4.8 Article

Direct and indirect effects of biological factors on extinction risk in fossil bivalves

出版社

NATL ACAD SCIENCES
DOI: 10.1073/pnas.1100572108

关键词

macroecology; macroevolution; Paleogene; Bivalvia; multigroup analysis

资金

  1. National Science Foundation [DEB-0607922]
  2. American Museum of Natural History
  3. Conchologists of America
  4. Geological Society of America
  5. Society for Sedimentary Geology
  6. University of Chicago

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Biological factors, such as abundance and body size, may contribute directly to extinction risk and indirectly through their influence on other biological characteristics, such as geographic range size. Paleontological data can be used to explicitly test many of these hypothesized relationships, and general patterns revealed through analysis of the fossil record can help refine predictive models of extinction risk developed for extant species. Here, I use structural equation modeling to tease apart the contributions of three canonical predictors of extinction-abundance, body size, and geographic range size-to the duration of bivalve species in the early Cenozoic marine fossil record of the eastern United States. I find that geographic range size has a strong direct effect on extinction risk and that an apparent direct effect of abundance can be explained entirely by its covariation with geographic range. The influence of geographic range on extinction risk is manifest across three ecologically disparate bivalve clades. Body size also has strong direct effects on extinction risk but operates in opposing directions in different clades, and thus, it seems to be decoupled from extinction risk in bivalves as a whole. Although abundance does not directly predict extinction risk, I reveal weak indirect effects of both abundance and body size through their positive influence on geographic range size. Multivariate models that account for the pervasive covariation between biological factors and extinction are necessary for assessing causality in evolutionary processes and making informed predictions in applied conservation efforts.

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