a) Study area
The study area for the Species Distribution Model (SDM) encompasses the diverse and ecologically rich Gujarat coast, a pivotal region for Molluscan diversity in India. Stretching along the country's western coastline, this region is characterized by a varied topography that includes sandy beaches, rocky shores, mudflats, and estuarine complexes. Such varied substrates provide an ideal natural laboratory for studying the habitat preferences and distribution of Molluscan species(Mahapatra et al., 2015). The Gujarat coast is washed by the nutrient-rich waters of the Arabian Sea, influencing a high productivity level that supports a vast array of marine life(Kumar et al., 2015). This coast, extending from the Sir Creek in the northwest to Umbergaon in the southeast, boasts a significant representation of Molluscan fauna, which forms an essential component of the marine biodiversity and holds substantial promise for sustainable aquaculture development(Bhatt et al., 2016).
For the purpose of this SDM study, specific sites along the Gujarat coast have been selected based on their prominence in Molluscan distribution(Vadher et al., 2023). These sites fall within the coordinates ranging from approximately 20.6°N to 23.7°N latitude and 68.9°E to 72.6°E longitude. These coordinates cover critical habitats from the Gulf of Kachchh, known for its rich coral reefs and seagrass meadows, to the estuarine regions of the Gulf of Khambhat and the expansive coastline of Saurashtra, which provides a home to numerous gastropod and bivalve species(Sivakumar, 2019). It will also offer insights into potential areas for expansion and intensification of Molluscan farming, providing economic benefits while ensuring ecological sustainability.
b) Background on the importance of species distribution mapping
Regular field visits were conducted to evaluate the diversity and distribution of Molluscan species along a designated coastal area. During these investigations, a total of 60 Molluscan species were identified, with a collective count of 3,261 individual organisms being recorded. From this extensive data collection, four dominant species were distinguished based on their dominance and abundance, highlighting the necessity for focused on farming strategies namely Cerithium caeruleum, Lunella coronatus, Peronia verruculata and Trochus radiatus. The primary aim of this research paper is to highlight the pivotal role played by species distribution mapping in the efficient cultivation of molluscs. The elucidation of specific habitat preferences and distribution patterns of various Molluscan species is shown to make a significant contribution towards the optimization of Molluscan aquaculture practices. This optimization is facilitated by arranging farming locations with the Molluscs natural ecological needs, thus improving their chances of survival, growth rates, and overall productivity of the farms. Furthermore, the paper explores the wider ecological ramifications of species distribution data, especially in terms of understanding the effects of environmental changes on Molluscan communities. The meticulous monitoring of changes in species distribution in reaction to environmental factors such as water temperature, salinity, and chlorophyll a concentration is discussed. The species distribution models (SDMs) are identified as crucial in protecting Molluscan aquaculture operations from environmental challenges, thereby enhancing their sustainability and resilience amidst shifting ecological conditions.
c) Species sampling and identification techniques.
Quadrat sampling stands as an advantageous technique employed in the study of Molluscan species along the Gujarat coast, offering a systematic and quantifiable method for species sampling and identification (Wells et al., 2008). This method involves laying out square plots of a set size, known as quadrats, at regular intervals across the study area to ensure a representative sample of the Molluscan population is assessed. The use of quadrats is particularly beneficial in delineating the distribution of stationary or slow-moving organisms, such as many molluscs (HAAG et al., 2012). It facilitates the precise recording of species presence, abundance, and spatial distribution, enabling researchers to generate accurate data that is crucial for effective species distribution modelling (SDM). Additionally, quadrat sampling allows for the comparison of data across different habitats and time periods, making it a robust tool for monitoring environmental changes and their impact on Molluscan communities. By adopting this technique, the research on the Gujarat coast provides reliable and repeatable results that are integral to understanding and managing the region's diverse Molluscan populations.
d) Occurrence data collection methods.
For the collection of occurrence data of Molluscan species along the Gujarat coast, a systematic approach was employed to ensure comprehensive and accurate representation of the distribution of species. Field surveys were meticulously planned to coincide with seasonal cycles and tidal patterns, which are known to influence Molluscan activity and visibility(Tran et al., 2011). The Research conducted transect walks and utilized quadrat sampling methods, where predetermined square plots of specific dimensions were laid out at regular intervals along various habitats of the coastline. This approach allowed for the standardized collection of data on the presence and abundance of Molluscan species.
