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).