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Description

<b>Abstract</b><br/>

When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric--the mean pairwise distance (MPD) between taxa--and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model of freeze tolerance, suggesting it evolved recently. We suggest that data ordered on phylogenies such as trait values, species interactions, or community presence/absence are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package <em>kitchen</em> to perform CKS for generic application of the technique.</p>; <b>Methods</b><br />

Data were simulated, processed, and analyzed in R. These analyses were performed to investigate the ability of two methods, a machine learning technique termed Convolutional Kitchen Sinks (CKS) and models trained on series of Mean Pairwise Distance (MPD) metrics, also termed MPD curve, to recover the evolutionary model of traits that communities are assembled on. Communities were simulated on both simulated and empirical phylogenies by evolving traits on the phylogenies according to an Early Burst transformation governed by a normally distributed evolutionary parameter. Data were separated into training and test data. The evolutionary parameters used in simulation were then modeled as a function of the observed simulated communities in the training data, using both the CKS method and MPD method. The models were then tested by examining the relationship between the predicted and known parameters of the test data. Finally, predictions using trained models were made on the known community of freeze-tolerant dicots.</p>