Distance sampling: Difference between revisions

Revision as of 23:44, 15 April 2019

Revision as of 18:54, 1 July 2019

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==Assumptions and sources of bias==

Since distance sampling is a comparatively complex survey method, the reliability of model results depends on meeting a number of basic assumptions. The most fundamental ones are listed below. Data derived from surveys that violate one or more of these assumptions can frequently, but not always, be corrected to some extent before or during analysis.<ref name=buckland1993/><ref name=buckland2001/>

{| class="wikitable"

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|+ Basic assumptions of distance sampling

! Assumption

! Violation

! Prevention/post-hoc correction

! Data example

|-

| All animals on the line are detected (i.e., ''P(0)'' = 1)

| This can often be assumed in terrestrial surveys, but may be problematic in shipboard surveys. Violation may result in strong bias of model estimates

| In dual observer surveys, one observer may be tasked to "guard the centerline".

Post-hoc fixes are sometimes possible but can be complex.<ref name=buckland1993/> It is thus worth avoiding any violations of this assumption

|

|-

| 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

|

|-

| Animals do not move before detection

| Resulting bias is negligible if movement is random. Movement in response to the observer (avoidance/attraction) will incur a negative/positive bias in detectability

| Avoidance behaviour is common and may be difficult to prevent in the field. An effective post-hoc remedy is the averaging-out of data by partitioning detections into intervals, and by using detection functions with a shoulder (e.g., hazard-rate)

| [[File:Distance sampling observer avoidance.png|thumb|An indication of avoidance behaviour in the data - detections initially increase rather than decrease with added distance to the transect line]]

|-

| Measurements (angles and distances) are exact

| Random errors are negligible, but systematic errors may introduce bias. This often happens with rounding of angles or distances to preferred ("round") values, resulting in heaping at particular values. Rounding of angles to zero is particularly common

| Avoid dead reckong in the field by using [[range finder]]s and angle boards. Post-hoc smoothing of data by partitioning into detection intervals is effective in addressing minor biases

| [[File:Distance sampling rounding to zero.png|thumb|An indication of angle rounding to zero in the data - there are more detections than expected in the very first data interval]]

|}

==Software implementations==

== References ==