2. The effects of temperature on mosquito population dynamics and pathogen transmission
Numerous studies have demonstrated that mosquito-borne pathogen transmission is both seasonally and geographically limited at various spatial scales by variation in ambient temperature (e.g., malaria (Sirajet al. 2014; Ryan et al. 2015; Villena et al.2022), Zika (Siraj et al. 2018; Tesla et al. 2018; Ryanet al. 2020a), chikungunya (Johansson et al. 2014), and dengue (Mordecai et al. 2017)). The effects of temperature on ectotherm performance, including mosquito vectors, are typically non-linear, with performance steadily increasing from zero at a minimum critical temperature (CTmin) up to an optimum temperature (Topt), followed by a steep decline towards the critical thermal maximum (CTmax) (Fig. 1). The CTmin and CTmax represent the operational limits for trait performance because temperatures that exceed their range are not permissive for ectotherm development, survival, or reproduction (Brown et al. 2004; Deutsch et al. 2008; Hoffmann et al. 2013; Corkrey et al. 2016; Sinclair et al. 2016). These thermal limits in ectotherm performance are consistent with the metabolic theory of ecology, which posits that organismal physiological and enzymatic rates will increase predictably with temperature because of increased efficiency of biochemical reactions (Huey & Kingsolver 2019) up to Topt. The steep decline in performance above the Topt is attributed to the declining efficiency of metabolic processes due to decreases in protein stability as temperatures increase, eventually resulting in organismal death at the Tmax. Collectively, this information gives us a Thermal Performance Curve (TPC), which can be used to infer ecological and evolutionary outcomes.
Mosquitoes, like other ectotherms, are highly susceptible to changes in ambient temperature, which demonstrably affects their growth rate (Tun-Lin et al. 2000; Alto & Juliano 2001; Monteiro et al. 2007; Delatte et al. 2009; Paaijmans et al. 2013; Evans et al. 2018a; Huxley et al. 2022), reproduction (Carrington et al. 2013; Miazgowicz et al. 2020), metabolic rate (Vorhees et al. 2013), lifespan (e.g., Alto & Juliano 2001; Gunay et al. 2010; Christofferson & Mores 2016; Miazgowicz et al. 2020), biting rate (Afrane et al. 2005; Lardeux et al. 2008; Shapiro et al. 2017; Miazgowiczet al. 2020), immunity (Suwanchaichinda & Paskewitz 1998; Murdock et al. 2012, 2013, 2014b; Adelman et al. 2013; Ferreira et al. 2020), and ability to acquire, carry, and transmit pathogens (Lambrechts et al. 2011; Paaijmans et al. 2012; Mordecai et al. 2013, 2017; Murdock et al.2014b, 2016; Johnson et al. 2015; Shocket et al. 2018a, 2020; Tesla et al. 2018) in a non-linear, unimodal fashion. These temperature-trait relationships can vary in overall shape (e.g., symmetric or asymmetric non-linear relationships) due to differences in the temperatures that optimize and constrain various traits, which in combination will determine the predicted thermal minimum, maximum, and optimum for mosquito fitness, intrinsic growth rates of mosquito populations, and pathogen transmission (Fig. 1).
Process-based models, which traditionally have relied upon temperature relationships grounded in metabolic theory, have enhanced our ability to predict the effects of environmental drivers on spatial and temporal dynamics of vector-borne disease. Several key biological insights have resulted from this general approach. First, temperate areas of the world that currently experience relatively cool temperatures are expected to increase in thermal suitability for many mosquito-borne diseases with future climate warming (Siraj et al. 2014; Ryan et al.2015; Tesla et al. 2018; Ryan et al. 2020a), and, in temperate regions, mosquito-borne pathogens can invade or spread during the summer in seasonally varying environments (Huber et al. 2018; Ngonghala et al. 2021). Secondly, areas that are currently permissive (near the Topt) or warmer than the Topt for transmission are expected to experience a decline in thermal suitability with future warming (Ryan et al.2015, 2020b; Murdock et al. 2016). Third, because mosquito and pathogen species can have different qualitative and quantitative relationships with temperature (resulting in different CTmin, CTmax, and Topt) (Mordecai et al. 2013, 2017, 2019; Johnson et al. 2015; Shapiro et al. 2017; Shocket et al. 2018a, 2020; Teslaet al. 2018; Miazgowicz et al. 2020; Villena et al.2022), shifts in thermal suitability with climate and land use change could also alter the prevalence and magnitude of mosquito-borne diseases in a given area (Tesla et al. 2018), such as sub-Saharan Africa (Mordecai et al. 2020). Fourth, small variations in ambient temperature at fine spatial scales can contribute to high heterogeneity in predicted suitability for pathogen transmission across various environments (Okech et al. 2004; Afrane et al. 2005; Paaijmans & Thomas 2011; Cator et al. 2013; Pincebourde et al. 2016; Murdock et al. 2017; Thomas et al. 2018; Evanset al. 2019; Verhulst et al. 2020; Wimberly et al.2020), which can have important ramifications for predicting mosquito-borne pathogen transmission and targeting interventions (Thomaset al. 2018; Wimberly et al. 2020). Finally, disease intervention efforts can also be directly or indirectly affected by variation in ambient temperature. Various insecticides (Glunt et al. 2014; Akinwande et al. 2021), entomopathogenic fungi (Kikankie et al. 2010; Darbro et al. 2011), andWolbachia transinfections (Murdock et al. 2014a; Ulrichet al. 2016; Ross et al. 2017, 2019, 2020; Foo et al. 2019; Gu et al. 2022) are thermally sensitive, indicating that the efficacy and cost of these interventions could vary seasonally, across geographic regions, and with future climate and land use change (Parham & Hughes 2015).