Optimally designing drone-based surveys for wildlife abundance estimation with N-mixture models

• Autor(a) principal: Brack, Ismael Verrastro
• Data de Publicação: 2023
• Outros Autores: Kindel, Andreas, Oliveira, Luiz Flamarion Barbosa de, Lahoz-Monfort, José Joaquín
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/274707
• topic: Animais selvagens; Vida selvagem; Abundance modelling; Aerial surveys; Count data; Double observer; Effort allocation; Hierarchical models; Imperfect detection; Sampling design

Hierarchical N-mixture models have been suggested for abundance estimation from spatiotemporally replicated drone- based count surveys, since they allow modeling abundance of unmarked individuals while accounting for detection er-rors. However, it is still necessary to understand how these models perform in the wide variety of contexts and species in which drone surveys are being used. This knowledge is fundamental to plan study designs with optimal allocation of scarce resources in ecology and conservation. We conduct a simulation study to address N- mixture model (binomial and multi-nomial) performance and optimal survey effort allocation in different scenarios of local abundance and detectability of individuals, focusing on their application for drone- based surveys. We also investigate the benefits of using a double-observer protocol (either human or algorithm) in image review to decompose the detection process in availability and perception. Finally, we illustrate our simulation- based survey design considerations by applying them to abundance estimation of marsh deer in the Pantanal wetland (Brazil). Accuracy of abundance estimation with N- mixture models increases with local abundance in sites and especially with the availability of individuals. The opti-mal design requires more visits at fewer sites when the availability probability is lower, and the optimal design is more flexible as local abundance increases. Two observers checking images can increase the estimator performance even at very high perception probabilities. We quantified how much the use of a double- observer protocol in image review can reduce fieldwork effort while achieving the same accuracy. N-mixture models can deliver accurate abundance estimates from spatiotem-porally replicated drone surveys in a wide variety of contexts while accounting for imperfect detection. The improvements achieved by a consciously planned design, rearranging survey efforts among sites and visits, as well as using a sec-ond observer in image review, can be crucial to detect trends when monitoring a population or to categorize a species as threatened or not.