A large‐scale approach can help detect general processes driving the dynamics of brown trout populations in extensive areas

• Published in: Ecology of Freshwater Fish
• Main Authors: Alonso, Carlos, García de Jalón, Diego, Álvarez, Javier, Gortázar, Javier
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2011
• Subjects: Ecology; Aquatic Science; Ecology, Evolution, Behavior and Systematics; North Atlantic; North Atlantic oscillation
• Online Access: http://dx.doi.org/10.1111/j.1600-0633.2011.00484.x; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1600-0633.2011.00484.x; https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1600-0633.2011.00484.x

Abstract – Most studies on the population dynamics of stream‐living salmonids have been conducted at the scale of a reach, a stream or a river basin. This can lead to overestimating the importance of local factors acting on a reduced scale compared to the more general factors that drive the dynamics of several populations. Two models were built on the basis of a data set from 60 sampling stations representing separated populations inhabiting a large heterogeneous area encompassing 18 years of quantifications. Our analyses showed the following: (i) Population growth rate (pgr) of a set of independent brown trout populations can be described by means of a single model; (ii) the youngest and the oldest year classes of these populations seem to be limited by the same constraints; (iii) there is a climatic control of the recruitment because of spring weather conditions, but also the abundance of oldest age class may be controlled by the climate, (iv) there is a nonlinear positive effect of winter North Atlantic Oscillation on pgr; (v) there is a 3‐year lagged positive feedback tracing the upward trend of a stock‐recruitment curve, and 1‐year lagged negative feedback showing the downward trend of the curve; (vi) a strong cohort has a positive effect on the whole population that can be detected throughout the time. Our fitted models allowed to predict the mean population densities at a regional scale with <10% error and shed light onto the main factors and associated ecological processes that control these large‐scale dynamics.