Molluscan Marvels of Gujarat: Unveiling Biodiversity and Conservation Strategies with the aid of Spatial approach

Molluscan Marvels of Gujarat: Unveiling Biodiversity and Conservation Strategies with the aid of Spatial approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Molluscan Marvels of Gujarat: Unveiling Biodiversity and Conservation Strategies with the aid of Spatial approach Pooja Agravat, Ajay Baldaniya, Biplab Banerjee, Agradeep Mohanta, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4195930/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract This study delves into the Molluscan diversity along the Gujarat coast, India, focusing on the distribution and habitat suitability of four key species: Cerithium caeruleum, Lunella coronata, Peronia verruculata , and Trochus radiatus . Utilizing Species Distribution Models (SDMs) integrated with machine learning algorithms, we assessed the impact of environmental variables on the distribution patterns of these molluscs. Our findings reveal a nuanced understanding of habitat preferences, highlighting the critical roles of salinity, chlorophyll concentration, and water temperature. The MaxEnt model, with the highest Area Under the Curve (AUC) value of 0.63, demonstrated moderate discrimination capability, suggesting room for enhancement in capturing complex ecological interactions. The spatial distribution analysis indicated a random arrangement of species, with no significant spatial autocorrelation observed. This research underscores the significance of advanced modelling techniques in predicting Molluscan distributions, providing insights crucial for the conservation and sustainable management of marine biodiversity along the Gujarat coast. Coastal Biodiversity Ecological Modelling Habitat Suitability Environmental Variables Conservation Strategies Sustainable Management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Gujarat, India's westernmost state, boasts a uniquely diverse geographical landscape, which ranges from vast desert expanses to long, picturesque coastlines. This state is endowed with an extensive 1,600 km coastline that stretches along the Arabian Sea, encompassing a variety of coastal and marine habitats such as estuaries, mudflats, mangroves, coral reefs, and sandy and rocky shores. Diverse habitats support a rich tapestry of marine biodiversity, including a significant variety of Molluscan species (Alonso, 2008). The ecological and socio-economic fabric of Gujarat is intricately linked with its marine biodiversity, making the study of Molluscans in this region not only a subject of ecological interest but also of socio-economic necessity (Desai & Nair, 2015). Molluscans, a diverse group of invertebrates, play pivotal roles in the marine ecosystems of Gujarat (Bhatt et al., 2020). They serve as key species in maintaining the ecological balance and contribute significantly to the state's socio-economic development. Molluscan species such as clams, oysters, mussels, and squids are not just integral to the marine food web, but also form the backbone of local fisheries and aquaculture sectors (Patil et al., 2018). The fisheries sector in Gujarat is a major economic driver, supporting the livelihoods of thousands of coastal communities (Joshi et al., 2020). Molluscans, being a significant part of the catch composition, contribute to the nutritional security and economic stability of these communities (Berthou et al., 2009). The aquaculture industry in Gujarat has also seen a substantial rise, with Molluscan aquaculture emerging as a promising sector. Species such as the Indian backwater oyster and the green mussel have been identified as suitable for commercial cultivation, providing sustainable livelihood options for coastal populations (Sekar Megarajan et al., 2018). Moreover, the Molluscan aquaculture of the state is not just limited to domestic consumption but also has a considerable export potential, contributing to the economy of the state. Beyond their direct economic value, Molluscans play crucial roles as bioindicators, serving as natural monitors of the environmental health of marine ecosystems (Markert et al., 2002). Their sensitivity to changes in water quality and habitat conditions makes them effective indicators for assessing the impacts of pollution, climate change, and anthropogenic disturbances on marine habitats (Moraitis et al., 2018). This bioindicator role is particularly valuable in a state like Gujarat, where rapid industrialization and urbanization pose significant environmental challenges to coastal and marine ecosystems (Moraitis et al., 2018). Despite their ecological and economic significance, Molluscan populations in Gujarat face numerous threats (Gadhavi et al., 2022). Overexploitation, habitat destruction due to coastal development, pollution from industrial and domestic sources, and the impacts of climate change are some of the critical challenges (Watson & Neo, 2021). These threats not only jeopardize the survival of Molluscan species but also threaten the socio-economic fabric of coastal communities dependent on these resources (Simon, 2023). Understanding the distribution, abundance, and diversity of Molluscans is crucial for their conservation and sustainable management (Sor et al., 2020). However, comprehensive data on these aspects are often lacking, hindering effective conservation planning and management. This gap underscores the need for robust methods to predict and map the distribution of Molluscan species across diverse coastal and marine habitats of Gujarat. Species Distribution Models (SDMs) emerge as powerful tools. SDMs use species occurrence data and environmental variables to predict the potential distribution of species across geographical landscapes (Hankins, 2023). By applying SDMs, researchers can gain insights into the habitat preferences of Molluscans, identify areas of high conservation value, and predict shifts in distribution patterns in response to environmental changes (Moraitis et al., 2018). This information is invaluable for devising strategies for the conservation of Molluscan biodiversity and the sustainable management of fisheries and aquaculture sectors in Gujarat. The socio-economic implications of Molluscan distribution patterns are profound (Gallagher & Albano, 2023). Identifying areas with high Molluscan diversity and abundance can help optimize the allocation of resources for fisheries and aquaculture, enhancing productivity and sustainability (Theuerkauf et al., 2022). Moreover, understanding the impacts of environmental changes on Molluscan distributions can inform adaptive management strategies, ensuring the resilience of coastal communities to ecological and socio-economic shifts. The threats facing these species underscore the need for a comprehensive understanding of their distribution and ecology, for which SDMs provide an effective approach (Gutt et al., 2012). By leveraging the predictive power of SDMs, conservationists, policymakers, and stakeholders can ensure the sustainable management of Molluscan resources, safeguarding the ecological integrity of Gujarat's marine environments and the socio-economic resilience of its coastal communities. a) Challenges in Molluscan Conservation and Management Conserving and sustainably managing Molluscan species in Gujarat faces significant challenges, including over-exploitation, habitat destruction, and climate change impacts (Biju Kumar & Ravinesh, 2017). Over-exploitation due to high demand in fisheries leads to unsustainable harvesting, disrupting marine ecosystems and reducing species populations. Habitat destruction, driven by coastal development and pollution, further threatens Molluscan habitats such as coral reefs and mangroves, essential for their survival and biodiversity (Ryan et al., 2019). Additionally, climate change introduces stressors like rising sea temperatures and ocean acidification, adversely affecting Molluscan physiology and distribution. In Gujarat, case studies like the conservation efforts for the Indian backwater oyster ( Saccostrea cucullata ) and the green mussel (Perna viridis ) highlight the complexity of these challenges. For instance, the Gulf of Kutch, a biodiversity hotspot, has seen significant declines in Molluscan species due to industrial pollution and habitat degradation (Mitra et al., 2022). Efforts to restore these populations have been hampered by the lack of accurate distribution data, essential for effective conservation planning and policymaking (Borges et al., 2017). The need for comprehensive distribution data is critical to inform conservation strategies, identify critical habitats, and assess the impacts of environmental changes (Villero et al., 2017). Recent initiatives leveraging Species Distribution Models (SDMs) analysis in Gujarat offer promising insights into Molluscan distributions, aiding in the development of targeted conservation strategies. Effective conservation policies must address these multifaceted challenges through sustainable fishing practices, habitat protection and restoration, and climate change mitigation (Riisager-Simonsen et al., 2022). Case studies in Gujarat demonstrate the importance of integrating scientific research with community engagement and adaptive management approaches, ensuring the long-term sustainability of Molluscan populations and the ecological and socio-economic benefits they provide (Mohan Joseph, 2007). b) Overview of Machine Learning Models in SDM Machine learning has revolutionized ecological modelling, including the prediction of species distributions through Species Distribution Models (SDMs)(Zhang et al., 2020). By leveraging complex datasets, machine learning models uncover patterns and relationships between species occurrences and environmental variables, offering nuanced insights into species habitats and potential distribution areas (Zhang et al., 2020). This method is especially useful for tackling conservation issues, as it allows for the forecasting of potential shifts in species distributions due to evolving environmental scenarios, as illustrated in Table 1 with multiple examples. Among the machine learning models applied in SDMs, MaxEnt (Maximum Entropy) is widely used for its effectiveness in handling presence-only data, making it ideal for species with limited occurrence records (Hankins, 2023) . Biomod, an ensemble forecasting package, integrates multiple models to provide more robust predictions, accounting for uncertainties inherent in ecological data (Zhang et al., 2020). Random Forest , an ensemble learning method, excels in classification and regression tasks, offering high accuracy and the ability to handle complex interactions between variables (Koudenoukpo et al., 2021). Bayesian models incorporate prior knowledge into the modelling process, enhancing predictions by integrating existing ecological theories and expert opinions (Kocot et al., 2020). The table below showcases case studies involving the application of these machine learning models in SDMs for marine invertebrates, providing a glimpse into their diverse applications: Table 1: Machine Learning Models Utilized Species Distribution Modelling (SDM) Marine Invertebrate Model Description Reference Endemic Brazilian coral ( Mussismilia harttii ) MaxEnt Predicting potential distribution under climate change scenarios. (de Oliveira et al., 2019) Octopus vulgaris