Dataset Information

Increasing morphological complexity in multiple parallel lineages of the Crustacea.

ABSTRACT: The prospect of finding macroevolutionary trends and rules in the history of life is tremendously appealing, but very few pervasive trends have been found. Here, we demonstrate a parallel increase in the morphological complexity of most of the deep lineages within a major clade. We focus on the Crustacea, measuring the morphological differentiation of limbs. First, we show a clear trend of increasing complexity among 66 free-living, ordinal-level taxa from the Phanerozoic fossil record. We next demonstrate that this trend is pervasive, occurring in 10 or 11 of 12 matched-pair comparisons (across five morphological diversity indices) between extinct Paleozoic and related Recent taxa. This clearly differentiates the pattern from the effects of lineage sorting. Furthermore, newly appearing taxa tend to have had more types of limbs and a higher degree of limb differentiation than the contemporaneous average, whereas those going extinct showed higher-than-average limb redundancy. Patterns of contemporary species diversity partially reflect the paleontological trend. These results provide a rare demonstration of a large-scale and probably driven trend occurring across multiple independent lineages and influencing both the form and number of species through deep time and in the present day.

SUBMITTER: Adamowicz SJ

PROVIDER: S-EPMC2290764 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature

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Increasing morphological complexity in multiple parallel lineages of the Crustacea.

Adamowicz Sarah J SJ Purvis Andy A Wills Matthew A MA

Proceedings of the National Academy of Sciences of the United States of America 20080317 12

The prospect of finding macroevolutionary trends and rules in the history of life is tremendously appealing, but very few pervasive trends have been found. Here, we demonstrate a parallel increase in the morphological complexity of most of the deep lineages within a major clade. We focus on the Crustacea, measuring the morphological differentiation of limbs. First, we show a clear trend of increasing complexity among 66 free-living, ordinal-level taxa from the Phanerozoic fossil record. We next ...[more]

PMID: 18347335

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Project description:BackgroundIn microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes.ResultsIn this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes.ConclusionsWe show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events.

Dataset Information

Increasing morphological complexity in multiple parallel lineages of the Crustacea.

Publications

Increasing morphological complexity in multiple parallel lineages of the Crustacea.

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