0559-C1

Implementing Active Adaptive Management in Alberta’s Boreal Forest

Matthew Carlson^[1], Stan Boutin, Mark Boyce, Steve Cumming, Elston Dzus, Dan Farr, Lee Foote, Werner Kurz, Fiona Schmiegelow, Richard Schneider, Brad Stelfox, Michael Sullivan and Shawn Wasel

Abstract

Rapid growth in oil and gas exploration and forestry in recent decades has brought into question the sustainability of land use practices in northeastern Alberta, Canada. While many current management strategies are criticized, solutions are notably absent. The Adaptive Management Experiment Team is implementing active adaptive management to improve understanding of land use, compare the outcomes of alternative land use strategies, and transfer acquired knowledge to decision-makers and managers. The team, which includes resource management scientists and managers from academic, industry, government and non-government organizations, has defined adaptive management as the systematic process of modelling, experimentation and monitoring to compare the outcomes of alternative management actions. Three key uncertainties that preclude prediction of the ecological effects of land use options have been identified: the ecological effects of linear features such as pipelines and roads; the relationship between older forest and biodiversity; and the effect of a changing landscape on fire behaviour. A number of field research projects have been initiated to reduce the uncertainties. Four landscape simulation tools integrate acquired knowledge, facilitating comparison of the ecological consequences of alternative management strategies. Scenario analyses conducted using the tools illustrate the capacity of integrated landscape management strategies to reduce forest disturbance, and the improved approximation of large-scale fire pattern achieved by aggregating timber harvest. Tight collaboration with a forestry company in the region provides capacity to influence policy and practice with research findings. Knowledge transfer initiatives, such as workshops attended by industry, are being used to motivate change in resource management strategies.

Introduction

Sustainable development, almost universally attractive in concept, has proven difficult to implement. Poor progress towards international commitments made at the 1992 Earth Summit is evidenced by a four percent decline in global forest cover over the past decade (United Nations 2002). Canada, considered a predominantly pristine landscape, is not immune to such issues. The western province of Alberta has lost 21% of its forest to permanent conversion, 80% of the remaining forest is accessed by roads (Smith and Lee 2000), and levels of human activity have brought into question the sustainability of land use practices (Schneider 2002). While many current management practices are thought to be ineffective, solutions are notably absent. Required is collaborative, directed, and long-term research to understand the effects of land use, compare the outcomes of alternative land use strategies, and transfer acquired knowledge to decision makers and managers. The Adaptive Management Experiment Team is implementing such a research program to foster improved land use in northeastern Alberta.

Northern Alberta, situated over part of the Western Canada Sedimentary Basin’s vast reserves of hydrocarbons, and covered by over 346,000 km² of boreal forest (Alberta Environmental Protection 1998), has experienced a rapid increase in energy exploration and forestry over the past 50 years (Figure 1). Sustainable management of the development requires an understanding of the likely ecological implications of land use options. However, predicting management outcomes is beyond the capacity of the traditional forest management research paradigm. Management questions typically involve numerous variables and large spatial and temporal scales. Research has focused on systems that are grossly simplified in comparison, because the scientific method works best when applied to small scales and few variables. The disjunct between research focus and the reality of management ensures little relevancy of results to managers, and reliance on academic journals for knowledge transfer ensures poor dissemination of results to managers.

Figure 1: Growth in Alberta’s forestry and energy sectors, as demonstrated by temporal trends in timber harvest volume (m³) and number of operated natural gas, conventional oil, and bitumen wells. Source: Stelfox and Wynes 1999, CAPP 2000.

Over 20 years ago, adaptive management was introduced as a solution to the failure of research to reduce resource management uncertainties (Walters and Hilborn 1976). Under the approach, management and science merge as scientists and managers work together to experimentally manage ecosystems. The integration of science and management focuses research on important management questions, facilitates large scale experiments, and fosters effective knowledge transfer. Despite its popularity and apparent simplicity, adaptive management has rarely been successfully implemented (Walters 1997). Instead adaptive management has become synonymous with trial and error management that is poorly equipped to cope with uncertainty. Required is active adaptive management, whereby multiple alternative management policies are compared using simulation tools and field experiments, a practice that greatly increases knowledge acquisition rates (Walters and Holling 1990).