Random Forest Written in ink: Elemental signatures in octopus ink successfully trace geographical origin (Duarte et al., 2022) Pacific oyster ( Crassostrea gigas ) Biomod Early detection of marine invasive species following the deployment of an artificial reef: Integrating tools to assist the decision-making process (Castro et al., 2021) Neon Flying Squid ( Ommastrephes bartramii ) Bayesian models Genralized linear Bayesian models for standardizing CPUE: an application to a squid jigging fishery in the north – west pacific ocean (Cao et al., 2011) These studies highlight the versatility and effectiveness of machine learning models in SDMs, providing critical insights for conservation biology and ecological research. The potential distribution of key Molluscan species, the research will identify critical habitats that necessitate conservation efforts, thereby aiding in the preservation of these vital marine resources. The findings will also illuminate the socio-economic interdependencies between local communities and Molluscan populations, highlighting the importance of sustainable management practices to support livelihoods. This complex understanding of the ecological and socio-economic dynamics at play will provide a robust foundation for informed policymaking. Policymakers can leverage the insights gained to devise regulations and initiatives that balance conservation needs with economic development, ensuring the long-term sustainability of Molluscan resources. Ultimately, this study aims to foster a symbiotic relationship between human activities and marine biodiversity, contributing to the resilience of coastal ecosystems and the communities that depend on them in Gujarat coast for their socio- economic activities. Materials and Methods 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: 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. Results and Discussion a) Model Evaluation In the context of the research conducted on Species Distribution Modelling (SDM), a detailed analysis was undertaken to evaluate the comparative performance of four prevalent machine learning models, namely Maxent, BIOMOD, Bayesian models, and Random Forest. This evaluation was centred around the interpretation of Receiver Operating Characteristic (ROC) curves and the corresponding Area Under the Curve (AUC) values, which serve as critical metrics for assessing the ability of these models to accurately discriminate between species presence and absence across varied environmental conditions. The ROC curve, by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR), offers a nuanced visual representation of a model's discriminative capacity at various threshold levels. This curve essentially delineates the balance a model maintains between correctly identifying locations where a species is present (sensitivity) and erroneously predicting species presence in locales where it is absent (1 - specificity)(Florkowski, 2008). Ideally, a model's ROC curve would closely align with the upper left corner of the plot, signifying optimal sensitivity and specificity levels. Upon comparative analysis of the ROC curves and AUC values across the studied models, a spectrum of discriminative capabilities was unveiled. Maxent emerged with the highest AUC value of 0.63, indicating a moderate level of discrimination that, while surpassing random chance, highlighted potential areas for enhancement. The model's ROC curve depicted a deviation from the diagonal, suggesting a capability to distinguish between presence and absence locations to a certain extent. However, the observed moderate AUC value raised considerations regarding Maxent's ability to fully encapsulate complex ecological interactions or effectively manage data inconsistencies as depicted in Fig.3. BIOMOD, with an AUC value of 0.53, showcased a performance closely aligned with Maxent, albeit with a marginally reduced discriminative prowess. The model's ROC curve exhibited a trajectory slightly inferior to that of Maxent, hinting at a potential compromise in sensitivity or specificity. This could be attributed to BIOMOD's ensemble approach, which amalgamates various models, potentially introducing a higher variance in predictions and a slight dip in overall efficacy(Singleton et al., 2023) as shown in Fig. 3. In the case of Bayesian models, the AUC value further declined to 0.43, signifying a more pronounced reduction in discrimination capability relative to Maxent and BIOMOD. The ROC curve for Bayesian models indicated a greater deviation from the diagonal, reflecting lower levels of sensitivity and specificity. This suggested challenges in accurately representing species distribution patterns, possibly due to the intrinsic assumptions and constraints of the Bayesian methodology or the complexity of the ecological niches under consideration. Random Forest, with the lowest AUC value of 0.42 among the evaluated models, demonstrated the least discriminative ability. Its ROC curve approached the diagonal more closely, underscoring a marked difficulty in differentiating between presence and absence locations as illustrated in Fig. 3. This was potentially linked to the model's reliance on decision trees and its challenges in capturing intricate non-linear data relationships. Subsequent post-hoc multiple pairwise comparison tests revealed statistically significant differences in model accuracies. Maxent models were found to exhibit significantly higher accuracy compared to BIOMOD, Bayesian models, and Random Forest, with p-values less than 0.01 for both AUC and True Skill Statistics (TSS) metrics. Despite these significant disparities in accuracy, the magnitude of differences remained relatively modest. The gap between the highest (Maxent) and lowest (Random Forest) mean accuracy measures was merely 0.022 AUC points and 0.034 TSS points. Among the algorithms, all pairwise comparisons of accuracy were significantly distinct for both AUC and TSS metrics (p<0.01), except for the comparison involving Maxent. The sequence of mean accuracies observed was Maxent (mean AUC: 0.63; TSS: 0.777), followed by Bayesian models (mean AUC: 0.53; TSS: 0.702), BIOMOD (mean AUC: 0.49; TSS: 0.56), and Random Forest (AUC: 0.42; TSS: 0.693). This comprehensive analysis underscored the intricate nuances and inherent challenges in accurately modelling species distributions. While Maxent and BIOMOD displayed relatively superior discriminative capacities, Bayesian models and Random Forest highlighted the complexities involved in SDM. The ROC curve and AUC metrics proved instrumental in dissecting the strengths and limitations of each model, facilitating informed decisions regarding model selection, threshold optimization, and data interpretation in the realm of ecological research and conservation endeavours. b) Probability of Finding the Species through Abiotic Factors The exploration of oceanographic data revealed intricate details about the marine environment, shedding light on the dynamic interplay of ecological and physical factors. The study commenced with an analysis of salinity, a parameter indicative of the total dissolved salt content in water, typically expressed in Practical Salinity Units (PSU). It was found that the average salinity across the observed regions stood at approximately 35.80 PSU, aligning with the general range for the world's oceans and denoting a marine environment of typical salinity levels. However, the noted variability in salinity, as evidenced by a standard deviation of 2.44 PSU, underscored the complex interplay of factors such as riverine input, precipitation, evaporation, and ocean currents, which could significantly alter local salinity levels. The observed salinity ranged from 31.60 PSU to 39.40 PSU, highlighting the diverse conditions under which marine organisms thrive as shown Fig. 4. The investigation further delved into chlorophyll concentrations, a crucial proxy for the abundance of phytoplankton, the microscopic, plant-like organisms at the base of the marine food web. An average chlorophyll concentration of about 5.02 mg/m³ was recorded, reflecting a healthy presence of phytoplankton essential for supporting a diverse marine life. The variability in chlorophyll levels, with a standard deviation of 2.23 mg/m³, pointed to the varying productivity of different marine areas, influenced by factors such as nutrient availability, light penetration, and water temperature. The range of chlorophyll concentrations observed, from 0.89 mg/m³ to 8.90 mg/m³, indicated significant ecological variability, attributable to natural phenomena such as algal blooms and seasonal changes, or anthropogenic impacts like pollution and eutrophication. The temperature of the water bodies emerged as a critical parameter influencing numerous biological and chemical processes within aquatic ecosystems. The study noted an average temperature of 18.73°C, reflecting the temperate nature of the sampled environments. However, the considerable standard deviation of 6.07°C highlighted the wide range of temperatures to which marine organisms are exposed. The temperature span from 7.90°C to 27.90°C underscored the diversity of thermal habitats in the marine environment, each supporting unique communities of organisms. Temperature variations were found to affect metabolic rates, reproductive cycles, migration patterns, and even the solubility of gases in water, thus playing a pivotal role in shaping marine biodiversity as shown in Fig. 4. The examination of probability values associated with the observations offered a lens through which the reliability and confidence in the data could be assessed. An average probability of 0.85 signified a high level of confidence in the observations or the predictions made by the model, with a relatively low standard deviation of 0.06 indicating a consistent level of reliability across the dataset. The range of probabilities, from 0.74 to 0.96, though not exceedingly wide, reflected a degree of variability in the confidence levels associated with different observations as illustrated in Fig. 4 . The derived model for the multiple linear regression analysis further illuminated the relationships between the parameters: This equation suggested that temperature exerted a positive effect on probability, with a coefficient of 0.01, implying a slight increase in probability with rising temperatures. The coefficients for salinity and chlorophyll were found to be negligible, indicating a minimal direct effect on probability within this linear framework. The intercept of the equation, set at 0.69, represented the baseline probability when all independent variables were held constant at zero. It was noted that the small coefficients for salinity and chlorophyll might not necessarily signify an absence of relationship but could indicate that the relationship might not be linear or could be influenced by interactions between variables not captured by this simple linear model. The growth probabilities of Molluscan species Cerithium caeruleum, Lunella coronata, Peronia verruculata, and Trochus radiatus were analyzed against environmental factors such as salinity, chlorophyll concentration, and water temperature through Pearson correlation analysis. The findings revealed a strong negative correlation between salinity and growth probability (-0.84), suggesting that higher salinity levels might inhibit growth across these