Back

JARA: ‘Just Another Red list Assessment’

Winker, H.; Sherley, R.

2019-06-18 ecology
10.1101/672899 bioRxiv
Show abstract

Identifying species at risk of extinction is necessary to prioritise conservation efforts. The International Union for Conservation of Natures (IUCN) Red List of Threatened Species is the global standard for quantifying extinction risk, with many species categorised according to population reduction thresholds. We introduce the Bayesian state-space framework JARA (Just Another Red-List Assessment). Designed as decision-support tool, JARA allows both process error and uncertainty to be incorporated into IUCN Red List assessments under criterion A. JARA is implemented via an R package that is designed to be easy to use, rapid and widely applicable, so conservation practitioners can apply it to their own count or relative abundance data. JARA outputs display easy to interpret graphics of the posterior probability of the population trend against the corresponding IUCN Red List categories, as well as additional graphics to describe the timeseries and model diagnostics. We illustrate JARA using three real-world examples: (1) relative abundance indices for two elasmobranchs, Yellowspotted Skate Leucoraja wallacei and Whitespot Smoothhound Mustelus palumbes; (2) a comparison of standardized abundance indices for Atlantic Blue Marlin Makaira nigricans and (3) absolute abundance data for Cape Gannets Morus capensis. Finally, using a simulation experiment, we demonstrate how JARA provides greater accuracy than two approaches commonly used to assigning a Red List Status under criterion A. Tools like JARA can help further standardise Red List evaluations, increasing objectivity and lowering the risk of misclassification, with substantial benefits for global conservation efforts.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.