Effects of uncertainty and variability on population declines and IUCN Red List classifications

Abstract

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.

Site‐selection bias and apparent population declines in long‐term studies

“…Accurately detecting and measuring population declines is a major challenge for several reasons. Studying population dynamics over time is labor intensive and expensive, and because total population censuses are rarely possible, biologists must deal with substantial measurement uncertainty (e.g., Reynolds et al 2011;d'Eon-Eggertson et al 2015;Rueda-Cediel et al 2018), especially for species with low or variable detection probabilities (Nichols et al 2000;Bailey et al 2004). Furthermore, populations often fluctuate randomly, exhibit temporally autocorrelated changes, or both, and such phenomena reflect complex underlying dynamics (e.g., Mosnier et al 2015;McCain et al 2016;Öst et al 2016).…”

Reducing uncertainty in impact assessments for alien species

“…However, few studies have yet explored the potential types and sources of uncertainty in undertaking alien species impact assessments and how these may affect the final classification outcome (Kumschick et al 2017a) who compared results from two different impact classification frameworks for alien amphibians). Better understanding of the sources of uncertainty in risk assessment is essential to protocol improvement, enabling more effective risk communication (McGeoch et al 2012, Milner-Gulland andShea 2017), and identifying general solutions for improving their reliability and value (Rueda-Cediel et al 2018, Latombe et al 2019.…”

Estimating IUCN Red List population reduction: JARA—A decision‐support tool applied to pelagic sharks

“…BSSMs have been used to assess extinction risk under the U.S. Endangered Species Act (Boyd et al., ), but rarely to assign IUCN categories (Regehr et al., ; Rueda‐Cediel, Anderson, Regan, & Regan, ).…”