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

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.

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.

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.

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.