Old growth is disappearing globally with implications for biodiversity, forest resilience and carbon storage; yet uncertainty remains about how much exists, partly because assessments stratify ecosystems differently, sometimes obscuring relevant patterns. This paper compares portrayals of BC’s old growth forest stratified in two ways: by biogeoclimatic variant, as per policy, and by relative site productivity. Our analyses confirm provincial government claims that about a quarter of BC’s forests are old growth, but find that most of this area has low realized productivity, including subalpine and bog forests, and that less than 1% is highly productive old growth, growing large trees. Within biogeoclimatic variant, nearly half of high productivity forest landscapes have less than 1% of the expected area of old forest. Low productivity ecosystems are over-represented in protected forest. We suggest that the experiment of managing old growth solely by biogeoclimatic variant has failed, and that current forest policy, in combination with timber harvesting priorities, does not maintain representative ecosystems, counter to the intent of both policy and international conventions. Stratifying old growth by relative productivity within biogeoclimatic variant seems an appropriate method to portray ecosystem representation, potentially increasing the probability of maintaining ecosystem resilience.
For landscape models to be applied successfully in management situations, models must address appropriate questions, include relevant processes and interactions, be perceived as credible and involve people affected by decisions. We propose a framework for collaborative model building that can address these issues, and has its roots in adaptive management, computer-supported collaborative work and landscape ecology. Models built through this framework integrate a variety of information sources, address relevant questions, and are customized for the particular landscape and policy environment under study. Participants are involved in the process from the start, and because their input is incorporated, they feel ownership of the resulting models, increasing the chance of model acceptance and application. There are two requirements for success: a tool that supports rapid model prototyping and modification, that makes a clear link between a conceptual and implemented model, and that has the ability to implement a wide range of model types; and a core team with skills in communication, research and analysis, and knowledge of ecology and forestry in addition to modelling. SELES (Spatially Transactions in GIS, 2001, 5(1): 67±86 ß 2001 Blackwell Publishers,Explicit Landscape Event Simulator) is a tool for building and running models of landscape dynamics. It combines discrete event simulation with a spatial database and a relatively simple modelling language to allow rapid development of landscape simulations, and provides a high-level means of specifying complex model behaviours ranging from management actions to natural disturbance and succession. We have applied our framework in several forest modelling projects in British Columbia, Canada. We have found that this framework increases the interest by local experts and decision-makers to participate actively in the model building process. The workshop process and resulting models have efficiently provided insight into the dynamics of large landscapes over long time frames. The use of SELES has facilitated this process by providing a flexible, transparent environment in which models can be rapidly implemented and refined. As a result, model findings may be more readily incorporated into decision-support systems designed to assist resource managers in making informed decisions.
Resource managers, planners, and the public are unified in their calls for monitoring of land-use plans. Unfortunately, many monitoring initiatives fall short of their potential for several reasons: indicators are not explicitly linked to objectives, hindering feedback to planning; knowledge is not represented in a manner that facilitates learning; and monitoring priorities are driven subjectively. We describe a framework that links indicators to existing objectives, presenting knowledge as hypotheses about the probability of achieving an objective as a function of various indicator levels. Uncertainty is explicitly included in the models. The framework can be used for management decision support and to prioritize objectives for implementation, effectiveness, and validation monitoring, and research. Monitoring priority is determined first by probability of success and uncertainty and then by the importance of an objective. We present a case study for the Babine Watershed, an area in the interior of British Columbia with high resource values and decades of controversy and ineffective monitoring. The framework sifted through existing objectives to focus effort on those most critical to monitor. By concentrating on publicly derived, regionally applicable objectives and strategies taken from existing land-use plans, the framework provided relevant results and enabled rapid feedback.
British Columbia’s (BC) diverse forest ecosystems include highly productive old growth with global importance for carbon storage and biodiversity. Current estimates of the remaining amount of “big-treed” old growth vary 10-fold, creating uncertainty that challenges provincial attempts to shift management policy toward ecological integrity. This uncertainty arises from using different remotely sensed indicators and definitions of tree size. No ideal indicator exists. We attempt to improve clarity by evaluating the reliability of candidate indicators, calibrating selected indicators to improve consistency, and generating multiple estimates of the amount of big-treed old growth using calibrated indicators. To evaluate reliability, we compared inventory estimates of tree size and site productivity with measured tree size in 1,945 ground plots. To assess the amount of big-treed old growth, we determined an equivalent “big” size threshold for each indicator and calculated the area of old growth above the size threshold. Stand volume, tree density, basal area, and diameter estimates performed poorly; we selected tree height and two measures of site productivity for further analysis. Estimated tree height best indicated the current old growth size, followed by inventory-based site index and finally ecosystem-based site index. The calibrated indicators agreed that very little remaining old growth supports large trees (1.5–3.3% for the biggest trees; 6–13% including medium-sized trees that represent the largest growing trees in lower productivity interior ecosystems). Tree height cannot be used to compare the remaining area of big-treed old growth to the area expected naturally, an important input for ecological risk assessment and conservation planning because height data are lost from the inventory after harvest. The two calibrated site productivity indicators agreed that the amount remaining is less than 30% of the expected historical amount, posing a high risk to biodiversity and resilience. We recommend using estimated height to identify the biggest remaining old-growth stands for regional planning and calibrated inventory-based site index for risk assessment until a detailed ecosystem mapping has been verified to represent old-growth variability. To reduce uncertainty, we suggest that planning groups compare several indicators and analysis approaches, adjusted to ensure equivalence, and use precaution to avoid any unknowingly increasing risks.
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