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Anthozoan cnidarians (corals and sea anemones) include some of the world’s most important foundation species, capable of building massive reef complexes that support entire ecosystems. Although previous molecular phylogenetic analyses have revealed widespread homoplasy of the morphological characters traditionally used to define orders and families of anthozoans, analyses using mitochondrial genes or rDNA have failed to resolve many key nodes in the phylogeny. With a fully resolved, time-calibrated phylogeny for 234 species constructed from hundreds of ultraconserved elements and exon loci, we explore the evolutionary origins of the major clades of Anthozoa and some of their salient morphological features. The phylogeny supports reciprocally monophyletic Hexacorallia and Octocorallia, with Ceriantharia as the earliest diverging hexacorals; two reciprocally monophyletic clades of Octocorallia; and monophyly of all hexacoral orders with the exception of the enigmatic sea anemone Relicanthus daphneae. Divergence dating analyses place Anthozoa in the Cryogenian to Tonian periods (648–894 Ma), older than has been suggested by previous studies. Ancestral state reconstructions indicate that the ancestral anthozoan was a solitary polyp that had bilateral symmetry and lacked a skeleton. Colonial growth forms and the ability to precipitate calcium carbonate evolved in the Ediacaran (578 Ma) and Cambrian (503 Ma) respectively; these hallmarks of reef-building species have subsequently arisen multiple times independently in different orders. Anthozoans formed associations with photosymbionts by the Devonian (383 Ma), and photosymbioses have been gained and lost repeatedly in all orders. Together, these results have profound implications for the interpretation of the Precambrian environment and the early evolution of metazoans.

Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep‐learning approaches for single‐cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single‐cell level for unseen dosages, cell types, time points, and species. Using newly generated single‐cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single‐cell Perturb‐seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single‐cell level and thus accelerate therapeutic applications using single‐cell technologies. Synopsis The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations. CPA can be trained on highly multiplexed, single‐cell experiments with thousands of conditions to predict unmeasured phenotypes (e.g., specific dose responses). It can generalize to predict responses to small molecules never seen in the training by adding priors on chemical space. Validations using a newly generated combinatorial drug perturbation dataset demonstrate the accuracy of CPA in predicting unseen drug combinations. CPA is also applicable to genetic combinatorial screens, as shown by imputing in silico 5,329 missing combinations in a single‐cell perturb‐seq experiment with diverse genetic interactions. Graphical Abstract The compositional perturbation autoencoder (CPA) is a deep learning model for predicting the transcriptomic responses of single cells to single or combinatorial treatments from drugs and genetic manipulations.

Long-distance dispersal is believed to strongly influence coral reef population dynamics across the Tropical Pacific. However, the spatial scale and strength at which populations are potentially connected by dispersal remains uncertain. To determine the patterns in connectivity between the Eastern (ETP) and Central Tropical Pacific (CTP) ecoregions, we used a biophysical model incorporating ocean currents and larval biology to quantify the seascape-wide dispersal potential among all population. We quantified the likelihood and determined the oceanographic conditions that enable the dispersal of coral larvae across the Eastern Pacific Barrier (EP-Barrier) and identified the main connectivity pathways and their conservation value for dominant reef-building corals. Overall, we found that coral assemblages within the CTP and ETP are weakly connected through dispersal. Although the EP-Barrier isolates the ETP from the CTP ecoregion, we found evidence that the EP-Barrier may be breached, in both directions, by rare dispersal events. These rare events could explain the evolutionary genetic similarity among populations of pocilloporids in the ecoregions. Moreover, the ETP may function as a stronger source rather than a destination, providing potential recruits to CTP populations. We also show evidence for a connectivity loop in the ETP, which may positively influence long-term population persistence in the region. Coral conservation and management communities should consider eight-key stepping stone ecoregions when developing strategies to preserve the long-distance connectivity potential across the ETP and CTP.

Stony Coral Tissue Loss Disease (SCTLD) has caused widespread coral mortality in the Caribbean Region. However, how the disease presence alters the microbiome community, their structure, composition, and metabolic functionality is still poorly understood. In this study, we characterized the microbial communities of the tissues of apparently healthy and diseased SCTLD colonies of the species Siderastrea siderea , Orbicella faveolata , and Montastraea cavernosa to explore putative changes related to the presence of SCTLD. Gammaproteobacteria , Alphaproteobacteria , and Bacteroidia were the best represented classes in the healthy tissues of all coral species, and alpha diversity did not show significant differences among the species. The microbial community structure between coral species was significantly different (PERMANOVA: F = 3.46, p = 0.001), and enriched genera were detected for each species: Vibrio and Photobacterium in S . siderea , Spirochaeta2 and Marivivens in O . faveolata and SAR202_clade and Nitrospira in M . cavernosa . Evidence of SCTLD in the microbial communities was more substantial in S . siderea , where differences in alpha diversity, beta diversity, and functional profiles were observed. In O . faveolata , differences were detected only in the community structure, while M . cavernosa samples showed no significant difference. Several microbial groups were found to have enriched abundances in tissue from SCTLD lesions from S . siderea and O . faveolata , but no dominant bacterial group was detected. Our results contribute to understanding microbial diversity associated with three scleractinian coral species and the shifts in their microbiomes associated with SCTLD in the Mexican Caribbean.

