The natural control of salmon and trout populations in streams

• Published in: Fisheries research Vol. 62; no. 2; pp. 111 - 125
• Main Authors: Milner, N.J., Elliott, J.M., Armstrong, J.D., Gardiner, R., Welton, J.S., Ladle, M.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2003; Elsevier
• Subjects: Animal, plant and microbial ecology; Applied ecology; Biological and medical sciences; Density-dependence; Density-independence; Exploitation and management of natural biological resources (hunting, fishing and exploited populations survey, etc.); Freshwater; Fundamental and applied biological sciences. Psychology; Management; Population dynamics; Salmo salar; Salmo trutta; Salmonidae; Stock–recruitment; Variation; British Isles; Density-independence; Salmo trutta; Stock–recruitment; Salmo salar; Population dynamics; Management; Variation; Density-dependence; Salmonidae; Population management; Mortality; Review; Freshwater environment; Vertebrata; Fishery; Pisces; Population recruitment; Stream; Density dependence; Population density; Stock; Stock-recruitment
• ISSN: 0165-7836; 1872-6763
• DOI: 10.1016/S0165-7836(02)00157-1

This paper reviews current understanding of factors controlling salmonid populations in streams and how this contributes to better fisheries management. Salmonid populations are regulated by density-dependent mortality, typically during the early stages of free-living life after fry emerge from spawning gravels. After the early regulatory phase, mortality is controlled mainly by density-independent factors. The relative contributions of density-dependent and density-independent factors to population variability are outlined, noting the special importance of environmental impacts such as flow and temperature extremes. Stock–recruitment relationships are discussed, with an emphasis on understanding the uncertainties and risks inherent in modelling wild populations. Key subjects for future research are identified. The challenge for science in the future lies in two areas: first, incorporating uncertainties into population modelling and management decision making, and second improving the understanding of processes regulating populations through long term studies.