Mikhail V. Matz

With advances in sequencing technology, the prospect of studying microbes from ancient samples to reconstruct past environments and host–microbe interactions has been growing rapidly. However, the field remains constrained by computational challenges and accuracy problems due to the difficulty of validating truly ancient microbes within noisy datasets dominated by modern contaminants. Existing pipelines often demand substantial memory resources, limiting their use to researchers with access to advanced computational systems. Here, we present MAT-classifier, a pipeline for genus-level profiling of ancient taxa designed to increase accuracy while substantially reducing computational requirements. Using simulated bacterial datasets, we showed that the MAT-classifier achieves more accurate detection of ancient taxa with substantially lower memory usage and shorter runtime than the existing counterpart, the aMeta pipeline. Validation on deeply sequenced ancient metagenomic data further confirmed its low memory footprint and practical utility. Overall, the MAT-classifier provides a reliable, efficient, and accessible alternative to current pipelines, lowering technical barriers to enable broader adoption of ancient microbiome research.

Identifying environmental gradients driving genetic adaptation is one of the major goals of ecological genomics. We present RDAforest, a methodology that leverages the predominantly polygenic nature of adaptation and harnesses the versatility of random forest regression to solve this problem. Instead of computing individual SNP‐environment associations, RDAforest seeks to explain the overall genetic covariance structure based on multiple environmental predictors. By relying on random forest instead of linear regression, this method can detect non‐linear and non‐monotonous dependencies as well as all possible interactions between predictors. It also incorporates a novel procedure to select the best predictor out of several correlated ones, and uses jackknifing to model uncertainty of genetic structure determination. Lastly, our methodology incorporates delineation and plotting of “adaptive neighbourhoods”—areas on the landscape that are predicted to harbour differentially adapted individuals. Such maps can be used as a guide for planning conservation and ecological restoration efforts. We demonstrate the use of RDAforest in two simulated scenarios and one real dataset (North American grey wolves).

Coral bleaching is the largest global threat to coral reef ecosystem persistence this century. Advancing our understanding of coral bleaching and developing solutions to protect corals and the reefs they support are critical. In the present article, we, the US National Science Foundation–funded Coral Bleaching Research Coordination Network, outline future directions for coral bleaching research. Specifically, we address the need for embedded inclusiveness, codevelopment, and capacity building as a foundation for excellence in coral bleaching research and the critical role of coral-bleaching science in shaping policy. We outline a path for research innovation and technology and propose the formation of an international coral bleaching consortium that, in coordination with existing multinational organizations, could be a hub for planning, coordinating, and integrating global-scale coral bleaching research, innovation, and mitigation strategies. This proposed strategy for future coral bleaching research could facilitate a step-function change in how we address the coral bleaching crisis.

Increasingly frequent marine heatwaves devastate coral reefs around the world, so there is great interest in finding warm‐adapted coral populations that could be used as sources for assisted gene flow and restoration. Here, we evaluated the relative power of various environmental factors to explain coral genetic variation, suggestive of differential local adaptation to these factors, across the Florida Keys Reef Tract. We applied a machine learning population genomic method (RDAforest) to two coral species—the mustard hill coral Porites astreoides and the lettuce coral Agaricia agaricites —sampled from 65 sites covering the whole reef tract. Both species comprised three genetically distinct lineages distributed across depths in a remarkably similar way. Within these lineages, there was additional genetic divergence explained by depth, but even more within‐lineage variation was cumulatively explained by water chemistry parameters related to nitrogen, phosphorus, silicate, and salinity. Visualizing the predicted environment‐associated genetic variation on a geographic map suggests that these associations reflect adaptation to certain aspects of the inshore‐offshore environmental gradient, and, to a lesser extent, to difference of Middle and Lower Keys from the rest of the reef tract. Thermal parameters, most notably maximal monthly thermal anomaly, were also consistently identified as putative drivers of genetic divergence, but had a relatively low explanatory power compared to depth and water chemistry. Overall, our results indicate that temperature was not the most important driver of coral genetic divergence in the Florida Keys, and underscore depth and water chemistry as more important environmental factors from the corals' perspective. Our study emphasizes the need for considering a variety of environmental variables, rather than solely focusing on temperature, when predicting how corals may respond to transplantation.

