Species distribution modeling and assessment of environmental drivers responsible for distribution and preferred niche of critically endangered and endemic ornamental freshwater fish species of the genus Sahyadria

The concept of "state fishes of India": Current status, knowledge gaps, and strategic plans for conservation and sustainable utilization

The concept of "state fishes of India" highlights the importance and significance of the prioritized fish species distributed across various states within the country. This review article systematically documents the current status of state fishes from various perspectives, identifies the prevailing knowledge gaps, and also highlights the issues and strategic plans essential for the conservation and sustainable utilization of these valuable genetic resources. A total of 8357 publications were checked for the consolidated information on state fish species, and the appropriate items were selected under eight categories: biology, physiology and nutrition, aquaculture, habitat and environmental parameters, genetics and biotechnology, harvest and postharvest, fish health management, and others. The synthesized information was used to present the current status of research and development on state fish species. The knowledge gaps that are to be addressed are also depicted under the perspectives of fisheries management and conservation aquaculture. Based on the findings, strategic plans for the targeted conservation programmes are proposed and discussed under various in situ and ex situ conservation measures. Further, the departmental processes involved in the declaration, the importance of stakeholder involvement, namely, local communities and policymakers, in fostering effective conservation measures, and planning for utilization of these valuable fish genetic resources are also indicated.

Predicting present spatial distribution and habitat preferences of commercial fishes using a maximum entropy approach

The knowledge of the geographical distribution and habitat preferences of marine species is the key to protecting marine ecosystems. Modeling the distribution of marine species through environmental variables is an essential step to understanding and reducing climate change effects on marine biodiversity and related human populations. In this study, the present distributions of commercial fishes including Acanthopagrus latus, Planiliza klunzingeri, and Pomadasys kaakan were modeled using the maximum entropy (MaxEnt) modeling technique and a set of 22 environmental variables. In total, 1531 geographical records belonging to three species were extracted from online databases Ocean Biodiversity Information System (OBIS, 829 records, 54%), Global Biodiversity Information Facility (GBIF, 17 records, 1%), and literature (685 records, 45%) during September to December 2022. The findings showed the values of area under the receiver operating characteristic (ROC) curve (AUC) above 0.99 for all species indicating the high performance of this technique to reflect the actual distribution of species. Environmental factors such as depth (19.68%), sea surface temperature (SST) (19.40%), and wave height (20.71%) were the strongest environmental predictors determining the present distribution and habitat preferences of the three commercial fish species. The Persian Gulf, Iranian coasts of the Sea of Oman, North Arabian Sea, North-East areas of the Indian Ocean, and North coasts of Australia are among the areas with ideal environmental conditions for the species. For all species, the percentage of habitats with high suitability (13.35%) was higher compared to habitats with low suitability (6.56%). However, a high percentage of species occurrence habitats had unsuitable conditions (68.58%) showing the vulnerability of these commercial fishes. Significant management strategies are needed to protect preferred habitats to minimize the effect of fishery and climate change on the population stocks of these commercial fishes.

MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models

Species distribution models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well-performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly-used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced-biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model-specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine-tuned models was assessed based on recently identified localities of the species. The results indicated that although the fine-tuned version of all models shows great performance in predicting training data (AUC > 0.9 and TSS > 0.5), they produce different results in classifying out-of-bag data. The GBM and RF with higher sensitivity of training data showed more different performances. The GLM, despite having high predictive performance for test data, showed lower specificity. It was only the MaxEnt model that showed high predictive performance and comparable results for identifying test data in both random and background weighting procedures. Our results highlight that while GBM and RF are prone to overfitting training data and GLM over-predict nonsampled areas MaxEnt is capable of producing results that are both predictable (extrapolative) and complex (interpolative). We discuss the assumptions of each model and conclude that MaxEnt could be considered as a practical method to cope with imbalanced-biased data in species distribution modeling approaches.