Literature DB >> 20836466

Unmodeled observation error induces bias when inferring patterns and dynamics of species occurrence via aural detections.

Brett T McClintock1, Larissa L Bailey, Kenneth H Pollock, Theodore R Simons.   

Abstract

The recent surge in the development and application of species occurrence models has been associated with an acknowledgment among ecologists that species are detected imperfectly due to observation error. Standard models now allow unbiased estimation of occupancy probability when false negative detections occur, but this is conditional on no false positive detections and sufficient incorporation of explanatory variables for the false negative detection process. These assumptions are likely reasonable in many circumstances, but there is mounting evidence that false positive errors and detection probability heterogeneity may be much more prevalent in studies relying on auditory cues for species detection (e.g., songbird or calling amphibian surveys). We used field survey data from a simulated calling anuran system of known occupancy state to investigate the biases induced by these errors in dynamic models of species occurrence. Despite the participation of expert observers in simplified field conditions, both false positive errors and site detection probability heterogeneity were extensive for most species in the survey. We found that even low levels of false positive errors, constituting as little as 1% of all detections, can cause severe overestimation of site occupancy, colonization, and local extinction probabilities. Further, unmodeled detection probability heterogeneity induced substantial underestimation of occupancy and overestimation of colonization and local extinction probabilities. Completely spurious relationships between species occurrence and explanatory variables were also found. Such misleading inferences would likely have deleterious implications for conservation and management programs. We contend that all forms of observation error, including false positive errors and heterogeneous detection probabilities, must be incorporated into the estimation framework to facilitate reliable inferences about occupancy and its associated vital rate parameters.

Mesh:

Year:  2010        PMID: 20836466     DOI: 10.1890/09-1287.1

Source DB:  PubMed          Journal:  Ecology        ISSN: 0012-9658            Impact factor:   5.499


  7 in total

1.  Observer aging and long-term avian survey data quality.

Authors:  Robert G Farmer; Marty L Leonard; Joanna E Mills Flemming; Sean C Anderson
Journal:  Ecol Evol       Date:  2014-05-26       Impact factor: 2.912

2.  Designing better frog call recognition models.

Authors:  Paul S Crump; Jeff Houlahan
Journal:  Ecol Evol       Date:  2017-03-30       Impact factor: 2.912

3.  Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification.

Authors:  Katharine M Banner; Kathryn M Irvine; Thomas J Rodhouse; Wilson J Wright; Rogelio M Rodriguez; Andrea R Litt
Journal:  Ecol Evol       Date:  2018-05-20       Impact factor: 2.912

4.  Dependent double-observer method reduces false-positive errors in auditory avian survey data.

Authors:  Kaitlyn M Strickfaden; Danielle A Fagre; Jessie D Golding; Alan H Harrington; Kaitlyn M Reintsma; Jason D Tack; Victoria J Dreitz
Journal:  Ecol Appl       Date:  2019-11-13       Impact factor: 4.657

5.  Determining Occurrence Dynamics when False Positives Occur: Estimating the Range Dynamics of Wolves from Public Survey Data.

Authors:  David A W Miller; James D Nichols; Justin A Gude; Lindsey N Rich; Kevin M Podruzny; James E Hines; Michael S Mitchell
Journal:  PLoS One       Date:  2013-06-19       Impact factor: 3.240

6.  Integrating occurrence and detectability patterns based on interview data: a case study for threatened mammals in Equatorial Guinea.

Authors:  Chele Martínez-Martí; María V Jiménez-Franco; J Andrew Royle; José A Palazón; José F Calvo
Journal:  Sci Rep       Date:  2016-09-26       Impact factor: 4.379

7.  Dynamic wildlife occupancy models using automated acoustic monitoring data.

Authors:  Cathleen Balantic; Therese Donovan
Journal:  Ecol Appl       Date:  2019-02-27       Impact factor: 4.657

  7 in total

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