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 T. maximaexhibits 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 T. maxima .
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 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 ofT. 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 ofT. maxima , offering scientific guidance for effective
conservation management and recommendations for the establishment of
future protected areas.
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.
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.