species, possibly due to osmotic stress affecting physiological processes. Chlorophyll concentration showed a high correlation (0.78) with growth probability, indicating that the abundance of phytoplankton, as inferred from chlorophyll levels, might directly impact the growth of these species, possibly due to varied diets or the overriding influence of other environmental or ecological factors as shown in Fig. 5 . Conversely, a very strong positive correlation (0.98) was observed between water temperature and growth probability, highlighting temperature's critical role in promoting growth, likely due to its influence on metabolic rates and physiological functions in these ectothermic organisms. These insights emphasize the importance of monitoring and managing salinity and temperature within the habitats of these species to support their growth and conservation, while also suggesting that factors beyond primary food availability, such as food quality and ecological interactions, might be significant for their growth as shown in Fig. 5. c) Projection onto the Gujarat Coast: In the assessment of potential habitats for the Molluscan species Cerithium caeruleum, Lunella coronata, Peronia verruculata, and Trochus radiatus along the Gujarat coast, the Maximum Entropy (MaxEnt) modelling technique was utilized to delineate their prospective distributions. Employed extensively in ecological modelling for predicting species distributions, MaxEnt capitalizes on presence-only data coupled with environmental variables to approximate the likelihood of species occurrences across varied landscapes. This approach has been demonstrated to be particularly efficacious in forecasting species distributions under both existing and future environmental conditions, thereby offering invaluable insights for the formulation of conservation strategies and resource management plans. The MaxEnt model, integrating pivotal environmental predictors such as salinity, chlorophyll concentration, and water temperature—parameters previously identified to exert significant influences on the growth and survivability of these Molluscan entities—generated intricate distribution maps for each species across the Gujarat coastline. The projections derived from the model indicated distinctive habitat preferences among the species, mirroring their unique ecological niches and tolerance levels to the environmental variables under consideration. For Cerithium caeruleum and Trochus radiatus , the model delineated potential habitats within regions characterized by comparatively moderate salinity levels, corroborating prior findings that elevated salinity could adversely affect these species. Conversely, the projections for Lunella coronata and Peronia verruculata suggested a more expansive distribution along the coastal stretch, indicative of a heightened tolerance to fluctuating salinity levels, potentially attributable to their adaptive osmoregulatory capacities. Furthermore, the model incorporated the variable of chlorophyll concentration, reflecting a strong correlation with growth probability, to signify the availability of food resources. These highlighted areas endowed with ample primary productivity as potential focal points for these Molluscan species as shown in Fig. 6,7,8,9. This aspect accentuates the necessity of acknowledging not merely the direct impacts of environmental factors on species growth but also their indirect ramifications through the dynamics of the food web. The initial analysis, revealing a pronounced positive impact of water temperature on Molluscan growth, was reaffirmed by the MaxEnt projections Fig. 6,7,8,9. Zones featuring optimal temperature ranges were identified as potential high-probability locales for the occurrence of all four Molluscan species. This observation holds particular pertinence in the context of climate change, wherein rising temperatures may instigate shifts or expansions in the suitable habitats for these species along the Gujarat coast. The projections formulated by the MaxEnt model provide a holistic overview of the potential distribution patterns of Cerithium caeruleum, Lunella coronata, Peronia verruculata, and Trochus radiatus within the Gujarat region, encapsulating the critical environmental determinants pivotal to Molluscan habitat suitability. These insights are imperative for the conservation and management of these species, particularly in the wake of ongoing environmental alterations and anthropogenic activities that may modify their natural habitats. Moreover, this study underscores the complexity inherent in species-environment interactions, highlighting the imperative for an integrated approach that encompasses multiple environmental variables and their potential synergistic effects on species distribution. The observed variability in habitat preferences among the examined species underscores the significance of devising species-specific conservation strategies, underpinned by rigorous ecological modeling and a comprehensive understanding of each species' ecological niche Fig 6,7,8,9. d) Spatial Auto-correlation In the conducted research, Species Distribution Modelling (SDM) was performed for four marine taxa: Cerithium caeruleum, Lunella coronatus, Peronia verruculata, and Trochus radiatus. Subsequent analysis for spatial autocorrelation was undertaken, yielding the following metrics: Moran's I coefficient was determined to be 0.053, diverging from the anticipated index of -0.0116. The variance of Moran's I was computed at 0.0028, and the statistical significance was evaluated through a Z-score of 1.2097, with an associated p-value of 0.226. In the executed study, Species Distribution Modelling (SDM) was applied to four marine taxa: Cerithium caeruleum, Lunella coronatus, Peronia verruculata, and Trochus radiatus , with the aim of elucidating their spatial dispersion patterns. To quantify the degree of spatial autocorrelation and ascertain whether the distribution of these taxa was clustered, random, or dispersed, Moran's I statistic was employed. The computed Moran's I index stood at 0.053, marginally surpassing the hypothesized mean of -0.0116. This positive Moran's I index intimated a slight propensity towards a clustered disposition; however, its proximity to zero suggested that the clustering was not pronounced. The expected index, inherently negative for spatial datasets, represents the Moran's I value under the null hypothesis of a stochastic spatial distribution as shown in Fig. 10. The ascertained variance for Moran's I was recorded at 0.0028, providing insight into the dispersion of the index values and facilitating the derivation of the Z-score. The resultant Z-score was calculated to be 1.2097, serving as an indicator of the statistical significance of Moran's I as shown in Fig. 10. Within this context, the Z-score elucidates the deviation, measured in standard units, of the observed Moran's I from the expected value under the null hypothesis. The derived p-value, corresponding to this Z-score, was 0.226, surpassing the conventional alpha level of 0.05. This elevated p-value suggests that the observed spatial pattern does not significantly deviate from a random distribution, leading to the non-rejection of the null hypothesis that postulates a random spatial arrangement. Therefore, it was inferred that the spatial distribution of Cerithium caeruleum, Lunella coronatus, Peronia verruculata , and Trochus radiatus did not exhibit significant spatial autocorrelation. The marginally positive Moran's I index, the absence of statistical significance, as evidenced by the p-value, led to the inference that the spatial distribution of the aforementioned taxa is characterized by randomness within the study area. This outcome implies that the spatial dispersion of these taxa might be governed by variables not encapsulated in the current model or that their distribution patterns are inherently stochastic. Future investigations may benefit from exploring additional environmental or biological factors that could potentially influence the spatial distribution patterns of these taxa. Conclusion In the study along the Gujarat coastline, the distribution of Molluscan species was extensively analyzed, focusing on their ecological and socio-economic importance for conservation and management. Using Species Distribution Models (SDMs) and machine learning, the research aimed to predict potential habitats and understand the impact of environmental factors on Molluscan distribution. The study combined field surveys, quadrat sampling, and data from Bio-Oracle to explore Molluscan habitat preferences. The assessment of machine learning models in SDMs, including MaxEnt, BIOMOD, Bayesian models, and Random Forest, was conducted using ROC curves and AUC values to measure their discriminative ability. MaxEnt was the most effective, with an AUC value of 0.63, indicating moderate accuracy and highlighting the need for model refinement. Random Forest, with the lowest AUC of 0.42, faced challenges in accurately distinguishing presence from absence locations, likely due to the complexity of its decision-tree-based approach. Correlation analysis revealed a strong negative correlation (-0.84) between salinity and Molluscan growth probability, suggesting adverse effects of high salinity on growth. Conversely, a very strong positive correlation (0.98) between water temperature and growth probability underscored the importance of thermal conditions in Molluscan development. Spatial autocorrelation analysis using Moran's I statistic indicated a nearly random spatial distribution of Molluscan species, with a Moran's I index of 0.053 and a non-significant p-value of 0.226. This suggests that other unaccounted variables or stochastic processes might influence the spatial patterns of these species. The research contributes to marine biodiversity conservation by highlighting the relationship between Molluscan species and their environment, emphasizing the need for targeted conservation strategies that consider both ecological and socio-economic factors. The findings of the study on species-environment interactions and the performance of SDMs offer valuable insights for developing informed conservation policies to protect Gujarat's marine biodiversity and support local communities. Declarations Acknowledgement The authors express their gratitude to the Heads of the Department of Zoology, The Maharaja Sayajirao University of Baroda, Vadodara, and the Department of Life Sciences, Bhakta Kavi Narsinh Mehta University, Junagadh, for providing laboratory, storage, and museum facilities. Additionally, the authors extend their appreciation to the anonymous reviewers for their valuable feedback on the manuscript. Pooja Agravat, Ajay Baldaniya and Agradeep Mohanta acknowledge the fellowship received from the SHODH - ScHeme Of Developing High-quality research, provided by the Education Department, Gujarat. Author’s Contribution: 1. Pooja Agravat: Conceptualization, Methodology, Original Draft Preparation 2. Ajay Baldaniya: Literature review, Data collection, Validation 3. Agradeep Mohanta: Methodology, Data Collection, Data interpretation 4. Biplab Bannerjee: Statistical analysis, Graph preparation, Data interpretation 5. Jatin Raval: Visualization, Research supervision, Reviewing and editing 6. Pradeep Mankodi: Research Design, Research supervision, Reviewing and editing Funding: There was no external funding for this study. Data availability: The authors declare that all data and materials support their published claims and comply with field standards. Code availability: The authors declare that software application or custom code supports their published claims and comply with field standards. The R studio codes are available in Appendix 1. The authors declare that, • The manuscript has not been published anywhere nor submitted to another journal. • The manuscript is not currently being considered for publication in any another journal. • All authors have been personally and actively involved in substantive work leading to the manuscript, and will hold themselves jointly and individually responsible for its content. • Research does not involve any Human Participants and/or Animals. Ethics: approval Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Conflict of interest: The authors declare that there are no conflicts of interest References Alonso, A. (2008). Biodiversity: connecting with the tapestry of life . DIANE Publishing. Berthou, P., Poutiers, J.