Abstract
SummaryExceptionally preserved fossil sites have allowed specimen-based identification of trophic interactions to which network analyses have been applied. However, network analyses of the fossil record suffer from incomplete and indirect data, time averaging that obscures species coexistence, and biases in preservation. Here, we present a high-resolution fossil data set from Raymond Quarry member of the mid-Cambrian Burgess Shale (7,549 specimens, 61 taxa, ∼510 Mya) and formulate a measure of “preservation bias” that aids identification of assemblage subsets to which network analyses can be reliably applied. For these sections, abundance correlation network analyses predicted longitudinally consistent trophic and competitive interactions. Our analyses predicted previously postulated trophic interactions with 83.5% accuracy and demonstrated a shift from specialist interaction-dominated assemblages to ones dominated by generalist and competitive interactions. This approach provides a robust, taphonomically corrected framework to explore and predict in detail the existence and ecological character of putative interactions in fossil data sets.
Highlights
The Cambrian Period (541–485 Mya) is unique because it witnessed the emergence and rapid diversification of phylum-level extant animal body plans and featured the highest morphological and genetic rates of animal evolution (Erwin et al, 2011; Lee et al, 2013)
Network analyses of the fossil record suffer from incomplete and indirect data, time averaging that obscures species coexistence, and biases in preservation
We present a high-resolution fossil data set from Raymond Quarry member of the mid-Cambrian Burgess Shale (7,549 specimens, 61 taxa, 510 Mya) and formulate a measure of ‘‘preservation bias’’ that aids identification of assemblage subsets to which network analyses can be reliably applied
Summary
The Cambrian Period (541–485 Mya) is unique because it witnessed the emergence and rapid diversification of phylum-level extant animal body plans and featured the highest morphological and genetic rates of animal evolution (Erwin et al, 2011; Lee et al, 2013). Network-based studies provide critical insight on the structure and function of ecological systems (Ings et al, 2009; Poisot et al, 2016; Delmas et al, 2017), but paleo-assemblages often suffer from incomplete and indirect data (Roopnarine, 2010; Shaw et al, 2021), time-averaging across large stratigraphic sections that obscure species coexistence (Kidwell et al, 1991; Dunne et al, 2008; Roopnarine, 2010; Muscente et al, 2018), and biases in preservation, collection, and identification of both specimens and interactions (Koch, 1978; Smith, 2001; Dunne et al, 2008).
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