Analysing the spatial and temporal dynamics of species interactions in mixed-species forests and the effects of stand density using the 3-PG model
Introduction
Mixed-species forests can be more productive than monocultures when complementary traits and species interactions enable them to use a higher proportion of the sites resources or to use them more efficiently. However, mixtures are certainly not always more productive than monocultures. A given combination of species can be more productive than monocultures on some sites or ages but less productive elsewhere (Forrester, 2014a). This is because the interactions between species changes spatially and temporally with changes in resource availability or climatic conditions (Forrester, 2014a). Species interactions can also be modified by differences in stand density (Garber and Maguire, 2004, Amoroso and Turnblom, 2006, Condés et al., 2013) and probably also by management activities or disturbances that influence density. To make the most of these interactions and to determine how to manage forests under changing management and climatic conditions, tools that can predict these spatial and temporal dynamics and disturbance effects are required.
There are countless combinations of species, silvicultural treatments, soil and climatic conditions in any given region. In many cases, foresters are interested in combinations of these variables that may not currently exist, especially when the goal is to establish new mixed-species plantations. Traditional empirical growth and yield models often unrealistically assume stable site conditions (Battaglia and Sands, 1998). In some tree-level models inter-specific interactions are estimated from empirical relationships based on competition indices calculated from the size of, or shading from, neighbouring trees (Peng, 2000, Pretzsch et al., 2015). These empirical relationships often require large data sets to develop and are restricted to the stand structures or site and climatic conditions represented in the data set (Battaglia and Sands, 1998, Peng, 2000, Landsberg, 2003). This is because they do not consider the main factors that influence forest growth including resource availability, climatic conditions or different silvicultural regimes (Korzukhin et al., 1996, Monserud, 2003). On the other hand, models that are based on general and fundamental ecophysiological processes can potentially provide robust extrapolations to untested conditions, silvicultural regimes (Weiskittel et al., 2010) and species combinations and proportions. While some important physiological processes work at high temporal (seconds, hours) or spatial (leaves, trees) resolutions, models that operate at these resolutions often require detailed and expensive physiological data to parameterise and validate (Battaglia and Sands, 1998). Calculations at high resolutions can also lead to errors that are propagated when upscaling, and are not necessary when the desired outputs are at lower temporal (months or years) or spatial (stands) resolutions, such as those often required by forest managers.
In an attempt to bridge the gap between conventional empirical models and process-based models, hybrid models have been developed to capture some of the advantages of both (Landsberg and Waring, 1997, Battaglia and Sands, 1998, Sands and Landsberg, 2002, Landsberg, 2003, Härkönen et al., 2010, Landsberg and Sands, 2010, Weiskittel et al., 2010). These include the generality and ability of process-based models to describe the forests’ interaction with its environment and hence its response to changes in environmental conditions and management (Battaglia and Sands, 1998). In addition, complex physiological relationships can be replaced with simpler and lower resolution (and sometimes more empirical) relationships. This can reduce data input and parameterisation requirements and provide outputs at the desired resolution without any need to scale up. In some cases, the hybrid models provide even better growth predictions than empirical models developed for the same regions, probably partly because they account for intra- and inter-annual climatic variability (Weiskittel et al., 2010, Pérez-Cruzado et al., 2011).
The forest growth model 3-PG (Physiological Principles Predicting Growth) is an example of such a model. It is a simple process-based (or hybrid) model that has been widely used and well validated for all the processes it considers, such as growth, biomass partitioning, light absorption, water balance, responses to CO2, diameter distributions and mortality (Landsberg and Sands, 2010). It is widely used in South America as a management tool to complement routine inventory and strategic planning (Landsberg, 2003, Almeida et al., 2004b, Battaglia et al., 2007). It has been incorporated into a decision support system to assess plantation water yield and productivity in South Africa (Dye, 2005) and into the FullCam model for carbon accounting of Australian forests (Paul et al., 2006). Many more examples are described in Landsberg and Sands (2010). This wide-spread use is unusual for a process-based model and reflects its simple and transparent structure, the feasible input requirements, the relatively easily testable outputs for each process that it models, and importantly, the fact that it has always been freely available and well documented (Landsberg and Sands, 2010).
