4 research outputs found

    Species distribution modelling of seaweeds in Indian Seas

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    Seaweeds, the green pastures of the ocean, hold immense potential for India’s coastal communities and its burgeoning blue economy. These marine macroalgae are not just ecological keystones, responsible for oxygen production and forming the base of marine food webs, but also a multi-billion-dollar industry offering a cornucopia of products, from food supplements to hydrocolloids. With projections for exponential growth in the coming decades, harnessing the potential of seaweeds requires precise and sustainable approaches. While India stands strong as a seaweed cultivating nation, identifying suitable regions for this marine bounty remains a complex puzzle. The vast and diverse coastline demands advanced tools to unlock hidden opportunities. The realm of species distribution modelling (SDM) aids in identifying the suitable habitats along our coast. This book chapter offers a comprehensive exploration of how SDM can illuminate the path for a thriving seaweed industry in India. Through a real-world case study of an SDM exercise, we paint a vivid picture of how this technology can translate knowledge into action

    Change in habitat suitability of the invasive Snowflake coral (Carijoa riisei) during climate change: An ensemble modelling approach

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    Global species range dynamics are intrinsically influenced by the interplay between human activities and climate compatibility. Snowflake coral (Carijoa riisei) is a soft octacoral species that belongs to the family Clavulariidae and can rapidly grow to colonise new habitats. This species has successfully colonised numerous habitats, displacing native species and disrupting the ecological balance in the introduced habitats. Recent investigations into species invasions in aquatic ecosystems suggest that anthropogenic activities and climate change will accelerate the introduction, establishment, and spread of invasive species to new habitats. In this study, we utilised ensemble species distribution modelling to investigate shifts in the invasive potential of Snowflake coral in current and future climatic settings on a global scale. Future distribution was forecasted using four Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0, and 8.5) across two periods (2040–2050 and 2090–2100). The results accurately predicted the known distributional range of the species. Temperature, distance to the port, and bathymetry were identified as the three most significant predictor variables. The low and medium habitat suitability regions increased in all scenarios and periods. In the high habitat suitability category, only RCP 4.5 and RCP 6.0 in the 2090–2100 period exhibited an increase in percentage area. Under the worst-case climate scenario, RCP 8.5 (2090–2100), the high-suitability regions displayed a surprising decline in area percentage, which can be attributed to the temperature thresholds of the species. Our findings indicate that the species has a greater potential to spread under current climatic conditions than previously reported, and its expansion may further accelerate in the future. This highlights the urgent need for more intensive surveys employing advanced detection tools and the implementation of proactive management measures to protect vulnerable ecosystems that could be impacted by this species

    Muscling mussels: Understanding the invasive potential of the South American bivalve Mytella strigata (Hanley, 1843) in the Northern Indian Ocean

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    In past decades, non-native species invasion has emerged as one of the leading drivers of biodiversity loss in terrestrial and aquatic ecosystems globally. In aquatic ecosystems, invasion by bivalve species has increased substantially due to their evolutionary resilience and adaptability. This study aimed to determine the habitat suitability of the South American bivalve Mytella strigata in the northern Indian Ocean using Species distribution modelling. The species occurrence and environmental data for model building were extracted from GBIF, Bio-ORACLE, The World Bank Data Catalogue and GMED. Pearson's correlation (<0.7) and Variance inflation factor (<10) analyses were used to select the environmental covariates. Individual models were built by combining the native range occurrence data of Mytella strigata with the bioclimatic data under the current climatic setting. Ten individual models were built and ensembled to create the final model using the biomod2 package. The variable importance score and the response curve plot were used to identify the most crucial variable and its influence on the models. Distance to port had the highest influence on predicting the distribution of Mytella strigata. The results indicated that the western coast of India as more susceptible to invasion. Our predictions indicate that the species has the potential to become highly invasive in the region, given the vast habitat suitability and documented introduction and presence of the species in the region. This research generated baseline information on the habitat suitability of M. strigata that will aid in managing and restricting its spread in the region. Considering the substantial impact of the species in other introduced ranges worldwide, immediate action should be initiated for the swift management of M. strigata from the Indian coast

    Ensemble modelling of Pirarucu (Arapaima gigas) distribution in biodiversity hotspot to understand its invasion risk

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    Invasive species pose a severe threat to biodiversity around the world. Managing the consequences of invasion is difficult in aquatic settings, as the rate at which invaders establish typically outpaces the resources available to eradicate them. For proactive management measures to be implemented, prior knowledge of the probability of invasion is required. In this study, we created a spatial model of the probability of the Pirarucu (Arapaima gigas) invasion in the Western Ghats. The Western Ghats, one of the world's top biodiversity hotspots, is home to numerous endemic species, many of which are now threatened. An ensemble modelling approach using 10 models, including machine learning techniques such as Artificial Neural Network (ANN), Maximum Entropy (MaxEnt), Random Forest (RF), Generalised Boosted Regression Model (GBM) and Classification Tree Analysis (CTA), was adopted. The model was built using the species' occurrence data and nine climate variables. The findings revealed that southern regions of the Western Ghats have a high risk of Pirarucu invasion. Sri Lanka also has a much greater geographical area with a higher percentage of appropriate habitats for the species. The study becomes vital as this exotic species was repeatedly reported from the rivers since the extensive floods in the region in 2018. The developed model will assist managers in prioritising locations and initiating monitoring and management steps to prevent the spread before they establish in the wild. With earlier Pirarucu invasions in Bolivia, Peru and East Asia and recent climatic vagaries in the Western Ghats, the native biodiversity of the region is in grave danger of being displaced
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