Using stand-scale forest models for estimating indicators of sustainable forest management

Abstract

Criteria and indicators (C & I) to evaluate the sustainability of forest management have been proposed by the Ministerial Conference on the Protection of Forests in Europe. Although primarily defined at the national scale, these C & I also have implications at scales ranging from forest stands to the forest management unit. In this paper, we review existing forest growth and ecosystem models from the point of view of applicability to prediction of indicators of sustainable management, focusing on stand scale models and management. To do this, we first present a conceptual framework for understanding the role of models in assessing forest management at the stand level in the context of sustainability criteria and indicators. We classify the criteria into those predictable using models operating at the stand scale, and those derivable either through scaling up or as solutions of a multi-objective management optimisation problem.

We conclude that to date, no comprehensive models exist that could be used to predict all the indicators simultaneously. The most promising approach seems to be a modular system where different models are combined and run simultaneously, with shared inputs and well defined mutual links. More modelling efforts are needed especially regarding the state of the soil, including carbon, nitrogen and water balances and physical effects. Models also need development in their ability to deal with heterogeneous stand structures and with non-woody forest products such as berries, mushrooms or cork. The outputs of the models need to be developed in a direction where they can be interpreted in terms of the recreational or biodiversity value of the forest.

Data requirements are most pronounced on the same issues as the gaps in model availability. It would be important to consider amending the national forest inventories and other similar standard data collection protocols with variables required for sustainability assessment. Importantly, combining different models in a modular system and with variable data sources requires advanced model parameterisation and evaluation methods and assessment of parameter and model uncertainty. The probabilistic, Bayesian approaches hold a lot of promise in this respect. Predictions using several different models or model systems, with systematic analysis of e.g. inter-model variability, could also be considered.

Introduction

Since the end of the last century the concept of sustainability has become an important focus of forest management (FM). Sustainable forest management refers to the management of forests according to the principles of sustainable development, which integrates social, economic and environmental goals in a manner characterised by the Rio Declaration on Environment and Development and, more specifically, the Statement of Principles for the Sustainable Management of Forests.¹ Following the Rio declaration, sustainable development was subsequently applied to forest management in Europe by the Ministerial Conference on the Protection of Forests in Europe (MCPFE), who defined sustainable forest management (SFM)² and further a series of criteria and indicators (C & I) as tools to evaluate the sustainability of forest management (Appendix A). Around this time, analogous initiatives dealing with SFM and C & I began simultaneously in non-European countries (the Montreal Process, the Tarapoto process, etc.).

In the MCPFE approach, the criteria define and characterise the essential elements, as well as a set of conditions or processes, by which the sustainability of forest management may be assessed. The indicators are quantitative or qualitative variables that measure aspects of the criteria and are meant to be evaluated periodically to reveal the direction of change with respect to each criterion. While these definitions outline the type of issues that are relevant for sustainable management, subsequent developments have taken the concepts further. Lammerts van Bueren and Blom (1997) developed a hierarchical approach to the analysis and definition of forest management standards under the sustainability framework, separating underlying principles from the more detailed and case-specific criteria (principles, criteria and indicators, PCI). These guidelines have since been used for defining sustainable management for different conditions and scales, including country-level principles (Prabhu et al., 1999) and more detailed, operational certification schemes at the stand and forest management unit (FMU) level (PECF Council, 2010).

The PCI approach is meant for ex post assessment and is very practical in the sense that the indicators directly combine the physical state of the system and the methods of management of the system. This is somewhat different from the ex ante approach to management planning by means of forest growth modelling, where the state of the system is conceptually separated from the management methods. The model provides a prediction of how the state of the system will develop in time, given any set of management actions. If criteria are set for the desired/acceptable development of the state of the system, the model may then be used for assessing which management methods comply with the set objectives. Obviously, this restricts the use of models in sustainability assessment to questions where it is relevant to compare the implications of different management methods on the state of the system. If the methods themselves are judged unsustainable, models become redundant. For example, one might use a model to analyse whether continuous-cover forestry differs from even-aged forestry in terms of wood production, carbon sequestration, nutrient and water retention, etc., but this will be of no use if the up-front objective has already been defined as the avoidance of clearcuts.

Because the PCI approach combines the state of the system and the management methods, the scale of the analysis is critical, as management methods cannot be defined irrespective of scale (although similar definitions of SFM may exist at different spatial scales (Lammerts van Bueren and Blom, 1997)). While stand scale management alternatives cover basic silvicultural decisions, country scale methods include forest policies put to effect through legislation, subsidies and other policy instruments. As noted above, sustainable management has therefore been defined separately for different scales. From the point of view of modelling the state of the system, however, the scale is of less significance, as the physical state can – at least in principle – be scaled up and down between stand, FMU and country. What is more critical is the ability of the model to describe the processes relevant for the criteria of sustainability.

In forest management planning, stand-scale forest growth models are conventional tools that might be applied to individual stands and FMUs or to larger forest areas including country-level (Weiskittel et al., 2011). Until now, such models have mainly been developed for predicting wood production under different management regimes and in different sites. The requirements of sustainable forest management have set an increasing demand for models to expand their predictions to a variety of ecosystem services and to evaluate trade-offs between them. In order to do this, the models must include variables that allow us to assess the quality of the ecosystem services. Sustainability C & I offer a comprehensive definition and operational quantification for such variables. While the general set-up of forest growth models as a tool for management planning is still valid in the context of sustainable forest management (Monserud, 2003), it requires some important developments in both the outputs of the models, including variables relevant for sustainability assessment, and in the methods of evaluating the management operations, accounting not only for the economic returns of wood production, but for the multitude of criteria defining sustainability.

The latter problem has already received much attention in the scientific literature. Multicriteria optimisation methods have been proposed and developed as tools in SFM to account for the various, possibly competing goals defining sustainability (Monserud et al., 2003, Díaz-Balteiro and Romero, 2008, Kangas et al., 2008). Stakeholder involvement and participatory methods have become focal for defining and balancing the different objectives (Prabhu et al., 1999, Pukkala, 2002, Kangas et al., 2008, Nordström et al., 2010). However, these methodological developments have largely operated on the assumption that the relationship between management and indicators of sustainability is well understood, and less attention has been paid to the actual derivation of the indicators from the state of the stand. Brang et al. (2001) pointed out that the choice of indicators is often driven by data availability rather than theory, that the connection between indicators and the state of the stand is not explicit, and that important causal links between the indicators have not been appreciated.

Several studies have reviewed different forest and ecosystem models from the point of view of their usefulness for assessing SFM. Peng (2000) compared three models based on different approaches (empirical, succession and process models) for predicting future forest stocks under different management options, combined with the potential effects of climate change and fire disturbances. Monserud (2003) reviewed the expected utility of different classes of forest growth models for assessing the sustainability of alternative forest management regimes. Pretzsch et al. (2008) discussed the role of models in the societal process of decision-making about natural resources, providing a broad review of the significance of different types of model in the assessment and design of SFM. The general conclusion from these studies is that a wide suite of models would be required in order to analyse not only growth and yield but also the different aspects of ecosystem functioning and societal value that play a role in the sustainability criteria and indicators. However, an explicit derivation of indicators from dynamic growth models in the SFM context is rare (but see Huth et al., 2005, Azevedo et al., 2005).

Although they analyse the type of information required for sustainability assessment, most of the above-mentioned studies remain fairly abstract and conceptual on the question, “How do models provide information to assess SFM?” Assuming that sustainability indicators are an adequate tool to evaluate SFM, this translates into, “How do models provide information to estimate indicators?” Further important questions for the application of such models are, “What data are needed?”, and “How can we evaluate this aspect of growth models?”

In this review, we first present a conceptual framework for understanding the role of models in assessing forest management at the stand level in the context of sustainability criteria and indicators. We have chosen the MCPFE indicators as a basis because they are well-known national level C & I. They are regarded here as a reference standard to illustrate the potential of models to simulate sustainability indicators. We focus on criteria describing the physical state of the system and directly relevant for models operating at the stand scale. The remaining criteria can be seen as derivable either through scaling up the stand-scale results, or as solutions of a multi-objective management optimisation problem, where the alternative management actions need to be defined separately for each scale and forest type. We will then review models that can be used to predict the different indicators, and thereby try to extract the key model-related components and variables required to assess SFM goals. We will consider current data sources for such models and how they might be augmented to improve the efficacy of modelling in an SFM context. In this light, we assess different data collecting protocols, such as national forest inventories (NFI) and permanent sample plots (PSPs), and review possible problems related to the evaluation of models using such data sources.