-M., Goulletquer, P., & Dao, J.-C. 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jatin","middleName":"","lastName":"Raval","suffix":""},{"id":296324107,"identity":"92113015-eec9-4fc7-b1b4-8738b1f04544","order_by":5,"name":"Pradeep Mankodi","email":"","orcid":"","institution":"The Maharaja Sayajirao University of Baroda Faculty of Science","correspondingAuthor":false,"prefix":"","firstName":"Pradeep","middleName":"","lastName":"Mankodi","suffix":""}],"badges":[],"createdAt":"2024-03-31 13:57:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4195930/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4195930/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-025-35959-7","type":"published","date":"2025-01-28T15:58:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55795283,"identity":"92c98ec7-dc64-4c86-962f-a2cadf4f8f29","added_by":"auto","created_at":"2024-05-03 10:37:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":721449,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy area Showing the Gujarat Coast, Sample area Collection from Mangrol, Adri, Veraval .\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/eee13485403a5a450acad6f0.png"},{"id":55795284,"identity":"56b0332f-d3db-4ca5-9736-d2521e592fc8","added_by":"auto","created_at":"2024-05-03 10:37:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":231986,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethodology for Species Distribution Modelling of Molluscs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/c3c23947a22ede18254f796a.png"},{"id":55795594,"identity":"8120bed2-2bbf-4e70-bf6a-c23cce073f90","added_by":"auto","created_at":"2024-05-03 10:45:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":195309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curve and AUC values for machine learning models for potential zone distribution of Molluscans through SDM\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/7fafd5bdb835bae1cc4dd922.png"},{"id":55795286,"identity":"ba0e60f4-c356-4ea1-8da8-782fe9759f3c","added_by":"auto","created_at":"2024-05-03 10:37:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":318257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Fig. illustrating the likelihood of locating the species in relation to abiotic factors such as salinity, chlorophyll content, and temperature\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/ad718f6565b5bee13456d894.png"},{"id":55795290,"identity":"b0970499-3f23-443e-8dc3-10917c6ab77a","added_by":"auto","created_at":"2024-05-03 10:37:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":65795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure illustrates a correlation plot with various Bio-ORACLE data types\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/18e61dc47ecc1558f5422ee4.png"},{"id":55795293,"identity":"3a5e4209-4341-4d90-993e-17e1a6c91c8c","added_by":"auto","created_at":"2024-05-03 10:37:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":303844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjection for the species distribution model for\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Cerithium caeruleum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e using MaxEnt modelling\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/1e31cf33fceb517a5441fffa.png"},{"id":55795288,"identity":"a1a08f34-ad74-4a9e-9ebd-b77efb39a64a","added_by":"auto","created_at":"2024-05-03 10:37:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":366324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjection for the species distribution model for\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Lunella coronata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e using MaxEnt modelling.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/93debc0556dd0a4c156f1df0.png"},{"id":55795595,"identity":"07a127fa-7abf-406c-a478-ecfa28155ea8","added_by":"auto","created_at":"2024-05-03 10:45:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":397039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjection for the species distribution model for\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Peronia verruculata \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eusing MaxEnt modelling\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/f6c64ffe5f79344800aae5a6.png"},{"id":55795294,"identity":"456b9f6e-1dca-4598-a5f9-caed4951c99b","added_by":"auto","created_at":"2024-05-03 10:37:26","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":329056,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjection for the species distribution model for\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Peronia verruculata\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e using MaxEnt modelling.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/42e56c5182bd0a902d69b5a9.png"},{"id":55795287,"identity":"741d766a-82fe-489f-aecf-124898669e41","added_by":"auto","created_at":"2024-05-03 10:37:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":232968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneration of Spatial Autocorrelation Matrix for the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/cdb086e2016e0e2c27087ff2.png"},{"id":75351389,"identity":"0d21ebe5-eac6-4e3d-81a1-dec52562e47c","added_by":"auto","created_at":"2025-02-03 16:10:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4832269,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/bfe48ab4-940f-455f-8ec2-2011a46d2111.pdf"},{"id":55795292,"identity":"4713701b-fa43-4997-a474-16abb9fa3cda","added_by":"auto","created_at":"2024-05-03 10:37:26","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":19227,"visible":true,"origin":"","legend":"","description":"","filename":"Appendex1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4195930/v1/87fbc4d5214829cf8b6427ae.docx"}],"financialInterests":"","formattedTitle":"Molluscan Marvels of Gujarat: Unveiling Biodiversity and Conservation Strategies with the aid of Spatial approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGujarat, India\u0026apos;s westernmost state, boasts a uniquely diverse geographical landscape, which ranges from vast desert expanses to long, picturesque coastlines. This state is endowed with an extensive 1,600 km coastline that stretches along the Arabian Sea, encompassing a variety of coastal and marine habitats such as estuaries, mudflats, mangroves, coral reefs, and sandy and rocky shores. Diverse habitats support a rich tapestry of marine biodiversity, including a significant variety of Molluscan species\u0026nbsp;(Alonso, 2008). The ecological and socio-economic fabric of Gujarat is intricately linked with its marine biodiversity, making the study of Molluscans in this region not only a subject of ecological interest but also of socio-economic necessity\u0026nbsp;(Desai \u0026amp; Nair, 2015). Molluscans, a diverse group of invertebrates, play pivotal roles in the marine ecosystems of Gujarat\u0026nbsp;(Bhatt et al., 2020). They serve as key species in maintaining the ecological balance and contribute significantly to the state\u0026apos;s socio-economic development. Molluscan species such as clams, oysters, mussels, and squids are not just integral to the marine food web, but also form the backbone of local fisheries and aquaculture sectors\u0026nbsp;(Patil et al., 2018). The fisheries sector in Gujarat is a major economic driver, supporting the livelihoods of thousands of coastal communities\u0026nbsp;(Joshi et al., 2020). Molluscans, being a significant part of the catch composition, contribute to the nutritional security and economic stability of these communities\u0026nbsp;(Berthou et al., 2009).\u003c/p\u003e\n\u003cp\u003eThe aquaculture industry in Gujarat has also seen a substantial rise, with Molluscan aquaculture emerging as a promising sector. Species such as the Indian backwater oyster and the green mussel have been identified as suitable for commercial cultivation, providing sustainable livelihood options for coastal populations\u0026nbsp;(Sekar Megarajan et al., 2018). Moreover, the Molluscan aquaculture of the state is not just limited to domestic consumption but also has a considerable export potential, contributing to the economy of the state. Beyond their direct economic value, Molluscans play crucial roles as bioindicators, serving as natural monitors of the environmental health of marine ecosystems\u0026nbsp;(Markert et al., 2002). Their sensitivity to changes in water quality and habitat conditions makes them effective indicators for assessing the impacts of pollution, climate change, and anthropogenic disturbances on marine habitats\u0026nbsp;(Moraitis et al., 2018). This bioindicator role is particularly valuable in a state like Gujarat, where rapid industrialization and urbanization pose significant environmental challenges to coastal and marine ecosystems\u0026nbsp;(Moraitis et al., 2018).\u003c/p\u003e\n\u003cp\u003eDespite their ecological and economic significance, Molluscan populations in Gujarat face numerous threats (Gadhavi et al., 2022). Overexploitation, habitat destruction due to coastal development, pollution from industrial and domestic sources, and the impacts of climate change are some of the critical challenges (Watson \u0026amp; Neo, 2021). These threats not only jeopardize the survival of Molluscan species but also threaten the socio-economic fabric of coastal communities dependent on these resources (Simon, 2023). Understanding the distribution, abundance, and diversity of Molluscans is crucial for their conservation and sustainable management (Sor et al., 2020). However, comprehensive data on these aspects are often lacking, hindering effective conservation planning and management. This gap underscores the need for robust methods to predict and map the distribution of Molluscan species across diverse coastal and marine habitats of Gujarat. Species Distribution Models (SDMs) emerge as powerful tools. SDMs use species occurrence data and environmental variables to predict the potential distribution of species across geographical landscapes (Hankins, 2023). By applying SDMs, researchers can gain insights into the habitat preferences of Molluscans, identify areas of high conservation value, and predict shifts in distribution patterns in response to environmental changes (Moraitis et al., 2018). This information is invaluable for devising strategies for the conservation of Molluscan biodiversity and the sustainable management of fisheries and aquaculture sectors in Gujarat.