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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