During these surveys, detailed records of each Molluscan specimen encountered were kept, noting the species, size, and distinctive features. garmin gps etrex 10 device was used to record the precise locations of each sighting, providing georeferenced data points that are crucial for the creation of accurate distribution maps. In addition to field observations, local fishermen and other stakeholders were interviewed to gather ancillary data on less accessible or deeper coastal areas. Specimens collected were taken to laboratories, Division of Marine and freshwater biology, Department of Zoology, The Maharaja Sayajirao University of Baroda and Zoology Lab, Bhakta Kavi Narsinh Mehta University for further identification and validation, especially when dealing with cryptic or juvenile forms, to augment the field identification process. The accumulated data from these various methods form a robust dataset for analysing the distribution patterns and habitat preferences of molluscs on the Gujarat coast, providing a solid foundation for ecological studies and the development of conservation strategies.
e) Quadrate studies for key species selection and data collection
Quadrate studies for key species selection and data collection are central to understanding the distribution of Molluscan species along the coast. This methodology proved to be instrumental in identifying the dominant species within the ecosystem. Post-analysis, it was discovered that four species notably stood out due to their prevalence. This prominence was determined based on the ratio of individuals of the most abundant species relative to the total population of molluscs in the sampled ecosystem. Dominant species are typically those that have a significant impact on the community structure and the distribution of other organisms within the same habitat. The degree of dominance among different communities or samples, particularly when the number of species and total abundances vary in the study area is calculated by Whittaker’s index as shown below in Eq1.
The formula for calculating Whittaker's Index is:
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUcAAABQCAYAAACK7bf8AAAM1ElEQVR4Ae2dva7UOhSFzysg3gAkngAaWpCgpoCOjoIWUVEhOiREjQQ9okY8AT2iBtFyGjoeYK6+uXedu8fHTpyZTOJkVqSjTBz/bH+OV3ZsJ+ds480ETMAETOASgbNLIQ4wARMwARPYWBx9EZiACZhAhoDFMQPFQSZgAiZgcfQ1YAImYAIZAhbHDBQHmYAJmIDF0deACZiACWQIWBwzUBxkAiZgAhZHXwMmYAImkCFgccxAcZAJmIAJWBx9DZiACZhAhoDFMQPFQSZgAiZgcfQ1YAImYAIZAhbHDBQHmYAJmIDF0deACZiACWQIWBwzUBy0P4Hfv39vrl27tjk7O7v4e/z4cTHDN2/eXMRTmqtXr26+fftWTOMTJjAFAYvjFJRPsIzz8/PNs2fPtsKHWCKape3v37+bO3fubBDFL1++lKI53AQmJWBxnBT3aRX2+fPnC68QD7G0SRyJ780EWiFgcWylJVZoB4J4+/btrUDiGSKCuQ2v8u7du53eZS6dw0zgmAQsjseke8J5I4QPHz7cPiZrDLLkGRLeNS55whhd9RkJWBxnhL/moplQQRwRSYSPyZaSABLe9di9Zk6uW7sELI7tts2iLYveIELJZEtuFloepmenF93cqzTe4rjKZp2/