Latitudinal variation in growth of young brown trout Salmo trutta

Citation
Aj. Jensen et al., Latitudinal variation in growth of young brown trout Salmo trutta, J ANIM ECOL, 69(6), 2000, pp. 1010-1020
Citations number
58
Categorie Soggetti
Animal Sciences
Journal title
JOURNAL OF ANIMAL ECOLOGY
ISSN journal
00218790 → ACNP
Volume
69
Issue
6
Year of publication
2000
Pages
1010 - 1020
Database
ISI
SICI code
0021-8790(200011)69:6<1010:LVIGOY>2.0.ZU;2-F
Abstract
1. A new laboratory-based growth model for brown trout (Salmo trutta) was u sed to explore latitudinal variation in growth among natural populations. T he model included the effects of differences in ambient temperatures and fi sh size among populations. Annual growth rates of anadromous brown trout pa rr from 22. Norwegian populations at 61-70 degreesN were compared with pred ictions from the growth model. Published field data from one Spanish, 15 Br itish and four Danish populations at 44-58 degreesN were included in the an alysis to increase the latitudinal range. 2. Among the Norwegian populations, the ratio between observed and predicte d growth rates was not significantly different from 1.00 in eight rivers, b ut was significantly higher in eight, and was significantly lower in six. O bserved growth was highest, relative to predicted growth, in the coldest ri vers. In Spanish, British and Danish rivers, observed growth did not exceed predicted growth. 3. The ratio between observed and predicted annual growth rate decreased si gnificantly with increasing annual mean temperature. Observed annual growth was higher than predicted growth only in rivers with an annual mean temper ature lower than 5.1 degreesC, and this indicates that some kind of thermal adaptation may occur in trout populations in the coldest rivers. 4. Regression analyses showed that besides the direct effects of temperatur e and body size predicted by the growth model, annual growth rates were sig nificantly related to annual mean temperature, densities of juvenile salmon ids, duration of twilight (average for May-August) and latitude. Adding the se variables to the original model increased the explanatory power from 73. 9 to 80.6%.