Turvey, S. T. | Search bioRxiv

bioRxiv2024

Prioritising Functionally Distinct and Globally Endangered (FuDGE) sharks for conservation action

Robust species-level methods for quantifying ecological differences have yet to be incorporated into conservation strategies. Here, we present a conservation prioritisation approach that integrates species trait data and extinction risk to quantify the contribution of individual species to overall functional diversity. The Functionally Irreplaceable with Risk of Extinction (FIRE) metric directs conservation action to species whose extinction is expected to result in significant losses of functional diversity. We applied our framework to sets of species at the global scale. First we assessed the worlds birds, highlighting congruent and divergent priorities identified by trait-based and phylogenetic approaches. Second, we applied FIRE to the worlds sharks, exploring the impact of imputed traits on prioritisation robustness. For birds and sharks, we show that prioritising by functional irreplaceability is an effective strategy to conserve exploited species. The FIRE metric provides a robust tool to facilitate the incorporation of functional diversity into conservation policy and practice, revealing species that may be overlooked by existing approaches.

ecology↗

bioRxiv2020

Automated detection of Hainan gibbon calls for passive acoustic monitoring

1O_LIExtracting species calls from passive acoustic recordings is a common preliminary step to ecological analysis. For many species, particularly those occupying noisy, acoustically variable habitats, the call extraction process continues to be largely manual, a time-consuming and increasingly unsustainable process. Deep neural networks have been shown to offer excellent performance across a range of acoustic classification applications, but are relatively underused in ecology. C_LIO_LIWe describe the steps involved in developing an automated classifier for a passive acoustic monitoring project, using the identification of calls of the Hainan gibbon (Nomascus hainanus), one of the worlds rarest mammal species, as a case study. This includes preprocessing - selecting a temporal resolution, windowing and annotation; data augmentation; processing - choosing and fitting appropriate neural network models; and postprocessing - linking model predictions to replace, or more likely facilitate, manual labelling. C_LIO_LIOur best model converted acoustic recordings into spectrogram images on the mel frequency scale, using these to train a convolutional neural network. Model predictions were highly accurate, with per-second false positive and false negative rates of 1.5% and 22.3%. Nearly all false negatives were at the fringes of calls, adjacent to segments where the call was correctly identified, so that very few calls were missed altogether. A postprocessing step identifying intervals of repeated calling reduced an eight-hour recording to, on average, 22 minutes for manual processing, and did not miss any calling bouts over 72 hours of test recordings. Gibbon calling bouts were detected regularly in multi-month recordings from all selected survey points within Bawangling National Nature Reserve, Hainan. C_LIO_LIWe demonstrate that passive acoustic monitoring incorporating an automated classifier represents an effective tool for remote detection of one of the worlds rarest and most threatened species. Our study highlights the viability of using neural networks to automate or greatly assist the manual labelling of data collected by passive acoustic monitoring projects. We emphasise that model development and implementation be informed and guided by ecological objectives, and increase accessibility of these tools with a series of notebooks that allow users to build and deploy their own acoustic classifiers. C_LI

ecology↗