Challenge 3: Agents and targets of selection
General Application — Quantifying the agents and targets of
natural selection is essential for understanding local adaptation
(Kawecki and Ebert 2004) in any environment, yet is inherently difficult
(Endler 1986). Targets of selection may be misidentified or confounded
in both phenotypic and genomic approaches due to a poor understanding of
the relationships between genotype, phenotype, and environment (Linnen
and Hoekstra 2009, Bierne et al. 2011, Hoban et al. 2016). Disentangling
selection on single versus multiple correlated traits can be
particularly difficult because of genetic, developmental, and functional
constraints (Hill and Robertson 1966, Price 1970, Lande and Arnold
1983). The genetic architecture of a phenotype can also complicate
genomic tests for local adaptation as polygenic traits may be more
difficult to detect in selection scans compared to single locus traits
(Hoban et al. 2016). Given the suspected prevalence and importance of
polygenic adaptation and that rapid adaptation may involve soft rather
than hard selective sweeps, identifying genomic targets of selection may
be difficult for many complex phenotypes (Rockman 2011, Messer and
Petrov 2013). In addition, large sample sizes and powerful statistical
methods may be required to detect what are typically small selection
coefficients (Kingsolver et al. 2001), and episodic or age-specific
selection may lead to confusion as to when selection has occurred (Grant
and Grant 2014). The signatures of past and contemporary selection can
be difficult to differentiate (Haller and Hendry 2014) as phenotypes may
arise in response to selective pressures in the contemporary environment
but also may have arisen under ancestral selective regimes (i.e., are
exaptations) or as a consequence of non-adaptive processes (e.g., gene
flow). Lastly, in any environment humans can directly or indirectly
change factors affecting selection and adaptation such as resource
availability, resource distribution, population connectivity, and
habitat size.
Human Element — The urban environment is human-built, thus many
of the agents of selection are anthropogenic and not previously
encountered by organisms or researchers in non-urban environments (Lugo
et al. 2018, Alberti 2015). For example, extensive impervious surfaces
(e.g., asphalt) within cities can impact local climate because they
absorb and radiate solar energy differently than natural substrates (the
“urban heat island” effect, Oke 1973), and high concentrations of
anthropogenic pollutants in urban habitats could accelerate mutation
rates (Yauk et al. 2000, Somers et al. 2004, Johnson and Munshi-South
2017). Understanding these anthropogenic pressures may require
cross-disciplinary collaboration (e.g., engineering, physics, chemistry,
governance, urban planning; McPhearson et al. 2016). Moreover, teasing
apart the relative importance of local adaptation, exaptation, and
non-adaptive (e.g., gene flow) origins of urban phenotypes can be
particularly challenging in urban environments. For example, as a
consequence of human-associated population connectivity, pigeons
(Columba livia ) in the Northeastern United States form a large
continuous genetic metapopulation spanning city centers separated by
over 800 km (Carlen and Munshi-South 2020). In fact, due to
human-mediated movement, some organisms have a higher probability,
frequency, and distance of dispersal in somewhat predictable ways (e.g.,
intercity translocations; Gotzet et al. 2015, Bennett et al. 2019). For
example, urban areas act as hubs to increase connectivity among
populations of the Western black widow spider (Latrodectus
hesperus ), including among historically and geographically-distinct
populations locally adapted to desert environments (Miles et al. 2018a,
2018b).
Misconceptions — A misconception perpetuated by poorly
understood agents and targets of selection is that selection in urban
environments is strong primarily as a consequence of humans and human
activities as agents. Although rates of phenotypic change have been
demonstrated to be elevated in response to some anthropogenic agents
(Hendry et al. 2008, Alberti 2015), many studies rely on environmental
proxies such as impervious surface cover rather than identifying causal
relationships. Researchers may conflate environmental proxies with
drivers of selection if the selective agents are unclear,
multicollinear, or correlated with general environmental features— a problem that plagues adaptation research in any environment
(Endler 1986, Mitchell-Olds & Shaw 1987, Kawecki and Ebert 2004). For
example, in urban crested anoles (A. cristatellus ), limb length
differences can be connected to shifts in structural environment
directly related to locomotion (Winchell et al. 2016, 2018), although
this trait shift could also be explained by the proxy variable of
impervious surface cover correlated with structural environment. In
addition, contemporary movement patterns of urban organisms influenced
directly and indirectly by human activities can obscure the selective
landscape that shaped phenotypes. For example, populations of the
mosquito Culex pipiens were presumed to be locally adapted to
living in subway stations in London, yet a recent review instead
supports exaptive origins of these underground-adapted populations, with
adaptive phenotypes previously present in the ancestral populations
outside of Europe (Haba and McBride 2022). As in any environment, if we
fail to first characterize patterns of gene flow and genetic drift, we
may incorrectly conclude local adaptation to urban environments (e.g.,
Gould and Lewontin 1979, Hoban et al. 2016).
Moving Forward — To address the challenges of understanding
novel anthropogenic selective pressures, connecting phenotypes to
selective agents and accounting for nonadaptive processes is crucial
(Santangelo et al. 2018, Miles et al. 2019). Research that connects
adaptive urban phenotypes to selective agents through performance or
fitness quantification (e.g., Tüzün and Stoks 2020, Chick et al. 2020)
will provide more informative evidence of urban adaptation and reduce
the conflation of environmental proxies (e.g., general urban
characteristics) with drivers of phenotypic change. Genomic approaches
may be particularly valuable to examine adaptive responses while
accounting for underlying population structure. For example, Salmón et
al. (2021) used genotype-environment association tests to identify
adaptation in the great tit (Parus major ) across multiple cities,
interpreting results in light of population structure analyses
suggesting widespread gene flow across city centers. When populations
are highly connected, it can be unclear if adaptive phenotypes arose
repeatedly or swept across urban populations, a subtle distinction in
the evolutionary mechanism underlying adaptation. Teasing apart these
mechanisms is possible: Oziolor et al. (2019) used a model developed by
Lee and Coop (2017) to determine how both de novo mutation and
adaptive introgression contributed to pollution tolerance in Atlantic
killifish (Fundulus heteroclitus ). Lastly, long-term datasets,
including building museum resources (see Challenge 2) and research on
ancient DNA will provide important context for understanding urban
adaptation by addressing temporal variation and timescales in natural
selection. For example, in non-urban ecosystems, selection on beak size
in Galapagos finches (Geospiza spp.) fluctuates from year to year
in variable directions, and by building a multidecadal data set, Grant
and Grant (2014) were able to quantify these dynamics.