| Title |
From video recordings to whisker stable isotopes: a critical evaluation of timescale in assessing individual foraging specialisation in Australian fur seals
|
|---|---|
| Published in |
Oecologia, August 2015
|
| DOI | 10.1007/s00442-015-3407-2 |
| Pubmed ID | |
| Authors |
Laëtitia Kernaléguen, Nicole Dorville, Daniel Ierodiaconou, Andrew J. Hoskins, Alastair M. M. Baylis, Mark A. Hindell, Jayson Semmens, Kyler Abernathy, Greg J. Marshall, Yves Cherel, John P. Y. Arnould |
| Abstract |
Estimating the degree of individual specialisation is likely to be sensitive to the methods used, as they record individuals' resource use over different time-periods. We combined animal-borne video cameras, GPS/TDR loggers and stable isotope values of plasma, red cells and sub-sampled whiskers to investigate individual foraging specialisation in female Australian fur seals (Arctocephalus pusillus doriferus) over various timescales. Combining these methods enabled us to (1) provide quantitative information on individuals' diet, allowing the identification of prey, (2) infer the temporal consistency of individual specialisation, and (3) assess how different methods and timescales affect our estimation of the degree of specialisation. Short-term inter-individual variation in diet was observed in the video data (mean pairwise overlap = 0.60), with the sampled population being composed of both generalist and specialist individuals (nested network). However, the brevity of the temporal window is likely to artificially increase the level of specialisation by not recording the entire diet of seals. Indeed, the correlation in isotopic values was tighter between the red cells and whiskers (mid- to long-term foraging ecology) than between plasma and red cells (short- to mid-term) (R (2) = 0.93-0.73 vs. 0.55-0.41). δ(13)C and δ(15)N values of whiskers confirmed the temporal consistency of individual specialisation. Variation in isotopic niche was consistent across seasons and years, indicating long-term habitat (WIC/TNW = 0.28) and dietary (WIC/TNW = 0.39) specialisation. The results also highlight time-averaging issues (under-estimation of the degree of specialisation) when calculating individual specialisation indices over long time-periods, so that no single timescale may provide a complete and accurate picture, emphasising the benefits of using complementary methods. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 17% |
| United Kingdom | 4 | 14% |
| Canada | 2 | 7% |
| Chile | 1 | 3% |
| South Africa | 1 | 3% |
| Bosnia and Herzegovina | 1 | 3% |
| Ecuador | 1 | 3% |
| Spain | 1 | 3% |
| Norway | 1 | 3% |
| Other | 2 | 7% |
| Unknown | 10 | 34% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 17 | 59% |
| Scientists | 11 | 38% |
| Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | <1% |
| Portugal | 1 | <1% |
| Belgium | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 105 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 22 | 20% |
| Student > Ph. D. Student | 21 | 19% |
| Researcher | 20 | 18% |
| Student > Bachelor | 11 | 10% |
| Other | 9 | 8% |
| Other | 12 | 11% |
| Unknown | 14 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 62 | 57% |
| Environmental Science | 16 | 15% |
| Earth and Planetary Sciences | 4 | 4% |
| Biochemistry, Genetics and Molecular Biology | 2 | 2% |
| Veterinary Science and Veterinary Medicine | 1 | <1% |
| Other | 4 | 4% |
| Unknown | 20 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 August 2015.
All research outputs
#2,364,425
of 25,286,324 outputs
Outputs from Oecologia
#337
of 4,458 outputs
Outputs of similar age
#29,365
of 270,840 outputs
Outputs of similar age from Oecologia
#3
of 57 outputs
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