Uncertainty in the detection of disturbance spatial patterns in temperate forests
Authored by P Samonil, J Timkova, I Vasickova
Date Published: 2016
DOI: 10.1016/j.dendre.2015.12.002
Sponsors:
National Agency for Agricultural Research (Národní agentura pro zemědělský výzkum NAZV)
American Science Information Center (AMVIS)
Platforms:
R
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
The use of individual-based models in the study of the spatial patterns
of disturbances has opened new horizons in forest ecosystem research.
However, no studies so far have addressed (i) the uncertainty in
geostatistical modelling of the spatial relationships in
dendrochronological data, (ii) the number of increment cores necessary
to study disturbance spatial patterns, and (iii) the choice of an
appropriate geostatistical model in relation to disturbance regime. In
addressing these issues, we hope to contribute to advances in research
methodology as well as to improve interpretations and generalizations
from case studies.
We used data from the beech-dominated Zofinsky Prales forest reserve
(Czech Republic), where we cored 3020 trees on 74 ha. Block bootstrap
and geostatistics were applied to the data, which covered five decades
with highly different disturbance histories. This allowed us to assess
the general behavior of various mathematical models. Uncertainty in the
spatial patterns and stability of the models was measured as the length
of the 95\% confidence interval (CI) of model parameters.
According to Akaike Information Criterion (AIC), the spherical model
fitted best at the range of ca. 20 m, while the exponential model was
best at the range of ca. 60 m. However, the best fitting models were not
always the most stable. The stability of models grew significantly with
sample size. At <500 cores the spherical model was the most stable, while the Gaussian model was very unstable at <300 cores. The pure
nugget model produced the most precise nugget estimate. The choice of
model should thus be based on the expected spatial relations of the
forest ecosystem under study. Sill was the most stable parameter, with
an error of +/- 6-20\% for >1110 core series. By contrast, practical
range was the most sensitive, with an error of at least +/- 59\%. The
estimation of the spatial pattern of severe disturbances was more
precise than that of fine-scale disturbances.
The results suggest that with a sample size of 1000-1400 cores and a
properly chosen model, one reaches a certain precision in estimation
that does not increase significantly with growing sample size. It
appears that in temperate old-growth forests controlled by fine-scale
disturbances, it is necessary to have at least 500 cores to estimate
sill, nugget and relative nugget, while to estimate practical range at
least 1000 cores are needed. When choosing the best model, the stability
of the model should be considered together with the value of AIC. Our
results indicate the general limits of disturbance spatial pattern
studies using dendrochronological and geostatistical methods, which can
be only partially overcome by sample size or sampling design. (C) 2015
Elsevier GmbH. All rights reserved.
Tags
Dynamics
Variability
Soil
Old-growth forests
Hemlock-hardwood forest
Canopy gap formation
Dendroecological reconstruction
Lowland forest
Spruce forests
Carpathians