Data from: The importance of accounting for larval detectability in mosquito habitat-association studies

Background: Mosquito habitat-association studies are an important basis
for disease control programmes and/or vector distribution models. However,
studies do not explicitly account for incomplete detection during larval
presence and abundance surveys, with potential for significant biases
because of environmental influences on larval behaviour and sampling
efficiency. Methods: Data were used from a dip-sampling study for
Anopheles larvae in Ethiopia to evaluate the effect of six factors
previously associated with larval sampling (riparian vegetation, direct
sunshine, algae, water depth, pH and temperature) on larval presence and
detectability. Comparisons were made between: (i) a presence-absence
logistic regression where samples were pooled at the site level and
detectability ignored, (ii) a success versus trials binomial model, and
(iii) a presence-detection mixture model that separately estimated
presence and detection, and fitted different explanatory variables to
these estimations. Results: Riparian vegetation was consistently
highlighted as important, strongly suggesting it explains larval presence
(−). However, depending on how larval detectability was estimated, the
other factors showed large variations in their statistical importance. The
presence-detection mixture model provided strong evidence that larval
detectability was influenced by sunshine and water temperature (+), with
weaker evidence for algae (+) and water depth (−). For larval presence,
there was also some evidence that water depth (−) and pH (+) influenced
site occupation. The number of dip-samples needed to determine if larvae
were likely present at a site was condition dependent: with sunshine and
warm water requiring only two dips, while cooler water and cloud cover
required 11. Conclusions: Environmental factors influence true larval
presence and larval detectability differentially when sampling in field
conditions. Researchers need to be more aware of the limitations and
possible biases in different analytical approaches used to associate
larval presence or abundance with local environmental conditions. These
effects can be disentangled using data that are routinely collected (i.e.,
multiple dip samples at each site) by employing a modelling approach that
separates presence from detectability.