Expected rate of environmental change
Rates of environmental change will vary based on the specific temperature variable being considered (e.g., mean temperature of the hottest month or quarter, maximum temperature in the dry season, etc.), and depend on climate policy: projections of global mean annual surface temperatures in 2100 vary by over 3°C depending on the future climate scenario (Collins et al. 2013). However, while greater warming is projected for higher latitudes (IPCC 2007), faster rates of adaptation may be necessary for tropical mosquito species such as An. gambiae, Ae. aegypti , and Ae. albopictus that already experience environmental temperatures close to their thermal optima and may experience large fitness costs under additional warming in the absence of adaptation (Deutsch et al. 2008, Somero 2010, Ryan et al. 2015, 2019, Mordecai et al. 2019).
Priorities and approaches for measuring adaptive potential
Addressing several key data and knowledge gaps will improve our ability to estimate mosquito adaptive potential. As outlined above, there are virtually no estimates of the heritability of thermal tolerance traits, environmental sensitivity of selection, and within-population variation (and few estimates of between-population variation) in thermal tolerance for mosquitoes specifically. Additionally, we have a limited understanding of the role of phenotypic plasticity, particularly behavioral thermoregulation, in mosquito thermal tolerance. Although other parameters of the evolutionary rescue model, including the strength of selection imposed by temperature change, mosquito generation time, and maximum population growth rate, are often not measured directly or precisely, we have relatively more information about these parameters and they are unlikely to be the primary constraints on evolutionary adaptation (see ‘Estimating evolutionary rates’). We therefore recommend that future research focus on measuring environmental sensitivity of selection, plasticity, and within-population variation and heritability in thermal tolerance. We discuss the most promising and feasible approaches for doing so below.
Selection experiments are a powerful tool for investigating the evolution of complex traits (reviewed in Fuller et al. 2005, Garland and Rose 2009, Swallow et al. 2009) that can be used to estimate several of the parameters in evolutionary rescue model parameters. In artificial selection experiments, where individuals are chosen to advance to the next generation based on their value for a particular trait (e.g., time to thermal knockdown), heritability can be measured as:\(h^{2}=\ R\ /\ (i\ \sigma_{p})\) (Falconer and Mackay 1996). Here,R is the mean difference in the trait between control and selected lines, \(\sigma_{p}\) is the trait standard deviation in the control lines, and i , the intensity of selection, is determined based on what proportion of the population is selected each generation (see Box 1). In laboratory natural selection—in which the treatments, rather than the researcher, impose the selection pressure—selection strength itself can be approximated based on the survival rates between generations held at specific temperatures (see Box 1). Both selection designs have been used extensively with model organisms such asDrosophila spp., Daphnia spp., and Escherichia colito measure changes in upper limits of trait thermal tolerance. While no thermal selection experiments have yet been published on mosquitoes (Dennington et al., in prep), several major vector species, includingAe. aegypti and Cx. quinquefasciatus, can be readily maintained and manipulated in the lab (Munstermann 1997, Kauffman et al. 2017) and can therefore be used in experiments to obtain estimates of the heritability of thermal tolerance and the selection strength imposed by different temperature conditions.
Common garden experiments, where traits are measured for distinct populations or genotypes exposed to the same environmental conditions, enable measurement of nearly all rescue model parameters (Clausen et al. 1941, Merilä and Hendry 2014, Villemereuil et al. 2016, Berend et al. 2019). In mosquitoes, common garden experiments have been used to investigate variation in thermal tolerance between populations sampled across a thermal gradient (Mogi 1992, Reisen 1995, Rocca et al. 2009, Vorhees et al. 2013, Ruybal et al. 2016, Chu et al. 2019), but this approach could also be used to measure within-population variation if thermal tolerance traits were measured at the individual level. To measure genetically based variation in thermal tolerance, and to avoid confounding maternal effects and thermal acclimation in the original environment, collected populations should be reared for at least one generation in the lab before experimentation. However, plasticity itself can be measured by, for example, varying larval rearing temperature (Dodson et al. 2012) or measuring thermoregulatory behavior as the trait of interest (e.g., Logan et al. 2018). Common garden experiments can also be used to measure the environmental sensitivity of selection if fitness is measured in addition to thermal tolerance traits (Chevin et al. 2010). Lastly, by tracking parentage and measuring thermal tolerance traits, the heritability of thermal tolerance can be measured based on the slope of the trait values of parent and offspring (Falconer and Mackay 1996).
