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Updated: Mar 1, 2026

Design and Use of an Apparatus for Quantifying Bivalve Suspension Feeding at Sea
Published on: September 5, 2018
The depletion of algal beds by geese: a predictive model and test
J M Rowcliffe1,2, A R Watkinson1, W J Sutherland1
1Schools of Biological and Environmental Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK.
Abstract:
The short-term impact of herbivores on plant productivity has been well studied. Demonstrating long-term effects of grazing on plant populations is much more difficult, but knowledge of such long-term effects is vital in understanding plant-herbivore interactions. We address this issue in a relatively simple plant herbivore system involving the dark-bellied brent goose Branta bernicla bernicla and two marine macroalgae, Enteromorpha spp. and Ulva lactuca, on which the geese graze. In 3 years of sampling, goose grazing was responsible for depleting between 23% and 60% of the algae in the autumn, while wave damage caused between 15% and 70% depletion. The degree of depletion in autumn had no effect on the biomass of algae present the following summer, suggesting no long-term consequences of grazing for the population dynamics of the algae. A model simulating the change in mean algal biomass over the autumn and winter, incorporating changes due to depletion by geese, wave action and productivity, successfully described the date at which geese abandoned the algal bed in six different years. These years varied in numbers of geese, strength and timing of storms and initial biomass available. The most important factor determining the date of abandonment of the algal bed was a tradeoff between the timing of storms and the numbers of geese. When storms occur early, most depletion is due to wave action and geese must abandon the bed early, regardless of the numbers grazing there. As the algae become depleted, the geese switch to feeding on saltmarsh, pastures or arable crops. The rate of depletion of algae affects the timing of this movement, and the model presented here enables the switch to be predicted.
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