Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Artículo científico |
| Lingua: | en |
| Pubblicazione: |
Environmental science and pollution research international
2025
|
| Soggetti: | |
| Accesso online: | https://pubmed.ncbi.nlm.nih.gov/39875785/ |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1868266249908125698 |
|---|---|
| author | Agravat, Pooja Baldaniya, Ajay Banerjee, Biplab Mohanta, Agradeep Raval, Jatin Mankodi, Pradeep |
| author_facet | Agravat, Pooja Baldaniya, Ajay Banerjee, Biplab Mohanta, Agradeep Raval, Jatin Mankodi, Pradeep Agravat, Pooja Baldaniya, Ajay Banerjee, Biplab Mohanta, Agradeep Raval, Jatin Mankodi, Pradeep |
| collection | PubMed - marine biology |
| contents | Molluscan marvels of Gujarat: exploring species distribution and conservation strategies using a spatial approach. Agravat, Pooja Baldaniya, Ajay Banerjee, Biplab Mohanta, Agradeep Raval, Jatin Mankodi, Pradeep Animals India Mollusca Biodiversity Ecosystem Conservation of Natural Resources Salinity 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. The study examined the distribution and habitat suitability of four key molluscan species-C. caeruleum, L. coronata, P. verruculata, and T. radiatus-along the Gujarat coast, India. By integrating SDMs with machine learning algorithms, we assessed how environmental variables such as salinity, chlorophyll concentration, and water temperature influence their distribution patterns. The MaxEnt model was employed, achieving an AUC value of 0.63, indicating moderate discrimination capability and suggesting potential areas for model refinement to better capture complex ecological interactions. Our analysis revealed no significant spatial autocorrelation, suggesting a random spatial distribution of these species. The results highlight the importance of using advanced modeling techniques to predict the distribution of molluscs, which is essential for the conservation and sustainable management of marine biodiversity along the Gujarat coast. |
| format | Artículo científico |
| id | pubmed_39875785 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Environmental science and pollution research international |
| record_format | pubmed |
| spellingShingle | Molluscan marvels of Gujarat: exploring species distribution and conservation strategies using a spatial approach. Agravat, Pooja Baldaniya, Ajay Banerjee, Biplab Mohanta, Agradeep Raval, Jatin Mankodi, Pradeep Animals India Mollusca Biodiversity Ecosystem Conservation of Natural Resources Salinity Molluscan marvels of Gujarat: exploring species distribution and conservation strategies using a spatial approach. Agravat, Pooja Baldaniya, Ajay Banerjee, Biplab Mohanta, Agradeep Raval, Jatin Mankodi, Pradeep Animals India Mollusca Biodiversity Ecosystem Conservation of Natural Resources Salinity 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. The study examined the distribution and habitat suitability of four key molluscan species-C. caeruleum, L. coronata, P. verruculata, and T. radiatus-along the Gujarat coast, India. By integrating SDMs with machine learning algorithms, we assessed how environmental variables such as salinity, chlorophyll concentration, and water temperature influence their distribution patterns. The MaxEnt model was employed, achieving an AUC value of 0.63, indicating moderate discrimination capability and suggesting potential areas for model refinement to better capture complex ecological interactions. Our analysis revealed no significant spatial autocorrelation, suggesting a random spatial distribution of these species. The results highlight the importance of using advanced modeling techniques to predict the distribution of molluscs, which is essential for the conservation and sustainable management of marine biodiversity along the Gujarat coast. |
| title | Molluscan marvels of Gujarat: exploring species distribution and conservation strategies using a spatial approach. |
| topic | Animals India Mollusca Biodiversity Ecosystem Conservation of Natural Resources Salinity |
| url | https://pubmed.ncbi.nlm.nih.gov/39875785/ |