In addition to providing estimates of rescue model parameters, common garden experiments can be combined with genomic approaches to identify genetic variants associated with climate-adaptive traits (Kort et al. 2014, Villemereuil et al. 2016, Exposito-Alonso et al. 2019, Capblancq et al. 2020). In the closest example of this approach in mosquito populations, the hypothesized thermo-adaptive role of a particular genotype (chromosomal inversion 2La) associated with aridity clines in Africa in An. gambiae (Coluzzi et al. 1979) was confirmed based on thermal tolerance experiments on the two genotypes (homokaryotypic populations 2La+ and 2La) (Rocca et al. 2009). In other taxa, common garden experiments have been combined with genome scans to quantify and predict climate-driven selection along the genome of the plantArabidopsis thaliana (Exposito-Alonso et al. 2019), and to identify 162 candidate genes underlying climate adaptation in the harlequin fly Chironomus riparius (Waldvogel et al. 2018).In these studies, whole-genome sequencing would provide greater power to detect causal loci, thus this approach would be most feasible for mosquito species with available reference genomes, namely Ae. aegypti (e.g., Nene et al. 2007, Matthews et al. 2018), Ae. albopictus (Chen et al. 2015), An. darlingi (Marinotti et al. 2013), An. gambiae (Holt et al. 2002), and An. stephensi(Jiang et al. 2014).
Selection experiments and common garden experiments provide the means to obtain critical missing information on mosquito adaptive potential, but there are several challenges to these approaches. For any experimental test of adaptive potential, regardless of the methodology used, one must identify appropriate temperature treatments and pick fitness-relevant mosquito life history traits for which to assess thermal tolerance. Arbitrary choices for these details make it more difficult to extrapolate from these results to natural systems. Experiments commonly use treatments with constant temperatures above mean ambient temperatures. However, temperature minima or maxima, seasonal variability, and/or accumulated thermal stress may be more relevant to adaptive potential. For example, increases in minimum temperatures affect overnight recovery from heat stress in mosquitoes (Murdock et al. 2012, Bai et al. 2019). Further, given trade-offs in isolating the effect of temperature versus incorporating realistic ecological variation and in maximizing replication between versus within populations, no single study can definitively determine a species’ adaptive potential. As a first step, controlled and replicated lab studies measuring mosquito fitness (either directly or as a composite of individual life history traits) under realistic projected thermal regimes that incorporate natural diurnal variation in temperature, combined with genomics approaches, will greatly improve our understanding on current and potential mosquito thermal adaptation (Andriamifidy et al. 2019). Such studies will inform parameters of evolutionary rescue models and, more broadly, enable investigation of the dynamics and limits of thermal adaptation.
Discussion
Accurate predictions of mosquito-borne disease distributions under climate change require reckoning with the potential for mosquitoes to adapt to rising temperatures. Estimating this potential is challenging, however, because thermal tolerance is a complex trait, mosquito vectors vary in their current thermal sensitivity, and many aspects of climate regimes are projected to change. Here, we have outlined a framework for investigating mosquito adaptive potential that involves identifying the climate factors and mosquito traits currently limiting persistence, then comparing the projected rates of environmental change to potential evolutionary rates in these traits using a simple evolutionary model (Chevin et al. 2010). This approach makes clear that some aspects of mosquito demographics and strong temperature-imposed selection may facilitate rapid evolution and adaptation. However, missing information on the heritability and within-population variation of thermal tolerance, the environmental sensitivity of selection, and the role of phenotypic plasticity (particularly behavioral thermoregulation), constrains our ability to make predictions about mosquito persistence and adaptation under climate warming. Common garden and selection experiments can be used to fill these data gaps but require careful consideration of the most relevant temperature treatments and mosquito life history traits.
