Data · dataset · 2021
Data from: Large‐scale molecular diet analysis in a generalist marine mammal reveals male preference for prey of conservation concern
Listed in Borealis and Agri-environmental Research Data Dataverse — shown once because both records carry DOI 10.5683/sp2/jw6dei
Description
Abstract
Sex‐specific diet information is important in the determination of predator impacts on prey populations. Unfortunately, the diet of males and females can be difficult to describe, particularly when they are marine predators. We combined two molecular techniques to describe haul‐out use and prey preferences of male and female harbor seals (Phoca vitulina) from Comox and Cowichan Bay (Canada) during 2012–2013.
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DNA metabarcoding quantified the diet proportions comprised of prey species in harbor seal scat, and qPCR determined the sex of the individual that deposited each scat. Using 287 female and 260 male samples, we compared the monthly sex ratio with GLMs and analyzed prey consumption relative to sex, season, site, and year with PERMANOVA. The sex ratio between monthly samples differed widely in both years (range = 12%–79% males) and showed different patterns at each haul‐out site.
Male and female diet differed across both years and sites: Females consumed a high proportion of demersal fish species while males consumed more salmonid species. Diet composition was related to both sex and season (PERMANOVA: R2 = 27%, p < 0.001; R2 = 24%, p < 0.001, respectively) and their interaction (PERMANOVA: R2 = 11%, p < 0.001). Diet differences between males and females were consistent across site and year, suggesting fundamental foraging differences, including that males may have a larger impact on salmonids than females.
Our novel combination of techniques allowed for both prey taxonomic and spatiotemporal resolution unprecedented in marine predators.
Usage notes
Sex of depositor and diet proportions for individual scat samples The file lists the sex of the scat depositor, individual sample ID, sampling month, sampling season, sampling year, and sampling site, as well as the diet proportions of different prey species (in %) for each individual sample used in the study. Salmonid prey species proportions are listed separately for juvenile and adult salmon. The columns with prey species diet proportions are followed by columns showing diet proportions by order (for fish) or higher taxonomic level for invertebrates (Crustacea and Cephalopoda).
Data accessibility file for Dryad.csv
Links
Where it is published
- Dataverse dataset page borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP2%2FJW6DEI ↗
landing page · from borealisdata ca
- DOI doi.org/10.5683/sp2/jw6dei ↗
DOI / persistent id · from borealisdata ca
Catalogue records · 1
- Dataverse API borealisdata.ca/api/datasets/:persistentId/?persistentId=doi%3A10.5683%2FSP2%2… ↗
metadata API · from borealisdata ca
Topics
- From keywords
- Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Mathematics & Statistics · Medicine & Health · Ocean & Atmospheric Science · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Evolutionary biology 74%
Provenance · 2 source records, 21 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Borealis | doi:10.5683/SP2/JW6DEI | 10 d ago | JSON v1 |
| Agri-environmental Research Data Dataverse | doi:10.5683/SP2/JW6DEI | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3104 | enrichment · borealisdata ca | taxonomy-embedding@1.0.0 | title+keywords+description (74%) |
| concepts[field].dataverse_subject:other | source · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].dataverse_subject:other | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:chemistry | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:economics-finance | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:engineering | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:humanities | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:mathematics-statistics | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:medicine-health | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:ocean-atmospheric | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:ocean-atmospheric | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].local:field:social-science | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| created_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| description | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /description |
| publication_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| title | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /name |
| updated_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| version_label | source · borealisdata ca | connector:borealisdata_ca@1.0.0 |