Early identification of increasing mosquito activity is critical to effective mosquito control, particularly when increasing host-seeking behavior may be associated with increased risk of mosquito-borne disease. In this paper, we analyzed the temporal abundance pattern of the West Nile Virus vector, Culex tarsalis, in Fort Collins, CO, using an autoregressive integrated moving average model. We determined that an autoregressive model order 5 with lagged minimum temperatures was best at describing the seasonal abundance of Cx. tarsalis. We then tested the effect of using both temporal and spatial subsets of the data to determine the effect of reduced sampling effort on abundance predictions. We found that, if reduced trapping is necessary due to limited resources, removal of the least productive 1/3 or 1/4 of the traps produced the least erroneous predictions of seasonality represented in the observed data. We show that this productivity-based subset scheme performs better than other sampling effort reductions in generating the best estimate of Cx. tarsalis abundance per trap-night.
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