Distance sampling: Difference between revisions

Revision as of 18:58, 1 July 2019

Revision as of 02:52, 14 August 2019

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== Detection function ==

[[File:Distance sampling method function fitting basic.png|thumb|right|Half-normal detection function (red line) fitted to PDF of detection data. Data have been collated into distance bands (either collected as such, or combined after collection to improve model fitting). Detection probability decreases with distance from center line (''y'' = 0).]]

The drop-off of detectability with increasing distance from the transect line is modeled using a <b>'''detection function</b>''' g(''y'') (here ''y'' is distance from the line). This function is fitted to the distribution of detection ranges represented as a [[probability density function]] (PDF). The PDF is a [[histogram]] of collected distances and describes the probability that an object at distance ''y'' will be detected by an observer on the center line, with detections on the line itself (''y'' = 0) assumed to be certain (''P'' = 1).

By preference, g(''y'') is a [[robust statistics|robust]] function that can represent data with unclear or weakly defined distribution characteristics, as is frequently the case in field data. Several types of functions are commonly used, depending on the general shape of the detection data's PDF:

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| Animals are randomly and evenly distributed throughout the surveyed area

| The main sources of bias are

a) <b>'''clustered populations</b>''' (flocks etc.) but individual detections are treated as independent

b) transects are not placed independently of <b>'''gradients of density</b>''' (roads, watercourses etc.)

c) transects are <b>'''too close together</b> '''

| a) record not individuals but clusters + cluster size, then incorporate estimation of cluster size into the detection function

b) place transects either randomly, or <i>''across</i>'' known gradients of density

c) make sure that maximum detection range (''w'') does not overlap between transects