Biology subjects

Sutaria, D.

Publications and source records attributed to Sutaria, D..

4 recordsLinked to original sources

bioRxiv2025

Human activities drive novel behaviours and transitions in dolphins

Intensifying human activities are reshaping coastal ecosystems, yet their impact on wildlife behaviour and survival remain poorly understood. We conducted drone-based focal-group follows of endangered Indian Ocean humpback dolphin groups in Goa, India, to quantify how tourism and fisheries jointly impact behavioural states and transitions. By integrating machine learning and georeferencing techniques, we found that both human activities triggered distinct behavioural transitions absent in undisturbed groups. Dolphins expressed escape behaviours exclusively near tourist boats and were more likely to transition into foraging near fishing nets (mostly purse-seines). Smaller groups reacted more strongly to tourist boats and interacted more frequently with fishing nets, especially in seasons when fish landing data showed a decline in their prey. These findings offer new insights into the behavioural mechanisms underlying the co-occurrence of coastal dolphins with humans and provide broader guidelines for managing tourism and fisheries to reduce anthropogenic pressure on threatened delphinids.

ecology↗

bioRxiv2025

Integrated deep learning and geo-referencing for drone-based animal tracking with flexible camera angles

O_LIThe ability of drones to provide detailed information on animals and their surroundings makes them ideal for studying animal behaviour at fine scales. While drones can provide high-resolution images of what animals are doing, they should also, in theory, be able to provide data on where they are. However, reconstructing geo-referenced tracks from drone videos that follow animals is challenging, particularly because current methods require specific drone flight patterns and large computational power. C_LIO_LIHere, we combine deep learning and object tracking methods with a novel geo-referencing algorithm which allows us to track individuals across video frames and reconstruct their geo-referenced trajectories. We used a Region-based Convoluted Neural Network to detect animals and a Hungarian tracking algorithm to link detections across video frames, and then geo-referenced each detection in every frame to reconstruct individual trajectories. C_LIO_LIWe tested our geo-refencing algorithm through multiple drone flights with varying flight parameters over known Ground Control Points. The median (95% CI) geo-referencing error was 2.81 (0.74 - 23.23) meters, which reduced by 50% when the drone camera was positioned between -90{degrees} and -40{degrees}. Error increased with drone height and camera angle (-90{degrees} refers to the camera pointing towards the ground) but was not impacted by drone orientation. C_LIO_LIWe then demonstrate the utility of our framework with empirical examples using consumer-level drones. First, we tracked a volunteer carrying a high-resolution GPS unit and overlayed their GPS tracks on our estimated tracks to quantify tracking error. Next, we used drone videos of two delphinid species (Tursiops truncates gephyreus, and Sousa plumbea) representing varying environmental and flight conditions. We were able to successfully infer individual tracks across all conditions except when individuals formed tight clusters, in which case tracks were assigned a group identifier. C_LIO_LIOur framework demonstrates an easy and robust approach to translate drone videos of moving animals into geo-referenced animal tracks which is applicable in many research contexts. A major advance over previous methods is that our algorithm is robust to different camera angles, and provides tracks with accuracy on-par with, or even exceeding, the accuracy from GPS tracking. C_LI HeadlineAutomated drone-based animal tracking

ecology↗

bioRxiv2024

Species rich but data poor: leveraging distribution modelling techniques to map cetacean occurrence in South Asian waters

Cetaceans play important ecosystem roles but are challenging to study since they are highly mobile and spend their entire life at sea. More than a third of all cetaceans occur in South Asian waters where they overlap with several key threats including fisheries, pollution, and high vessel traffic. However, baseline information on their occurrence at the sub-continental scale is lacking even for relatively well studied species. To address this gap, we collated sighting information on cetaceans from the North Indian Ocean region (5-25{degrees}N, 65-95{degrees}E) and used species-tuned distribution models and ensemble modelling approaches with physiographic and oceanographic predictors to map habitat suitability and species richness. We used Multivariate Environmental Similarity Surfaces (MESS) indices and expert reviews to validate our predictions and better infer species distribution. We collated 2329 sighting records for 31 species and modelled the distribution of 18 species. Areas of high species richness, defined by high slope and bathymetric complexity, occurred along the east coast of India, in south Sri Lanka, and around the Lakshadweep and Andaman and Nicobar archipelagos. Species richness patterns were generally consistent with the six identified ecoregions in the area. Most of our predictions were given high scores by the expert reviewers. Our maps underpredicted the occurrence of a few oceanic species, highlighting the need for focused offshore surveys, and overpredicted areas with high MESS indices for all species. We discuss the occurrence patterns and their drivers for these cetacean species while highlighting knowledge gaps and the importance of fine-scale distribution data for conservation planning, especially during the development of regional management plans for cetaceans.

ecology↗

bioRxiv2020

Catch composition and life history characteristics of sharks and rays (Elasmobranchii) landed in the Andaman and Nicobar Islands, India.

ABSTRACTThe scientific literature on the diversity and biological characteristics of sharks and rays from the Andaman and Nicobar Archipelago fishing grounds is scarce and compromised by species misidentifications. We carried out systematic fish landing surveys in South Andamans from January 2017 to May 2018, a comprehensive and cost-effective way to fill this data gap. We sampled 5,742 individuals representing 57 shark and ray species. Of the 36 species of sharks and 21 species of rays landed, six species of sharks - Loxodon macrorhinus, Carcharhinus amblyrhynchos, Sphyrna lewini, Carcharhinus albimarginatus, Carcharhinus brevipinna, and Paragaelus randalli dominated landings and comprised 83.35 % of shark landings, while three species of rays were most abundant – Pateobatis jenkinsii, Himantura leoparda and H. tutul, and comprised 48.82 % of ray landings. We report size extensions for seven shark species as well as three previously unreported ray species, increasing the known diversity for the islands and for India. For sharks, mature individuals of small-bodied species (63.48 % males of total landings of species less than 1.5 m total length) and immature individuals of larger species (84.79 % males of total landings of species larger than 1.5 m total length) were mostly landed; whereas for rays, mature individuals were predominantly landed (80.71 % males of total landings) likely reflecting differences in fishing patterns as well as habitat preferences and life history stages across species. Further, juvenile sharks and gravid females were landed in large quantities which might be unsustainable in the long-term. Landings were female-biased in C. amblyrhynchos, S. lewini and P. jenkinsii, and male-biased in L. macrorhinus and H. leoparda, indicating either spatio-temporal or gear specific sexual segregation in these species. Understanding these nuances - the composition and biology of sharks and rays landed in different fisheries seasonally will inform future conservation and fishery management measures for these species in the Andaman and Nicobar Islands.View Full Text

zoology↗