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.