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Springer Verlag, Journal für Ornithologie, 1(142), p. 63-72, 2001

DOI: 10.1007/bf01651739

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Magnetic resonance imaging and spectroscopy (MRI, MRS) of seasonal patterns of body composition: A methodological pilot study in White Storks (Ciconia ciconia)

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Here we present a systematic application of magnetic resonance imaging (in the following called MRI) and magnetic resonance spectroscopy (MRS) to the White Stork. The main aim was to demonstrate the annual cycle of fat deposition in the same individuals for comparison to wild conspecifics, to clarify the energy metabolism of this migratory species. To obtain sharp, high-contrast images of the interior of the body, the birds were kept still by enclosing them in simple plastic tubes with additional fixation of legs and head, avoiding the problematic sedation with drugs. Altogether 12 test birds (young storks) were monitored systematically for 15 months, to follow seasonal changes in the internal organs (mainly breast muscles) and tissues (mainly fat depots). At each examination 22 high-contrast pixel images representing serial dorsoventral sections through the body were generated with the computer program MatLab, after which the pixels per section image were converted to tissue components in cm2 and the distances between consecutive sections used to calculate the tissue volumes in cm3. To measure the fat in the breast muscle spectroscopy was used to determine the fat : water ratio, from which changes in fat content could be derived. The study revealed pronounced seasonal changes in the visceral and cutaneous/subcutaneous fat depots, which precisely paralleled the annual variation in body weight of the birds (see also the preceding paper, Berthold et al. 2001). The breast muscles exhibited the prolonged growth typical of the juveniles of large species but no conspicuous change at the migration periods. In this project MRI and MRS proved to be successful methods that show great promise.