What is urbanization and how do we, as urban evolutionary biologists, ecologists, and conservation biologists, define this metric? We’ve written about this topic before and a new paper by Remington Moll et al. titled “What does urbanization actually mean? A framework for urban metrics in wildlife research” takes a deep dive into defining urbanization. Generally, we all have a basic understanding of what can be found in urban spaces (e.g. buildings, roads, pollution, anthropogenic noise, etc.). However, how each of these metrics relates to our specific study system is likely different. For example, a road might be a huge barrier to dispersal for a salamander, a partial barrier to dispersal for a squirrel, and not a barrier to dispersal for a bird. Thus, the scale at which we quantify and model urbanization is important.
Traditionally, researchers have taken a number of different approaches to defining urbanization, such as:
- urban-to-rural gradients measured in Euclidean distance from a city center
- paired comparisons of urban to less-developed sites
- land-cover measured using satellite imagery
- building density
- human population density
- multivariate methods such as Principal Component Analysis (PCA) used to coalesce multiple urban measurements into a single urbanization metric.
Additionally, the way urbanization is quantified may depend on the study design, even when trying to asses the same outcome. Moll et al. provide an example of trying to quantify the effects of urban noise pollution on avian singing behavior. Noise pollution could be quantified using acoustic monitors at the study site, measuring road traffic volume as a proxy for noise pollution, or by simulating anthropogenic noise in a controlled setting. The variation in these methods makes it difficult to directly compare results across studies or draw broad conclusions about the effects of anthropogenic noise.
To understand the many ways in which urbanization has been quantified in wildlife research, Moll et al. conducted a literature search of studies published between 2012-2016 and found 244 studies that contained “urban” in the title and “ecolog*” plus “mammal/ bird/ reptile/ amphibian/ wildlife” in the keywords. For each study, Moll et al. recorded the metric(s) that were used to quantify urbanization such as physical structures (e.g. buildings/roads), organisms inhabiting the landscape (e.g. wildlife/humans), or abiotic characters of the environment (e.g. light/noise). They then classified each metric according to four dimensions: (a) the urban component measured, (b) the method of measurement, (c) the metric’s spatial scale and (d) the metric’s temporal nature (figure 1).
The urban component measured was further broken down into structural (water, built environment, and vegetation), agent (human or animal), and abiotic (air, soil, and water). Next, the authors considered which method was used to measure the urban component, dividing this into two classifications: singular (in which a single component was measured) or composite (in which multiple urban components were measured). The spatial scale of each metric was examined. For example, if building density within a 1km buffer of the sample location was used, the spatial scale of building density would be ~314ha. Finally, the temporal nature of the metric was assessed and classified as either static or dynamic. A static metric would be a measurement taken at a specific point in time (e.g. human population density during the 2010 Census), while a dynamic metric would be a measurement that quantifies variation (e.g. change in human population density from the 1950 Census to the 2010 Census).
Of the 244 papers examined, Moll et al. identified 1,177 urban metrics! Over half the studies examined songbirds, while only about 10% studied herptofauna, 10% studied medium/large mammals. With regard to the urban component, most metrics were structural, with most of these quantifying the amount of vegetation (figure 2). Most approaches calculated the proportion of the metric in a spatial delineation, often related to a structural component, such as the percent impervious surface within a buffer. The second most common method was calculating the density of the metric in a spatial delineation, for example, the human population density within a buffer. The focal species home range was used by most metrics to estimate spatial scale, though some studies used spatial scales that were much smaller or much larger than this. Finally, the vast majority of studies that were examined included a static metric.
This summary by Moll et al. shows that recent urban wildlife research uses a wide variety of metrics to asses urbanization within the study area and the urgent need to clarify the terms “urban” and “urbanization”. Moll et al. suggest the future studies account for urbanization using more than one metric, which will help determine which metric drives the observed ecological phenomenon being studied. The authors suggest that landscape metrics (e.g. urban patch diversity) along with multivariate approaches are under-utilized by current studies and can help researchers discern the complex macroecological processes across urban gradients. Finally, the authors urge future studies to consider socio-economic dimensions when assessing urbanization; this increased inter-disciplinary research will help us more effectively understand how humans shape ecological processes.
