Audible bats provide opportunities for citizen scientists

Abstract

Bat conservation has been impeded by a lack of basic information about species' distributions and abundances. Public participation in closing this gap via citizen (community) science has been limited, but bat species that produce low-frequency calls audible to the unaided human ear provide an overlooked opportunity for collaborative citizen science surveys. Audible bats are rare in regional faunas but occur globally and can be under-surveyed by traditional methods. During 2019-2020, we were joined by community members to conduct aural surveys and expand our knowledge of rare audible desert bats in western North America through a structured survey design broadly adaptable for practitioners across the globe where audible bats occur. Our study was integrated into a statistically robust but flexible master sample in use by the North American Bat Monitoring Program (NABat), ensuring representativeness of data contributions. We used survey results to update a Bayesian species distribution model for the rare spotted bat, Euderma maculatum, accounting for imperfect detection and including land cover occupancy predictors. Detection probability was estimated~0.7 ± 0.1. Informative priors from a previous attempt to model E. maculatum were leveraged with the new citizen science data to support spatial predictions of occurrence previously impeded by data sparsity and which reinforced the biogeographic importance of arid cliffs and canyons. Our results are preliminary but encouraging, and future surveys can scale up through the NABat design structure and Bayesian modeling framework. We encourage future surveys to use recording devices to obtain voucher calls and double-observer methods to address false-positive detection errors that arise with inexperienced volunteers. Our design and model supported approach to integrating citizen science surveys into bat conservation programs can strengthen both the scientific understanding of rare species and public engagement in conservation practices.

Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology

“…Hence, many authors resort to Bayesian occupancy and species distribution models (e.g. Della Rocca & Milanesi, 2022; Sheard et al , 2021; Coomber et al , 2021; Ver Hoef et al , 2021; Erickson & Smith, 2021; Rodhouse et al , 2021). Ecological data is generally geo‐referenced and many of the models account for spatial variation by using for instance conditional autoregressive models (Arab et al , 2016; Croft et al , 2019; Dwyer et al , 2016; Purse et al , 2015; Santos Fernandez et al , 2021), covariance matrices (Reich et al , 2018), Gaussian processes (Sicacha‐Parada et al , 2021), SPDE (Girardello et al , 2019; Humphreys et al , 2019) or simply incorporating spatially varying covariates.…”