We hypothesize a series of mechanisms that could explain interspecific shifts in body size as a function of urban tolerance.

These hypotheses include reduced seed dispersal59, increased mobility42, thermophily30, the temperature-size rule15, size-biased competition79, size-biased survival80, and size-biased human preference56.

Species urbanness distributions (SUDs) exemplified for eight subrealms.

Plotted are all species per subrealm (A), with the images highlighting an example ‘hyper-exploiter’ species from each of these subrealms (i.e., with a high urban tolerance score). The x-axis shows the urban tolerance measure whereas the y-axis is the number of species within that bin. There were consistent patterns for kingdoms, classes, and orders (B) as shown by generally similar density histogram shapes for each of these. The vertical dashed line represents where species are neutral towards urban land cover. Photos acquired from iNaturalist CC BY-NC and background was removed by authors: Brown rat (© Ouwesok), Monk parakeet (© Juan Emilio), Cape dwarf chameleon (© Berkeley Lumb), American cockroach (© Len Worthington), Flaming kay (© Lyubo Gadzhev), Juno silverspot (© Rigoberto Ramírez Cortés), Hibiscus harlequin bug (© Sam Fraser-Smith), and Mascarene island leaf-flower (© Douglas Goldman).

Effect sizes between body size and urban tolerance across the tree of life and individual effect sizes for animals and plants families.

(A) Effect sizes of the relationship between body size and urban tolerance for the 371 families included in our analysis, plotted along a phylogenetic tree of life (see Methods); Plantae are highlighted and shaded in green. Colors indicate the direction of the effect: orange indicates negative, petrol indicates positive, grey indicates neutral (i.e. any effect sizes between -0.05 and 0.05). (B) and (C) histograms of individual effect sizes for each family, for animals (B) and plants (C). Orders are shown along the outside edge of the phylogenetic tree, each with a bar and icon, for any order with more than 3 families. An interactive version for full exploration of our results at both family and order level is available here.

We used subrealms as our geographical aggregation.

Subrealms were quantified from aggregating bioregions as identified by One Earth (see more here: https://www.oneearth.org/bioregions/). The subrealms level was chosen after exploring the tradeoff between accounting for geographic differences in urban tolerance and the number of species that could be included.

(A) Exploratory analyses show some correlation between the effect of body size on urban tolerance for plants and the mean dry seed mass of those plants. (B) A Bayesian model finds moderate evidence that this is a positive relationship. This indicates that dispersal may be an underlying mechanism influencing the effect of body size (i.e., plant height) on urban tolerance.

Exploratory analysis for 74 bird families, showing a potential relationship between dispersal (measured using the mean hand-wing index per family) and the effect of body size on urban tolerance.

This indicates that dispersal may be an underlying mechanism influencing the effect of body size on urban tolerance. The blue line is the linear model trend line and the grey shading represents the 95% confidence interval.

The distribution of Magnolia warbler Setophaga magnolia observations and the VIIRS values, in average radiance, of those observations in four different subrealms.

Shown using dashed lines are the species-specific mean (green), the subrealm-specific mean of VIIRS (violet), and the resulting urban tolerance measure (yellow).

An illustration of the species-specific distribution of observations and the VIIRS values, in average radiance, of those observations for six different species within the Great European Forests subrealm.

Of note is that the urban tolerance is not shown, as some are negative values, but these values are shown in the text.

The total number of species for which we calculated urban tolerance scores, stratified by subrealm and shown separately for Animalia (top) and Plantae (bottom).

The subrealms with their names are shown in Figure S1. The dataset of species’ urban tolerance scores is provided in Table S1.

The total number of subrealms for which a species had an urban tolerance score.

The majority of species (64%) only had an urban tolerance score from one subrealm, but the range was from 1 to 34.

An illustrative example, showing the relative urban tolerance scores for Hymenoptera in the Northeast American Forests subrealm.

The plot is for illustrative purposes and the values for each species can be found in Data S1.

An illustrative example, showing the relative urban tolerance scores for Lepidoptera in the Southeast Asian Forests subrealm.

The plot is for illustrative purposes and the values for each species can be found in Data S1.

An illustrative example, showing the relative urban tolerance scores for Asterales in the Scandinavia & West Boreal Forests subrealm.

The plot is for illustrative purposes and the values for each species can be found in Data S1.

A summary of the number of species, shown per class, gained through taxonomic harmonization, and therefore included in the analysis dataset.

The number of taxa, on a log10-transformed scale.

These can be found in Table S3.

We chose to model each taxonomic group (i.e., family, order, class, phylum, and kingdom) independently of one another to avoid the influence of where individual families could have no effect but result in a positive effect if modeled jointly.

The top panel shows simulated raw data for two families; the middle panel shows the posterior distribution of a bayesian model fit for each of these families separately; the bottom panel shows the posterior distribution of a bayesian model fit for a model including family as a random effect and for a model not including family as a random effect.

An illustrative example showing the influence of using a random slope for body size, by showing the (A) Interaction between body size (scaled and log10 transformed) and body size measurement type (metadata; see Methods for details) for the family Accipitridae.

Facets represent different types of body size metrics used in the dataset, labeled with a letter identifier and corresponding sample size (n). Lines represent predicted urban tolerance based on body size, with 95% credible intervals shown as shaded ribbons. These are extracted from a model fit with an interaction between metadata and body size to purposefully investigate the influence of metadata and the relationship with body size. (B) Posterior distributions of the slope for body size (scaled and log10 transformed) under two model structures: V1 includes a random slope for body size by subrealm and a random intercept for metadata (as presented in main results), while V2 adds a random slope for body size by metadata. The inclusion of this additional random slope in V2 increases uncertainty and pulls slope estimates toward zero, particularly when data are sparse within body size measurement types as illustrated here. Also see Fig. S15.

An illustrative example showing the influence of using a random slope for body size, by showing the (A) Interaction between body size (scaled and log10 transformed) and body size measurement type (metadata; see Methods for details) for the family Apidae.

Facets represent different types of body size metrics used in the dataset, labeled with a letter identifier and corresponding sample size (n). Lines represent predicted urban tolerance based on body size, with 95% credible intervals shown as shaded ribbons. These are extracted from a model fit with an interaction between metadata and body size to purposefully investigate the influence of metadata and the relationship with body size. (B) Posterior distributions of the slope for body size (scaled and log10 transformed) under two model structures: V1 includes a random slope for body size by subrealm and a random intercept for metadata (as presented in main results), while V2 adds a random slope for body size by metadata. The inclusion of this additional random slope in V2 increases uncertainty and pulls slope estimates toward zero, particularly when data are sparse within body size measurement types as illustrated here. Also see Fig. S14.

A list of 41 potential ‘types’ of body size that were used for potential inclusion in our body size dataset.

We aimed to incorporate as many types of body size measures as possible and were not restrictive in our searching for body size measures.