\u003c/p\u003e\n\u003cp\u003eThe socio-economic implications of Molluscan distribution patterns are profound\u0026nbsp;(Gallagher \u0026amp; Albano, 2023). Identifying areas with high Molluscan diversity and abundance can help optimize the allocation of resources for fisheries and aquaculture, enhancing productivity and sustainability\u0026nbsp;(Theuerkauf et al., 2022). Moreover, understanding the impacts of environmental changes on Molluscan distributions can inform adaptive management strategies, ensuring the resilience of coastal communities to ecological and socio-economic shifts. The threats facing these species underscore the need for a comprehensive understanding of their distribution and ecology, for which SDMs provide an effective approach\u0026nbsp;(Gutt et al., 2012). By leveraging the predictive power of SDMs, conservationists, policymakers, and stakeholders can ensure the sustainable management of Molluscan resources, safeguarding the ecological integrity of Gujarat\u0026apos;s marine environments and the socio-economic resilience of its coastal communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea) Challenges in Molluscan Conservation and Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConserving and sustainably managing Molluscan species in Gujarat faces significant challenges, including over-exploitation, habitat destruction, and climate change impacts\u0026nbsp;(Biju Kumar \u0026amp; Ravinesh, 2017). Over-exploitation due to high demand in fisheries leads to unsustainable harvesting, disrupting marine ecosystems and reducing species populations. Habitat destruction, driven by coastal development and pollution, further threatens Molluscan habitats such as coral reefs and mangroves, essential for their survival and biodiversity\u0026nbsp;(Ryan et al., 2019). Additionally, climate change introduces stressors like rising sea temperatures and ocean acidification, adversely affecting Molluscan physiology and distribution. In Gujarat, case studies like the conservation efforts for the Indian backwater oyster (\u003cstrong\u003e\u003cem\u003eSaccostrea cucullata\u003c/em\u003e\u003c/strong\u003e) and the green mussel \u003cstrong\u003e\u003cem\u003e(Perna viridis\u003c/em\u003e\u003c/strong\u003e) highlight the complexity of these challenges. For instance, the Gulf of Kutch, a biodiversity hotspot, has seen significant declines in Molluscan species due to industrial pollution and habitat degradation\u0026nbsp;(Mitra et al., 2022). Efforts to restore these populations have been hampered by the lack of accurate distribution data, essential for effective conservation planning and policymaking\u0026nbsp;(Borges et al., 2017).\u003c/p\u003e\n\u003cp\u003eThe need for comprehensive distribution data is critical to inform conservation strategies, identify critical habitats, and assess the impacts of environmental changes\u0026nbsp;(Villero et al., 2017). Recent initiatives leveraging Species Distribution Models (SDMs) analysis in Gujarat offer promising insights into Molluscan distributions, aiding in the development of targeted conservation strategies. Effective conservation policies must address these multifaceted challenges through sustainable fishing practices, habitat protection and restoration, and climate change mitigation\u0026nbsp;(Riisager-Simonsen et al., 2022). Case studies in Gujarat demonstrate the importance of integrating scientific research with community engagement and adaptive management approaches, ensuring the long-term sustainability of Molluscan populations and the ecological and socio-economic benefits they provide\u0026nbsp;(Mohan Joseph, 2007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Overview of Machine Learning Models in SDM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMachine learning has revolutionized ecological modelling, including the prediction of species distributions through Species Distribution Models (SDMs)(Zhang et al., 2020). By leveraging complex datasets, machine learning models uncover patterns and relationships between species occurrences and environmental variables, offering nuanced insights into species habitats and potential distribution areas\u0026nbsp;(Zhang et al., 2020). This method is especially useful for tackling conservation issues, as it allows for the forecasting of potential shifts in species distributions due to evolving environmental scenarios, as illustrated in Table 1 with multiple examples. Among the machine learning models applied in SDMs, \u003cstrong\u003eMaxEnt (Maximum Entropy)\u003c/strong\u003e is widely used for its effectiveness in handling presence-only data, making it ideal for species with limited occurrence records\u0026nbsp;(Hankins, 2023)\u003cstrong\u003e. Biomod,\u003c/strong\u003e an ensemble forecasting package, integrates multiple models to provide more robust predictions, accounting for uncertainties inherent in ecological data\u0026nbsp;(Zhang et al., 2020). \u003cstrong\u003eRandom Forest\u003c/strong\u003e, an ensemble learning method, excels in classification and regression tasks, offering high accuracy and the ability to handle complex interactions between variables\u0026nbsp;(Koudenoukpo et al., 2021). \u003cstrong\u003eBayesian models\u003c/strong\u003e incorporate prior knowledge into the modelling process, enhancing predictions by integrating existing ecological theories and expert opinions\u0026nbsp;(Kocot et al., 2020). The table below showcases case studies involving the application of these machine learning models in SDMs for marine invertebrates, providing a glimpse into their diverse applications:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Machine Learning Models Utilized Species Distribution Modelling (SDM)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarine Invertebrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.143094841930116%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.945091514143094%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.625623960066555%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003eEndemic Brazilian coral\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eMussismilia harttii\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.143094841930116%\" valign=\"top\"\u003e\n \u003cp\u003eMaxEnt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.945091514143094%\" valign=\"top\"\u003e\n \u003cp\u003ePredicting potential distribution under climate change scenarios.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.625623960066555%\" valign=\"top\"\u003e\n \u003cp\u003e(de Oliveira et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eOctopus vulgaris\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.143094841930116%\" valign=\"top\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.945091514143094%\" valign=\"top\"\u003e\n \u003cp\u003eWritten in ink: Elemental signatures in octopus ink successfully trace geographical origin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.625623960066555%\" valign=\"top\"\u003e\n \u003cp\u003e(Duarte et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003ePacific oyster (\u003cem\u003eCrassostrea gigas\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.143094841930116%\" valign=\"top\"\u003e\n \u003cp\u003eBiomod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.945091514143094%\" valign=\"top\"\u003e\n \u003cp\u003eEarly detection of marine invasive species following the deployment of an artificial reef: Integrating tools to assist the decision-making process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.625623960066555%\" valign=\"top\"\u003e\n \u003cp\u003e(Castro et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.286189683860233%\" valign=\"top\"\u003e\n \u003cp\u003eNeon Flying Squid\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eOmmastrephes bartramii\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.143094841930116%\" valign=\"top\"\u003e\n \u003cp\u003eBayesian models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.945091514143094%\" valign=\"top\"\u003e\n \u003cp\u003eGenralized linear Bayesian models for standardizing CPUE: an application to a squid jigging fishery in the north \u0026ndash; west pacific ocean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.625623960066555%\" valign=\"top\"\u003e\n \u003cp\u003e(Cao et al., 2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese studies highlight the versatility and effectiveness of machine learning models in SDMs, providing critical insights for conservation biology and ecological research. The potential distribution of key Molluscan species, the research will identify critical habitats that necessitate conservation efforts, thereby aiding in the preservation of these vital marine resources. The findings will also illuminate the socio-economic interdependencies between local communities and Molluscan populations, highlighting the importance of sustainable management practices to support livelihoods. This complex understanding of the ecological and socio-economic dynamics at play will provide a robust foundation for informed policymaking. Policymakers can leverage the insights gained to devise regulations and initiatives that balance conservation needs with economic development, ensuring the long-term sustainability of Molluscan resources. Ultimately, this study aims to foster a symbiotic relationship between human activities and marine biodiversity, contributing to the resilience of coastal ecosystems and the communities that depend on them in Gujarat coast for their socio- economic activities.\u003c/p\u003e"},{"header":"Materials and Methods ","content":"\u003cp\u003e\u003cstrong\u003ea) Study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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\u0026apos;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\u0026nbsp;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).\u003c/p\u003e\n\u003cp\u003eFor 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\u0026deg;N to 23.7\u0026deg;N latitude and 68.9\u0026deg;E to 72.6\u0026deg;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Background on the importance of species distribution mapping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegular 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\u003cstrong\u003e\u0026nbsp;\u003cem\u003eCerithium caeruleum\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Lunella coronatus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Peronia verruculata\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Trochus radiatus\u003c/em\u003e\u003c/strong\u003e. 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec) Species sampling and identification techniques.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuadrat 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\u0026nbsp;(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\u0026nbsp;(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\u0026apos;s diverse Molluscan populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed) Occurrence data collection methods.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor 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.