Journal article
Estimating animal populations and body sizes from burrows: Marine ecologists have their heads buried in the sand
Journal of Sea Research, Vol.112, pp.55-64
2016
Abstract
Most ecological studies require knowledge of animal abundance, but it can be challenging and destructive of habitat to obtain accurate density estimates for cryptic species, such as crustaceans that tunnel deeply into the seafloor, beaches, or mudflats. Such fossorial species are, however, widely used in environmental impact assessments, requiring sampling techniques that are reliable, efficient, and environmentally benign for these species and environments. Counting and measuring the entrances of burrows made by cryptic species is commonly employed to index population and body sizes of individuals. The fundamental premise is that burrow metrics consistently predict density and size. Here we review the evidence for this premise. We also review criteria for selecting among sampling methods: burrow counts, visual censuses, and physical collections. A simple 1:1 correspondence between the number of holes and population size cannot be assumed. Occupancy rates, indexed by the slope of regression models, vary widely between species and among sites for the same species. Thus, 'average' or 'typical' occupancy rates should not be extrapolated from site- or species specific field validations and then be used as conversion factors in other situations. Predictions of organism density made from burrow counts often have large uncertainty, being double to half of the predicted mean value. Whether such prediction uncertainty is 'acceptable' depends on investigators' judgements regarding the desired detectable effect sizes. Regression models predicting body size from burrow entrance dimensions are more precise, but parameter estimates of most models are specific to species and subject to site-to-site variation within species. These results emphasise the need to undertake thorough field validations of indirect census techniques that include tests of how sensitive predictive models are to changes in habitat conditions or human impacts. In addition, new technologies (e.g. drones, thermal-, acoustic- or chemical sensors) should be used to enhance visual census techniques of burrows and surface-active animals.
Details
- Title
- Estimating animal populations and body sizes from burrows: Marine ecologists have their heads buried in the sand
- Authors
- Thomas Schlacher (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringSerena Lucrezi (Author) - North-West University, South AfricaCharles H Peterson (Author) - University of North Carolina at Chapel Hill, United StatesRod M Connolly (Author) - Griffith UniversityAndrew D Olds (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringFranziska Althaus (Author) - CSIRO Oceans and AtmosphereGlenn A Hyndes (Author) - Edith Cowan UniversityBrooke Maslo (Author) - State University of New Jersey, United StatesBen Gilby (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringJavier X Leon (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringMichael A Weston (Author) - Deakin UniversityMariano Lastra (Author) - Universidad de Vigo, SpainAlan Williams (Author) - CSIRO Oceans and AtmosphereDavid S Schoeman (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
- Publication details
- Journal of Sea Research, Vol.112, pp.55-64
- Publisher
- Elsevier BV
- Date published
- 2016
- DOI
- 10.1016/j.seares.2016.04.001
- ISSN
- 1385-1101
- Copyright note
- Copyright © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99449354602621
- Output Type
- Journal article
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