Downloads provided by UsageCountsA 450 million years long latitudinal gradient in age‐dependent extinction
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Daniele Silvestro; Silvia Castiglione; Alessandro Mondanaro; Carmela Serio; Marina Melchionna; Paolo Piras; Mirko Di Febbraro; +3 Authors
Daniele Silvestro; Silvia Castiglione; Alessandro Mondanaro; Carmela Serio; Marina Melchionna; Paolo Piras; Mirko Di Febbraro; Francesco Carotenuto; Lorenzo Rook; Pasquale Raia;
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A 450 million years long latitudinal gradient in age‐dependent extinction
AbstractLeigh Van Valen famously stated that under constant conditions extinction probability is independent of species age. To test this 'law of constant extinction', we developed a new method using deep learning to infer age‐dependent extinction and analysed 450 myr of marine life across 21 invertebrate clades. We show that extinction rate significantly decreases with age in > 90% of the cases, indicating that most species died out soon after their appearance while those which survived experienced ever decreasing extinction risk. This age‐dependent extinction pattern is stronger towards the Equator and holds true when the potential effects of mass extinctions and taxonomic inflation are accounted for. These results suggest that the effect of biological interactions on age‐dependent extinction rate is more intense towards the tropics. We propose that the latitudinal diversity gradient and selection at the species level account for this exceptional, yet little recognised, macroevolutionary and macroecological pattern.
United Kingdom, Italy, Switzerland
- University of Florence Italy
- University of Gothenburg Sweden
- Liverpool John Moores University United Kingdom
- University Federico II of Naples Italy
- Roma Tre University Italy
Fossil occurrences, Law of constant extinction, 'law of constant extinction'; deep learning; fossil occurrences; mass extinction; neural networks; animals; biological evolution; fossils; invertebrates; biodiversity; extinction, biological, Animals; Biodiversity; Biological Evolution; Extinction, Biological; Fossils; Invertebrates; 'law of constant extinction'; deep learning; fossil occurrences; mass extinction; neural networks, Extinction, Biological, Law of constant extinction; Deep learning; Fossil occurrences; Neural networks; Mass extinction, Mass extinction, 'law of constant extinction'; deep learning; fossil occurrences; mass extinction; neural networks, Animals, Letters, Ecology, Evolution, Behavior and Systematics, GE, Fossils, 'law of constant extinction'; deep learning; fossil occurrences; mass extinction; neural networks; Animals; Biological Evolution; Fossils; Invertebrates; Biodiversity; Extinction, Biological, Deep learning, Biodiversity, Biological Evolution, Invertebrates, Neural networks, GE Environmental Sciences
Fossil occurrences, Law of constant extinction, 'law of constant extinction'; deep learning; fossil occurrences; mass extinction; neural networks; animals; biological evolution; fossils; invertebrates; biodiversity; extinction, biological, Animals; Biodiversity; Biological Evolution; Extinction, Biological; Fossils; Invertebrates; 'law of constant extinction'; deep learning; fossil occurrences; mass extinction; neural networks, Extinction, Biological, Law of constant extinction; Deep learning; Fossil occurrences; Neural networks; Mass extinction, Mass extinction, 'law of constant extinction'; deep learning; fossil occurrences; mass extinction; neural networks, Animals, Letters, Ecology, Evolution, Behavior and Systematics, GE, Fossils, 'law of constant extinction'; deep learning; fossil occurrences; mass extinction; neural networks; Animals; Biological Evolution; Fossils; Invertebrates; Biodiversity; Extinction, Biological, Deep learning, Biodiversity, Biological Evolution, Invertebrates, Neural networks, GE Environmental Sciences
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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