UniDeoxGABlzxHtMPcToYc5vtS0wgf8JWBz/Z+FfIxHITbAgiohjuqyH8FQwRzLD2ZjAQQQsjgfhc+IcgZw3GBeHy6MkbfQwc3k5zATmImBxnIv8issteYMIYZyYyXmYK8biqi2MgMVxYQ3WurldEyzpxEyctGm9Xrbv9AhYHE+vzY9a4y5vMJ2Y8RKeozaFMz+QgMXxQIBOvkugzxvkPI/WLOvh7Rkv4dnl56N2CFgc22mLVVjSN8ESJ2a6XilcBQxXYtEELI6Lbr62jEf4bt261esNalmPl/C01X62ZpeAxXGXx84RY2R8QuvJkyfbR8G4BGUn4okcwIPPkOU4cA6vMfcWTIpHEzO5fNK4OiYuZVOONxOYgsBo4hgflxhTin81j0+I0P3793fScfzjx48pOFwqg07Iu8F0dtVlSGe+lOHIAQjMhw8ftsywsdY2eEbOtYwlaC9fvtwRKDjp7RdxYt/V5qR5/vz5oK/wkIaya8R3ZNTO7kQJjCaO4vfx48cLQaGD1Igbj1fRK1BHpJOlb1SonKn2UfRrBejYtsHq0aNH20dYCVKNbcRBXPDwEBv+yKtPcJSupoxj151rZcjN4Nj2OP/1EhhdHKOw1XQm4tNB040OTMfv67hpurGPWxRH1XGIbWqX1KNDIAkr3YSUrqXxwdrHd3Hyvn0Cv3792rx+/XrDfsj29evXbbohaWrjji6OCGLfY1U0jk6X63itdMohAhTrNcXvIbbpZpNjTRhtRpy49QlnjDvlb9U7FfopbXBZ4xH4/v375t69e5s/f/5kM+Vp9ObNmxuEMLcRzpNUKX0uTU3Y6OKoTph2tJIxiGnJaymlmTJcHRHxqPGEW7RNN5qSF67zaTtINHOCOmU9c2W1bFvOXoflCSCMCF9O2BDFp0+fXoz5l8SRnCWguXzyJfeHjiqO8jSGCInSlDpufxWOG2MN4ighScVP5HJ1bL1dJOj2HtWKy9sjZNevX98KW5f1eIVoSpc4kh7v88WLF11ZDTo3qjjqgi11wpJl6ogAiBMzpfhThueEIy2fu9aNGzcu7nAIfV893r9/v5OG5UI1k1ex7BrbiC9vvtQukb+8RA2PlNJEO+b4XVv3OWxzmXUEELIrV670RmYsskYcWfFSE6+3wP8ijCqO8lCG3s3pnAgMokLlhqavrew+8bo6oUSFhc8SNsIQRupR8oYlVtSZjUZV3UlXy6DLNtVVNnblGeNoOGTftlS5x95HmyXoxy7T+Y9HAK+Ra5LH5r6tVhzJhzzxNMfYRhPHfS5WrW2U1xQ7uzppTSXVkQEz5K9mDDHalMbHxpIAyqbU85JHlt4AFH9IvbtsE7fYLmmZuTiUH9MMsUf5TbXHNtq7VK+p7HA5wwm8e/du23bs+7Yh4sj4JdfEGGOPo4mjOmpJLCIAOh/eVfS4dF7iUZOP0hxzr3oBPIqjhhBKHTOmi56NOnQqOqp3KqZddYtlRNtimih0JVtjHOyK+aZ2xrzn/i2WQ5jNbbPL/5dA7TgisYeIo/LVU9khvEcTx9rOrY5YuqBjxyx1+EMqPDRtyZ4aT0+dN4pSKZ3EtsQlZ3fJthhXvLs8rBgnFcco7DHfFn6L5RBmLdhtGzbbsUauyb5JFlgNEUfFZX/oNpo4SgjYd226oEudrqbDd+U/9rmSPTX1VV1j5y3dRBQehbSvLiXb0nSytZR3zAeb0+M0Px1zcU/xp/LSfY5vGsfHbRLQdVOz6FuCN0RImbk+dBtFHKPn0eXt1XhHNXEOrfSQ9FEoYt0kOF03g5LgqVOTFnZM5iBcDCXEMvrsLNmWplN5UaRjnDSfeNxVv5jHHL/76jWHTS6zjoDEsSb2osVRgtY3TqiLuavDKQ772k1pBLx2XyNEUShifOpAOSVvDNsljml9EUQ+ahGX/9R+ACIyKdkW4/C7r310XuIZb3ap7Wnecx7XtMGc9rnsMgH1URaB922LFkeJU5dQAEAXc6nDqZP25dMHc8zzJQGS8NHIUTRj2eISz0t4YlhMM+R3ybZcHmKfu+nIztguCmupLWK9xBH+0e4Yx7/bJcD6RtpuyKPykLhNjDnGizTX8WLzqMPlLmbl01pnjAKU1k+Ck7O5VB+J6hizadG2PrEt3Xhkp7xGtZfsTMN1fu79kLpjK8vGSsxZ9sGSkpIXo48i5JaHEEZHLI2d0aG7lqtgE7blNuwhba5c4nel7aoT5aU2qR4lBjn7DgnTrPIQwauJq9cNS209xOaDxhzpWHxjTy5yKh6pIeqgxI/G01gs6+l7qyTNb4pj7FT9UhGUsHCec1oIrjFE6kQnjptuEMoz3TM0wdszNVvkWcNOgqdvMsr+3HBI17ka244dR3WvEW+ES5xzHQyh4HzpbQ0G9zmfW7CszliaAJCHlIoRfLBFduXElVfrOJ/zghAxpc0Jmh5FySPdlC6yUPxSPdI8Dj1Wv8rVLeaNaGvtYs2rgWqrHM+Yb83vvcRRHUeQ032XSCIciIbS0DHp2Ofn5zX2ThYneiayVfvopcHi7du3O+OHjCUSxrl0I0xfFld+uX0sI81DIpdLlwp4mhZRify1AD+Nx7HK6WrPXLopwnSTqbWNDoZQ5ToNIsG50psVdEpY5zw8wjhX6rjkSd5RiMQHWziHbbmtq1xEA+Hjj9/p1mVXjkVX/DTvMY6xuYs5ZUiw0+s8x1I2EbfUjopTu99LHGszd7w8AW4GiFRu4ybx4MGDJsbRdBOs8c5ydTlWmOzquxEcq3znOw4BxA+BHGuTwHeJ55CyLI5DaI0QF08nN+Yas0Y4EdAWNj2+9tk8pa0wzA0FTGmDyzqcgLzfOMR2SK4McZQ8+H3ytTjuQ23PNBKarkdmsuZ8X5w9TdgrmeyufYTdq5DKRHBBGFviU2m6o2UIMF5aGhrIRC8G4S0yXJAbYigm6jlhcewBNOZpxIUxkS6R4ZGRf5zV2oZAzjlpBhf/g63Wropx7EEgmUjZV9gOTV+qhcWxROYI4RorQyCZCEFw4sbxq1evshM5Md5cv7Gfx/05vDbKpGxs8LY+AggjM/q5CbOu2uIx5lYCdKWpPWdxrCU1YjwGjuO/R0UsOSbcmwmYQBsELI5ttIOtMAETaIyAxbGxBrE5JmACbRCwOLbRDrbCBEygMQIWx8YaZE3m/Pz5czvzzhsLjKnGVylZ26avEjE55YmWNbX8OupicVxHOzZXCy1bYrKJPy0i14y3wrWfYwa8OWg2qCkCFsemmmN9xujLRRI/jlkvyTv2eJL89tsu62v3NdTI4riGVmy0Dvp4h8QPbzKuVdT51t7dbhSnzZqYgMVxYuCnVBzeIo/NfCCCMcYojHDQeT1ynxIb17V9AhbH9ttosRZq3JEJGf4tRDrpkj5yL7aiNnyVBCyOq2zW+SuFEOIx4jnqsTpa5UfqSMO/WyRgcWyxVVZgk77kgzjmPrQhr9KP1Cto7JVWweK40oadu1oaT8x9kDZ6lZrFnttel28CKQGLY0rEx6MQ6BpPlFepWepPnz5dGo8cxQhnYgIHELA4HgDPSfME+sYT5VXy7yD4dmX66bZ8rg41gWkJWByn5X0SpckzzD1SA0D/eU6LwU8Ciiu5OAIWx8U1mQ02AROYgoDFcQrKLsMETGBxBCyOi2syG2wCJjAFAYvjFJRdhgmYwOIIWBwX12Q22ARMYAoCFscpKLsMEzCBxRGwOC6uyWywCZjAFAQsjlNQdhkmYAKLI2BxXFyT2WATMIEpCPwDNhVEIlDDZE8AAAAASUVORK5CYII=)
Where:
N = Total number of individuals in the sample
n = Number of individuals of the most abundant species
In the scope of this study, Whittaker's Index (Iδ) is a measure of dominance that quantifies the degree to which the most abundant species dominates the community relative to the number of species present (CASTRO & JAKSIC, 2008). In the study area, which spans the upper, middle, and lower intertidal zones, 4 out of 60 species were identified as dominant. The species that stood out due to their prevalence are Cerithium caeruleum with a mean value of 0.066, Lunella coronatus at 0.056, Peronia verruculata with 0.074, and Trochus radiatus, which had the highest mean value of 0.083. Their high numbers not only illustrate their successful adaptation to local environmental conditions but also their potential impact on the ecological dynamics of the region. Understanding the abundance and spatial distribution of these species provides invaluable insights into the health of the ecosystem and aids in the socio-economic development. The prevalent presence and potential ecological resilience of these dominant species might also suggest their viability for aquaculture projects in the area.
f) Acquisition and processing of environmental data from Bio-Oracle
The acquisition and processing of environmental data from Bio-Oracle underpin the ecological assessments and predictive modelling for marine species distribution, including molluscs (Bolam et al., 2023). Bio-Oracle is a comprehensive marine data repository that offers a wide array of global environmental layers which are crucial for Species Distribution Models (SDM). These layers typically include various oceanographic and biotic variables, such as sea surface temperature, salinity, and chlorophyll -a concentration levels, which are often provided at high spatial resolution as depicted in table 2.
