Ecological differences among species influence population response to historical environmental changes, and genetic simulations now allow us to directly incorporate this variation into inferential models. However, the impact of life history strategies in demographic inference has been far less explored relative to the impact of other ecological differences, such as dispersal capacity and habitat preference. Here, we utilize individual-based simulations of a non-Wright-Fisher population to ask whether differences in the average age of first reproduction of individuals, the average adult mortality and the average number of mates per reproductive season lead to consistent and predictable differences in summary statistics of genetic diversity commonly used for simulation-based parameter estimation and demographic inference. Using a Random Forest model, we estimate three population parameters (variance in reproductive success, generation time, and effective population size) from genome-wide SNP variation for two bird species with distinct life history strategies that are directly built into the simulation machinery. Our results show that life history variation leads to predictable differences in patterns of genetic diversity. While prediction accuracy is low, parameter estimates from empirical datasets agree with the expectation that species with extreme polygamy, long adult longevity and later onset of reproduction will exhibit higher variance in reproductive success, longer generation time and smaller effective population sizes. Since the signal of life history differences is observed in the genetic summary statistics, we suggest that simulation- and model-based multi-species demographic inference should incorporate life history parameters.

Many studies suggest that aside from environmental variables, such as topography and climate, species-specific ecological traits are relevant to explain the geographic distribution of intraspecific genetic lineages. Here, we investigated whether and to what extent incorporating such traits systematically improves the accuracy of random forest models in predicting genetic differentiation among pairs of localities. We leveraged available ecological datasets for birds and tested the inclusion of two categories of ecological traits: dispersal-related traits (i.e., morphology and foraging ecology) and demographic traits (such as species survival rate and generation length). We estimated genetic differentiation from published mitochondrial DNA sequences for 31 species of birds (1,801 total genetic samples, 526 localities) in the Atlantic Forest of South America. Aside from the aforementioned ecological traits, we included geographic, topographic and climatic distances between localities as environmental predictors. We then created models using all available data to evaluate model uncertainty both across space and across the different categories of predictors. Finally, we investigated model uncertainty in predicting genetic differentiation individually for each species (a common challenge in conservation biology). Our results show that while environmental conditions are the most important predictors of genetic differentiation, model accuracy largely increases with the addition of ecological traits. Additionally, the inclusion of dispersal traits improves model accuracy to a larger extent than the inclusion of demographic traits. Similar results are observed in models for individual species, although model accuracy is highly variable. We conclude that ecological traits improve predictive models of genetic differentiation, refining our ability to predict phylogeographic patterns from existing data. Additionally, demographic traits may not be as informative as previously hypothesized. Finally, prediction of genetic differentiation for species with conservation concerns may require further careful assessment of the environmental and ecological variations within the species range.