The subset of multiple detection-state models with non-zero Akaike weights; models are ranked by their AICc scores.

*The probability of site-occupancy (or overall infestation prevalence, Ψ) was held constant in all models. Detection parameters include p11 (probability of detecting infestation in an infested ecotope, or vector-search sensitivity); p10 (probability of misclassifying a non-infested ecotope as infested); and b (probability that a detection is classified as ‘certain’ in an infested ecotope where at least one detection occurred). Each detection parameter was allowed to have a distinct intercept, whereas all parameters had a common slope, as estimated for p11, for each covariate (see text and Table 5). Detection covariates include: NB, “Number of bugs”; S1, “Search 1”; “SDEc”, detection in same-dwelling ecotopes; BP, “Brick pile”; WP, “Woodpile”; TP, “Tile pile”; Ho, “House”; PS, “Pigsty”; AE, “Animal enclosure”; SR, “Storeroom”; GC, “Goat/sheep corral”; HH, “Henhouse”. See text for the definitions and values of covariates.
AICc, sample size-corrected Akaike's information criterion (or second-order AIC); ΔAICc, difference in AICc between each model and the lowest-AICc (top-ranking) model; wi, Akaike model weight; Likelihood, likelihood of each model, given the data (or relative strength of evidence for each model); k, number of estimable parameters; −2log, twice the negative log-likelihood of each model. See ref. [22] for formulae and details on AICc and related metrics.
The subset of multiple detection-state models with non-zero Akaike weights; models are ranked by their AICc scores.