DOI: 10.5061/dryad.98q2r
title: Data from: The importance of accounting for larval detectability in mosquito habitat-association studies

description: 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.
description: individual dip sampling
 data for mosquito larvae in EthiopiaSite: sites visited
 labelled from A-Z (with 2 meaning second visit). Sample: count of
 individual samples taken at each site. Depth: depth in cm at the point of
 each individual dip sample. Area: area of the water body being sampled.
 Mean_depth: mean of depth measures from site. ph: ph at each site.
 Tall_rip: percentage estimate of tall (&gt;20cm) riparian vegetation
 around the water body. Temp: temperature in degrees celsius at the
 sampling point. Anoph10: yes/no variable for whether Anopheles larvae were
 found in an individual dip sample. Algae: whether algae were visibly
 present in the water body. Sunny: if it was sunny on the surface of the
 water body at the time of collection.dryad_data_Lowetal_malaria.csv