Meeting Abstract
There are intrinsic mathematical patterns in nature. A Fibonacci sequence describes the arrangements of seeds in a sunflower and the spiral of a Nautilus shell. Species are natural units, that populate the world around us, and they are formed from branching phylogenetic processes that also have a mathematical structure. So it follows that should be able develop a set of general principles that describe global patterns of species groups, like genera, or families. Understanding such patterns would lend considerable power to the approach of “taxonomic surrogacy”. In environmental assessments, ecology, and palaeontology, it is common to substitute genus-level or family-level identification where definitive species identification is impractical. A more robust assessment of the error introduced by taxonomic surrogacy could also improve comparisons of living diversity (where we can hope to identify everything to species, in theory) to the fossil record (which is intrinsically more data limited). But species and species groups are fundamentally not the same. And some higher taxa are “natural” or monophyletic groups, while others are mixed or paraphyletic melting pots awaiting taxonomic revision. Finally, the use of species group designations are potentially different in living and fossil taxa. All of these issues can be addressed through simulation approaches, in silico approximations of both large scale phylogenetic scenarios that underpin the evolution of species groups, but also simulation of an “idealised” taxonomic practice. Clarity and confidence in fundamental patterns for taxonomy based on a robust null model – there are more species in the tropics, species-poor genera are very common, large genera are very rare – can accelerate species discovery. We cannot wait to identify all the species on earth before we assess and anthropogenic damage to biodiversity.
