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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 Il faut se poser deux questions à grande échelle lorsqu'on planifie pour des forêts anciennes : quelle étendue faut-il prévoir pour elles? et où peuvent-elles subsister dans un paysage? Pour répondre à ces questions, la connaissance de la physionomie et de la dynamique des forêts anciennes au niveau du peuplement ne suffit pas. Nous faisons valoir que les régimes des perturbations de grande échelle pourraient représenter une clé importante pour la compréhension des patrons spatiotemporels de vieillissement qui déterminent le potentiel pour une forêt ancienne dans les paysages forestiers. Les approches de description de ces régimes vont de l'élaboration de scénarios en s'appuyant sur des données historiques jusqu'à la simulation de paysages à l'aide de modèles prédictifs. Dans cette communication, nous décrivons une approche de modélisation produisant des simulations du vieillissement d'un paysage pour en déterminer le patron de vieillissement et, donc, le potentiel de développement de forêts anciennes. Un modèle de simulation stochastique spatialement explicite de la dynamique du feu et du couvert forestier a été appliqué à un paysage d'étude de 1,8 million d'hectares dans la forêt boréale en vue de quantifier la variation spatiotemporelle du vieillissement. Vingt-...

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Ontario is a spatially heterogenous province. Natural resource policies and management plans must therefore address and account for this heterogeneity.An eco-regionalization scheme must possess certain minimum criteria to be effective. These criteria are: 1) an explicit explanation of spatial and temporal scales and variation; 2) a hierarchical construct of eco-regional domains; 3) an explicit quantitative description of the eco-regional domains; and, most importantly, 4) an ability to test a given eco-regional scheme as a hypothesis.This paper describes a hierarchical eco-regional framework (HEF) currently being constructed for Ontario. HEF is based on the scale-specific expression of ecological domain structure (geoclimatological parameters) and function (primary productivity). The approach relies on current advances in ecological hierarchy theory, remote sensing techniques, GIS methodologies, and statistical techniques. When completed, HEF will serve as a hypothesis which may be tested and validated at several different spatial scales.

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