Effects of starting conditions of SORTIE-ND model growth predictions: tree spatial coordinate randomization

Transcription

1 Effects of starting conditions of SORTIE-ND model growth predictions: tree spatial coordinate randomization Marie-Lou Lefrancois 1, K. David Coates 2, and Rasmus Astrup 1, 3 (1) Bulkley Valley Centre for Natural Resources Research and Management, Box 4274, Smithers, BC, VJ 2N, Canada. Phone: Fax: (2) British Columbia Forest Service, Research Section, Bag 6, Smithers, BC, VJ 2N, Canada. (3) Current address: Norwegian Forest and Landscape Institute Høgskoleveien 8, Ås Postboks 115, 1431 Ås, Norway. April, 29 1

2 Introduction The spatial distribution of individual trees within a forest stand is pivotal in understanding dynamics at the tree, stand and landscape-level (Kuuluvainen et al. 1998; Mäkelä, 23; Schumacker et al. 24). The structural forces behind the spatial distribution of trees are: 1) competition, which creates a negative spatial dependence and 2) micro-site effects, which usually have opposite impact by attracting trees to resources. While most studies show that the effects of micro-site quality is dominant over competition (Fox et al. 27), much of the spatial distribution can be explained by the successional stage (Oliver and Larson, 1996; Fox et al. 27), hence the importance of competition. The role of competition is especially relevant when the stand is at a self-thinning stage, where the individuals are established but have not yet reached the climax stage (Fajardo and McIntire, 27; Fox et al. 27; Oliver and Larson, 1996). It is recognised that competition negatively affects individual growth (Canham et al. 24, 26) and can initiate spatial patterns in the stand (Fox et al. 27). But despite the fact that the effect of competition on growth is recognised and that the competition can in turn depend on the spatial pattern, it is still debated if this type of information is necessary in stand management. More precisely, the importance of whether or not spatial information is necessary when forecasting tree growth (Fox et al 27; Stadt et al. 27) is still discussed. In monospecific stands where individuals are evenly spaced (such as plantations), it is more likely that growth can be accurately predicted without knowing the neighbourhood of individuals, and that including a measure of stand density would be sufficient (Stadt et al. 27). Although in more complex stands where species interactions are important and where there is vertical and horizontal heterogeneity, spatially-explicit models proved to be a better tool to understand and manage the forest (Canham et al. 24, 26; Mailly et al. 23; Stadt et al. 27). In this study, we investigate the effects of initial starting conditions on the outcome of model predictions. Specifically, we investigate how the initial spatial 2

3 distribution of trees affect projected growth rates. This is achieved by using SORTIE-ND, a spatially-explicit forest growth model, where species are assigned a competition coefficient (neighbourhood competition index, (NCI)). The NCI is function of tree size and distance between competitors and directly affects growth. By modifying the initial spatial coordinates of the trees present in the stand, we are in a position to determine if the initial spatial distribution is important to the outcome of the simulation. Methods The study area is located in the biogeoclimatic Sub-Boreal Spruce zone (SBSmc2) in the Northern Interior of British Columbia (54 49 N, W.). During the summer of 27, individual trees were mapped using a Criterion laser 4 survey transit. A total of 12 plots were selected in young stands (<1 years old) of secondary structure, which contained various proportions of interior (or hybrid) spruce (Picea glauca X Picea engelmannii (Moench) Voss), lodgepole pine (Pinus contorta Doug. Ex. Loud.), subalpine fir (Abies lasioscarpa (Hook.) Hutt.) and trembling aspen (Populus tremuloides Michx.). Small amounts of paper birch (Betula papyrifera Marsh.) and western hemlock (Tsuga heterophylla (Raf.) Sarg.) were also recorded and were analysed as species similar in shade-tolerance such as trembling aspen (for paper birch) and subalpine fir (for western hemlock). This allowed simulations with SORTIE-ND with an existing parameter file (see below). In order to generate tree maps of 9 hectares (ha) in size (for simulations in SORTIE-ND) from the original plots, which ranged from.6 to.66 ha, the existing tree maps were processed with a spatial pattern analysis program (SPA, Canham, 27). Using an empty-space function based on a nearest neighbour analysis performed on the original tree maps, larger tree maps were generated for all plots. Individuals below 5 cm in diameter at breast height (DBH) were discarded. A copy of the generated tree maps was then processed in MS Excel in order to randomize the X and Y coordinates of individual trees (using the RAND function). 3