Table 2: Selected environmental predictors suitable for benthic species distribution modelling along with their biological importance
Predictor
|
Unit
|
Biological Importance
|
Mean Surface Salinity
|
pss
|
Salinity is used to define different water masses and depth zones and is considered as a primary driver for the distribution of benthic invertebrates(Russell et al., 2012)
|
chlorophyll -a concentration levels
|
mg/m3
|
Primary productivity proxies indicate food availability for suspension feeding mollusks (Rodil et al., 2014)
|
Mean sea surface temperature
|
c
|
Temperature is a limiting factor for marine species distribution that controls metabolic rates and affects physiological functions in all growth stages(Velaoras et al., 2013)
|
To utilize the Bio-Oracle for SDMs, first acquired relevant environmental data layers that align with the scope and scale of their study. This usually involves selecting variables known or hypothesized to influence the distribution of the target Molluscan species. Once these layers are downloaded, the data undergo processing which might include clipping to the study area's spatial extent, ensuring compatibility with other data sets, and statistical analyses to discern patterns and correlations. Processing also involves cleaning the data to remove any anomalies or errors and standardizing the datasets to a common format and spatial resolution to ensure consistency across the variables. The quality and resolution of these data layers are paramount, as they can significantly impact the predictive accuracy of the SDMs. With properly processed environmental data from Bio-Oracle, it can then correlate the presence or absence of Molluscan species with environmental conditions, leading to robust predictive models that can inform sustainable socio economy spots for mollusc populations along coastlines of the study area.
g) Methodologies employed for predictive mapping.
In ecological studies, predictive mapping is essential for understanding species distribution patterns, and four distinctive modelling approaches are commonly utilized to achieve this, each offering unique advantages and mechanisms suitable for various types of data. The Maximum Entropy Model, known as Maxent, is founded on the maximum entropy principle (Wiltshire & Tanner, 2020) as depicted in appendix 1. It excels in predicting species distributions using incomplete data by estimating the widest possible probability distribution of species occurrences within the given constraints. Maxent proves especially advantageous when dealing with presence-only data, as it does not rely on absence information, making it a robust tool for modelling the distribution of rare or elusive species as illustracted in Fig. 2.
The BIOMODelling framework, or BIOMOD, is a sophisticated R-based system designed for ensemble forecasting that incorporates a multitude of species distribution models(Li et al., 2010) as depicted in appendix 1. It works with both presence-absence and presence-only data, enabling users to cross-validate and compare outcomes from various modelling methods like generalized linear models, generalized additive models, and classification trees(Thuiller et al., 2009). BIOMOD’s ensemble method amalgamates multiple predictions, yielding more precise and confident projections that are crucial for conservation efforts and understanding the potential impacts of climate change on species distributions(Thuiller, 2003). Bayesian models utilize Bayes' theorem to refine the probability estimates for hypotheses based on new information, allowing them to incorporate prior knowledge into species distribution modelling as depicted in appendix 1. These models are particularly valuable when historical data or expert insights are available, enhancing predictive accuracy by integrating these with current observations. Their ability to manage complex data and quantify prediction uncertainties makes Bayesian models increasingly popular in ecological and geographical research(Dormann et al., 2018). The Random Forest model is a robust non-parametric method that generates numerous decision trees and uses their collective outcomes for classification or regression tasks(Ho, 1995). In species distribution modelling, Random Forest is adept at processing large sets of predictor variables and capturing intricate interactions within the data as depicted in appendix 1. Its high precision and provision of variable importance metrics make it an essential model for pinpointing the crucial environmental factors influencing species distributions.
The research objective is to identify the most effective algorithm Species Distribution Model (SDM) for optimizing Molluscan farming. The hypothesis posits that among the various modelling approaches, an ensemble model that combines the predictive capabilities of Maxent, BIOMOD, Bayesian models, and the Random Forest model will yield the highest accuracy and reliability in forecasting suitable habitats for Molluscan aquaculture. This ensemble approach is anticipated to leverage the strengths of each individual model, such as Maxent's efficiency with presence-only data, BIOMOD's ensemble forecasting power, Bayesian models' incorporation of prior knowledge, and the Random Forest model's handling of complex data. The synergistic integration of these models is expected to provide a nuanced, multi-faceted view of habitat suitability that can be directly applied to improve the sustainability and yield of Molluscan farming practices.
h) Model training, testing, and evaluation methods
Model training, testing, and evaluation are critical phases in the development of Species Distribution Models (SDMs), ensuring that the models are both accurate and reliable for predicting the distribution of species such as molluscs along the Gujarat coast. During the training phase, the model is built using a portion of the collected occurrence and environmental data. This process involves adjusting the model parameters to best fit the known distribution of the species based on the selected environmental variables. Techniques such as cross-validation, where the dataset is partitioned into complementary subsets, are commonly used to train the model while avoiding overfitting(Kuhn et al., 2013).