The Adaptive Management Experiment (AME) Team, composed of resource scientists and managers from academic, industry, government, and non-government organizations, is applying active adaptive management to identify land use strategies that balance human activity with ecological integrity in northeastern Alberta. We have defined adaptive management as the systematic process of modeling, experimentation, and monitoring to compare the outcomes of alternative management actions, explicitly identifying analysis of multiple policies as part of the approach. Human actions being investigated include energy exploration, forestry, transportation, fish and wildlife harvest, and fire suppression. Options to improve sustainability include altering the amount, intensity, and spatial and temporal distribution of human activity. Various levels and combinations of these general strategies will be evaluated using landscape, ecological, and resource indicators (Table 1). The project is large-scale (>100,000 km²) and expected to last decades. Over the past three years, the AME Team has committed substantial intellectual and financial resources to set up an active adaptive management framework, and this paper describes our approach. Early results are presented, in the form of simulation studies that compare management strategies, and challenges inherent with adaptive management are discussed.

Table 1: Indicators being used to compare outcomes of land use strategies.

Methods

Figure 2 portrays the adaptive management process as consisting of six successive results linked by seven tasks. Our methodology is now explained by describing activities related to each task.

Figure 2: The adaptive management process being implemented by the AME Team. The rectangles are results, and each numbered box indicates the task required to progress towards the results. The tasks are: assimilation of existing knowledge into models (1a); identification of key uncertainties (1b); research to reduce uncertainty (2); integration of research results into models (3); policy analysis using models (4); knowledge transfer (5); identification of new uncertainties (6); and monitoring (7).

Evaluating the effects of management scenarios requires specification of relationships linking actions to indicators. If relationships are understood but not included in simulation tools, model development proceeds (Figure 2, task 1a). If, on the other hand, relationships are uncertain they must be prioritized for research (Figure 2, task 1b). We have identified three key uncertainties: the ecological effects of linear features; the relationship between older forest and biodiversity; and the effect of a changing landscape on fire behaviour. Increase in linear features and reduction in older forest are the most striking landscape changes associated with northeastern Alberta land use. Linear features such as roads, seismic lines, and pipelines are increasing due to their association with forestry and energy exploration. Older forest will decline due to Alberta government policy directing harvesting of older forest first (Timoney and Lee 2001). The effects of these changes on wildlife, including habitat loss and fragmentation and increased human access, are poorly understood. How landscape changes influence fire is also uncertain, and of importance given the capacity of fire to influence ecological and resource indicators.

We are involved in a number of projects to reduce the key uncertainties (Figure 2, task 2). Bird and small mammal communities are being sampled on numerous townships (100 km²) selected to cover a spectrum of linear feature, older forest, and forestry activity amounts. The degree to which road waterway crossings inhibit fish migration is being quantified for species such as northern pike (Esox lucius) and arctic grayling (Thymallus arcticus). Human access and hunting are being manipulated to investigate their effects on black bear (Ursus americanus). Remotely sensed data and field observations are quantifying the response of riparian vegetation to linear features. A modeling project will evaluate how linear features affect fire ignition and spread, and field observations will determine the effect of fire on linear feature reclamation. These preliminary studies will provide data required to design efficient, large-scale management experiments expected to be initiated in 2005. Through collaboration with industry, the experiments will manipulate human access, timber harvest, and linear feature density to discern effects to boreal fauna.

Results from current field studies and future large-scale management experiments will be incorporated into four simulation tools (Figure 2, task 3): TELSA (Kurz et al. 2000), TARDIS, FEENIX (Cumming et al. 1998), and ALCES (Stelfox and Martin 2002). These models act as the interface between research and management by allowing resource managers to use current knowledge to explore the consequences of alternative management actions. The models are currently able to simulate a subset of the indicators and have been applied (Figure 2, task 4) to estimate disturbance reductions achievable through integrated landscape management, and to evaluate strategies to approximate landscape fire pattern through aggregation of timber harvest.

The primary goal of the AME Team is to use applied research to influence policy and practice (Figure 2, task 5). Useful in this respect are workshops, where simulation tools and other research findings are used by scientists and managers to identify solutions to specific management questions. Workshops attended by managers from energy and forestry companies have been held to discuss three management issues: cumulative disturbance of forestry and energy development, management of fish in industrial landscapes, and landscape scale approximation of fire through timber harvest.

As portrayed in Figure 2, tasks 1-5 are highly iterative as each step invariably identifies new uncertainties (task 6). In addition, long-term landscape scale monitoring of biodiversity (task 7) provides a feedback loop to reinitiate the process. Monitoring allows managers to remain vigilant for unintended management outcomes, identifying new issues to be investigated. The AME Team is collaborating in the development of the Alberta Forest Biodiversity Monitoring Program, a province wide monitoring initiative to track large-scale trends in forest taxa.

