Development of a large-scale juvenile density model to inform the assessment and management of Atlantic salmon (Salmo salar) populations in Scotland. (January 2019)

• Record Type: Journal Article
• Authors: Malcolm, Iain A.; Millidine, Karen J.; Glover, Ross S.; Jackson, Faye L.; Millar, Colin P.; Fryer, Robert J.
• Is Part Of: Ecological indicators. Volume 96(2019)Part 1
• Page Start: 303
• Page End: 316
• Publication Date: 2019-01
• Subjects: Capture probability -- Electrofishing -- Habitat -- Spatial statistical model -- GIS -- Juvenile assessment; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2018.09.005
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: Fish data from 25 organisations, 179 Catchments, 1861 sites, 3848 visits, 19 years. Landscape proxies for habitat derived from large-scale spatial datasets. Capture probability model harmonises complex and diverse dataset. Density model allows national scale predictions of juvenile abundance. Benchmark developed from density model provides basis for juvenile assessment. Abstract: Electrofishing data are commonly collected to assess the status of salmonid populations. However, their interpretation can be challenging without a benchmark measure of abundance against which they can be compared, leading some practitioners to question the value of these data. Juvenile density models that relate salmonid abundance to habitat characteristics offer one approach for developing spatially explicit benchmark abundances. This study collated and analysed an electrofishing dataset for Atlantic salmon ( Salmo salar ) collected across Scotland between 1997 and 2015. Habitat was characterised using landscape proxies, derived from large scale spatial datasets. A two stage modelling approach related (1) capture probability to landscape and other covariates (2) fish density to landscape and other covariates, having adjusted for differences in capture probability. Capture probability varied with monitoring organisation, year and region, responded modally to day of the year, and decreased with upstream catchment area, river distance to sea and gradient. Salmon density increased non-linearly with upstream catchment area and river distance to sea and decreased with the percentage of the riparian zone containing conifer trees. There was a south-north gradient in density, with higher densities in the north. The density model was used to develop benchmark salmon densities (reference conditions) that would be expected in river catchments that are relatively un-impacted by anthropogenic pressures and are associated with high spawner densities, resulting in near-saturation of available freshwater habitat. The benchmark densities were based on the fixed effects from the density model, excluding the effects of riparian conifer woodland and the south-north gradient, adjusted for the site-wise mean observed density in the dataset. Comparing catchment scale predictions of juvenile production against this benchmark (or some percentage of it) could provide a valuable assessment tool.