\u003c/p\u003e\n\u003cp\u003eDuring 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\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee) Quadrate studies for key species selection and data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuadrate 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\u0026rsquo;s index as shown below in Eq1.\u003c/p\u003e\n\u003cp\u003eThe formula for calculating Whittaker\u0026apos;s Index is:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"327\" height=\"80\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003eN = Total number of individuals in the sample\u003c/p\u003e\n\u003cp\u003en = Number of individuals of the most abundant species\u003c/p\u003e\n\u003cp\u003eIn the scope of this study, Whittaker\u0026apos;s Index (I\u0026delta;) 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 \u0026amp; 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 \u003cem\u003eCerithium caeruleum\u003c/em\u003e with a mean value of 0.066, \u003cem\u003eLunella coronatus\u003c/em\u003e at 0.056, \u003cem\u003ePeronia verruculata\u003c/em\u003e with 0.074, and \u003cem\u003eTrochus radiatus\u003c/em\u003e, 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef) Acquisition and processing of environmental data from Bio-Oracle\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe acquisition and processing of environmental data from Bio-Oracle underpin the ecological assessments and predictive modelling for marine species distribution, including molluscs\u0026nbsp;(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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eSelected environmental predictors suitable for benthic species distribution modelling along with their biological importance\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.379876796714576%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.50718685831622%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.1129363449692%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiological Importance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.379876796714576%\" valign=\"top\"\u003e\n \u003cp\u003eMean Surface Salinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.50718685831622%\" valign=\"top\"\u003e\n \u003cp\u003epss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.1129363449692%\" valign=\"top\"\u003e\n \u003cp\u003eSalinity 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)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.379876796714576%\" valign=\"top\"\u003e\n \u003cp\u003echlorophyll -a concentration levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.50718685831622%\" valign=\"top\"\u003e\n \u003cp\u003emg/m3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.1129363449692%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary productivity proxies indicate food availability for suspension feeding mollusks\u0026nbsp;(Rodil et al., 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.379876796714576%\" valign=\"top\"\u003e\n \u003cp\u003eMean sea surface temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.50718685831622%\" valign=\"top\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.1129363449692%\" valign=\"top\"\u003e\n \u003cp\u003eTemperature is a limiting factor for marine species distribution that controls metabolic rates and affects physiological functions in all growth stages(Velaoras et al., 2013)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo 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\u0026apos;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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg) Methodologies employed for predictive mapping.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 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\u0026nbsp;(Wiltshire \u0026amp; Tanner, 2020)\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003eThe 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)\u0026nbsp;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\u0026rsquo;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\u0026apos; 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\u0026nbsp;as\u0026nbsp;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.\u003c/p\u003e\n\u003cp\u003eThe 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\u0026apos;s efficiency with presence-only data, BIOMOD\u0026apos;s ensemble forecasting power, Bayesian models\u0026apos; incorporation of prior knowledge, and the Random Forest model\u0026apos;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh) Model training, testing, and evaluation methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eModel 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).\u003c/p\u003e\n\u003cp\u003eThe 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\u0026apos;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\u0026apos;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\u0026apos;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\u0026apos;s outputs align with established understanding of the species\u0026apos; 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei) Validation procedures using ground data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValidation 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\u0026apos;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\u0026apos;s full range of predictions.\u003c/p\u003e\n\u003cp\u003eThe 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\u0026apos;s predictions to assess the congruence between predicted and observed species occurrences.\u003c/p\u003e\n\u003cp\u003eStatistical measures are employed to quantify the model\u0026apos;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\u0026apos;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej) Projection of models onto the Gujarat coast.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProjecting Species Distribution Models (SDMs) onto the Gujarat coast for Molluscan species involves translating the model\u0026apos;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.\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eBy 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.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003ea) Model Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the context of the research conducted on Species Distribution Modelling (SDM), a detailed analysis was undertaken to evaluate the comparative performance of four prevalent machine learning models, namely Maxent, BIOMOD, Bayesian models, and Random Forest. This evaluation was centred around the interpretation of Receiver Operating Characteristic (ROC) curves and the corresponding Area Under the Curve (AUC) values, which serve as critical metrics for assessing the ability of these models to accurately discriminate between species presence and absence across varied environmental conditions. The ROC curve, by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR), offers a nuanced visual representation of a model\u0026apos;s discriminative capacity at various threshold levels. This curve essentially delineates the balance a model maintains between correctly identifying locations where a species is present (sensitivity) and erroneously predicting species presence in locales where it is absent (1 - specificity)(Florkowski, 2008). Ideally, a model\u0026apos;s ROC curve would closely align with the upper left corner of the plot, signifying optimal sensitivity and specificity levels. Upon comparative analysis of the ROC curves and AUC values across the studied models, a spectrum of discriminative capabilities was unveiled. Maxent emerged with the highest AUC value of 0.63, indicating a moderate level of discrimination that, while surpassing random chance, highlighted potential areas for enhancement. The model\u0026apos;s ROC curve depicted a deviation from the diagonal, suggesting a capability to distinguish between presence and absence locations to a certain extent. However, the observed moderate AUC value raised considerations regarding Maxent\u0026apos;s ability to fully encapsulate complex ecological interactions or effectively manage data inconsistencies as depicted in Fig.3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBIOMOD, with an AUC value of 0.53, showcased a performance closely aligned with Maxent, albeit with a marginally reduced discriminative prowess. The model\u0026apos;s ROC curve exhibited a trajectory slightly inferior to that of Maxent, hinting at a potential compromise in sensitivity or specificity. This could be attributed to BIOMOD\u0026apos;s ensemble approach, which amalgamates various models, potentially introducing a higher variance in predictions and a slight dip in overall efficacy(Singleton et al., 2023) as shown in Fig. 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the case of Bayesian models, the AUC value further declined to 0.43, signifying a more pronounced reduction in discrimination capability relative to Maxent and BIOMOD. The ROC curve for Bayesian models indicated a greater deviation from the diagonal, reflecting lower levels of sensitivity and specificity. This suggested challenges in accurately representing species distribution patterns, possibly due to the intrinsic assumptions and constraints of the Bayesian methodology or the complexity of the ecological niches under consideration. Random Forest, with the lowest AUC value of 0.42 among the evaluated models, demonstrated the least discriminative ability. Its ROC curve approached the diagonal more closely, underscoring a marked difficulty in differentiating between presence and absence locations as illustrated in Fig. 3. This was potentially linked to the model\u0026apos;s reliance on decision trees and its challenges in capturing intricate non-linear data relationships. Subsequent post-hoc multiple pairwise comparison tests revealed statistically significant differences in model accuracies.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Maxent models were found to exhibit significantly higher accuracy compared to BIOMOD, Bayesian models, and Random Forest, with p-values less than 0.01 for both AUC and True Skill Statistics (TSS) metrics. Despite these significant disparities in accuracy, the magnitude of differences remained relatively modest. The gap between the highest (Maxent) and lowest (Random Forest) mean accuracy measures was merely 0.022 AUC points and 0.034 TSS points. Among the algorithms, all pairwise comparisons of accuracy were significantly distinct for both AUC and TSS metrics (p\u0026lt;0.01), except for the comparison involving Maxent. The sequence of mean accuracies observed was Maxent (mean AUC: 0.63; TSS: 0.777), followed by Bayesian models (mean AUC: 0.53; TSS: 0.702), BIOMOD (mean AUC: 0.49; TSS: 0.56), and Random Forest (AUC: 0.42; TSS: 0.693).