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The influence of functional traits on species survivorship has been evaluated in various contexts in both modern and ancient ecosystems, but an important direction for research is to integrate datasets that include both extinct and extant taxa. This approach can provide a more reliable understanding of the effects of functional traits on macroecological and macroevolutionary dynamics. Knowledge of the links between individual traits and survivorship is crucial for developing accurate extinction risk predictive models. Here we test the impact of numerous functional traits on the survival and extinction of species through time, using bivalve and gastropod species from the rich fossil record of the western Atlantic over the last ~3 million years, along with the associated extant biota. We also compare the impact of these organismic traits on survival relative to a group level trait: geographic distribution. Analyses use a dataset of 12 functional traits including life habit, feeding behavior and basal metabolic rate (BMR), for 115 species from 36 families. Traits were observed and measured from specimens in the collections of the Paleontological Research Institution, Florida Museum of Natural History, and University of Kansas, as well as surveys of the literature and online databases such as the Neogene Marine Biota of Tropical America (NMITA). Results derived from Principal Coordinates Analysis (PCoA) show there is a clear distinction between extinct and extant species, overall, when comparing them based on life habit, maximum body size, shell composition and BMR. Most traits showed little direct relation with survival, except BMR and associated maximum body size, supporting the Metabolic Theory of Ecology. Since many functional traits do not explain survival, their function may be mis- or over-interpreted, and traits posited to represent important organismic adaptations may not play a prominent role in long-term species survival, especially during the major climate changes over the last ~ 3 million years. Some traits do show significant interactions, and these were more fully explored using additional multivariate analyses. The relative importance of geographic range size suggests group-level characters may be the primary determinant of extinction patterns over macroevolutionary time scales.
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High-throughput micro-CT scanning and deep learning segmentation workflow for analyses of shelly invertebrates and their fossils: Examples from marine Bivalvia
The largest source of empirical data on the history of life largely derives from the marine invertebrates. Their rich fossil record is an important testing ground for macroecological and macroevolutionary theory, but much of this historical biodiversity remains locked away in consolidated sediments. Manually preparing invertebrate fossils out of their matrix can require weeks to months of careful excavation and cannot guarantee the recovery of important features on specimens. Micro-CT is greatly improving our access to the morphologies of these fossils, but it remains difficult to digitally separate specimens from sediments of similar compositions, e.g., calcareous shells in a carbonate rich matrix. Here we provide a workflow for using deep learning—a subset of machine learning based on artificial neural networks—to augment the segmentation of these difficult fossils. We also provide a guide for bulk scanning fossil and Recent shells, with sizes ranging from 1 mm to 20 cm, enabling the rapid acquisition of large-scale 3D datasets for macroevolutionary and macroecological analyses (300–500 shells in 8 hours of scanning). We then illustrate how these approaches have been used to access new dimensions of morphology, allowing rigorous statistical testing of spatial and temporal patterns in morphological evolution, which open novel research directions in the history of life.
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- Award ID(s):
- 2049627
- PAR ID:
- 10415820
- Date Published:
- Journal Name:
- Frontiers in Ecology and Evolution
- Volume:
- 11
- ISSN:
- 2296-701X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fevo.2023.1127756
