Mixture enhances productivity in a two-species forest: evidence from a modeling approach
Authored by Thomas Perot, Nicolas Picard
Date Published: 2012
DOI: 10.1007/s11284-011-0873-9
Sponsors:
French National Forest Office
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
The effect of mixture on productivity has been widely studied for
applications related to agriculture but results in forestry are scarce
due to the difficulty of conducting experiments. Using a modeling
approach, we analyzed the effect of mixture on the productivity of
forest stands composed of sessile oak and Scots pine. To determine
whether mixture had a positive effect on productivity and if there was
an optimum mixing proportion, we used an aggregation technique involving
a mean-field approximation to analyze a distance-dependent
individual-based model. We conducted a local sensitivity analysis to
identify the factors that influenced the results the most. Our model
made it possible to predict the species proportion where productivity
peaks. This indicates that transgressive over-yielding can occur in
these stands and suggests that the two species are complementary. For
the studied growth period, mixture does have a positive effect on the
productivity of oak-pine stands. Depending on the plot, the optimum
species proportion ranges from 38 to 74\% of oak and the gain in
productivity compared to the current mixture is 2.2\% on average. The
optimum mixing proportion mainly depends on parameters concerning
intra-specific oak competition and yet, intra-specific competition
higher than inter-specific competition was not sufficient to ensure
over-yielding in these stands. Our work also shows how results obtained
for individual tree growth may provide information on the productivity
of the whole stand. This approach could help us to better understand the
link between productivity, stand characteristics, and species growth
parameters in mixed forests.
Tags
Competition
Dynamics
Diversity
Biodiversity
growth
France
Trees
Current knowledge
Stand productivity
Mixed stands