Introduction
Urbanization is among the most permanent and transformative types of anthropogenic land use (McKinney, 2009). The loss, degradation, and conversion of natural habitats with urbanization poses a mounting threat to the planet’s biodiversity, especially as the pace of urban expansion continues to accelerate to accommodate human population growth (Angel et al., 2011). However, urban expansion does not affect all species equally, nor does it affect all species negatively. Wildlife responses to urbanization rely on a suite of species-specific ecological, behavioral, physiological, and life history characteristics, which can sometimes be linked to shared evolutionary history (Betke et al., 2023; Iglesias-Carrasco et al., 2022; Rodewald & Gehrt, 2014). In other words, although the net effect of urbanization on biodiversity is negative, any given species occupies a continuum of urban tolerance based on how well its functional traits suit it to urban life (the urban tolerance hypothesis; (Sol et al., 2014). At one end of this continuum, “urban avoiders” are sensitive to anthropogenic disturbance and tend to be excluded from urban environments (Blair, 1996). At the other extreme, species termed “urban exploiters”, “urban adapters”, or “synanthropes” are highly tolerant of modified habitats and can effectively use, occupy, and achieve high population densities in urban environments (Blair, 1996; Francis & Chadwick, 2012; Santini et al., 2019).
The ecological traits typifying synanthropes are relatively well-characterized in wild birds. Globally, urban-tolerant bird species tend to be smaller-bodied, gregarious, more dispersal-prone, and have broader niche breadths in terms of both dietary and habitat generalism (Bonier et al., 2007; Møller, 2009; Neate-Clegg et al., 2023). The contribution of these functional traits to urban tolerance can vary with geography (Neate-Clegg et al., 2023) but is relatively consistent. While urban-tolerant mammals have adopted diverse and taxonomically stratified strategies for urban life (Betke et al., 2023; Santini et al., 2019), urban bird communities are generally defined by this set of “winning” traits irrespective of phylogeny (Evans et al., 2011), (Santini et al., 2019). As the species-specific traits associated with urban tolerance filter the rapidly homogenizing bird diversity present in anthropogenic habitats, these traits should become increasingly represented in urban bird communities (Luck & Smallbone, 2011).
Depauperate diversity in urban communities and increased densities of urban-adapted species can have consequences for infectious diseases. Urban-adapted species often harbor greater parasite richness than species unassociated with urban landscapes and can serve as reservoir hosts, increasing infection prevalence in their communities (Albery et al., 2022; Ecke et al., 2022; Marm Kilpatrick et al., 2006). While most comparative work to date has focused on how urban tolerance shapes variation in parasite diversity and abundance, this characteristic could also favor parasites with certain traits. For example, urbanization may favor transmission of pathogens spread through direct contact owing to density-dependent transmission and close contact around clumped anthropogenic resources (Murray et al., 2019). Beyond transmission mode, an equally fundamental feature of parasites is their host specificity, a measure of ecological specialization that can reflect both the number and phylogenetic breadth of hosts a parasite can exploit (Poulin, 2007). Parasites with high host specificity are associated with a single or few closely related hosts (i.e., “host specialists”), whereas parasites with low specificity may use numerous, phylogenetically distant hosts (i.e., “host generalists”). Host specialists are often assumed to have diversified via cospeciation, resulting in evolutionary histories that mirror that of their hosts’. In contrast, host generalists are expected to have their co-evolutionary congruence disrupted by relatively frequent host-switching events.
Host specificity and emergent macro-evolutionary patterns (e.g., evolutionary histories shaped predominantly by host-switching or by cospeciation) are influenced by both host and vector ecology, particularly in parasites that are not free-living (Huyse et al. 2005). For example, solitary social systems in hosts can reinforce high specificity and cospeciation with their parasites (Hafner et al., 1994), while host migratory propensity or dispersal ability can instead lead to host-switching opportunities and lower co-evolutionary congruence (Jenkins et al., 2012; Boyd et al., 2022); but see (de Angeli Dutra et al., 2022). However, whether co-phylogenetic congruence and host switching of parasites is modified by the unique ecological traits associated with urban tolerance remains poorly understood.
