bioRxiv Science⌕ Search

bioRxiv · 10.1101/454132

Whatever You Want: Inconsistent Results Is The Rule, Not The Exception, In The Study Of Primate Brain Evolution

Abstract

Primate brains differ in size and architecture. Hypotheses to explain this variation are numerous and many tests have been carried out. However, after body size has been accounted for there is little left to explain. The proposed explanatory variables for the residual variation are many and covary, both with each other and with body size. Further, the data sets used in analyses have been small, especially in light of the many proposed predictors. Here we report the complete list of models that results from exhaustively combining six commonly used predictors of brain and neocortex size. This provides an overview of how the output from standard statistical analyses changes when the inclusion of different predictors is altered. By using both the most commonly tested brain data set and a new, larger data set, we show that the choice of included variables fundamentally changes the conclusions as to what drives primate brain evolution. Our analyses thus reveal why studies have had troubles replicating earlier results and instead have come to such different conclusions. Although our results are somewhat disheartening, they highlight the importance of scientific rigor when trying to answer difficult questions. It is our position that there is currently no empirical justification to highlight any particular hypotheses, of those adaptive hypotheses we have examined here, as the main determinant of primate brain evolution.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wartel, A., Lindenfors, P., Lind, J.. 2018-10-26. Whatever You Want: Inconsistent Results Is The Rule, Not The Exception, In The Study Of Primate Brain Evolution. https://doi.org/10.1101/454132

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Ocean current models predict spatial structure for coastal biodiversity

Genomic diversity provides the raw material through which all evolutionary processes occur. Accurately describing how environmental features affect patterns of diversity shows promise in predicting the resilience of populations and entire communities to dynamic environments and novel stressors. Identifying the contribution of purely neutral responses to environmental effects can be difficult, especially across large spatial ranges with many covarying environmental features. Using an individual-based model of larvae carried by ocean currents, we predict patterns of neutral genomic diversity in a passively dispersed coastal species, suggesting large geographic regions of relative homogeneity with ancestry determined by the nearest "upstream" population with limited larval input. The genomic identity that dominates each region sharply transitions at the next such interruption, generating hierarchical structure and violating assumptions of isolation-by-distance. We call these upstream populations "lineage source regions" (LSRs), and we find strong concordance between LSR model predictions and an empirical population genomic survey of a keystone salt marsh mussel (Geukensia demissa). We quantitatively validate model performance and goodness-of-fit, updating baseline predictions of genomic diversity patterns from the classic isolation-by-distance model to this LSR framework. This spatially explicit coastal ancestry model has potential for generalization across passive dispersing species.

evolutionary biology↗

TaxaAudit: A statistical model of consensus in phylogenetics identifies non-monophyletic taxa

Interpreting variation in genes and traits across species requires understanding their evolutionary relationships. Taxonomic hierarchies provide a practical approximation of the tree of life, with the NCBI taxonomy linking this framework directly to public sequence data. Some named groups, however, are not evolutionary clades, and recognizing these exceptions requires knowledge scattered across the phylogenetics literature. Here we introduce TaxaAudit, a statistical model that identifies taxonomic groups whose monophyly is repeatedly contradicted by published phylogenies. The model accounts for background taxonomic conflict and the ability of each study's species sampling to expose non-monophyly. Applied to 5,865 molecular phylogenies from TimeTree, TaxaAudit flagged 516 NCBI taxa at family rank and below for significant recurrent contradiction of monophyly. Excluding these taxa's monophyly requirements improved the taxonomy's agreement with independent phylogenomic reference trees and phylogenies withheld from discovery. Flagged taxa were also 3.61 times as likely as matched controls to have their monophyly challenged in publications outside the discovery set. In tree-inference experiments, excluding these monophyly constraints improved agreement with reference phylogenies relative to enforcing the unmodified taxonomy. By identifying groups whose monophyly should not be assumed, TaxaAudit lets researchers draw on published phylogenetic knowledge without reviewing the specialist literature for every taxon. This supports broader use of the NCBI taxonomy as an evolutionary framework for biological comparisons.

evolutionary biology↗

Shared landscape, divergent genomes: Comparative population genomics of co-occurring flora in an isolated Australian mountain range

Understanding how landscape heterogeneity shapes genomic variation is key to conserving biodiversity and evolutionary potential. Yet conservation genomics is typically conducted at the single species level, limiting our ability to identify shared genomic patterns or understand how co-occurring taxa differ in their response to common environmental gradients. We assessed spatial patterns of genomic variation and their associations with geographic and environmental features among populations of nine co-existing plant species with contrasting life-histories and ecological traits across Gariwerd, a threatened biodiversity hotspot in south-eastern Australia. All species exhibited weak overall genetic structure, although the magnitude and spatial distribution of differentiation varied among species. Despite this variation, broadly consistent spatial trends were apparent, with higher genomic divergence largely concentrated in central zones and higher genomic diversity localised in southern regions of the landscape. Significant associations between genetic differentiation and geographic distance were observed in three species, while another species showed associations with environmental distance. These findings demonstrate that coexisting species can show overlapping spatial patterns in genomic variation while differing markedly in their associations with geographic and environmental variation across the same landscape. This comparative approach provides a basis for spatial prioritisation and conservation planning, while highlighting the complex eco-evolutionary processes operating across heterogeneous landscapes.

evolutionary biology↗