Long-term effects of changes in goose grazing intensity on arrowgrass populations: A spatially explicit model
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Authors: C. P. H. Mulder, and R. W. Ruess
Date: 2001
Journal: Journal of Ecology
Volume: 89
Number: 3
Pages: 406-417
Summary of Methods: The effect goose grazing intensity on arrowgrass (Triglochin palustris) populations along the Tutakoke River on the Yukon–Kuskokwim Delta, Alaska (61° 5' N, 165° 30' W) was investigated using a cellular automata model. The results from previous studies by Mulder and associates were used. The vegetation grid underwent two transitions (within-year and between-year) during each time step of the model. Each model version was run 10 times at grazing intensities of 0, 10, 25, 50, 75 and 100% with geese (number was constant).
Article Summary / Main Points: Arrowgrass populations declined after 25 years and this decline was greatest at medium to high levels (50 to 100%) of grazing. At all grazing intensities arrowgrass populations decreased with increased aggregation if distributions of geese were patchy. To predict the long-term effects of goose grazing on arrowgrass, knowledge of both small and large-scale foraging behavior is required. When herbivory reduced plants below threshold level and grazing was suspended the ability of plant population to persist locally may be decreased.
Vegetation Types: Riparian and Wetlands
MLRA Ecoregions: 238 Yukon-Kuskokwin Coastal Plain
Agrovoc Control Words: Riparian zones Grazing Wildlife
Article Review Type: Refereed
Article Type: Experimental Research
Keywords: branta bernicla nigricans, cellular automata, foraging behavior, subarctic saltmarsh, triglochin palustris
Annotation: These results and the resulting model are applicable to salt marsh areas influenced by grazing intensity. The model can be used to predict long-term grazing effects on riparian or rangelands both at small-scale and on large scale. The findings from the previous the two studies by Mulder and Ruess in 1998 (Can J Bot, 76:2164-2176; Ecol Monograph 68:275-293; both available in the RSIS database) and the present study can be used as an approach for long-term grazing management studies and model application across the landscape.
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