Abstract

AbstractQuantifying the abundance of species is essential for their management and conservation. Much effort has been invested in surveys of freshwater dolphins in the Amazon basin but river dimensions and complex logistics limit replication of such studies across the region. We evaluated the effectiveness of using unmanned aerial vehicles (UAVs) for surveying two Amazon dolphin species, the tucuxi Sotalia fluviatilis and pink river dolphin Inia geoffrensis, in tropical rivers. In 2016 we conducted drone and visual surveys over 80 km of the Juruá River in Brazil. The aerial surveys provided higher accuracy than human observers in counting individuals detected in groups. Compared to estimates derived from visual surveys, the use of UAVs could provide a more feasible, economical and accurate estimate of Amazon river dolphin populations. The method could potentially be replicated in other important areas for the conservation of these species, to generate an improved index of river dolphin populations in the Amazon.

Highlights

  • Quantifying the abundance of species is essential for their management and conservation

  • Conservation plans have been developed for the Amazon river dolphins (Trujillo et al, ), implementation has been hampered by a lack of knowledge of their ecology, distribution and behaviour

  • The potential of unmanned aerial vehicles (UAVs) for environmental monitoring is being increasingly recognized because of the opportunities they offer for cost- and timeefficient surveys (Hardin & Hardin, ; Hodgson et al, ), including for aquatic mammals (Jones et al, ; Martin et al, ; Hodgson et al, )

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Summary

Introduction

Quantifying the abundance of species is essential for their management and conservation. Keywords Amazon, drone, Inia geoffrensis, population estimate, river dolphin, Sotalia fluviatilis, UAV, unmanned aerial vehicle Conservation plans have been developed for the Amazon river dolphins (Trujillo et al, ), implementation has been hampered by a lack of knowledge of their ecology, distribution and behaviour.

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