Figure Captions}

\textbf{Figure 1} The global distribution map of Tridacna maxima (A) and
the study area map (B). The red dots represent existing occurrence
records of the T. maxima worldwide, while the yellow and blue dots
represent occurrence records of the East Indian Ocean-South China Sea
population (EIOS) and the West Pacific-Indonesian population (WPI),
respectively.

\textbf{Figure 2} Results of collinearity analysis of nine predictors.
Scatter collinearity analysis (A), Pearson's correlation analysis (B),
and variance inflation factor analysis (C) results for nine predictor
variables. Depth - ocean depth; LandD - land distance; Cv - current
velocity; Do - dissolved oxygen; Sal - salinity; Tmean -- temperature
mean; Trange -- temperature mean; Lb - Light at bottom; Pp -
Phytoplankton.

\textbf{Figure 3}Ecological niche differentiation. (A) Percentage of
explained variance of each principal component of principal component
analysis for the nine selected predictors. (B) The niches of the two
populations of\emph{Tridacna maxima} quantified via four-dimensional
hypervolumes. To visualize the shape and boundary of the hypervolumes in
two dimensions, a random selection of 20,000 stochastic points for each
hypervolume was used. The large blue and orange points indicate the mean
niche position (niche centroid) of EIOS and WPI, respectively. (C)
Contribution of environmental predictors to each principal component
(PC). The number in the bar indicates the contribution rate (\%) of each
predictor to PC axes, and only values\textgreater{}15\% are shown. Depth
- ocean depth; LandD - land distance; Cv - current velocity; Do -
dissolved oxygen; Sal - salinity; Tmean -- temperature mean; Trange --
temperature mean; Lb - Light at bottom; Pp - Phytoplankton.

\textbf{Figure 4}Model evaluation and importance of environmental
factors. Predictive abilities of the ten modeling algorithms in
projecting the distribution of\emph{Tridacna maxima} at the population
and species levels. (A) the True Skill Statistics (TSS) value; (B) the
Area Under the receiver operating characteristic Curve (AUC) value. The
black horizontal lines indicate the cutoff values of the AUC (0.8) and
TSS (0.7) of the single model used to build the ensemble model. (C)
Relative importance of the nine predictor variables in the three
ensemble models built at population and species levels. Data are
expressed as mean ± standard error. Depth - ocean depth; LandD - land
distance; Cv - current velocity; Do - dissolved oxygen; Sal - salinity;
Tmean -- temperature mean; Trange -- temperature mean; Lb - Light at
bottom; Pp - Phytoplankton.

\textbf{Figure 5}The response curves of \emph{Tridacna maxima}
occurrence probability against the two most important driving factors
based on the population-level (A1, A2) and species-level model
(including the Eastern Indian Ocean -- South Sea population (B1, B2) and
the Western Pacific -- Indonesia population (C1, C2)). LandD - land
distance; Tmean -- temperature mean; Lb - Light at bottom.

\textbf{Figure 6}Habitat suitability maps of \emph{Tridacna maxima}
predicted by species and population level integrated models under
current climate scenarios. Panels (A1, A2) show the corresponding
continuous and binary maps for the species; panels (B1, B2) show the
corresponding maps for EIOS; panels (C1, C2) show the corresponding maps
for WPI.

\textbf{Figure7}Future predictions and changes. Habitat suitability maps
of\emph{Tridacna maxima} predicted by an integrated model established at
both species and population levels under future climate scenarios.
Panels (A1, B1, C1) are binary maps for each species, and panels (A2,
B2, C2) show the predicted changes in suitable habitats for the 2100s
under RCP 8.5 (0085). The category ``loss'' represents areas projected
to be suitable under current climatic conditions but unsuitable under
future climatic conditions; ``stable'' represents areas projected to be
suitable under both current and future climatic conditions; ``gain''
represents areas projected to be unsuitable under current climatic
conditions but suitable under future climatic conditions; and
``unsuitable'' represents areas projected to be unsuitable under current
and future climatic conditions.

\textbf{Figure 8} Analysis of Marine Protected Area (MPA) Gaps. (A)
Analysis of suitable habitats for \emph{Tridacna maxima} in the current
climate scenario and existing MPA gaps. (B) Analysis of suitable
habitats for\emph{Tridacna maxima} in the 2100s under the RCP 8.5
climate scenario and existing MPA gaps.

\textbf{Table 1}The nine environmental variables selected for this study\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Environment variable} & \textbf{Unit} & \textbf{Source} &
\textbf{Data for the future}\tabularnewline
\midrule
\endhead
Current velocity & m\selectlanguage{ngerman}·S\textsuperscript{-1} &
\emph{http://www.bio-oracle.org} & Downloaded from
Bio-oracle\tabularnewline
Salinity & PSS & \emph{http://www.bio-oracle.org} & Downloaded from
Bio-oracle\tabularnewline
Temperature Mean &  & \emph{http://www.bio-oracle.org} & Downloaded
from Bio-oracle\tabularnewline
Temperature Range &  & \emph{http://www.bio-oracle.org} & Downloaded
from Bio-oracle\tabularnewline
Depth & m & \emph{http://www.bio-oracle.org} & Remain
unchanged\tabularnewline
Land Distance & km & \emph{http://gmed.auckland.ac.nz} & Remain
unchanged\tabularnewline
Dissolved oxygen & mol\selectlanguage{ngerman}·m\textsuperscript{-3} &
\emph{http://gmed.auckland.ac.nz} & Remain unchanged\tabularnewline
Light at bottom & / & \emph{http://www.bio-oracle.org} & Remain
unchanged\tabularnewline
Phytoplankton & \selectlanguage{greek}μ\selectlanguage{english}mol\selectlanguage{ngerman}·m\textsuperscript{-3} &
\emph{http://www.bio-oracle.org} & Remain unchanged\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} Mean values of the true skill statistics (TSS) and the
area under the receiver operating characteristic curve (AUC) for the
ensemble models built at the species level (species model) and
populations level (EIOS model and WPI model).\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Ensemble models} & \textbf{TSS} & AUC\tabularnewline
\midrule
\endhead
Species model & 0.753 & 0.892\tabularnewline
EIOS model & 0.822 & 0.928\tabularnewline
WPI model & 0.773 & 0.886\tabularnewline
\bottomrule
\end{longtable}

EIOS, East Indian Ocean-South China Sea; WPI, West Pacific-Indonesia.

\textbf{Table 3} Size of predicted changes (\%) in species range based
on population-level and species-level models for the middle (2050s) and
end (2100s) of the 21st century under RCPs 2.6 and 8.5.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Year RCP\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
EIOS\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
EIOS\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
WPI\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
WPI\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
SPECIES\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
SPECIES\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2050s\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2100s\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2050s\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2100s\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2050s\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2100s\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
RCP 2.6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.17\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.15\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-8.60\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-8.43\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-10.72\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-15.13\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
RCP 8.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.87\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.43\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-10.11\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-17.79\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-18.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-25.86\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

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