Download entropy dynamics in cone production of longleaf pine forests in the

Xiongwen Chen
Department of Biological
& Environmental Sciences
139 ARC Building
Alabama A&M University
Normal, AL 35762
Dear Editor,
Thanks for your and two reviewers’ suggestions and comments on our manuscript. We have
incorporated the most of the suggestions. A high resolution figure is provided. All the
suggestions in the text have been incorporated. The detailed answer to each suggestion/comment
and clarification to some questions are listed in the followings:
Reviewer A
The result from log2 based analysis should be consistent with the current (log10 based) result
because the log2 transformation will not change the general pattern. It is easy to see the
converging trend based on log10 values in Figure 1. Also, this is consistent with our previous
result. Otherwise, it will have more variations in Figure 1 if log2 values are used. log2 values
have advantage for estimating cone production in the future. However, it is not necessary to have
both values in this study.
Reviewer B
The introduction is improved by adding some relevant information that you mentioned (please
see the marked part).
There is limited study on the cone production of longleaf pine due to the complexity. Some
previous results from one single site cannot be applied to other sites; with the increase of time
scale, some previous results turned into invalid. Therefore, currently there is limited general
assessment to compare the spatial and temporal complexity in cone production for longleaf pine
across the region (Chen et al. 2016; Guo et al. 2016). The reduction approach indicated the
complexity and inconsistent for environmental contribution to cone production among different
sites. Our recent study with a top down approach indicated the high correlation between the
entropy of cone production and entropy of precipitation or temperature (Chen et al. 2016). Thus,
further study on entropy based information flow is necessary.
The importance of entropy and its implications in cone production assessment in the
southeast has been added in the Discussion part (see page 4 and 5). The implications of our
results to the longleaf pine forest management can be emphasized from several perspectives. The
similar trajectory of entropy dynamics at all sites means that the sporadic cone production of
longleaf pine may share some common characteristics of self-organization. Based on the
trajectory at each site and overall trend in the region, the cone production at corresponding sites
might be estimated early. The sudden change of entropy might indicate major or abrupt
environmental changes. The joint entropy (both minimum and maximum) could be used not only
to estimate the emergent property of cone production in the entire region, but also to identify the
abnormal change in entropy dynamics at each site based on the joint entropy of minimum or
maximum values in cone production. The turning points in entropy dynamics provide clues for
further studying local ecosystem and environmental changes, such as those at Blackwater (FL),
Eglin (FL), and Jones Center (GA). These results suggest that necessary monitoring at ecosystem
level should be set up at each study site in order to better understand the dynamics of cone
production.
As for the suggestion on the Bayesian statistics, such as Jim Clark and others’ work, our joint
entropy (both joint minimum and maximum) is a kind of Bayesian statistics because it selected
only minimum or maximum data among research sites in each year. It is a special Bayesian
statistics. But other quantitative methods may be applied in the future.
Enclosed is a revised version of our manuscript. Thank you very much for your consideration.
Sincerely,
Xiongwen Chen