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ABSTRACT Colonization of a novel habitat is often followed by radiation in the wake of ecological opportunity. Alternatively, some habitats should be inherently more constraining than others if the challenges of that environment have few evolutionary solutions. We examined the push-and-pull of these factors on evolution following habitat transitions, using anglerfishes (Lophiiformes) as a model. Deep-sea fishes are notoriously difficult to study, and poor sampling has limited progress thus far. Here we present a new phylogeny of anglerfishes with unprecedented taxonomic sampling (1,092 loci and 40% of species), combined with three-dimensional phenotypic data from museum specimens obtained with micro-CT scanning. We use these datasets to examine the tempo and mode of phenotypic and lineage diversification using phylogenetic comparative methods, comparing lineages in shallow and deep benthic versus bathypelagic habitats. Our results show that anglerfishes represent a surprising case where the bathypelagic lineage has greater taxonomic and phenotypic diversity than coastal benthic relatives. This defies expectations based on ecological principles since the bathypelagic zone is the most homogeneous habitat on Earth. Deep-sea anglerfishes experienced rapid lineage diversification concomitant with colonization of the bathypelagic zone from a continental slope ancestor. They display the highest body, skull and jaw shape disparity across lophiiforms. In contrast, reef-associated taxa show strong constraints on shape and low evolutionary rates, contradicting patterns suggested by other shallow marine fishes. We found that Lophiiformes as a whole evolved under an early burst model with subclades occupying distinct body shapes. We further discuss to what extent the bathypelagic clade is a secondary adaptive radiation, or if its diversity can be explained by non-adaptive processes. 

Abstract Phylogenetic regression is a type of generalised least squares (GLS) method that incorporates a modelled covariance matrix based on the evolutionary relationships between species (i.e. phylogenetic relationships). While this method has found widespread use in hypothesis testing via phylogenetic comparative methods, such as phylogenetic ANOVA, its ability to account for non‐linear relationships has received little attention.To address this, here we implement a phylogenetic Kernel Ridge Regression (phyloKRR) method that utilises GLS in a high‐dimensional feature space, employing linear combinations of phylogenetically weighted data to account for non‐linearity. We analysed two biological datasets using the Radial Basis Function and linear kernel function. The first dataset contained morphometric data, while the second dataset comprised discrete trait data and diversification rates as response variable. Hyperparameter tuning of the model was achieved through cross‐validation rounds in the training set.In the tested biological datasets, phyloKRR reduced the error rate (as measured by RMSE) by around 20% compared to linear‐based regression when data did not exhibit linear relationships. In simulated datasets, the error rate decreased almost exponentially with the level of non‐linearity.These results show that introducing kernels into phylogenetic regression analysis presents a novel and promising tool for complementing phylogenetic comparative methods. We have integrated this method into Python package named phyloKRR, which is freely available at:https://github.com/ulises‐rosas/phylokrr.