Here, we test the contribution of urban tolerance in avian hosts to cophylogenetic relationships with their haemosporidian parasites. Haemosporidian parasites of birds (in the genera Plasmodium, Haemoproteus, and Leucocytozoon ) are cosmopolitan, intra-erythrocytic protozoan parasites vectored by mosquitoes, biting midges, hippoboscid flies, and black flies. Haemosporidian infections are highly prevalent in wild birds and are often associated with sparse and sub-lethal effects on their hosts, such as decreases in body condition and reproductive success (Valkiūnas 2005); however, their pathogenicity is both lineage- and context-dependent, and haemosporidian infections have been implicated in the decline and extirpation of multiple bird populations (Dadam et al., 2019; Woodworth et al., 2005). Avian haemosporidia demonstrate variable degrees of host specificity, ranging from host specialists recovered consistently from a single species to generalist lineages associated with a global range of hosts (Ricklefs et al., 2005). The prevalence, diversity, and varying host specialization strategies used by haemosporidians underpin their status as a model system for studying host–parasite interactions. For example, bird–haemosporidian associations are a valuable empirical system for testing hypotheses at the interface of host migration and parasitism (de Angeli Dutra et al., 2021; Emmenegger et al., 2018). Avian haemosporidia are also especially useful for testing hypotheses related to the contributions of host–parasite links to overall cophylogenetic congruence, because they have patterns of diversification driven both by host-switching and cospeciation (Ricklefs et al., 2004; Santiago-Alarcon et al., 2014).
We predict the broader niche breadth and higher dispersal ability of urban-tolerant bird species could expose haemosporidian parasites of these hosts to greater environmental heterogeneity, favoring selection for host generalism and diversification via host-switching events (Kassen 2002). Alternatively, bird species more prone to using disturbed habitats may expose their parasites to less avian host diversity, which could curb selection for host generalism (Moens & Pérez-Tris, 2016). Migration strategy may also influence cophylogenetic congruence either independently or in tandem with urban tolerance, given the widespread taxonomic and geographic distribution of migratory behavior among birds (Dufour et al., 2020; Tobias et al., 2022). A migratory host could promote host-switching events by increasing encounters with parasites and periodically suppressing host immunity (Arriero & Møller, 2008; Eikenaar & Hegemann, 2016); alternatively, migration could instead favor co-evolution of parasites to better exploit a host with seasonally variable internal and external environments (Clayton et al., 2003). An urban-tolerant, generalist host species may subject its parasites to similar pressures at a smaller ecological scale, which could result in convergent cophylogenetic patterns between parasites and birds that are resident but urban-tolerant and birds that are migratory but urban-intolerant. To test these predictions, we employed a global database of associations between haemosporidian lineages and avian host species and then used the Procrustean Approach to Cophylogeny (PACo) and phylogenetic generalized linear mixed models (PGLMMs) to test if host–parasite links associated with urban-adapted and migratory bird species have lower contributions to overall cophylogenetic congruence.
Host–parasite data
We used the malaviR package in R to programmatically download all available haemosporidian lineages from the MalAvi database (accessed 4/4/2023), a comprehensive, standardized, and dynamically updated record of haemosporidian cytochrome b sequences from avian hosts (Bensch et al., 2009). We conservatively pruned the dataset to include only hosts identified to species level and limited our data to those host–parasite links recorded at least twice to reduce including erroneous associations while attempting to preserve signal from rare and/or specialist haemosporidian lineages. The trimmed MalAvi alignment included a total of 739 haemosporidian lineages (288 in the genus Plasmodium and 451 in the genus Haemoproteus ). Lineages in the genus Leucocytozoon were used to construct the haemosporidian phylogeny but excluded from downstream analyses of host–parasite associations for computational feasibility. The resulting dataset comprised 1,655 associations between birds and haemosporidian parasites.
