Red-breasted Pygmy-parrot, photographed on 10 August 2009 in the Arfak mountains, West Papua, Indonesia. Photo: Collaertsbrothers / Flickr

17 January 2019 (Radboud University) – Currently approximately 600 species might be inaccurately assessed as non-threatened on the Red List of Threatened Species. More than a hundred others that couldn’t be assessed before, also appear to be threatened. A new more efficient, systematic and comprehensive approach to assess the extinction risk of animals has shown this. The method, designed by Radboud University ecologist Luca Santini and colleagues, is described in Conservation Biology on 17 January 2019.

Using their new method, the researchers’ predictions of extinction risks are quite consistent with the current published Red List assessments, and even a bit more optimistic overall. However, they found that 20% of 600 species that were impossible to assess before by Red List experts, are likely under threat of extinction, such as the brown-banded rail and Williamson’s mouse-deer. Also, 600 species that were assessed previously as being non-threatened, are actually likely to be threatened, such as the red-breasted pygmy parrot and the Ethiopian striped mouse. “This indicates that urgent re-assessment is needed of the current statuses of animal species on the Red List”, Santini says.

Limited amount of data leads to misclassification

Once every few years, specialized researchers voluntarily assess the conservation status of animal species in the world, which is then recorded in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species. Species are classified into five extinction risk categories ranging from Least Concern to Critically Endangered, based on data such as species distribution, population size and recent trends.

“While this process is extremely important for conservation, experts often have a limited amount of data to apply the criteria to the more than 90,000 species that are currently covered by the Red List”, Santini says. “Often these data are of poor quality because they are outdated or inaccurate because certain species that live in very remote areas have not been properly studied. This might lead to species to be misclassified or not assessed at all.”

New method: information and statistics lead to more efficiency

It’s time for a more efficient, systematic and comprehensive approach, according to Santini and his colleagues. They designed a new method that provides Red List experts with additional independent information, which should help them to better assess species.

The method uses information from land cover maps, that show how the distribution of species in the world has changed over time. The researchers’ method couples this information with statistical models to estimate a number of additional parameters, such as species’ abilities to move through fragmented landscapes, to classify species into a Red List extinction risk category.

Algorithms for a more dynamic Red List

The new approach is meant to complement the traditional methods of Red List assessments. “As the Red List grows, keeping it updated becomes a daunting task. Algorithms that use near-real time remote sensing products to scan across vast species lists, and flag those that may be nearing extinction, can improve dramatically the timeliness and effectiveness of the Red List”, says Carlo Rondinini, Director of the Global Mammal Assessment Programme for the Red List.

Santini: “Our vision is that our new method will soon be automated so that data is re-updated every year with new land cover information. Thus, our method really can speed up the process and provide an early warning system by pointing specifically to species that should be re-assessed quickly.”

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More animal species under threat of extinction, new method shows


Effect of decreasing proportions of population size and suitable habitat on the number of Data Deficient species predicted to be threatened, and the number of species predicted to be more threatened than currently classified under Red List criteria. Graphic: Santini, et al., 2019 / Conservation Biology

ABSTRACT: The IUCN Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends and structure of species’ distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification, or classification as Data Deficient.

Here we propose an approach combining data on land‐cover change and species‐specific habitat preferences, population abundance and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence IUCN Red List categories.

We demonstrate the applicability of our approach for non‐pelagic birds and terrestrial mammals globally (∼15,000 species), generating predictions fairly consistent with published Red List assessments, but more optimistic overall. We predict 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed, and 20.2% of Data Deficient species (10 birds and 114 mammals) to be at risk of extinction. However, incorporating the habitat fragmentation sub‐criterion reduced these predictions 1.5‐2.3% and 6.4‐14.9% (depending on the quantitative definition of fragmentation) of threatened and Data Deficient species respectively, highlighting the need for improved guidance to Red List assessors on applying this aspect of the Red List criteria.

Our approach can be used to complement traditional methods of estimating parameters for Red List assessments. Furthermore, it can readily provide an early warning system to identify species potentially warranting changes in their extinction risk category based on periodic updates of land cover information. Given that our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment.

Applying habitat and population‐density models to land‐cover time series to inform IUCN red list assessments

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