Supplementary Information - Collated Entomological Data from A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations

Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species–questions rarely answerable from individual entomological studies (that typically focus on a single location or species). We develop a novel statistical framework enabling identification and classification of time series with similar temporal properties; and use this framework to systematically explore variation in population dynamics and seasonality in <i>Anopheline</i> mosquito time-series catch data spanning seven species, 40 years and 117 locations across India. Our analyses reveal pronounced variation in dynamics across locations and between species in the extent of seasonality and timing of seasonal peaks. However, we show that these diverse dynamics can be clustered into four ‘dynamical archetypes’, each characterized by distinct temporal properties and associated with a largely unique set of environmental factors. Our results highlight that a range of environmental factors including rainfall, temperature, proximity to static water bodies and patterns of land use (particularly urbanicity) shape the dynamics and seasonality of mosquito populations; and provide a generically applicable framework to better identify and understand patterns of seasonal variation in vectors relevant to public health.

阐明媒介传播疾病传播过程中蚊虫种群的时间动态规律，是优化防控策略的核心关键。目前学界对这类动态的驱动机制，以及其在不同物种间的变异模式仍存在诸多未解问题；而这类问题往往难以通过单一昆虫学研究（通常仅聚焦单个研究地点或单一物种）得到解答。本研究构建了一套全新的统计框架，可实现对具有相似时间特征的时间序列的识别与分类；并借助该框架，对覆盖印度境内117个地点、跨越40年、涉及7个物种的按蚊属（Anopheline）蚊虫时间序列捕获数据中的种群动态与季节节律差异开展系统性探究。分析结果显示，不同研究地点以及不同蚊虫物种间，种群动态在季节性强度与季节峰值出现时间上均存在显著差异。不过本研究证实，这些多样的种群动态可被聚类为4类"动态原型"，每一类均具备独特的时间特征，且与一组高度专属的环境因子相关联。研究结果表明，降雨、气温、距静止水体的距离以及土地利用模式（尤其是城市化程度）等一系列环境因子，共同塑造了蚊虫种群的动态与季节节律；同时本研究还提供了一套通用可推广的分析框架，可用于更精准地识别与理解与公共卫生相关的媒介生物季节变异模式。