Results

Results from field studies are not yet available. Instead, results from two scenario analyses are presented. The study area for the projects was L1, a 278,000 ha Forest Management Unit (FMU) in Alberta-Pacific Forest Industries’ 5,800,000 ha Forest Management Agreement (FMA) area in northeastern Alberta. One project focused on integrated landscape management as a strategy to reduce land use intensity, and the other on aggregated timber harvest as a strategy to approximate landscape scale fire pattern.

Integrated landscape management (ILM) strategies decrease land use intensity by reducing the size and increasing the reclamation rate of disturbances, and integrating infrastructure. Eight ILM strategies (Table 2) were simulated for 50 years and compared to projections of current practices. The strategies, all of which were identified through consultation with industry, achieved substantial reductions in linear feature density (Figure 3) and, due to reduced forest conversion, conserved 1.7 million tones of biotic carbon compared to current practices.

Table 2: Land use strategies simulated to evaluate benefits of integrated landscape management.

Figure 3: 50-year linear feature density projection for current practice and integrated landscape management scenarios. Simulations completed using ALCES.

The Natural Disturbance Model for timber harvest seeks to minimize risk to forest ecosystems by approximating forest pattern created by fire. Between 1961 and 2000, 98% of the area burned in Alberta was due to just 5% of the fires (Schneider 2002), creating large areas of same-aged forest interspersed with patches of older, residual forest. Current timber harvest strategy disperses cut blocks across the landscape, causing excessive fragmentation of similar-aged forest compared to fire. In comparison, aggregating cut blocks should better approximate fire. To evaluate the concept, 240 year projections of dispersed and aggregated harvest were compared in terms of timber supply and forest demography. The dispersed harvest scenario attempted to harvest 200,000 m³ of timber annually. The aggregated scenario harvested the 80 year timber supply from L1 (16,000,000 m³) in 20 years, followed by 60 years of no harvest during which it was assumed that timber supply was provided by other FMU’s within the FMA area. After the 60 years, a subsequent 20 year harvest period occurred and so on. The maximum cut block size in the aggregated scenario was increased from 200 to 10,000 ha, and 7% of the merchantable forest area was not harvested to emulate fire skips. While both strategies achieved the timber supply goal, projections of the average age of merchantable forest highlights the difference between the strategies (Figure 4). Forest age was relatively constant under dispersed harvest but fluctuated dramatically under aggregated harvest. Temporal variability in forest age at large scales is the pattern expected from fire, and implies maintenance of large aggregations of older forest on the FMA area at any given time.

Figure 4: Projected trend in average age of merchantable forest for dispersed harvest and aggregated harvest scenarios. Simulations completed using FEENIX.

Discussion

The simplicity of the adaptive management idea belittles the effort required to successfully implement the concept. As noted by Rogers (1998), scientists and managers have divergent operational philosophies. Adaptive management, however, requires tight collaboration at all steps, from identification of key uncertainties, to communication and implementation of lessons learned. Insufficient collaboration at any step sacrifices the entire process.

Recognizing the importance of cooperation, we have dedicated substantial effort to building relationships and transferring knowledge amongst our diverse membership. A business plan identifies the team’s mission, "To conduct innovative research to improve the compatibility of human actions with ecological integrity of the boreal forest", and specifies six goals, including influence policy and practice, implement active adaptive management, and transfer scientific knowledge. The business plan ensures all participants share common expectations, and is indicative of the sacrifices and commitments demanded by active adaptive management. Adaptive management requires scientists to step beyond the academic realm and allocate effort to tasks beyond research. Five of the six goals are not directly related to research and, accordingly, much of our effort is spent on non-research tasks such as knowledge transfer. Managers must be committed to developing and implementing innovative approaches to maintain the full spectrum of values associated with the forest. This commitment is expressed in our mission, and by the range of ecological and economic indicators we are using to compare management outcomes. Managers must also be willing to implement experimental strategies that, while perhaps contrary to present management paradigms, represent the best opportunity to accumulate knowledge required to sustain forest values. The goal of implementing active adaptive management expresses the commitment to experiment, and the goal of influencing policy and practice states intention to act on lessons learned.

The ILM and aggregated harvest studies illustrate the valuable insights achieved through the large spatial and temporal perspective that simulation tools provide. To achieve our primary goal, the insights must be used to influence policy and practice. We have learned that, in the context of achieving change, knowledge is of little value without buy-in from stakeholders. Knowledge transfer activities, especially workshops, are therefore of utmost importance. Workshops can achieve broad-based support for alternative land use strategies because managers are involved in identifying the solutions.

