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.2022 Mar 18;8(11):eabl3644.
doi: 10.1126/sciadv.abl3644. Epub 2022 Mar 18.

Increasing morphological disparity and decreasing optimality for jaw speed and strength during the radiation of jawed vertebrates

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Increasing morphological disparity and decreasing optimality for jaw speed and strength during the radiation of jawed vertebrates

William J Deakin et al. Sci Adv..

Abstract

The Siluro-Devonian adaptive radiation of jawed vertebrates, which underpins almost all living vertebrate biodiversity, is characterized by the evolutionary innovation of the lower jaw. Multiple lines of evidence have suggested that the jaw evolved from a rostral gill arch, but when the jaw took on a feeding function remains unclear. We quantified the variety of form in the earliest jaws in the fossil record from which we generated a theoretical morphospace that we then tested for functional optimality. By drawing comparisons with the real jaw data and reconstructed jaw morphologies from phylogenetically inferred ancestors, our results show that the earliest jaw shapes were optimized for fast closure and stress resistance, inferring a predatory feeding function. Jaw shapes became less optimal for these functions during the later radiation of jawed vertebrates. Thus, the evolution of jaw morphology has continually explored previously unoccupied morphospace and accumulated disparity through time, laying the foundation for diverse feeding strategies and the success of jawed vertebrates.

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Figures

Fig. 1.
Fig. 1.. Example pipeline for adaptive landscape generation using Pareto methods.
(A) Lateral images of 121 gnathostome jaws were collected and characterized via EFA. (B) EFA results were input to a PCA to build a theoretical morphospace of evenly spaced theoretical jaw shapes. (C) Each theoretical shape is tested 1000 times with random input constraints using finite element analysis (FEA) to assess their functional performance in RE and VMS. (D) Each shape was plotted in performance space with its individual performance metrics. These shapes are then ranked using a Pareto system, with the assumption that lower VMS (higher strength) is optimal and that higher RE (higher speed and efficiency) is optimal. (E) Ancestral state reconstruction (ASR) is used with EFA data and a timed phylogeny to construct a phylomorphospace. (F) The Pareto rank from the performance space is used to construct an adaptive landscape, and the evolution of taxa within this adaptive landscape is observed via the phylomorphospace.
Fig. 2.
Fig. 2.. Empirical and theoretical morphospace.
Theoretical morphospace (B) is built by extending the limits of empirical morphospace (A) and calculating the shape data at each point in a regular 23-by-21 grid. Legend shows symbols and colors for individual taxa from four clades: Sarcopterygii (Sarc.), Chondrichthyes and acanthodians (Chon.), Placodermi (Plac.), and Actinopterygii (Acti.). Blue area in theoretical morphospace represents the extent of empirical morphospace.
Fig. 3.
Fig. 3.. Performance surfaces generated from theoretical shapes.
RE (A) and median VMS (B) performance surfaces showing the mean value from 1000 random constraint inputs. Superimposed theoretical shapes are colored on the basis of their individual performance. Gray shapes and area represent geometrically impossible shapes and morphospace. (C toJ) Random inputs shown on four theoretical shapes from different grid positions for RE (C to F) (blue lines represent the boundaries of random joint and tooth placement) and VMS (G to J) (blue dots represent constraint positions, red line represents boundaries of random force placement, and red area represents boundaries of random force direction).
Fig. 4.
Fig. 4.. Pareto optimality and the adaptive landscape.
(A) Performance space. Each theoretical shape is represented by a black dot, plotted at by its individual VMS and RE performance. Note the heterogeneous occupation densities. Gray area represents the region of possible solutions. Solid black line represents the Pareto front. Red dashed area represents the area shown in (B), a zoom of plot (A), showing extrapolated taxon performances and their phylogenetic relationships. (C) The adaptive landscape, with phylomorphospace superimposed. Note that only 99 of the 121 taxa are included in the phylogeny; the remainder are plotted unconnected to the phylogeny. Pareto rank represents optimality, with 0 being least optimal and 1 representing optimal (on the Pareto front). Gray region represents geometrically impossible morphospace. Legend shows symbols and colors for individual taxa from four clades: Sarcopterygii (Sarc.), Chondrichthyes and acanthodians (Chon.), Placodermi (Plac.), and Actinopterygii (Acti.).
Fig. 5.
Fig. 5.. Disparity and optimality through time.
Mean optimality decreases steadily through time, while the sum of variances and mean pairwise distance increase through the Devonian. White symbols and error bars represent mean and 95 confidence intervals of 10,000 bootstrap replicates. Columns on the right represent the occupation change in performance space (VMS versus RE) and morphospace (PC1 versus PC2) over time. Legend shows symbols and colors for individual taxa from four clades: Sarcopterygii (Sarc.), Chondrichthyes and acanthodians (Chon.), Placodermi (Plac.), and Actinopterygii (Acti.).
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