4 Simulations in the model SORTIE-ND were conducted by adding two 9 ha tree maps (with random and original tree locations) for each stand to an existing parameter file build for the same forest type. The parameter file ensures that all species functions (growth, mortality, reproduction) are replicated accordingly to their behaviour in nature. Because the objective of this project is to observe potential changes in growth, the regeneration was not activated and snags were absent from the parameter file. Results Results show that stands with randomized coordinates are not different from those with the original spatial distribution. Stands with randomized coordinates have similar basal area increment, similar density and DBH distributions as the original stands (Fig. 1 to 59). Otherwise, results show the normal evolution of stand succession by displaying the interior spruce dominating with large individuals towards the end, sometimes codominant with subalpine fir, while intolerant species decrease in abundance with time, but are still maintained, probably by a tree-fall gap dynamic. The lack of discrepancies between original stands and stands where trees were randomly dispersed might be explained by the characteristics of the stands selected for this study, and has some implications at the management level. Discussion Randomness in stands One possible explanation for the lack of difference in the results between the original stand and the one with a random spatial distribution is that the original spatial distribution might exhibit a random pattern. In order to understand if the trees in the stands used in this study were randomly distributed, the spatial pattern analyst (SPA, Canham 27) was used. 4

5 When generating a tree map, SPA can use an algorithm based on an emptyspace function or a complete spatial random algorithm. The former was employed to generate the tree maps in this study. The original tree maps were also generated using the complete spatial random function in order to illustrate if there were differences between this map and the one generated with an empty space function. The comparison of a nearest neighbourhood analysis results revealed very minimal discrepancies between both maps (results not shown) and therefore suggests that the generated tree maps (using the empty-space function) from the original plot data were exhibiting a random distribution of trees. In nature, it has been observed that in stands where trees are competing, a random distribution of trees might prevail (Nigh, 1996; Oliver and Larson, 1996), along with a high structural heterogeneity in the canopy (Kuuluvainen et al. 1998). Conversely, when a stand has a homogenous canopy and where competition is more asymmetrical (dominant vs. suppressed pattern), a structure is well established and its disruption may lead to changes in individual tree growth. It is also possible that when the stand has reached a climax stage (where dominant trees are usually regularly spaced), there can be a random mortality pattern among the mature trees which result in a random spatial arrangement (Oliver and Larson, 1996). The choice of the scale is essential in interpreting the results of studies on the spatial patterns of trees (Kuuluvainen et al. 1998; Mäkelä, 23). In this case, the 9 ha area is quite large to account for the effects of a very local process such as tree-level competition. Furthermore, saplings below 5 cm in DBH were discarded, but concurrently these individuals can be vulnerable to competition. The results observed in this study might not hold if the scale is decreased or if smaller trees are included. Implication for management The results suggest that this particular type of stand does not require details on the spatial distribution of trees in order for the model SORTIE-ND to make robust 5

6 predictions. In fact, the similar output in terms of basal area increment and successional patterns of the stand with randomized coordinates suggests that the labour-intensive tree mapping might not be essential. The only input needed is species identification and size (in DBH), while the spatial coordinates can be randomly assigned to the individuals. This applies only to stands of similar size (9 ha) comparable to those selected in this study and that satisfy the following conditions; 1) The stands exhibit a random pattern in the spatial distribution of the trees, 2) They are located in the SBSmc2 biogeoclimatic subzone, and are dominated by interior spruce, or by lodgepole pine or subalpine fir with a component of spruce, 3) The stands are young and of secondary structure, and only trees over 5 in DBH are included. 4) No major disturbance has recently reset the successional patterns, such as the mountain pine beetle epidemic, harvest, fire or a blowdown. Concluding remarks Even though the importance of a spatially-explicit approach to growth modelling has been highlighted in previous studies (Canham et al 24, 26; Mailly et al. 23; Stadt et al. 27) and is essential to yield the benefits of managing for complex stands, this study has shown that the growth predictions for stands of secondary structure in the SBSmc2 can be completed with a minimum of information, where spatial coordinates of trees are not necessary. Further studies are needed to aim at the importance of the spatial distribution of trees in older stands (where competition might be asymmetrical), and of much younger stands, where clumps with a high density seedlings are found. 6

7 Reference list Canham, C. D., P. T. LePage, and K. D. Coates. 24. A neighborhood analysis of canopy tree competition: effects of shading versus crowding. Can. J. For. Res. 34: Canham, C. D., Papaik, M. J., Uriarte, M., McWilliams, W., Jenkins, J.C., Twery, M.J. 26. Neighborhood analyses of canopy tree competition along environmental gradient in New England forests. Ecol. App. 16 (2) Fox, J., Bi, H. and Ades, P.K. 27. Spatial dependence and individual-tree growth models I. Characterising spatial dependence. For. Ecol. Manage. 245: 1 19 Fox, J., Bi, H. and Ades, P.K. 27b. Spatial dependence and individual-tree growth models II. Modelling spatial dependence. For. Ecol. Manage. 245: 2 3. Fajardo, A., and McIntire, E.J.B. 27. Distinguishing microsite and competition processes in tree growth dynamics: an a priori spatial modeling approach. Am. Nat. 169 (5): Kuuluvainen, T., Jarvinen, E., Hokkanen, T.J., Rouvinen, S. And Heikkinen, K Structural heterogeneity and spatial autocorrelation in a natural mature Pinus sylvestris dominated forest. Ecography 21:

