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Predicting the potential for old-growth forests by spatial simulation of landscape ageing patterns
Author(s) -
Ajith H. Perera,
David J. B. Baldwin,
Dennis Yemshanov,
Frank Schnekenburger,
Kevin Weaver,
Den Boychuk
Publication year - 2003
Publication title -
the forestry chronicle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 49
eISSN - 1499-9315
pISSN - 0015-7546
DOI - 10.5558/tfc79621-3
Subject(s) - forest dynamics , taiga , disturbance (geology) , old growth forest , geography , landscape ecology , ecology , boreal , physiognomy , spatial ecology , range (aeronautics) , scale (ratio) , environmental resource management , environmental science , habitat , cartography , forestry , biology , paleontology , physics , materials science , astronomy , composite material
Planning for old-growth forests requires answers to two large-scale questions: How much old-growth forest should exist? And where can they be sustained in a landscape? Stand-level knowledge of old-growth physiognomy and dynamics are not sufficient to answer these questions. We assert that large-scale disturbance regimes may provide a strong foundation to understand the spatio-temporal ageing patterns in forest landscapes that determine the potential for old growth. Approaches to describe large-scale disturbance regimes range from scenarios reconstructed from historical evidence to simulation of landscapes using predictive models. In this paper, we describe a simulation modelling approach to determine landscape-ageing patterns, and thereby the landscape potential of old-growth forests. A spatially explicit stochastic simulation model of landscape fire–forest cover dynamics was applied to a 1.8 million-ha case study boreal forest landscape to quantify the spatio-temporal variation of landscape ageing. Twenty-five replicates of 200-year simulation runs of the fire disturbance regime, at a 1-ha resolution, generated a suite of variables of landscape ageing and their error estimates. These included temporal variation of older age cohorts over 200 years, survivorship distribution at the 200 th year, and spatial tendencies of ageing. This information, in combination with spatial tendency of species occurrence, constitutes the contextual framework to plan how much old-growth forest a given landscape can sustain, and where such forest could be located. Key words: landscape management, old growth, spatial simulation modelling, landscape ecology, boreal forest, Ontario, fire regime simulation, natural forest disturbances, stochastic models, age-class distribution

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