A quantitative review of density-dependence in salmonids: biological mechanisms, methodological biases, and management implications

Thème:
Population Ecology
Quoi:
Talk
Quand:
vendredi 20 déc. 03:40 PM (15 minutes)
Où:
Salle AB
Discussion:
0

Understanding the complex variation in patterns of density-dependent growth and survival across populations is critical to adaptive fisheries management, but the extent to which this variation is caused by biological or methodological differences is unclear. Consequently, we conducted a meta-analysis of published literature to investigate the relative importance of methodological and biological predictors on the shape and strength of density-dependent growth and survival in salmonids. We obtained 160 effect sizes from 75 studies of 12 species conducted between 1977-2019 that differed in experimental approach (sensu Hurlbert, 1984; 65 laboratory experiments, 60 observational field studies, and 35 field experiments). The experimental approach was the strongest factor influencing the strength of density-dependence across studies: density-dependent survival was stronger than growth in field observational studies, whereas laboratory experiments detected stronger density-dependent growth than survival. The difference between density-dependent growth and survival was minimal in field experiments, and between lotic and lentic habitats. The shape of density-dependence (i.e. logarithmic, linear, exponential, density-independent) could be predicted with 33.7% error based solely on the experimental approach and the density gradient (highest/lowest *100) of the study. Overall, the strength and shape of density-dependence were primarily influenced by methodological predictors, while biological factors (predator presence, food abundance, and species) had predictable but modest effects. For both empirical studies and adaptive fisheries management, we recommend using field experiments with a density gradient of at least 440% to detect the proper shape of the density-dependent response, or by accounting for potential biases if observational or laboratory studies are conducted.

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