Building Decision Support Tools and Functions for Forest Landscape Planning and Restoration

Across the globe, forest managers are faced with the daunting task of mitigating the impacts of climate change and past land-use practices on future forested landscape dynamics. This is taking place alongside the provisioning of key ecosystem services that human populations are dependent upon, such as fresh water, clean air, and biodiversity. These shared goals of improving landscape resilience to future disturbances have strong roots in ecological literature, but frameworks for directly applying these concepts to management and prioritizing treatments across large landscapes are lacking. Such efforts must recognize the role of landscape-and regional-level processes as drivers of change at local scales and that management decisions must consider the impacts of proposed forest treatments across multiple temporal and spatial scales to be most effective. Furthermore, managing across large landscapes requires a recognition of the gradient of ecological and social conditions that drive management decisions.

Spatial decision support tools are uniquely adept at ingesting and evaluating a wide variety of data sources. This evaluation can inform management decisions ranging from strategic (where to go), to tactical (what to do when you get there) in scope. Such models not only evaluate forested landscapes based on their ecological potential but also incorporate barriers, limitations, and considerations to the implementation of treatments across the landscape. Decision support models are developed in a collaborative environment where scientists, managers, policymakers, and stakeholders are involved in the co-production of the model. As such these models represent the socio-ecological context through which management decisions are made. Advances in the development and accessibility of high-resolution spatial data sets have made resource assessments possible at scales large enough to address landscape resilience goals and impact key disturbance processes driving system behavior in the 21st century.

Through this Research Topic, we intend to highlight efforts that have formalized decision support approaches to address 21st century management issues. Such issues transcend traditional approaches that consider only a single spatial scale, single process, or neglect future landscape dynamics or climate change. We intend this Research Topic to incorporate decision support modeling from forested landscapes across the world that represent a gradient of social and environmental issues affecting regional decision making. Manuscripts can represent original research or review recent and novel applications of decision support models to pressing ecological problems.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Decision support, Climate change, Ecosystem services, Wildfire, Resilience, Landscape ecology, Socioecological systems

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.