A selection harvesting algorithm for use in spatially explicit individual-based forest simulation models
Authored by Ken Arii, John P Caspersen, Trevor A Jones, Sean C Thomas
Date Published: 2008
DOI: 10.1016/j.ecolmodel.2007.09.007
Sponsors:
National Science and Engineering Research Council of Canada (NSERC)
Sustainable Forest Management Network
Canadian Network Center of Excellence
Platforms:
R
SORTIE
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
There is growing interest in using spatially explicit, individual-based
forest simulation models to explore the ecological and silvicultural
consequences of various harvesting regimes. However, simulating the
dynamics of managed forests requires harvesting algorithms capable of
accurately mimicking the harvest regimes of interest. Under selection
silviculture, trees are harvested individually or in small groups, with
the aim of retaining trees across a full range of size classes. An
algorithm that reproduces selection harvesting must therefore be able to
recreate both the spatial and the structural patterns of harvest. Here
we introduce a selection harvest algorithm that simulates harvests as a
contagious spatial process in which the cutting of one tree affects the
probability that neighboring trees are also cut. Three simple and
intuitive parameters are required to implement this process: (1) the
probability of cutting a ``target{''} tree (P-t) (often a function of
tree size), (2) the probability of cutting its nearest neighbor (P-n), and (3) the total number of target trees to cut (N-t). Specification of
these parameters allows representation of both the spatial and the
structural patterns of harvest expected under selection silviculture.
Based on this simple process, we built two different versions of the
harvesting algorithm. An ``empirical{''} algorithm was designed and
calibrated to reproduce the observed spatial and size distribution of
stumps (harvested trees) at a study site in central Ontario, and was
successful in reproducing harvesting patterns found in the field, notably variability in the cluster size of harvested trees. The
``user-defined{''} algorithm implements alternative harvesting regimes
(user-defined harvest targets), which may differ in the intensity of
harvesting, the size-specificity of harvesting, and the spatial pattern
of harvesting. We show that the user-defined harvesting algorithm
succeeds in meeting harvest targets specified by the user (e.g., size
class distribution and basal area of trees harvested), while
simultaneously adjusting the gap size specified (i.e., the distribution
of harvested trees per cluster). Incorporation of this harvesting
algorithm into spatially explicit, individual-based models will permit
analyses of long-term responses of forest stands to harvesting scenarios
that more realistically capture the complex patterns of within-stand
variability generated by selection silviculture as practiced in actual
managed forests. (c) 2007 Elsevier B.V. All rights reserved.
Tags
Dynamics
Diversity
growth
Succession
Disturbance
History
Hardwood forests
Temperate forest
Point patterns
Single-tree