Host and parasite phylogeny
We standardized host species names in MalAvi against the BirdTree taxonomy and then used BirdTree to produce a consensus phylogeny for passerine hosts using 2,000 randomly sampled trees generated from the Hackett backbone (Jetz et al., 2012). We removed bird species not present in the MalAvi database, resulting in a total of 640 host species spanning 89 avian families. We constructed a maximum likelihood phylogeny of haemosporidian parasite lineages in IQ-TREE 2.2.2.6 (Minh et al., 2020) using 1,000 bootstrap replicates and a GTR+I+G substitution model (Abadi et al., 2019).
Host species traits
We next assigned urban occurrence, disturbed habitat occurrence, an urban tolerance index (UTI), and an avoider/exploiter metric to each host species present in the MalAvi database using the dataset curated by González-Lagos et al., 2021. Briefly, this dataset defines species-level urban and human-disturbed occurrence using the International Union for Conservation of Nature (IUCN) habitat classification data to determine if a species’ native range overlaps with modified environments. The urban tolerance index (UTI) is a continuous value using the ratio between individuals of a species occupying an urban area and those occupying surrounding natural environments to account for the dependence of urban occurrence on natural populations bordering urban sites (Evans et al., 2011; Sol et al., 2017). Large positive UTI values are thus indicative of species very tolerant of urban habitat, whereas large negative UTI values suggest low urban tolerance (Evans et al., 2011; González-Lagos et al., 2021). Lastly, the exploiter/avoider metric uses community simulations to designate species as urban exploiters or avoiders based on whether they are significantly more or less likely to be abundant in urban environments than expected by chance dispersal from surrounding habitats (Sol et al., 2014). Because the UTI and exploiter/avoider metrics rely on population surveys of bird assemblages across urban gradients, these metrics characterize fewer species ( n =639 for urban or disturbed habitat occurrence, n =235 for urban tolerance, and n =171 for exploiter/avoider status) but represent their tolerance of urban habitats more effectively than occurrence data alone (Sol et al., 2014). We also assigned migratory status to each host species per the AVONET database (Tobias et al., 2022). We collapsed migratory status into two categories: fully migratory species, wherein most of the population are long-distance migrants, and non-fully migratory species, which comprise both partially migratory and sedentary species.
Statistical analysis
To first test for overall congruence between the host and parasite phylogenies and the relative contribution of host–parasite links associated with urban-tolerant hosts to this global fit, we used PACo as implemented with the paco package in R (Hutchinson et al., 2017). We constructed a host–parasite association matrix and separate phylogenetic distance matrices for hosts and parasites using the ape package (Paradis et al., 2004). Phylogenetic distance matrices were then converted to principal coordinates prior to Procrustes imposition. The sum of squares residuals yielded by the superimposition between the host and parasite principal coordinates indicates their overall congruence, and we used a goodness-of-fit test against 1,000 random permutations of the association matrices to determine if this level of congruence was greater than expected by chance. To assess the contribution of individual interactions to overall congruence, we used a jackknife procedure to calculate the corrected Procrustes residuals for each host–parasite link. A lower residual value indicates a larger contribution of a given interaction to global cophylogenetic congruence.
We then analyzed these PACo residuals in a series of PGLMMs using the brms package (Bürkner, 2017). We averaged residuals per host and used their log-transformed values as a Gaussian response in eight models. These models differed in the predictor variable used to describe urban tolerance (i.e., urban occurrence, disturbed habitat occurrence, UTI, or exploiter/avoider metric) and additive or interaction effects between migration strategy and urban tolerance metric (Table 1). All models included a host phylogenetic random effect via covariance matrix from the host phylogeny as well as a fixed effect of number of citations per host species as a proxy for research effort, programmatically collected with the easyPubMed package. We ran all PGLMMs using four chains for 20,000 iterations, a burn-in of 50%, and default priors. We verified model convergence by inspecting trace plots and R-hat values. We extracted posterior means and 95% credible intervals for each model coefficient in brms . We compared models representing the same number of host species (Table 1) using the leave-one-out cross-validation information criterion (LOOIC) via the brms package, wherein the model with the lowest LOOIC score has the highest predictive performance.
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