Coastal organisms live in a dynamic environment where a myriad of environmental stressors, including climate change, ocean acidification, and human harvesting, act on variable spatio‐temporal scales. Each of these stressors may impose unique selective forces on a population, shaping a species' adaptive potential and its ability to persist under future climatic conditions. Genomic investigations of adaptive responses to environmental and anthropogenic disturbances remain rare, especially in marine systems. Here, we use whole genome sequencing data from the owl limpet, Lottia gigantea, and outlier detection methods to pinpoint signals of selection (1) across long‐standing environmental gradients spanning the species' distribution, (2) at the poleward edge of the species' range where it experienced a recent expansion, and (3) between sites vulnerable to or protected from human size‐selective harvesting within California. Loci associated with environmental gradients across the entire range show the strongest differentiation at the southern end of the species' range, potentially driven by adaptation to sea surface temperature and pH. Additional ad‐hoc outlier analyses revealed a distinct set of loci potentially under selection in the expanded range, with different functional roles than the range‐wide outliers. Despite demographic models suggesting that protection from harvesting has a positive impact on the abundance of large individuals, we did not find strong signals of selection or changes in genetic diversity between sites differing in harvesting vulnerability. Our findings suggest that range‐wide environmental selective signals established over longer time scales are distinct from those imposed by climatic anomalies at finer spatio‐temporal scales. We found that climatic variation has a stronger selective imprint than human harvesting, and thus conservation interventions should consider prioritizing the maintenance of climate‐related adaptive potential. Understanding how climatic trends and anomalies interact with anthropogenic pressures will allow us to make more informed decisions to sustain the evolutionary capacity of L. gigantea and other key coastal species.

Integrating multiple lines of evidence that support molecular taxonomy analysis has proven to be a robust method for species delimitation in scleractinian corals. However, morphology often conflicts with genetic approaches due to high phenotypic plasticity and convergence. Understanding morphological variation among species is crucial to studying coral distribution, life history, ecology, and evolution. Here, we present an application of Random Forest models for coral species identification based on morphological annotation of the corallum and corallites. We show that the integration of molecular and morphological trait analysis can be improved using machine learning. Morphological traits were documented for Porites and Pocillopora coral species that were collected and genotyped through genome-wide, genetical hierarchical clustering, and coalescence analyses for the Tara Pacific Expedition. While Porites only included three tentative species, most Pocillopora species were accounted by included specimens from the western Indian Ocean, tropical Southwestern Pacific, and southeast Polynesia. Two Random Forest models per genus were trained on the morphological annotations using the genetic lineage labels. One model was developed for in-situ image identification and used corallum traits measured from in-situ photographs. Another model for integrative species identification combined corallum and corallite data measured on scanning electron micrographs. Random Forest models outperformed traditional dimension reduction methods like PCA and FAMD followed by k-means and hierarchical clustering by classifying the correct genetic lineage despite morphological clusters overlapping. This machine learning approach is reproducible, cost-effective, and accessible, reducing the need for taxonomic expertise. It can complement molecular and phylogenetic studies and support image identification, highlighting its potential to advance a coral integrative taxonomy workflow.

Pocillopora corals are the main reef builders in the eastern tropical Pacific. The validity of Pocillopora morphospecies remains under debate because of disagreements between morphological and genetic data. To evaluate the temporal stability of morphospecies in situ, we monitored the shapes of individual colonies in three communities in the southern Gulf of California for 44 months. Twenty-three percent of tagged colonies of Pocillopora damicornis changed to Pocillopora inflata morphology during this time. This switch in identity coincided with a shift to a higher frequency of storms and lower water turbidity (i.e., lower chlorophyll a levels). Seven months after the switch, P. inflata colonies were recovering their original P. damicornis morphology. All colonies of both morphospecies shared a common mitochondrial identity, but most P. damicornis colonies undergoing change were at a site with low-flow conditions. This is the first in situ study to document switching between described morphospecies, and it elucidates the influence of temporal shifts in environmental conditions on morphologically plastic responses.

Many species are composed of two or more genetically distinct clades, indicating ongoing or past evolutionary divergence. Often however, there are no obvious morphological differences between clades, making it difficult to accurately assess specific aspects of biodiversity or to enact targeted conservation efforts. New advancements in artificial intelligence tools can be used to categorise individuals into their respective genetic clades and to highlight their distinguishing morphological characters that would otherwise be hidden from human observers. Here, we applied computer vision and explainable artificial intelligence techniques to four limpet species that display well-defined phylogeographic breaks along the Baja California and California coasts. A fine-tuned convolutional network, trained and evaluated over 100 resampling iterations, classified individuals into their genetic clades with median F1-scores of up to 0.96. F1-score performance was markedly higher for true clade groups than the controlled mixed-groups, confirming the presence of features specific to the clades. Saliency maps consistently emphasised structures such as the keyhole in Fissurella volcano and the ridge tips in Lottia conus as distinguishing features, and subsequent shape analyses confirmed significant divergence between clades. These results demonstrate the power of computer vision and explainable artificial intelligence to expose otherwise cryptic morphological diversity and provide a scalable, reproducible workflow that can broaden the biodiversity toolkit and refine eco-evolutionary research across taxa.