In addition to the important data gaps we have emphasized within the evolutionary rescue framework, the model itself (Chevin et al. 2010) has several important limitations. Notably, these include the lack of potential genotype-by-environment interactions in the expression of phenotypes, evolution in plasticity, gene flow, genetic correlations between traits associated with thermal tolerance, and demographic or environmental stochasticity (Chevin et al. 2010). These simplifying assumptions make the model tractable but may limit the accuracy of the predictions if these factors play a large role in adaptation. Adding complexity would require additional data collection and may make predictive models too computationally intensive to solve analytically but can be implemented through simulations (Bürger and Lynch 1995). Several studies have effectively used simulations to incorporate environmental stochasticity (Ashander et al. 2016), demographic stochasticity (Martin et al. 2013), dispersal (Schiffers et al. 2013), carrying capacity (Bridle et al. 2010), and evolution in plasticity (Scheiner et al. 2017) into an evolutionary rescue model framework. Simulation results can be used to investigate transient evolutionary dynamics and can be compared with analytic results to determine the impact of these processes on evolutionary rescue. For example, Ashander et al. 2016 estimated population extinction risk using both analytic approximations and simulations to find that evolving plasticity only facilitated evolutionary rescue when the environmental change was sufficiently predictable. Using simulation to model more realistically complex evolutionary scenarios will likely be necessary when more precise forecasting is a priority, and is becoming a more approachable method through the availability of evolutionary simulation tools such as SLiM (Haller and Messer 2017).
Given the potential for thermal adaptation in the form of physiological changes in the thermal tolerance of mosquito life history traits, we now consider the potential implications for disease transmission. Mosquito thermal niche shifts could maintain, increase, or decrease disease transmission depending on whether evolved increases in thermal tolerance are accompanied by shifts in lower thermal limits, on the strength of thermodynamic constraints, and on genetic correlations between traits. In the absence of other changes to thermal performance, upward shifts in thermal limits could maintain current levels of disease transmission under rising temperatures, particularly if lower temperatures are infrequently experienced. However, disease transmission may increase if peak performances for mosquito traits like fecundity and biting rate increase with their thermal optima. This is an expectation of the “hotter-is-better” hypothesis, but how the shape of thermal performance curves evolves is a point of ongoing debate and empirical uncertainty (Angilletta et al. 2010, Latimer et al. 2011, Kontopoulos et al. 2020). Regardless, genetic correlations between mosquito traits under direct selection and other traits that may impact disease transmission (e.g., development time and immunocompetence, as observed in Ae. aegypti ; Koella and Boëte 2002) could still constrain mosquito-borne disease transmission under thermal adaptation (Lande and Arnold 1983).
Mosquitoes, like other ectotherms, may cope with warming temperatures through a variety of other mechanisms besides shifts in thermal physiology, such as accelerated life cycles, phenological shifts, and behavioral thermoregulation, with varying consequences for disease transmission (Huey and Kingsolver 1993, Bradshaw et al. 2000, Stearns et al. 2000, Angilletta et al. 2003, Waldvogel et al. 2020). Evolved increases in life cycle speed can mitigate increases in daily mortality rates, and were suggested to occur in Anopheles spp. in response to vector control interventions (Ferguson et al. 2012). As adult mosquito longevity is already the main limitation on transmission near upper thermal limits for many major mosquito-borne diseases (Mordecai et al. 2019), further reductions could cause large declines in transmission for pathogens with longer incubation periods. In particular, transmission of malaria parasites, which have a minimum incubation period of approximately nine days (Paaijmans et al. 2012, Blanford et al. 2013), may be more negatively impacted under this strategy than viral pathogens, such as dengue virus and chikungunya virus, which have generally faster incubation periods—as low as three to five days at temperatures above 30°C (Tjaden et al. 2013, Rudolph et al. 2014, Mordecai et al. 2019, Winokur et al. 2020). The implications of warming-driven life cycle adaptation therefore depend on the interaction between vector and pathogen traits, which vary across species and environments.