The 3-PG model was developed for even-aged monocultures and the aim of this study was to modify 3-PG so that it could be used to examine the growth dynamics of each species within a mixed-species forest and their responses to disturbances that modify stand density, such as thinning. The 3-PG model was chosen because no other models with comparable simplicity could be found that have been so rigorously tested in terms of observed-predicted comparisons for all of their component processes in so many different types of forests. In addition, to reproduce the spatial and temporal dynamics of species interactions in mixtures, models probably need to account for the physiological functioning of the trees, the environmental conditions within the stand, the stand structure, and interactions between these, as well as the external influences of climate and disturbances (Pretzsch et al., 2015). These components are all included in 3-PG or could be added with minor modifications. This was done for subtropical forests containing Castanopsis sclerophylla, Cunninghamia lanceolata, Cyclobalanopsis glauca and Liquidambar formosana, with a wide range of stand densities in Shitai County, Anhui Province, China. Mixed-species plantations of the same or related species have also been considered in Southern China (Meng et al., 2014). Specifically, the objectives were (1) to convert 3-PG into a mixed-species model and parameterise it for these species, one of which is deciduous; (2) to calibrate it using monospecific stands and assess its performance when predicting mixed-species dynamics and (3) to examine the spatial and temporal dynamics of the interactions between these species.
Section snippets
Description of 3-PG
3-PG was developed by Landsberg and Waring (1997). A review of the 3-PG model, its calibration and validation and many examples of its application is in Landsberg and Sands (2010). 3-PGPJS 2.7 (Sands, 2010) was used to develop the mixed-species version of 3-PG, 3-PGmix, which can still be run as 3-PGPJS 2.7. The 3-PGmix model is described in detail in Appendix A. The following provides an overview of the model followed by the changes that were required for it to work in mixed-species forests,
Changes made to produce 3-PGmix
The modifications described below were made to 3-PGPJS 2.7, which is freely available as an Excel file at http://3pg.forestry.ubc.ca/. The 3-PGmix model is also freely available as an Excel file at the same web site.
Site description
Model predictions were compared with data collected from subtropical monospecific Cunninghamia lanceolata plantations that were about 14- to 20-years-old and monospecific and mixed-species forests containing C. sclerophylla, C. lanceolata and L. formosana that were about 16- to 42-years-old and located in Shitai County, Anhui Province, China (29°59′–30°24′N, 117°12′–117°59′E). The stands varied in terms of L from <1 to 10.2, trees per ha from 88 to 2741 and basal area from 1 to 57 m2 ha−1. In
Results
Predicted and observed comparisons were made for monocultures using the light sub-model of 3-PGPJS 2.7 (L1) and the new light sub-model (L2) (Table 2). The comparisons were very similar and the new light model did not offer any significant improvement or result in any significant deterioration of predictions for these monospecific stands. Comparisons for mixtures could not be made because L1 cannot be used to predict the light absorption of mixtures.
The 3-PGmix model produced accurate
Discussion
The stands used for this study included a wide range of stand structures, in terms of stand density, species composition and species proportions as well as soil fertility. The 3-PGmix model was able to predict the growth, biomass partitioning and light absorption of the individual species within these mixed-species forests. The simulations also indicated that the interactions between these species (and complementarity effects) varied with stand age, stand density, fertility and rainfall. The
Acknowledgements
This study was part of a Sino-German Cooperation on Innovative Technologies and Service Capacities of Multifunctional Forest Management project (Lin2Value, projec number 033L049-CAFYBB2012013) supported by the Federal Ministry of Education and Research (BMBF) and Chinese Academy of Forestry. We thank Dr. Lutz Fehrmann, Prof. Christoph Kleinn, Prof. Christian Ammer, Dr. Hans Fuchs, Sabine Schreiner, Dr. Haijun Yang, Dr. Torsten Vor, Rubén Guisasola and Dengkui Mo for assistance with the plot
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