Effects of altered river network connectivity on the distribution of Salmo trutta: Insights from a metapopulation model. (12th August 2019)

• Record Type: Journal Article
• Authors: González‐Ferreras, Alexia M.; Bertuzzo, Enrico; Barquín, José; Carraro, Luca; Alonso, Carlos; Rinaldo, Andrea
• Is Part Of: Freshwater biology. Volume 64:Number 11(2019)
• Page Start: 1877
• Page End: 1895
• Publication Date: 2019-08-12
• Subjects: age‐structured models -- continuous metapopulation model -- discrete metapopulation model -- longitudinal barriers -- salmonids; Freshwater biology -- Periodicals; Biologie d'eau douce -- Périodiques; 577.605
• DOI: 10.1111/fwb.13379
• Languages: English
• ISSNs: 0046-5070

Abstract

Abstract: Network connectivity is a key feature of rivers that affects patterns and processes in lotic ecosystems. Few studies have considered how changes in river reach connectivity might affect ecosystem attributes at a whole river network scale. The use of population dynamics models of keystone species at a river network scale allows exploration of how the effects of altered natural connectivity patterns might propagate through a river network. In this study, we present a metapopulation model to estimate the spatial distribution of the population density of brown trout ( Salmo trutta ), an ecologically and socioeconomically important top predator, in a river network in northern Spain. The model accounts for the presence of barriers that limit longitudinal connectivity in upstream and downstream directions. The model estimates the spatial distribution of densities of three age‐classes (young‐of‐the‐year, juveniles, and adults) in all river reaches that make up the network based on topology, connectivity and population dynamics (e.g. age‐class specific mortality, spawning, age‐class dispersal, and spawning migration patterns). Seventy‐five percent of the modelled population densities fell within the 95% confidence intervals of the empirical data (84.6% for young‐of‐the‐year, 69.2% for juveniles, and 69.2% for adults) collected in 13 reaches. The simulated removal of all longitudinal barriers to migration in the river network (re‐naturalisation of the whole catchment) produced a modelled increase in brown trout density in the most downstream reaches of the river network and lowered fish densities in the upstream portion of the network when bias in juvenile and adult movement direction was assumed. Furthermore, the simulated removal of a single obstacle affected fish density even in distant tributaries. The proposed model is an appropriate tool for the evaluation of spatial patterns of brown trout density at a river network scale and for the assessment of the impact of altered connectivity. This might help simulate the results of management strategies regarding river connectivity and show where population decreases or increases could be expected, although empirical knowledge of overall trout movement in the studied river networks is required for drawing realistic scenarios.