Natural and anthropogenic sources of habitat variation influence exploration behaviour, stress response, and brain morphology in a coastal fish.

Evolutionary ecology aims to better understand how ecologically important traits respond to environmental heterogeneity. Environments vary both naturally and as a result of human activities, and investigations that simultaneously consider how natural and human-induced environmental variation affect diverse trait types grow increasingly important as human activities drive species endangerment. Here, we examined how habitat fragmentation and structural habitat complexity affect disparate trait types in Bahamas mosquitofish Gambusia hubbsi inhabiting tidal creeks.

Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning.

Premise: X-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organization. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small data sets, restricting its utility for phenotyping experiments and limiting our confidence in the inferences of these studies due to low replication numbers.

Methods And Results: We present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification that dramatically reduces the time required to process single-leaf microCT scans into detailed segmentations.