The testing phase involves applying the trained model to a separate set of data not used during the training phase. This step is crucial for assessing the model's predictive performance on new, unseen data, providing an indication of its generalizability and reliability in real-world applications. Various metrics, such as the Area Under the Receiver Operating Characteristic Curve (AUC) for binary classification tasks, are used to quantify the model's accuracy, sensitivity, and specificity in predicting species presence or absence(Shabani et al., 2018). Evaluation methods extend beyond statistical metrics and include comparing model predictions against independent occurrence records or expert knowledge to gauge the model's ecological plausibility. Model evaluation may also involve assessing the spatial patterns of predicted suitable habitats against known biological and ecological principles, ensuring that the model's outputs align with established understanding of the species' habitat requirements and behaviours. Through iterative refinement, incorporating feedback from testing and evaluation, the model is honed to provide reliable and ecologically meaningful predictions of Molluscan distribution along the Gujarat coast as given in Fig. 2.
i) Validation procedures using ground data.
Validation of Species Distribution Models (SDMs) using ground data is an integral part of ensuring the accuracy and reliability of the model predictions. In this methodology, the model's predicted distributions of Molluscan species along the Gujarat coast are cross-referenced with independently collected ground-truth data. This ground data is obtained through field surveys and observations conducted after the model has been developed, specifically targeting areas where the model predicts high suitability for the species as well as areas of low predicted suitability to test the model's full range of predictions.
The validation process involves systematically recording the presence or absence of the target Molluscan species within these areas, using standardized sampling techniques such as quadrat sampling or transect walks, consistent with the initial data collection methods. These observations are then compared to the model's predictions to assess the congruence between predicted and observed species occurrences.
Statistical measures are employed to quantify the model's performance, including metrics such as accuracy, precision, recall, and the kappa statistic, which evaluates the agreement between observed occurrences and model predictions beyond chance. Additionally, confusion matrices may be used to provide a detailed breakdown of true positives (correctly predicted presences), false positives (incorrectly predicted presences), true negatives (correctly predicted absences), and false negatives (incorrectly predicted absences). This validation approach not only tests the model's predictive power but also highlights potential areas for refinement. Discrepancies between predicted and observed data can indicate the need for adjustments in model parameters, the inclusion of additional environmental variables, or further investigation into the ecological dynamics of the study area. Through rigorous validation using ground data, the reliability of SDMs in predicting the distribution of Molluscan species along the Gujarat coast can be significantly enhanced, contributing to more informed conservation and management decisions as shown in Fig. 2.
j) Projection of models onto the Gujarat coast.
Projecting Species Distribution Models (SDMs) onto the Gujarat coast for Molluscan species involves translating the model's predictions to generate detailed spatial maps that highlight potential habitats and distribution patterns across the region. This process entails overlaying the SDM outputs onto geographical maps of the Gujarat coast, utilizing GIS (Geographic Information System) software to visualize the correlation between environmental variables and the likelihood of Molluscan presence. These projections take into account the unique ecological characteristics of the Gujarat coastline, including its varied substrates, tidal regimes, and salinity gradients, which are critical determinants of Molluscan habitat suitability.
The resultant maps provide a comprehensive view of areas where environmental conditions align with the optimal habitat requirements of the target Molluscan species, identifying zones of high, moderate, and low suitability. This spatial representation allows for a nuanced understanding of the potential distribution areas, factoring in both the current state of the coast and projected changes due to factors like climate change or human activities. Moreover, these projections are instrumental in guiding conservation efforts, informing sustainable aquaculture practices, and identifying priority areas for further research and monitoring.
By integrating the SDMs with the geographical context of the Gujarat coast, researchers and policymakers can discern patterns and trends that may not be apparent from raw data alone. This approach enables the identification of habitat fragmentation, potential corridors for species migration, and areas vulnerable to environmental stressors, offering valuable insights for the management and preservation of Molluscan biodiversity in the region as shown in Fig. 2.