Increasingly frequent marine heatwaves devastate coral reefs around the world, so there is great interest in finding warm-adapted coral populations that could be used as sources for assisted gene flow and restoration. Here, we evaluated the relative power of various environmental factors to explain coral genetic variation, suggestive of differential local adaptation to these factors, across the Florida Keys Reef Tract. We applied a machine learning population genomic method (RDAforest) to two coral species - the mustard hill coral Porites astreoides and the lettuce coral Agaricia agaricites - sampled from 65 sites covering the whole reef tract. Both species comprised three genetically distinct lineages distributed across depths in a remarkably similar way. Within these lineages, there was additional genetic variation explained by depth, but even more within-lineage variation was cumulatively explained by water chemistry parameters related to nitrogen, phosphorus, silicate, and salinity. Visualizing the predicted environment-associated genetic variation on a geographic map suggests that these associations reflect adaptation to certain aspects of the inshore-offshore environmental gradient, and, to a lesser extent, to difference of Middle and Lower Keys from the rest of the reef tract. Thermal parameters, most notably maximal monthly thermal anomaly, were also consistently identified as putative drivers of genetic divergence, but had a relatively low explanatory power compared to depth and water chemistry. Overall, our results indicate that temperature is not the most important driver of differential coral adaptation in the Florida Keys, and underscore depth and water chemistry as more important environmental factors from the corals’ perspective. These findings are relevant for planning assisted gene flow and restoration efforts: not just any warmer reef would be an optimal source of transplants to promote thermal adaptation within the local coral population.

Significant genetic differentiation between Symbiodiniaceae populations in coral hosts can be induced by a range of factors including geography, latitude, depth, temperature and light utilisation. The conventional method of measuring Symbiodiniaceae diversity involving the ITS2 region of rDNA has several limitations, stemming from insufficient genetic resolution and the multi‐copy nature of the marker. This could be improved by using higher throughput whole genome sequencing to identify fine‐scale population genetic differences and provide new insight into factors influencing coral‐Symbiodiniaceae associations. The aim of this study was to investigate the genetic diversity of Symbiodiniaceae populations using low‐coverage whole genome sequencing in sympatric populations of Acropora cf. secale and allopatric populations of Acropora millepora that reproduce in different seasons in Western Australia. Genetic diversity of Symbiodiniaceae populations in these two species was examined using principal coordinates analysis and permutational analysis of variance. This analysis revealed that while all colonies were dominated by Cladocopium, there was a significant genetic difference between Symbiodiniaceae populations in both species. In A. millepora, this variation could be due to the latitudinal variation between populations or differences in reproductive seasonality, but in sympatric populations of A. cf. secale, genetic differences between Symbiodiniaceae populations were clearly aligned with the reproductive seasonality of the coral host. The use of whole genome sequencing improved the sensitivity to detect Symbiodiniaceae genetic population structure between coral populations, which increases our ability to identify genetic and potentially functional differences associated with variation in Symbiodiniaceae populations.

Coral populations across the Great Barrier Reef (GBR) could rapidly adapt to the warming climate if they have standing genetic variation for thermal tolerance. Here, we describe a locus likely involved in latitudinal adaptation of Acropora millepora. This locus shows a steep latitudinal gradient of derived allele frequency increasing at higher latitudes, and harbours a cluster of eight tandemly repeated Δ9‐desaturase genes adjacent to a region in the genome where a hard selective sweep likely occurred. In colonies reciprocally transplanted across 4.5° of latitude, the expression of Δ9‐desaturase is upregulated at the high‐latitude reef. Furthermore, corals from the low‐latitude reef bearing the derived Δ9‐desaturase allele express the gene more and grow faster than their peers when transplanted to the high‐latitude reef. In other organisms ranging from bacteria to fish, Δ9‐desaturase is upregulated under cold conditions to adjust membrane fluidity by introducing double bonds into fatty acid chains of membrane lipids. It is therefore plausible that the signal of latitudinal adaptation at the Δ9‐desaturase locus is due to its involvement in adaptation to cooler temperatures at higher latitudes.