Peter Karas1,
Paula BÖHLING2,Leili
JÄRV3,
Hannu Lehtonen4,
Andrey Makarchouk5
and Valdemaras Ziliukas6
1National
Board of Fisheries, Institute of Coastal Research, Gamla Slipvägen 19, S-740 71 Öregrund, Sweden.
2Finnish
Game and Fisheries Research Institute, FIN-00151 Helsinki, Finland.
3Estonian
Marine Institute, 32 Lai street, EE-0001 Tallinn, Estonia.
4University
of Helsinki, Department of Limnology and Environmental Protection, FIN-00014
Helsinki, Finland.
5Latvian
Fisheries Research Institute, 6 Daugavgrivas street, LV-1007 Riga, Latvia
6Institute
of Ecology, Laboratory of Marine Ecology, Akademijos 2, LT-2600 Vilnius,Lithuania
Key words: perch, pikeperch, year-class strength, Baltic Sea.
Abstract.
Variations in year-class strength of perch (Perca fluviatilis
L.) and pikeperch (Stizostedion lucioperca L.) were analyzed among
11 different Baltic populations from Finland, Estonia, Latvia, Lithuania
and Sweden using age distributions and estimates of year-class strength
from catches. Similar patterns in year-class strength appeared in perch
populations from archipelago areas in the Northern Quark, Archipelago Sea
and along the Swedish coast of the Baltic proper. This could be attributed
to large-scale weather variations similarly influencing water temperature
in similar habitats. Perch year-class stregth variations in the coastal
waters of Estonia, Latvia and Lithuania also demonstrated a common pattern,
although not as abvious. In general, it was not possible to relate these
variations to temperature. The data fot this type of analysis was, however,
weaker than for other areas. Also pikeperch demontrated different patterns
in year-class strength variations between the Archipelago Sea and coastal
populations in Estonia, Latvia and Lithuania. Two populations in the Gulf
of Riga were similar in this respect but the one studied in Lithuania deviated.
The analyses made showed that there were similarities in year-class strength
variations of the two species in different parts and habitats of the coastal
areas that are important to understand and use when evaluating monitoring
data and making prognoses of stock size variations. It is suggested for
the future that joint analyses and prognoses be performed for this purpose.