Variability in Population Abundance and the Classification of Extinction Risk

Classifying species according to their risk of extinction is a common practice and underpins much conservation activity. The reliability of such classifications rests on the accuracy of threat categorizations, but very little is known about the magnitude and types of errors that might be expected. The process of risk classification involves combining information from many sources, and understanding the quality of each source is critical to evaluating the overall status of the species. One common criterion used to classify extinction risk is a decline in abundance. Because abundance is a direct measure of conservation status, counts of individuals are generally the preferred method of evaluating whether populations are declining. Using the thresholds from criterion A of the International Union for Conservation of Nature (IUCN) Red List (critically endangered, decline in abundance of >80% over 10 years or 3 generations; endangered, decline in abundance of 50-80%; vulnerable, decline in abundance of 30-50%; least concern or near threatened, decline in abundance of 0-30%), we assessed 3 methods used to detect declines solely from estimates of abundance: use of just 2 estimates of abundance; use of linear regression on a time series of abundance; and use of state-space models on a time series of abundance. We generated simulation data from empirical estimates of the typical variability in abundance and assessed the 3 methods for classification errors. The estimates of the proportion of falsely detected declines for linear regression and the state-space models were low (maximum 3-14%), but 33-75% of small declines (30-50% over 15 years) were not detected. Ignoring uncertainty in estimates of abundance (with just 2 estimates of abundance) allowed more power to detect small declines (95%), but there was a high percentage (50%) of false detections. For all 3 methods, the proportion of declines estimated to be >80% was higher than the true proportion. Use of abundance data to detect species at risk of extinction may either fail to detect initial declines in abundance or have a high error rate.

[1]  Mark A. Burgman,et al.  Protocols for listing threatened species can forecast extinction , 2004 .

[2]  E. Slooten,et al.  An approach for regional threat assessment under IUCN Red List criteria that is robust to uncertainty: The Fiordland bottlenose dolphins are critically endangered , 2009 .

[3]  Mark A. Burgman,et al.  The Consistency of Extinction Risk Classification Protocols , 2005 .

[4]  S. Butchart,et al.  Rates of Movement of Threatened Bird Species between IUCN Red List Categories and toward Extinction , 2008, Conservation biology : the journal of the Society for Conservation Biology.

[5]  J. Rodríguez,et al.  National Threatened Species Listing Based on IUCN Criteria and Regional Guidelines: Current Status and Future Perspectives , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[6]  E J Milner-Gulland,et al.  Quantification of Extinction Risk: IUCN's System for Classifying Threatened Species , 2008, Conservation biology : the journal of the Society for Conservation Biology.

[7]  H. McCallum,et al.  Tasmanian devil facial tumour disease: lessons for conservation biology. , 2008, Trends in ecology & evolution.

[8]  B. Mapstone.  Scalable Decision Rules for Environmental Impact Studies: Effect Size, Type I, and Type II Errors , 1995 .

[9]  N. Dulvy,et al.  Comparison of threat and exploitation status in North‐East Atlantic marine populations , 2005 .

[10]  G. Mace,et al.  Making Consistent IUCN Classifications under Uncertainty , 2000 .

[11]  Todd S Bridges,et al.  Using Scalar Models for Precautionary Assessments of Threatened Species , 2006, Conservation biology : the journal of the Society for Conservation Biology.

[12]  Mark A. Burgman,et al.  The treatment of uncertainty and the structure of the IUCN threatened species categories , 1999 .

[13]  Michael Hoffmann,et al.  The value of the IUCN Red List for conservation. , 2006, Trends in ecology & evolution.

[14]  Alan Hastings,et al.  FITTING POPULATION MODELS INCORPORATING PROCESS NOISE AND OBSERVATION ERROR , 2002 .

[15]  S. Lele,et al.  ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR , 2006 .

[16]  Mike Hulme,et al.  Using expert knowledge to assess uncertainties in future polar bear populations under climate change , 2008 .

[17]  H. Resit Akçakaya,et al.  Measuring Global Trends in the Status of Biodiversity: Red List Indices for Birds , 2004, PLoS biology.

[18]  Bruce D. Patterson,et al.  The Status of the World's Land and Marine Mammals: Diversity, Threat, and Knowledge , 2008, Science.

[19]  Brian Dennis,et al.  ESTIMATING POPULATION TREND AND PROCESS VARIATION FOR PVA IN THE PRESENCE OF SAMPLING ERROR , 2004 .

[20]  Hugh P. Possingham,et al.  Minimizing the cost of environmental management decisions by optimizing statistical thresholds , 2004 .

[21]  Elizabeth E. Holmes,et al.  BEYOND THEORY TO APPLICATION AND EVALUATION: DIFFUSION APPROXIMATIONS FOR POPULATION VIABILITY ANALYSIS , 2004 .

[22]  Helen M. Regan,et al.  Treatments of Uncertainty and Variability in Ecological Risk Assessment of Single-Species Populations , 2003 .

[23]  Erica H. Dunn,et al.  Using Decline in Bird Populations to Identify Needs for Conservation Action , 2002 .

[24]  H. Resit Akçakaya,et al.  Value of the IUCN Red List , 2003 .

[25]  J. Staudenmayer,et al.  Measurement Error in a Random Walk Model with Applications to Population Dynamics , 2006, Biometrics.

[26]  Hugh P. Possingham,et al.  Limits to the use of threatened species lists , 2002 .

[27]  Using the IUCN Red List Criteria to Assess Species with Declining Populations , 2003 .

[28]  S. Lindley,et al.  ESTIMATION OF POPULATION GROWTH AND EXTINCTION PARAMETERS FROM NOISY DATA , 2003 .

[29]  Tim Gerrodette,et al.  A POWER ANALYSIS FOR DETECTING TRENDS , 1987 .