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Author (up) Hofmeester, T.R.; Cromsigt, J.P.G.M.; Odden, J.; Andrén, H.; Kindberg, J.; Linnell, J.D.C. url  doi
  Title Framing pictures: A conceptual framework to identify and correct for biases in detection probability of camera traps enabling multi-species comparison Type Journal Article
  Year 2019 Publication Ecology and Evolution Abbreviated Journal Ecol Evol  
  Volume Issue Pages  
  Keywords animal characteristics; detectability; environmental variables; mammal monitoring; reuse of data; trail camera  
  Abstract Abstract Obtaining reliable species observations is of great importance in animal ecology and wildlife conservation. An increasing number of studies use camera traps (CTs) to study wildlife communities, and an increasing effort is made to make better use and reuse of the large amounts of data that are produced. It is in these circumstances that it becomes paramount to correct for the species- and study-specific variation in imperfect detection within CTs. We reviewed the literature and used our own experience to compile a list of factors that affect CT detection of animals. We did this within a conceptual framework of six distinct scales separating out the influences of (a) animal characteristics, (b) CT specifications, (c) CT set-up protocols, and (d) environmental variables. We identified 40 factors that can potentially influence the detection of animals by CTs at these six scales. Many of these factors were related to only a few overarching parameters. Most of the animal characteristics scale with body mass and diet type, and most environmental characteristics differ with season or latitude such that remote sensing products like NDVI could be used as a proxy index to capture this variation. Factors that influence detection at the microsite and camera scales are probably the most important in determining CT detection of animals. The type of study and specific research question will determine which factors should be corrected. Corrections can be done by directly adjusting the CT metric of interest or by using covariates in a statistical framework. Our conceptual framework can be used to design better CT studies and help when analyzing CT data. Furthermore, it provides an overview of which factors should be reported in CT studies to make them repeatable, comparable, and their data reusable. This should greatly improve the possibilities for global scale analyses of (reused) CT data.  
  Corporate Author Thesis  
  Publisher John Wiley & Sons, Ltd Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2045-7758 ISBN Medium  
  Area Expedition Conference  
  Notes doi: 10.1002/ece3.4878 Approved no  
  Call Number Equine Behaviour @ team @ Serial 6518  
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