Resolving evolutionary relationships and establishing population structure depends on molecular diagnosability that is often limited for closely related taxa. Here, we use 3,200 ddRAD‐seq loci across 290 mallards, American black ducks, and putative hybrids to establish population structure and estimate hybridization rates. We test between traditional assignment probability and accumulated recombination events based analyses to assign hybrids to generational classes. For hybrid identification, we report the distribution of recombination events complements ADMIXTURE simulation by extending resolution past F4 hybrid status; however, caution against hybrid assignment based on accumulated recombination events due to an inability to resolve F1 hybrids. Nevertheless, both analyses suggest that there are relatively few backcrossed stages before a lineage's hybrid ancestry is lost and the offspring are effectively parental again. We conclude that despite high rates of observed interspecific hybridization between mallards and black ducks in the middle part of the 20th century, our results do not support the predicted hybrid swarm. Conversely, we report that mallard samples genetically assigned to western and non‐western clusters. We indicate that these non‐western mallards likely originated from game‐farm stock, suggesting landscape level gene flow between domestic and wild conspecifics.

When a lineage originates from hybridization genomic blocks of contiguous ancestry from different ancestors are fragmented through genetic recombination. The resulting blocks are delineated by so called junctions, which accumulate with every generation that passes. Modeling the accumulation of ancestry block junctions can elucidate processes and timeframes of genomic admixture. Previous models have not addressed ancestry block dynamics for chromosomes that consist of a finite number of recombination sites. However, genomic data typically consist of informative markers that are interspersed with fragments for which no ancestry information is available. Hence, repeated recombination events may occur between markers, effectively removing existing junctions. Here, we present an analytical treatment of the dynamics of the mean number of junctions over time, taking into account the number of recombination sites per chromosome, population size, genetic map length, and the frequency of the ancestral species in the founding hybrid swarm. We describe the expected number of junctions using equidistant molecular markers and estimate the number of junctions using random markers. This extended theory of junctions thus reflects properties of empirical data and can serve to study the genomic patterns following admixture.

1. Molecular phylogenies form a potential source of information on rates of diversification, and the mechanisms that underlie diversification patterns. Diversification models have become increasingly complex over the past decade, and we have reached a point where the computation of the analytical likelihood of the model given a phylogeny is either unavailable or intractable. For such models, a likelihood-free approach such as Approximate Bayesian Computation (ABC) offers a solution. ABC is a Bayesian framework that uses one or more summary statistics instead of the likelihood function. Crucial to the performance of an ABC algorithm is the choice of summary statistics. 2. Here, we analyse the applicability of three traditional and often-used summary statistics (Gamma statistic, Phylogenetic Diversity and tree size) within an ABC framework and propose a new summary statistic: the normalized Lineages-Through-Time (nLTT) statistic. 3. We find that the traditional summary statistics perform poorly and should not be used as a substitute of the likelihood. By contrast, we find that the nLTT statistic performs on par with the likelihood. 4. We suggest to include the nLTT statistic in future ABC applications within phylogenetics. We argue that the use of ABC in diversification rate analysis is a promising new approach, but that care should be taken which summary statistics are chosen.

The relative importance of niche‐based (e.g., competitive or stress‐based) and stochastic (e.g., random dispersal) processes in structuring ecological communities is frequently analyzed by studying trait distributions of co‐occurring species. While filtering processes, such as the exclusion of stress‐intolerant species from particular habitats, increase the trait similarity between co‐occurring species, other processes, such as resource competition, can limit the similarity of co‐occurring species. Comparing the observed trait distribution patterns in communities to null expectations from randomized communities (e.g., a draw of the same observed richness from the regional pool) therefore gives a first indication of the dominant process driving community assembly. However, such comparisons do not inform us about the relative contribution of these different processes in shaping community compositions in case of their joint operation (a likely scenario). Using an Approximate Bayesian Computation approach, we develop a new method that allows inference of the relative importance of dispersal, filtering, and limiting similarity processes for the assembly of observed communities with known species and trait composition. We applied this approach to a tree community data set, collected across 20 plots along strong rainfall and fire gradients in a South African savanna. Based on comparisons with simulations, we find that our new approach is powerful in identifying which community assembly scenario has the highest probability to generate the observed trait distribution patterns, while traditional null model comparisons perform poorly in detecting signs of limiting similarity. For the studied savanna tree communities, our analysis yields that dispersal processes are most important in shaping the functional trait distribution patterns. Furthermore, our models indicate that filtering processes were relatively most important in areas with high fire frequencies, while limiting similarity processes were relatively most important in areas with low fire frequency and high rainfall. We conclude that our new method is a promising improvement on current approaches to estimate the relative importance of community assembly processes across different species groups, ecosystems, and biomes. Future model modifications (e.g., the inclusion of individual‐based processes) could provide further steps in uncovering the underlying assembly processes behind observed community patterns.