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis comprehensive analysis underscored the intricate nuances and inherent challenges in accurately modelling species distributions. While Maxent and BIOMOD displayed relatively superior discriminative capacities, Bayesian models and Random Forest highlighted the complexities involved in SDM. The ROC curve and AUC metrics proved instrumental in dissecting the strengths and limitations of each model, facilitating informed decisions regarding model selection, threshold optimization, and data interpretation in the realm of ecological research and conservation endeavours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb) Probability of Finding the Species through Abiotic Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe exploration of oceanographic data revealed intricate details about the marine environment, shedding light on the dynamic interplay of ecological and physical factors. The study commenced with an analysis of salinity, a parameter indicative of the total dissolved salt content in water, typically expressed in Practical Salinity Units (PSU). It was found that the average salinity across the observed regions stood at approximately 35.80 PSU, aligning with the general range for the world\u0026apos;s oceans and denoting a marine environment of typical salinity levels. However, the noted variability in salinity, as evidenced by a standard deviation of 2.44 PSU, underscored the complex interplay of factors such as riverine input, precipitation, evaporation, and ocean currents, which could significantly alter local salinity levels. The observed salinity ranged from 31.60 PSU to 39.40 PSU, highlighting the diverse conditions under which marine organisms thrive as shown Fig. 4.\u003c/p\u003e\n\u003cp\u003eThe investigation further delved into chlorophyll concentrations, a crucial proxy for the abundance of phytoplankton, the microscopic, plant-like organisms at the base of the marine food web. An average chlorophyll concentration of about 5.02 mg/m\u0026sup3; was recorded, reflecting a healthy presence of phytoplankton essential for supporting a diverse marine life. The variability in chlorophyll levels, with a standard deviation of 2.23 mg/m\u0026sup3;, pointed to the varying productivity of different marine areas, influenced by factors such as nutrient availability, light penetration, and water temperature. The range of chlorophyll concentrations observed, from 0.89 mg/m\u0026sup3; to 8.90 mg/m\u0026sup3;, indicated significant ecological variability, attributable to natural phenomena such as algal blooms and seasonal changes, or anthropogenic impacts like pollution and eutrophication. The temperature of the water bodies emerged as a critical parameter influencing numerous biological and chemical processes within aquatic ecosystems. The study noted an average temperature of 18.73\u0026deg;C, reflecting the temperate nature of the sampled environments. However, the considerable standard deviation of 6.07\u0026deg;C highlighted the wide range of temperatures to which marine organisms are exposed. The temperature span from 7.90\u0026deg;C to 27.90\u0026deg;C underscored the diversity of thermal habitats in the marine environment, each supporting unique communities of organisms. Temperature variations were found to affect metabolic rates, reproductive cycles, migration patterns, and even the solubility of gases in water, thus playing a pivotal role in shaping marine biodiversity as shown in Fig. 4.\u003c/p\u003e\n\u003cp\u003eThe examination of probability values associated with the observations offered a lens through which the reliability and confidence in the data could be assessed. An average probability of 0.85 signified a high level of confidence in the observations or the predictions made by the model, with a relatively low standard deviation of 0.06 indicating a consistent level of reliability across the dataset. The range of probabilities, from 0.74 to 0.96, though not exceedingly wide, reflected a degree of variability in the confidence levels associated with different observations as illustrated in Fig. 4 .\u003c/p\u003e\n\u003cp\u003eThe derived model for the multiple linear regression analysis further illuminated the relationships between the parameters:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\" style=\"width: 795px; height: 48.6938px;\" width=\"795\" height=\"48.6938\"\u003e\u003c/p\u003e\n\u003cp\u003eThis equation suggested that temperature exerted a positive effect on probability, with a coefficient of 0.01, implying a slight increase in probability with rising temperatures. The coefficients for salinity and chlorophyll were found to be negligible, indicating a minimal direct effect on probability within this linear framework. The intercept of the equation, set at 0.69, represented the baseline probability when all independent variables were held constant at zero. It was noted that the small coefficients for salinity and chlorophyll might not necessarily signify an absence of relationship but could indicate that the relationship might not be linear or could be influenced by interactions between variables not captured by this simple linear model.\u003c/p\u003e\n\u003cp\u003eThe growth probabilities of Molluscan species \u003cstrong\u003e\u003cem\u003eCerithium caeruleum, Lunella coronata, Peronia verruculata, and Trochus radiatus\u003c/em\u003e\u003c/strong\u003e were analyzed against environmental factors such as salinity, chlorophyll concentration, and water temperature through Pearson correlation analysis. The findings revealed a strong negative correlation between salinity and growth probability (-0.84), suggesting that higher salinity levels might inhibit growth across these species, possibly due to osmotic stress affecting physiological processes. Chlorophyll concentration showed a high correlation (0.78) with growth probability, indicating that the abundance of phytoplankton, as inferred from chlorophyll levels, might \u0026nbsp; directly impact the growth of these species, possibly due to varied diets or the overriding influence of other environmental or ecological factors as shown in Fig. 5 . Conversely, a very strong positive correlation (0.98) was observed between water temperature and growth probability, highlighting temperature\u0026apos;s critical role in promoting growth, likely due to its influence on metabolic rates and physiological functions in these ectothermic organisms. These insights emphasize the importance of monitoring and managing salinity and temperature within the habitats of these species to support their growth and conservation, while also suggesting that factors beyond primary food availability, such as food quality and ecological interactions, might be significant for their growth as shown in Fig. 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec) Projection onto the Gujarat Coast:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the assessment of potential habitats for the Molluscan species \u003cem\u003eCerithium caeruleum, Lunella coronata, Peronia verruculata,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Trochus radiatus\u003c/em\u003e along the Gujarat coast, the Maximum Entropy (MaxEnt) modelling technique was utilized to delineate their prospective distributions. Employed extensively in ecological modelling for predicting species distributions, MaxEnt capitalizes on presence-only data coupled with environmental variables to approximate the likelihood of species occurrences across varied landscapes. This approach has been demonstrated to be particularly efficacious in forecasting species distributions under both existing and future environmental conditions, thereby offering invaluable insights for the formulation of conservation strategies and resource management plans.\u003c/p\u003e\n\u003cp\u003eThe MaxEnt model, integrating pivotal environmental predictors such as salinity, chlorophyll concentration, and water temperature\u0026mdash;parameters previously identified to exert significant influences on the growth and survivability of these Molluscan entities\u0026mdash;generated intricate distribution maps for each species across the Gujarat coastline. The projections derived from the model indicated distinctive habitat preferences among the species, mirroring their unique ecological niches and tolerance levels to the environmental variables under consideration. For \u003cstrong\u003e\u003cem\u003eCerithium caeruleum\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eTrochus radiatus\u003c/em\u003e\u003c/strong\u003e, the model delineated potential habitats within regions characterized by comparatively moderate salinity levels, corroborating prior findings that elevated salinity could adversely affect these species. Conversely, the projections for \u003cstrong\u003e\u003cem\u003eLunella coronata and Peronia verruculata\u003c/em\u003e\u003c/strong\u003e suggested a more expansive distribution along the coastal stretch, indicative of a heightened tolerance to fluctuating salinity levels, potentially attributable to their adaptive osmoregulatory capacities.\u003c/p\u003e\n\u003cp\u003eFurthermore, the model incorporated the variable of chlorophyll concentration, reflecting a strong correlation with growth probability, to signify the availability of food resources. These highlighted areas endowed with ample primary productivity as potential focal points for these Molluscan species as shown in Fig. 6,7,8,9. This aspect accentuates the necessity of acknowledging not merely the direct impacts of environmental factors on species growth but also their indirect ramifications through the dynamics of the food web. The initial analysis, revealing a pronounced positive impact of water temperature on Molluscan growth, was reaffirmed by the MaxEnt projections Fig. 6,7,8,9. Zones featuring optimal temperature ranges were identified as potential high-probability locales for the occurrence of all four Molluscan species. This observation holds particular pertinence in the context of climate change, wherein rising temperatures may instigate shifts or expansions in the suitable habitats for these species along the Gujarat coast.\u003c/p\u003e\n\u003cp\u003eThe projections formulated by the MaxEnt model provide a holistic overview of the potential distribution patterns of \u003cstrong\u003e\u003cem\u003eCerithium caeruleum, Lunella coronata, Peronia verruculata,\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eTrochus radiatus\u003c/em\u003e\u003c/strong\u003e within the Gujarat region, encapsulating the critical environmental determinants pivotal to Molluscan habitat suitability. These insights are imperative for the conservation and management of these species, particularly in the wake of ongoing environmental alterations and anthropogenic activities that may modify their natural habitats. Moreover, this study underscores the complexity inherent in species-environment interactions, highlighting the imperative for an integrated approach that encompasses multiple environmental variables and their potential synergistic effects on species distribution. The observed variability in habitat preferences among the examined species underscores the significance of devising species-specific conservation strategies, underpinned by rigorous ecological modeling and a comprehensive understanding of each species\u0026apos; ecological niche Fig 6,7,8,9.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed) Spatial Auto-correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the conducted research, Species Distribution Modelling (SDM) was performed for four marine taxa: \u003cstrong\u003e\u003cem\u003eCerithium caeruleum, Lunella coronatus, Peronia verruculata,\u003c/em\u003e and \u003cem\u003eTrochus radiatus.\u003c/em\u003e\u003c/strong\u003e Subsequent analysis for spatial autocorrelation was undertaken, yielding the following metrics: Moran\u0026apos;s I coefficient was determined to be 0.053, diverging from the anticipated index of -0.0116. The variance of Moran\u0026apos;s I was computed at 0.0028, and the statistical significance was evaluated through a Z-score of 1.2097, with an associated p-value of 0.226. In the executed study, Species Distribution Modelling (SDM) was applied to four marine taxa: \u003cstrong\u003e\u003cem\u003eCerithium caeruleum, Lunella coronatus, Peronia verruculata, and Trochus radiatus\u003c/em\u003e\u003c/strong\u003e, with the aim of elucidating their spatial dispersion patterns. To quantify the degree of spatial autocorrelation and ascertain whether the distribution of these taxa was clustered, random, or dispersed, Moran\u0026apos;s I statistic was employed. The computed Moran\u0026apos;s I index stood at 0.053, marginally surpassing the hypothesized mean of -0.0116. This positive Moran\u0026apos;s I index intimated a slight propensity towards a clustered disposition; however, its proximity to zero suggested that the clustering was not pronounced. The expected index, inherently negative for spatial datasets, represents the Moran\u0026apos;s I value under the null hypothesis of a stochastic spatial distribution as shown in Fig. 10.\u003c/p\u003e\n\u003cp\u003eThe ascertained variance for Moran\u0026apos;s I was recorded at 0.0028, providing insight into the dispersion of the index values and facilitating the derivation of the Z-score. The resultant Z-score was calculated to be 1.2097, serving as an indicator of the statistical significance of Moran\u0026apos;s I as shown in Fig. 10. Within this context, the Z-score elucidates the deviation, measured in standard units, of the observed Moran\u0026apos;s I from the expected value under the null hypothesis. The derived p-value, corresponding to this Z-score, was 0.226, surpassing the conventional alpha level of 0.05. This elevated p-value suggests that the observed spatial pattern does not significantly deviate from a random distribution, leading to the non-rejection of the null hypothesis that postulates a random spatial arrangement. Therefore, it was inferred that the spatial distribution of \u003cstrong\u003e\u003cem\u003eCerithium caeruleum, Lunella coronatus, Peronia verruculata\u003c/em\u003e, and \u003cem\u003eTrochus radiatus\u003c/em\u003e\u003c/strong\u003e did not exhibit significant spatial autocorrelation. The marginally positive Moran\u0026apos;s I index, the absence of statistical significance, as evidenced by the p-value, led to the inference that the spatial distribution of the aforementioned taxa is characterized by randomness within the study area. This outcome implies that the spatial dispersion of these taxa might be governed by variables not encapsulated in the current model or that their distribution patterns are inherently stochastic. Future investigations may benefit from exploring additional environmental or biological factors that could potentially influence the spatial distribution patterns of these taxa.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the study along the Gujarat coastline, the distribution of Molluscan species was extensively analyzed, focusing on their ecological and socio-economic importance for conservation and management. Using Species Distribution Models (SDMs) and machine learning, the research aimed to predict potential habitats and understand the impact of environmental factors on Molluscan distribution. The study combined field surveys, quadrat sampling, and data from Bio-Oracle to explore Molluscan habitat preferences. The assessment of machine learning models in SDMs, including MaxEnt, BIOMOD, Bayesian models, and Random Forest, was conducted using ROC curves and AUC values to measure their discriminative ability. MaxEnt was the most effective, with an AUC value of 0.63, indicating moderate accuracy and highlighting the need for model refinement. Random Forest, with the lowest AUC of 0.42, faced challenges in accurately distinguishing presence from absence locations, likely due to the complexity of its decision-tree-based approach. Correlation analysis revealed a strong negative correlation (-0.84) between salinity and Molluscan growth probability, suggesting adverse effects of high salinity on growth. Conversely, a very strong positive correlation (0.98) between water temperature and growth probability underscored the importance of thermal conditions in Molluscan development. Spatial autocorrelation analysis using Moran's I statistic indicated a nearly random spatial distribution of Molluscan species, with a Moran's I index of 0.053 and a non-significant p-value of 0.226. This suggests that other unaccounted variables or stochastic processes might influence the spatial patterns of these species. The research contributes to marine biodiversity conservation by highlighting the relationship between Molluscan species and their environment, emphasizing the need for targeted conservation strategies that consider both ecological and socio-economic factors. The findings of the study on species-environment interactions and the performance of SDMs offer valuable insights for developing informed conservation policies to protect Gujarat's marine biodiversity and support local communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the Heads of the Department of Zoology, The Maharaja Sayajirao University of Baroda, Vadodara, and the Department of Life Sciences, Bhakta Kavi Narsinh Mehta University, Junagadh, for providing laboratory, storage, and museum facilities. Additionally, the authors extend their appreciation to the anonymous reviewers for their valuable feedback on the manuscript. Pooja Agravat, Ajay Baldaniya and Agradeep Mohanta acknowledge the fellowship received from the SHODH - ScHeme Of Developing High-quality research, provided by the Education Department, Gujarat.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Pooja Agravat: Conceptualization, Methodology, Original Draft Preparation\u003c/p\u003e\n\u003cp\u003e2. Ajay Baldaniya: Literature review, Data collection, Validation\u003c/p\u003e\n\u003cp\u003e3. Agradeep Mohanta: Methodology, Data Collection, Data interpretation\u003c/p\u003e\n\u003cp\u003e4. Biplab Bannerjee: Statistical analysis, Graph preparation, Data interpretation\u003c/p\u003e\n\u003cp\u003e5. Jatin Raval: Visualization, Research supervision, Reviewing and editing\u003c/p\u003e\n\u003cp\u003e6. Pradeep Mankodi: Research Design, \u0026nbsp; Research supervision, Reviewing and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e There was no external funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The authors declare that all data and materials support their published claims and comply with field standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e The authors declare that software application or custom code supports their published claims and comply with field standards. The R studio codes are available in Appendix 1.\u003c/p\u003e\n\u003cp\u003e The authors declare that,\u003c/p\u003e\n\u003cp\u003e\u0026bull; The manuscript has not been published anywhere nor submitted to another journal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; The manuscript is not currently being considered for publication in any another journal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; All authors have been personally and actively involved in substantive work leading to the manuscript,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eand will hold themselves jointly and individually responsible for its content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; Research does not involve any Human Participants and/or Animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics:\u003c/strong\u003e approval Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare that there are no conflicts of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlonso, A. 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Comparing maximum entropy modelling methods to inform aquaculture site selection for novel seaweed species. \u003cem\u003eEcological modelling\u003c/em\u003e,\u003cem\u003e\u0026nbsp;429\u003c/em\u003e, 109071.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang, C., Chen, Y., Xu, B., Xue, Y., \u0026amp; Ren, Y. (2020). Temporal transferability of marine distribution models in a multispecies context. \u003cem\u003eEcological Indicators\u003c/em\u003e,\u003cem\u003e\u0026nbsp;117\u003c/em\u003e, 106649. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coastal Biodiversity, Ecological Modelling, Habitat Suitability, Environmental Variables, Conservation Strategies, Sustainable Management","lastPublishedDoi":"10.21203/rs.3.rs-4195930/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4195930/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study delves into the Molluscan diversity along the Gujarat coast, India, focusing on the distribution and habitat suitability of four key species: \u003cem\u003eCerithium caeruleum, Lunella coronata, Peronia verruculata\u003c/em\u003e, and \u003cem\u003eTrochus radiatus\u003c/em\u003e. Utilizing Species Distribution Models (SDMs) integrated with machine learning algorithms, we assessed the impact of environmental variables on the distribution patterns of these molluscs. Our findings reveal a nuanced understanding of habitat preferences, highlighting the critical roles of salinity, chlorophyll concentration, and water temperature. The MaxEnt model, with the highest Area Under the Curve (AUC) value of 0.63, demonstrated moderate discrimination capability, suggesting room for enhancement in capturing complex ecological interactions. The spatial distribution analysis indicated a random arrangement of species, with no significant spatial autocorrelation observed. This research underscores the significance of advanced modelling techniques in predicting Molluscan distributions, providing insights crucial for the conservation and sustainable management of marine biodiversity along the Gujarat coast.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Molluscan Marvels of Gujarat: Unveiling Biodiversity and Conservation Strategies with the aid of Spatial approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-03 10:37:20","doi":"10.21203/rs.3.rs-4195930/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-06-08T02:34:40+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-05-02T21:46:23+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-28T09:32:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-04-22T16:56:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-03T04:19:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-03-31T09:57:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9e99728a-daf5-4bc4-bce2-20c5aa10c15f","owner":[],"postedDate":"May 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:04:52+00:00","versionOfRecord":{"articleIdentity":"rs-4195930","link":"https://doi.org/10.1007/s11356-025-35959-7","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2025-01-28 15:58:14","publishedOnDateReadable":"January 28th, 2025"},"versionCreatedAt":"2024-05-03 10:37:20","video":"","vorDoi":"10.1007/s11356-025-35959-7","vorDoiUrl":"https://doi.org/10.1007/s11356-025-35959-7","workflowStages":[]},"version":"v1","identity":"rs-4195930","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4195930","identity":"rs-4195930","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}