Organisms modify and choose components of their local environments. This 'niche construction' can alter ecological processes, modify natural selection and contribute to inheritance through ecological legacies. Here, we propose that niche construction initiates and modifies the selection directly affecting the constructor, and on other species, in an orderly, directed and sustained manner. By dependably generating specific environmental states, niche construction co-directs adaptive evolution by imposing a consistent statistical bias on selection. We illustrate how niche construction can generate this evolutionary bias by comparing it with artificial selection. We suggest that it occupies the middle ground between artificial and natural selection. We show how the perspective leads to testable predictions related to: (i) reduced variance in measures of responses to natural selection in the wild; (ii) multiple trait coevolution, including the evolution of sequences of traits and patterns of parallel evolution; and (iii) a positive association between niche construction and biodiversity. More generally, we submit that evolutionary biology would benefit from greater attention to the diverse properties of all sources of selection.

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.