We used a validated matrix population model for mosquito population dynamics to investigate the efficiency of population control scenarios. Since the discrete mosquito population dynamics model implemented in this research considers each mosquito developmental stage and the transitions through the life cycle, it allows accurate prediction of variations in mosquito abundance due to control measures targeting different developmental stages.
Consistent with previous research, simulations clearly indicate greater effectiveness of combined larvicide-adulticide treatments than treatments targeting only one of these stages (Fig. 7). For example, White et al. (2011) demonstrate that combining interventions such as the application of larvicidal or pupacidal agents that target the aquatic stages of the mosquito life cycle with vector control interventions directed against adult mosquito stages (long-lasting insecticide-treated nets or indoor residual spraying) can lead to substantial reductions in adult mosquito density. Kiware et al. (2017) developed a model of mosquito population dynamics to optimize the impact of vector control intervention combinations to suppress malaria transmission. Based on model simulations, the strategy of “attacking mosquitoes on multiple fronts,” i.e. simultaneous implementation of different vector control interventions, is more effective than performing a single vector control intervention in isolation. A similar conclusion was drawn by Rafikov et al. (2015) who found that combination of three vector control strategies (namely insecticide control, sterile insect technique and reduction of the environmental carrying capacity) not only increased the efficacy of vector control compared to scenarios involving individual vector control strategies, but also significantly reduced the costs of mosquito population control.
Reducing the amount of insecticide used in combined treatments did not change results. In principle, combined treatments use more insecticides than treatments targeting a single developmental stage simply because multiple chemicals are used. Therefore, a greater probability of positive outcomes than targeting single developmental stages could simply result from the higher dosage. Our results, however, suggest this is not the case: reducing insecticide, modeled as halving the efficacy of treatments, does not change the positive outcome patterns (Fig. 8).
Counterintuitively, treatments can increase the mosquito population throughout the year. In mosquito populations, density-dependent growth occurs in the larval stage, with the development of individuals limited by available space and, in some environments, food (Jian et al. 2014; Legros et al. 2009). If this natural population regulation mechanism is disrupted at the wrong time, treatment can result in a singnificant increase in adult mosquitoes compared to no mosquito control interventions (Fig. 3b). Targeting the larval stage during a phase of accelerated development supported by favorable environmental conditions disrupts the density-dependence mechanism at its peak (when it is the strongest), thus triggering a strong compensation of destroyed larvae and a rapid increase in adult population abundance.
A similar mechanism operates when targeting the adult stage. Since the number of adult individuals directly determines the number of larvae, treatments targeting the adult stage during a strong density-dependence of the larval stage also trigger compensation in the larval stage. The compensation causes a rapid increase in adult abundance, with a delay corresponding to the time required for development from the larval to the adult stage. Combined treatments significantly reduce the likelihood of adverse outcomes driven by reduced activity of density-dependence mechanisms because combined treatments simultaneously act on the adult stage, thus reducing the population rebound.
Generally, optimal timing depends on the treatment type. Larvicidal treatments perform better early in the year, starting around day 100 (Fig. 7). This can be explained by considering the life cycle of the mosquito. The development of the first larval population of Aedes species is (given an adequate photoperiod) triggered by flooding (Porphyre et al. 2005; Shaman et al. 2002). First widespread flooding, therefore, synchronizes larval development, with most of the mosquitoes undergoing the sensitive stage simultaneously. Treatment at that particular time yields great results. Since treatments early in the season are more likely to correspond to the initial flooding correctly, such treatments also give better outcomes. Later reproductive cycles lose synchronicity, thus reducing the impact of larvicidal treatments.
On the other hand, adulticidal treatments are only effective when there is a significant number of adults. This, however, happens only later in the season and highly depends on the history of environmental drivers during the year. Therefore, only late (days 260-290) adulticide treatments consistently produce positive outcomes (Fig. 7). Additionally, there is a shift of about one month between periods favorable for larvicidal and adulticidal treatments. The shift corresponds to the time from the development of a critical number of larvae to the emergence of a critical number of adults.
Combined treatments offer the best chance of getting a positive outcome, and yield the best treatment result (Fig. 3a). At the same time, a larvicidal treatment gave the worst result (Fig. 3b). As discussed above, this is due to the interplay between treatment timing and density dependence: if timed correctly and early in the season, an appropriate treatment can almost completely suppress the population. If timed badly, the treatment negates larval density dependence, and the population can actually grow to larger than without any treatment. Larvicidal treatments result in a wide range of outcomes. Combined treatments, on the contrary, tend to have positive outcomes because they effectively bet-hedge by treating both larvae and adults, thus avoiding negation of density dependence.
Shorter, pulsed treatments offer a distinct advantage over longer treatments of the same total duration (Fig. 6). Even though the total amount of insecticide introduced is the same, pulsed treatments can affect the (sub)population during its recovery from the previous pulse or environmental disturbance, thus providing a better overall outcome even though the reduction in mosquito population during the first pulse in pulsed treatment may be much smaller than the reduction caused by a prolonged treatment. This finding is supported by Cailly et al. (2012), who used a continuous, mechanistic, climate-driven model of seasonal mosquito population dynamics to compare the efficiency of mosquito control strategies targeting larvae. They found larvicide spraying at regular time intervals is more efficient than spraying only when the abundance of host-seeking females reaches a given threshold.
Our results also indicate that the outcome of mosquito population control is not necessarily proportional to the amount of pesticide applied. The implementation of mosquito population control is based on applying a constant amount of larvicide and adulticide agents, most often expressed in units of volume or mass per area (e.g., mL ha−1 or g ha−1) (Boubidi et al. 2016). Although such an approach is not practiced (as far as the authors know), there is a need to optimize the amount of pesticide applied according to the type and timing of treatment, thus reducing environmental contamination and mosquito control costs.
We have shown that timing is crucial, particularly timing relative to important environmental cues such as flooding. However, these general results are becoming less relevant due to climate change. In the past, weather patterns, and therefore these cues, were somewhat predictable. Climate change, however, is rapidly affecting weather patterns, reducing the relevance of past experiences. Unless easy-to-use tools for optimizing insecticide treatments are developed, the efficacy of insecticide use will decline significantly (Rocklöv and Dubrow 2020; Colón-González et al. 2021). The decline will either prompt an increase in insecticide use and the related environmental toxicity and risk, or cause an increase in mosquito populations, thus negatively affecting human wellbeing.
There have been significant advances in the use of available datasets and targeted data collection to predict mosquito populations, particularly in preventing the spread of mosquito-borne diseases. Joshi and Miller (2021) give a comprehensive overview of state-of-the-art. However, there is a lack of software solutions that would serve as auxiliary tools and a basis for decision-making for local authorities to optimize mosquito population control and change its nature from reactive to preventive. We, therefore, call for a significant additional effort in spatially explicit modelling of mosquito populations and insecticidal treatments, which should rely on weather forecasts and flood modelling to inform adaptive mosquito population control measures.