Connor Panter

and 7 more

Understanding whether current macroecological biodiversity patterns are driven by shared evolutionary history remains a central question in biogeography. Here, we examine whether species’ geographic range sizes and abundance–distance relationships (ADRs) exhibit phylogenetic signal across terrestrial taxa, and what this reveals about their evolutionary structuring. We compiled published ADRs together with global range size data and phylogenetic information for 2,545 species, including 1,685 birds, 647 plants, and 213 mammals. Variation in ADRs and range sizes was quantified across taxonomic levels and across clades of increasing phylogenetic distance, and compared with random dispersion null expectations. Phylogenetic signal was evaluated with Bloomberg’s K and Moran’s I, and alternative evolutionary models were compared to assess which processes best described the distribution of ADRs and range sizes across phylogenies. We also examined whether any detected phylogenetic structure remained after accounting for dispersal-related traits, including plant height, seed mass, and body size. We found that range size showed consistent phylogenetic clustering across most taxonomic and phylogenetic levels, indicating that closely related species tend to have similar geographic extents. Contrastingly, ADRs exhibited limited phylogenetic structure, with weak under-dispersion detected only among plant species at intermediate phylogenetic depths. Trait evolution for both ADRs and range sizes was most consistent with an Ornstein–Uhlenbeck model, suggesting convergence toward optimal values rather than unrestricted divergence. After accounting for dispersal-related traits, range size retained significant phylogenetic signal, whereas ADRs did not differ from random expectations. Together, these findings indicate that geographic range sizes, but not ADRs, are strongly structured by phylogenetic relatedness across birds, plants, and mammals. Our findings suggest that broad-scale patterns of species’ range size are more evolutionarily conserved than ADRs, implying a fundamental decoupling between macroecological and population-level processes.

Stephan Kambach

and 5 more

Meta-analyses often encounter studies with incompletely reported variance measures (e.g. standard deviation values) or sample sizes, both needed to conduct weighted meta-analyses. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta-analyses showing that the majority of meta-analyses encountered incompletely reported studies. We then simulated meta-analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs. Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size-based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SDs and SSs. The performance of different imputation methods depends on the structure of the meta-analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best-case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta-analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness.