In many forest landscapes a desirable management objective is the sustainability of both economic productivity and healthy wildlife populations. Such dual-objective management requires a good understanding of the interactions between the many components and actors at several scales and across large extents. Computer simulation models have been enthusiastically developed by scientists to improve knowledge about the dynamics of forest growth and disturbance (for example by timber harvest or wildfire).

However, Papaik, Sturtevant and Messier write in their recent guest editorial for Ecology and Society that “models are constrained by persistent boundaries between scientific disciplines, and by the scale-specific processes for which they were created”. Consequently, they suggest that:

“A more integrated and flexible modeling framework is required, one that guides the selection of which processes to model, defines the scales at which they are relevant, and carefully integrates them into a cohesive whole”.

This new framework is illustrated by the papers in the Ecology and Society special feature ‘Crossing Scales and Disciplines to Achieve Forest Sustainability: A Framework for Effective Integrated Modeling’.

The papers in the special feature provide case studies that reflect two interacting themes:

1. interdisciplinary approaches for sustainable forest landscape management, and
2. the importance of scaling issues when integrating socioeconomic and ecological processes in the modeling of managed forest ecosystems.

These issues are well related to the project I’m currently working on that is developing an integrated ecological-economic model of a managed forest landscape in Michigan’s Upper Peninsula. One paper that caught my eye was by Sturtevant et al., entitled ‘A Toolkit Modeling Approach for Sustainable Forest Management Planning: Achieving Balance between Science and Local Needs’.

Sturtevant et al. suggest that forest managers are generally faced with a “devil’s choice” between using generic ‘off-the-shelf models’ where information flows primarily from researchers and planners down to local communities versus developing case-specific models designed for a specific purpose or locale and based on information from the local actors. To avoid this choice, which Sturtevant et al. believe will seldom result in a satisfactory management result, they outline their proposal for a hybrid ‘toolkit’ approach. Their alternative approach “builds on existing and readily adaptable modeling ‘tools’ that have been developed and applied to previous research and planning initiatives”.

Their toolkit approach is

1. collaborative – including stakeholders and decision-makers
2. a ‘meta-modelling’ approach – the model is derived from other models and tools.

They then illustrate their toolkit approach using a case study from Labrador, Canada, highlighting the stages of establishing the issues, developing a conceptual model, implementing the meta-model, and then refining the model iteratively. They conclude:

“A toolkit approach to SFM [Sustainable Forest Management] analytical support is more about perspectives on information flow than on technical details. Certainly expertise and enabling technology are required to allow a team to apply such a framework. However, the essence of this approach is to seek balance between top-down (off the shelf, science-driven) and bottom-up (case-specific, stakeholder-driven) approaches to SFM decision support. We aim to find a pivot point, with adequate information flow from local experts and stakeholders to scientists, while at the same time avoiding “reinventing the wheel” (e.g. Fig. 1) by making full use of the cumulative experience of scientists and tools they have constructed.”

Although this ‘meta-model’ approach may save time on the technical model building side of things, many resources (time, effort and money) will be required to build and maintain relationships and confidence between scientists, managers and local stakeholders. This approach is really a modelling toolkit for management, with very little emphasis on improving scientific understanding. In this case the modelling is the means to the end of integrative/participatory management of the forest landscape.

The authors continue:

“The mixture of local experts and stakeholders who understand how the tools work, scientists who are willing and able to communicate their science to stakeholders, and integrated analytical tools that can simulate complex spatial and temporal problems will provide powerful and efficient decision support for SFM.”

Unfortunately, unless the scientists in question have the explicit remit to offer their services for management purposes, this sort of modelling approach will not be very appealing to them. In a scientific climate of ‘publish or perish’, management outcomes alone are unlikely to be enough to lure the services of scientists. In some cases I’m sure I will be wrong and scientists will happily oblige. But more generally, unless funding bodies become less concerned with tangible outputs at specific points in time, and academic scientists are judged less strictly by their publishing output, this situation may be difficult to overcome.

This situation is one reason the two sides of the “devils’ choice” are more well developed to the expense of the ‘middle-ground’ toolkit approach. ‘Off-the-shelf’ models, such as LANDIS, are appealing to scientists as they allow the investigation of more abstract and basic science questions than asked by forest managers. The development of ‘customized’ models is appealing to scientists because they allow more detailed investigation of underlying processes and provide a framework for the collection of empirical data collection. No doubt the understanding gained from these approaches will eventually help forest managers – but not in the manner of direct decision-support as the toolkit modelling approach proposes.

As a case in point, the ‘customized’ Managed Forest Landscape Model for Michigan I am working on is raising questions about underlying relationships between deer and forest stand structure. I’m off into the field this week to get data collection started for just that purpose.

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.