Proposed solutions that conflict with the current management paradigm will likely face initial opposition. The ILM scenario analysis indicated that while best practices reduce ecological disturbance, maintenance of ecological integrity also requires reduction in the amount of industrial activity. However, the concept of finite resource supply and the need for human activity thresholds has received resistance. Radical land use strategies, such as aggregation of industrial activity, have also been contested, despite demonstrated benefits over current practice. Recognizing the difficulty of changing deeply held resource management ideologies, we are also participating in an initiative designed to educate tomorrow’s stakeholders. The Provincial Museum of Alberta is developing an interactive exhibit that demonstrates how humans impact Alberta’s natural systems, and the need to consider creative land use solutions. AME Team results, likely in the form of a landscape simulation tool, will be included in the museum to allow students to visualize future implications of land use decisions.

Conclusion

Slow progress towards sustainable development is perhaps of no great surprise given the complexity of the ecological and social systems involved. Knowledge required to implement sustainable development should not be underestimated. Indeed, given the variability of nature, understanding must continually evolve to meet new challenges. Active adaptive management is perfectly suited to obtaining, and perpetually updating, resource management knowledge. We must recognize that adaptive management is fundamentally different than Newtonian science and rigid management. Implementing adaptive management’s flexible systems approach requires new perspective and patience. The AME Team is committed to applying adaptive management to northeastern Alberta’s forests, and confident that our efforts will promote the learning and change so desperately needed.

Literature Cited

Alberta Environmental Protection. 1998. The Boreal Forest Natural Region of Alberta. Alberta Environmental Protection, Edmonton, AB, 313 p.

Canadian Association of Petroleum Producers (CAPP). 2000. Statistical Handbook for Canada’s Upstream Petroleum Producers. Canadian Association of Petroleum Produces, Calgary, Alberta.

Cumming, S., D. Demarchi, and C. Walters. 1998. A grid-based spatial model of forest dynamics applied to the boreal mixedwood region. Sustainable Forest Management Network Working Paper 1998-8. 30 p. Available from the Internet. URL: http://sfm-1.biology.ualberta.ca/english/pubs/PDF/WP_1998-08.pdf.

Kurz, W.A., S.J. Beukema, W. Klenner, J.A. Greenough, D.C.E. Robinson, A.D. Sharpe, and T.M. Webb. 2000. TELSA: the Tool for Exploratory Landscape Scenario Analyses. Computers and Electronics in Agriculture 27: 227-242.

Rogers K. 1998. Managing science/management partnerships: a challenge of adaptive management. Conservation Ecology [online] 2(2): R1. Available from the internet. URL: http://www.consecol.org/vol2/iss2/resp1.

Schneider, R. 2002. Alternative Futures: Alberta’s Forest at the Crossroads. Federation of Alberta Naturalists, Edmonton, AB, 152 p.

Smith, Wynet and Peter Lee (editors). 2000. Canada’s Forests at a Crossroads: an Assessment in the Year 2000. World Resources Institute, Washington, DC, 114 p.

Stelfox, B. and M. Martin. 2002. ALCES technical manual. Forem Technologies, Alberta. Available from the Internet. URL: http://www.foremtech.com/atm/index.htm.

Stelfox, J. B., and B. Wynes.1999. A physical, biological, and land-use synopsis of the boreal forest's natural regions of northwest Alberta. Daishowa-Marubeni International Ltd., Peace River, AB.

Timoney, Kevin and Peter Lee. 2001. Environmental management in resource-rich Alberta, Canada: first world jurisdiction, third world analogue? Journal of Environmental Management. 63: 387-405.

United Nations. 2002. Press Summary of the Secretary-General’s Report on Implementing Agenda 21. United Nations Department of Public Information, 4 p. Available from the Internet. URL: http://www.johannesburgsummit.org/html/media_info/pressreleases_factsheets/press_summary_sg_report2801.pdf.

Walters, C.J. and R. Hilborn. 1976. Adaptive control of fishing systems. Journal of the Fisheries Research Board of Canada 33:145-159.

Walters, C.J.. 1997. Challenges in adaptive mangement of riparian and coastal ecosystems. Conservation Ecology [online] 1(2):1. Available from the Internet. URL: http://www.consecol.org/vol1/iss2/art1.

Walters, C.J. and C.S. Holling. 1990. Large-scale management experiments and learning by doing. Ecology 71(6):2060-2068.