8 Nigh, G Identification and Simulation of the Spatial Pattern of Juvenile Lodgepole Pine in the Sub-Boreal Spruce Biogeoclimatic Zone, Stuart Dry Warm and Babine Moist Cold Variants.Victoria, BC : Ministry of Forests, Research Branch, Mailly, D., Turbis, S. and Pothier, D. 23. Predicting basal area increment in a spatiallyexplicit, individual tree model: a test of competition measures with black spruce. Can.J.For.Res. 33: Mäkelä, A. 23. Process-based modelling of tree and stand growth: towards a hierarchical treatment of mutliscale processes. Can.J.For.Res. 33: Oliver C. D. and Larson B.C Forest stand dynamics. John Wiley and sons.usa. 52p. Stadt, K.J., Huston, C., Coates, D., Feng, Z., Dale, M.R.T., and Lieffers, V. 27. Evaluation of competition and light estimation indices for predicting diameter growth in mature boreal mixed forests. Ann.For.Sci. 64: Schumacher, S., Bugmann, H. and Mladenoff, D.J. 24. Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol.Model

9 basal area (m2/ha) basal area (m2/ha) basal area (m2/ha) APPENDIX Figure 1 to 11: Basal area increment over 1 years. Red bars: interior spruce, blue bars: lodgepole pine, green bars: subalpine fir, yellow bars: trembling aspen, black bars: stand total. Hatched bars: stands with random spatial distribution. Site 1: Basal area for original and randomized coordinates Site 2: Basal area for original and randomized coordinates Site 3: Basal area for original and randomized coordinates

10 basal area (m2/ha) basal area (m2/ha) basal area (m2/ha) basal area (m2/ha) Site 4: Basal area for original and randomized coordinates Site 5: Basal area for original and randomized coordinates Site 6: Basal area for original and randomized coordinates Site 7: Basal area for original and randomized coordinates

11 basal area (m2/ha) basal area (m2/ha) basal area (m2/ha) basal area (m2/ha) Site 8: basal area for original and randomized coordinates Site 9: Basal area for original and randomized coordinates Site 1: Basal area for original and randomized coordinates Site 11: Basal area for original and randomized coordinates

12 Density (#/ha) Density (#/ha) stems (#/ha) stems (#/ha) Figure 12 to 22: Stand density over 1 years. Red bars: interior spruce, blue bars: lodgepole pine, green bars: subalpine fir, yellow bars: trembling aspen, black bars: stand total. Hatched bars: stand with random spatial distribution. Site 1: Density for original and randomized coordinates Site 2: Density for original and randomized coordinates Site 3: Density for original and randomized coordinates Site 4: Density for original and randomized coordinates

13 Density (#/ha) Density (#/ha) Density (#/ha) Density (#/ha) Site 5: Density for original and randomized coordinates Site 6: Density for original and randomized coordinates Site 7: Density for original and randomized coordinates Site 8: Density for original and randomized coordinates

14 Density (#/ha) Density (#/ha) Density (#/ha) Site 9: Density for original and randomized coordinates Site 1: Density for original and randomized coordinates Site 11: Density for original and randomized coordinates

15 Figure 23 to 59: DBH distribution at 5, 3 and 1 years. Black bars: number of trees at 5 years, Grey bars: number of trees at 3 years, Pale grey bars: number of trees at 1 years. Hatched bars: stand with random spatial distribution. site 1: Interior spruce site 1: Lodgepole pine site 1: Subalpine fir site 1: Trembling aspen

16 site 2: Interior spruce site 2: Lodgepole pine site 2: Subalpine fir site3: Interior spruce

17 site 3: Lodgepole pine site 3: Subalpine fir site 4: Interior spruce year 5 year 5 random year 3 year 3 random year 1 year 1 random site 4: Subalpine fir year 5 year 5 random year 3 year 3 random year 1 year 1 random

18 site 5: Interior spruce site 5: Lodgepole pine site 5: Subalpine fir site 5: Trembling aspen

19 site 6: Interior spruce site 6: Lodgepole pine site 6: Subalpine fir site 6: Trembling aspen

20 site 7: Interior spruce site 7: Lodgepole pine site 7: Subalpine fir site 7: Trembling aspen

21 site 8: Interior spruce site 8: Lodgepole pine site 8: Subalpine fir site 8: Trembling aspen

22 site 9: Interior spruce site 9: Lodgepole pine site 9:Trembling aspen site 1: Interior spruce

23 site 1: Lodgepole pine site 1: Trembling aspen site11: Interior spruce site11: Lodgepole pine

24 site11: Trembling aspen