Reflections on Treeline Studies and Ways to Improve Them.

Abstract:

The opposing feedbacks to climate warming by surface albedo (positive) and carbon storage (negative) of forests, partly explains the recent interest in understanding what controls the extent of forests while the need to understand visual patterns has long stimulated questions and studies. Studies into the reasons for limits to forest extent have generally focused on the transition zone (treeline) between the forest and the adjacent shrub, heath, or meadow. There, the questions have focused on two main themes, one relating to the small size of the tree individuals (growth) and the other to their low density (reproduction and establishment). The questions thus boil down to: Why so small?” “Why so few?” The assumption is that good answers will explain the location of the treeline.

I will address some weaknesses in our framing of the questions relating to treelines and in our approaches in answering them. What are the major knowledge gaps and what are some ways to make future research efforts more effective? These may include the identification of categories of treeline types and their distribution over short and long distances i.e. the need for both intensive and extensive sites within and between mountain ranges. They may also include preliminary assessment of the temporal and spatial data resolutions needed for generalizations. Both biotic and abiotic resources and stressors must be recognized, both in terms of direct physical (e.g. shelter, shade) and chemical impacts (e.g. litter quality, toxic release) and the former also in terms of their indirect impact where they serve as alternative hosts of a symbiont or a pathogen. I will point out that implicit assumptions of relevance of a factor and the tree response patterns may be faulty and give examples of the unintended consequences of some techniques designed to manipulate resource availability. To successfully address these shortcomings, I will discuss the benefits of multi-/inter-disciplinary teams to sharpen the formulation of the questions, to better focus the experimental design and to enhance the engineering of manipulations/experiments/monitoring and finally to improve the data analyses and explain the importance of the results including the boundaries of generalization.