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The demographic history of most species is complex, with multiple evolutionary processes combining to shape the observed patterns of genetic diversity. To infer this history, the discipline of phylogeography has (to date) used models that simplify the historical demography of the focal organism, for example by assuming or ignoring ongoing gene flow between populations or by requiring a priori specification of divergence history. Since no single model incorporates every possible evolutionary process, researchers rely on intuition to choose the models that they use to analyze their data. Here, we describe an approximate likelihood approach that reduces this reliance on intuition. PHRAPL allows users to calculate the probability of a large number of complex demographic histories given a set of gene trees, enabling them to identify the most likely underlying model and estimate parameters for a given system. Available model parameters include coalescence time among populations or species, gene flow, and population size. We describe the method and test its performance in model selection and parameter estimation using simulated data. We also compare model probabilities estimated using our approximate likelihood method to those obtained using standard analytical likelihood. The method performs well under a wide range of scenarios, although this is sometimes contingent on sampling many loci. In most scenarios, as long as there are enough loci and if divergence among populations is sufficiently deep, PHRAPL can return the true model in nearly all simulated replicates. Parameter estimates from the method are also generally accurate in most cases. PHRAPL is a valuable new method for phylogeographic model selection and will be particularly useful as a tool to more extensively explore demographic model space than is typically done or to estimate parameters for complex models that are not readily implemented using current methods. Estimating relevant parameters using the most appropriate demographic model can help to sharpen our understanding of the evolutionary processes giving rise to phylogeographic patterns.

Migratory insects are a key component of terrestrial ecosystems, but understanding their full contribution is challenging as they are difficult to track, and migration often takes place at high altitude. Migration hotspots offer an exceptional opportunity to study these otherwise indiscernible movements as migration can be visible at ground level; however these events are often also ephemeral and reported only from chance encounters. It is therefore often difficult to fully characterise the range and number of species involved, the drivers of migration or to appreciate the potential interactions and ecological roles of the migrants. Here we pursue field evidence suggesting that the Karpaz peninsula in northeast Cyprus is a suitable location to systematically collect data on migratory insects. In the spring of 2019, using a combination of timed‐counts, migration‐camera traps and netting we documented over 39 million day‐flying insects from eight orders arriving on Cyprus at rates of up to 5900 insects m‐1 min‐1. Mass arrivals were correlated with higher temperatures and easterly winds. Wind direction and normalised vegetation difference index (NDVI) data suggest that these insects had their natal origins in locations including Syria, Iraq and Saudi Arabia. It is estimated that many billions of insects left the coast of the Middle East heading west into Europe during the study period. While the migrant assemblage was diverse, Diptera were by far the most numerous insect order (86%) followed by Lepidoptera (10%). These migrating insects play a range of vital ecological roles including cross‐continental pollination and the transfer of important nutrients. We believe that the very infrequently explored processes described in this manuscript have important consequences for ecosystems in the destinations of these migratory insects across Europe.

We have provided a framework to better understand the mechanisms governing the balance between stochastic and deterministic processes and to integrate the shifts in community assembly processes with microbial carbon metabolism. Our study reinforced that environmental filtering and bacterial cooccurrence patterns influence the stochastic/deterministic continuum of soil bacterial community assembly and that stochasticity may act through deeper soil layers to influence carbon metabolism. Delineating theoretically the potential linkages between community assembly and SOC dynamics across a broad range of microbial systems represents an interesting topic for future research. Soil microbial community assembly is crucial for understanding the mechanisms of microbial communities that regulate ecosystem-level functioning. The relative contributions of stochastic and deterministic processes to microbial community assembly remain poorly defined, and major questions exist concerning the soil organic carbon (SOC) dynamics of microbial community assembly in deep soil. Here, the bacterial community assembly processes were explored across five soil profile depths (up to 80 cm) during a 15-year field experiment involving four fertilization regimes. We found that the bacterial community assembly was initially governed by deterministic selection in topsoil but was progressively structured by increasing stochastic dispersal with depth. The migration rate ( m ) and β-null deviation pattern supported the hypothesis of a relatively greater influence of dispersal in deep soil, which was correlated with bacterial community assembly by stochastic processes. These changes in the entire community assembly reflected consistent assembly processes of the two most dominant phyla, Acidobacteria and Chloroflexi . Structural equation modeling showed that soil features (pH and total phosphorus) and bacterial interactions (competition and network complexity) were significantly related to bacterial community assembly in the 0-to-10-cm and 10-to-20-cm layers. Partial Mantel tests, structural equation modeling, and random forest modeling consistently indicated a strong and significant correlation between bacterial community assemblages and SOC dynamics, implying that bacterial assembly processes would potentially suppress SOC metabolism and mineralization when the contributions of stochastic dispersal to communities increased in deeper layers. Our results have important implications for integrating bacterial community assembly processes into the predictions of SOC dynamics. IMPORTANCE We have provided a framework to better understand the mechanisms governing the balance between stochastic and deterministic processes and to integrate the shifts in community assembly processes with microbial carbon metabolism. Our study reinforced that environmental filtering and bacterial cooccurrence patterns influence the stochastic/deterministic continuum of soil bacterial community assembly and that stochasticity may act through deeper soil layers to influence carbon metabolism. Delineating theoretically the potential linkages between community assembly and SOC dynamics across a broad range of microbial systems represents an interesting topic for future research.