Behavioral thermoregulation and phenological shifts could increase, maintain, or decrease disease transmission, primarily depending on how these shifts impact mosquito – human contact rates and the effectiveness of vector control activities (Ferreira et al. 2017). For example, if rising temperatures promote shifts in biting activity towards the cooler, night-time hours when humans are more likely to be protected by bed nets, disease transmission may be reduced (Taylor 1975, Pates and Curtis 2005, Moiroux et al. 2012, Thomsen et al. 2017, Carrasco et al. 2019). However, in the absence of vector control, shifts towards night-time biting, as well as thermoregulatory shifts favoring indoor versus outdoor biting, could increase mosquito – human contact rates and transmission (Takken 2002). Similarly, phenological shifts in mosquito activity could lead to changes in the length or timing of disease transmission, potentially maintaining, increasing, or decreasing disease transmission. For example, increasing monthly mean temperatures in portions of California have effectively doubled the potential transmission season of St. Louis encephalitis virus, such that elderly persons traveling to California for the winter are newly at risk (Patz and Reisen 2001). Failing to account for phenological shifts in mosquito activity may render vector control programs less effective at reducing mosquito populations and disease transmission. In general, the impact of mosquito thermal adaptation on disease transmission will vary based on the mechanism of thermal adaptation, making identifying what adaptive strategies are most likely in different contexts a priority for future research.
Ultimately, how mosquitoes will adapt to climate change may depend on context-specific information (e.g., local human water storage practices that affect breeding habitat availability), additional evolutionary factors (e.g., rates of gene flow), and other ecological changes (e.g., land use change). Further, estimating the resulting effects on mosquito-borne disease distributions requires considering other mosquito adaptive mechanisms, eco-evolutionary responses of pathogens (Weaver 2006, Tabachnick 2016, Powell 2019), and climate effects on human immunology, behavior, and biogeography (reviewed in Reiter 2001, Patz and Reisen 2001, Sutherst 2004, Gage et al. 2008). This level of complexity can make the task of investigating mosquito climate adaptation daunting. However, the simple framework outlined here can be used to identify the mosquito species, geographic regions, and/or climate scenarios under which climate adaptation is more or less plausible. Targeted data collection efforts to address the key gaps outlined above will enable increasingly precise estimates of the probability of adaptation under different scenarios.
Many taxa that threaten human health and well-being share properties with mosquitoes that favor the potential for climate adaptation, such as short generation times, high population growth rates, and strong climate sensitivity. These include vectors of major human, wildlife, and plant disease (e.g., species of tsetse flies, biting midges, psyllids, and aphids), as well as pests of crops and forest resources (e.g., species of beetles, moths, fruit flies, and fire ants). Despite the substantial societal cost adaptation in pest and disease vector species could impose, their potential to adapt to climate change remains poorly understood. This remains challenging to predict given the many determinants of evolutionary rates, incomplete data on these determinants for most taxa, and the inability to perform a single, conclusive experiment. Drawing from conservation biology techniques used to study climate adaptive potentials in threatened and endangered species, we have outlined a framework and empirical approaches for investigating mosquito thermal adaptation that can be applied to any vector or pest species and type of environmental change. Combining available information on key components of evolutionary potential, leveraging information from related taxa where applicable, and using empirical approaches such as common garden or selection experiments to obtain multiple pieces of missing information at once will enable better estimates of adaptive potential. Understanding and estimating the potential for climate adaptation in taxa of concern to human health is critical for accurately predicting and preparing for their persistence or shifts in their distributions under climate change.
Acknowledgements
We thank George Somero for enlightening discussions about the molecular mechanisms of thermal tolerance. We thank Nina Dennington for sharing insight on experimental methods for studying mosquito thermal adaptation. We are grateful to Chris Anderson, Tejas Athni, Alex Becker, Caroline Glidden, and Morgan Kain for helpful feedback on the manuscript. EAM, DGK, JEF, and EBS were funded by the NIH National Institute of General Medical Sciences R35 MIRA program (R35GM133439). EAM, JEF, and MSS were funded by the NSF Ecology and Evolution of Infectious Diseases (EEID) program (DEB-1518681). EAM was funded by the NSF EEID program (DEB-2011147, with support from the Fogarty International Center), a Terman Award, and a Stanford King Center for Global Development seed grant. MLC was supported by the Illich-Sadowsky Fellowship through the Stanford Interdisciplinary Graduate Fellowship program. NN was supported by the Stanford Data Science Scholarship. LIC was funded by the Stanford Graduate Fellowship. JEF was funded by the Bing-Mooney Graduate Fellowship. MJH was funded by the Knight-Hennessy Scholarship.
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