Avian chemical communication, once largely overlooked, is a growing field that has revealed the important role that olfaction plays in the social lives of some birds. Leach’s storm-petrels (Oceanodroma leucorhoa) have a remarkable sense of smell and a strong, musky scent. This long-lived, monogamous seabird relies on olfaction for nest relocation and foraging, but whether they use scent for communication is less well studied. They are nocturnally active at the breeding colony and yet successfully reunite with their mate despite poor night-vision, indicating an important role for non-visual communication. We investigated the chemical profiles of Leach’s storm-petrels to determine whether there is socially relevant information encoded in their plumage odor. To capture the compounds comprising their strong scent, we developed a method to study the compounds present in the air surrounding their feathers using headspace stir bar sorptive extraction coupled with gas-chromatography mass-spectrometry. We collected feathers from Leach’s storm-petrels breeding on Bon Portage Island in Nova Scotia, Canada in both 2015 and 2016. Our method detected 142 commonly occurring compounds. We found interannual differences in chemical profiles between the two sampling years. Males and females had similar chemical profiles, while individuals had distinct chemical signatures across the two years. These findings suggest that the scent of the Leach’s storm-petrel provides sociochemical information that could facilitate olfactory recognition of individuals and may inform mate choice decisions.

The data were generated using gas chromatography-mass spectrometry (GC-MS) to measure chemicals in headspace of feathers collected from Leach’s storm-petrels (Oceanodroma leucorhoa) breeding on Bon Portage Island in Nova Scotia, Canada. In total, 30 individuals were sampled across 2015 and 2016. For each bird, 3 replicate samples consisting of 2 feathers each were processed in each year. The 30 individuals were equally split between the two sexes with 15 females and 15 males. We used these data to determine whether socially-relevant information that could inform chemical communication is encoded in the chemical profiles of this species.

To generate the chemical profile of each individual, we measured the peak areas of 127 compounds using Agilent MassHunter Quantitative Analysis Software Version B.08.00 for GC-MS. Each sample was analyzed with an added internal standard of known volume and concentration; 0.5 ml of 10ppm naphthalene-d8 in 100% ethanol. We measured the peak area of this compound in every sample to account for variation in instrument sensitivity. We standardized the chemical data by dividing each compound peak by the area of the internal standard in the corresponding run. To account for differences in feather quantity, we performed a second standardization where we divided by the pre-extraction feather sample mass. The standardized data are provided here.

The data were analyzed using the PRIMER v 7.0.13 software package with the PERMANOVA+ v1 add-on (www.primer-e.com). First, the data were log(x+1) transformed to reduce the influence of the most abundant compounds. We used a combination of Principal Coordinate Analysis (PCO) plots to visualize our data, as well as two types of distance-based multivariate statistics: PERMANOVA and Canonical Analysis of Principal Coordinates (CAP) to analyze the patterns in our data. All these methods can be found in the PRIMER program. The specific details of each analysis are provided in the corresponding publication.

There are two files included in this dataset:

1. Jennings_Ebeler_2020_chemicalprofiles_30birds.csv

This file lists the abundance of 127 compounds for 180 feather samples analyzed from 30 Leach’s storm-petrels across two sampling years, 2015 and 2016.

2. Jennings_Ebeler_chemicalprofiles_modelvalidationsamples.csv

This file lists the abundance of 127 compounds for additional feathers analyzed from a subset of 9 individuals where we obtained and analyzed an extra sample. These data were used for validating Canonical Analysis of Principal Coordinate models.

Please see the ReadMe file for further details.