Challenge 4: Habitat heterogeneity
General Application — The scale at which adaptation research is
conducted must consider the breadth of habitats in an environment (Levin
1992, Castillo and De León 2021), across which the strength and nature
of selection may vary. Qualitative habitat categorizations (e.g.,
montane and lowland) may not capture the habitat features underlying
selection and adaptation, particularly at organismally relevant (e.g.,
microhabitat) spatial scales (Castillo and De León 2021). Quantifying
habitat at local spatial scales is important because similar habitat use
(e.g., thermal niche) can impede adaptive divergence between populations
occupying divergent macrohabitats (e.g., cool montane versus warm
lowland; Muñoz and Losos 2020). In addition, quantifying the extent of
environmental divergence across habitat contrasts establishes the
premise that similar selective forces underlie the covariation between
phenotype and fitness, without which the selective landscape may be
oversimplified, and proxies (e.g., macrohabitat elements) may
erroneously appear to be the main drivers of selection (see Challenge
3). For example, macroclimatic variables (e.g., temperature and
precipitation) were weak predictors of niche evolution in plethodontid
salamanders in contrast to microhabitat variables (e.g., air
temperature, soil temperature, leaf litter depth; Farallo et al. 2020).
In addition to spatial variation, all habitats change over time as a
consequence of natural processes (e.g., hurricanes, succession) as well
as human activity (e.g., land management tied to social and political
priorities; Ian Perry and Ommer 2003). Adaptation research that
considers temporal variation in the selective landscape may help with
minimizing disruption of experiments (see Challenge 1) and identifying
appropriate temporal windows of selection (see Challenge 3).
Human Element — Modern urbanization represents a significant
shift in the complexity, speed, scale, and scope of human modification
of the environment (United Nations 2001). Examples of anthropogenic
habitat transformation include expansion or contraction of
infrastructure, landscaping, and extreme disturbances that radically and
rapidly obliterate entire metropolitan areas (such as the recent war
conflict in Ukraine). Anthropogenic environmental transformations have
long-lasting effects on evolutionary processes in urban environments by
altering habitat characteristics and connectivity (Pincetl 2015, Schell
et al. 2020, Des Roches et al. 2021). For example, railways in German
cities facilitated movement in admixed lineages of wall lizards
(Podarcis muralis ) derived from populations in other European
cities (Beninde et al. 2018). In addition, socio-cultural aspects of
urban environments, including the legacy of urban development and
discriminatory practices that promote structural racism (e.g.,
restrictive and discriminatory property sales), generate a heterogeneous
landscape and idiosyncratic variation within and between urban centers
(Yigitcanlar 2009, Pincetl 2015, United Nations 2018, Schell et al.
2020, Des Roches et al. 2021). For example, wealthy communities often
have more green space with abundant domesticated and invasive vegetation
compared to poorer communities (Aronson et al. 2014). In addition,
modern urbanization in North and South America is more recent than in
Asia and Europe (Fox and Goodfellow 2016), leading to less time for
urban adaptation to have occurred in American cities. It might be the
case that given the relatively recent age of most cities on Earth (a
large proportion of which emerged or radically expanded after the
Industrial Revolution and are less than 200 years old), adaptation may
occur primarily from standing genetic variation rather than de
novo mutation and result in primarily soft sweeps that are more
difficult to detect using classic genomic approaches (Messer and Petrov
2013). However, the importance of standing genetic variation for urban
adaptation, and how this relates to variation among cities, remains
understudied. Even in urban regions that have existed for centuries,
human interests and needs (e.g., roads and energy infrastructure) can
lead to drastically different selective landscapes at different points
in time. For example, Paris was radically transformed in the
19th century by demolishing overcrowded medieval
neighborhoods and building new parks and squares (Kirkland 2013).
Misconceptions — A misconception perpetuated by our nascent
understanding of the heterogeneity of cities is that urban environments
represent replicated natural experiments with parallel environmental
conditions and selective pressures across cities globally (Santangelo et
al. 2020a, 2020b, 2022, Szulkin et al. 2020b, Diamond and Martin 2021).
Although accumulating evidence suggests urban environments do converge
on multiple environmental variables (e.g., Santangelo et al. 2022), the
majority of urban adaptation research to date focuses on single
geographic regions (Santangelo et al. 2020a). However, we now recognize
that replication within a single city, as well as contrasts of urban
versus non-urban habitats or across urban to non-urban gradients, may
ignore the complex mosaic of anthropogenically impacted landscapes that
vary within and among cities (Szulkin et al. 2020b). Although we have
many operative definitions of “urban” environments, there is not a
universal consensus on what defines a city. For example, variation in
biotic (e.g., ecological dynamics), abiotic (e.g., temperature), and
social factors (e.g., political structures) across urban environments
may be underappreciated because of the North American and Western
European focus of much of urban evolutionary ecology research (Johnson
and Munshi-South 2017, Schell et al. 2020, Des Roches et al. 2021).
Therefore, we may reach incorrect conclusions about the generalizability
of urban adaptations globally based on this biased sample of
urbanization.
Moving Forward — To address the challenges presented by the
inherent heterogeneity within and among urban environments, it could
benefit researchers to move past a simplified assumption of cities as
replicates to incorporate heterogeneity and scale more explicitly.
Accomplishing this might involve quantification of urbanization at
multiple spatial scales and replication across diverse cities globally
(Pincetl 2015, Szulkin et al. 2020b). For example, Merckx et al. (2018)
employed spatially hierarchical sampling to capture regional and local
variation of temperature and fragmentation in three city centers to
understand adaptive patterns of invertebrate body size. When assessing
multiple spatial scales is not feasible (e.g., remote-sensing data of
appropriate resolution is unavailable or access to field locations is
restricted), a biologically-justified scale that reflects local
organismal interactions with their environment (e.g., dispersal or home
range) can be used as a proxy (Jackson and Fahrig 2015, Szulkin et al.
2020b). Critically, such decisions rely on natural history and
trait-environment information that may not yet be available for urban
organisms (see Challenge 2), and different methods may be more
appropriate (e.g., depending on spatial and temporal variation),
requiring flexibility in experimental designs and interdisciplinary
collaborations (see Challenge 1). In addition to a more quantitative
assessment of urban environments, the global study of cities that vary
in the intensity, age, and characteristics of urbanization will help
shed light on the process of urban adaptation and aid in our ability to
generalize findings. For example, Cosentino and Gibbs (2022) were able
to disentangle selective agents contributing to parallel and
non-parallel clines in Eastern Gray Squirrel (S. carolinensis )
melanic coat color associated with urbanization by comparing 43 North
American cities that differed in size, age, and geographic location. In
a global sample, Santangelo et al. (2022) collected data on white clover
(Trifolium repens ) from over 160 cities worldwide to demonstrate
that urbanization can lead to parallel adaptation despite considerable
environmental variation among cities.