1
FOR ONLINE PUBLICATION ONLY
2
Appendix A: iLand soil module
3
Soil dynamics
4
In developing a soil module for iLand our objectives were to capture the major processes of
5
detritus and soil C dynamics in forest ecosystems, and to potentially allow for dynamic
6
feedbacks on vegetation growth via dynamically simulated soil nutrient availability. However,
7
also parsimony and the ability to parameterize and initialize the model at the landscape scale
8
with widely available data (see Seidl and others 2009) were important factors for model design.
9
As the result of an extensive literature search with regard to these objectives we adopted the
10
ICBM/2N approach (Andrén and Kätterer 1997; Kätterer and Andrén 2001) for iLand.
11
Soil dynamics are simulated on annual time step in iLand, and are implemented at the spatial
12
level of resource units (RU, currently regular 100 m grid cells, see Seidl and others (2012)). Six
13
soil pools are modeled explicitly, that is, litter, coarse woody debris (CWD), and soil organic
14
matter (SOM) for C and N respectively. Mass dynamics builds on first order decay kinetics
15
where each pool is characterized by a pool-specific annual decomposition rate. This base rate is
16
modified by a climate response parameter representing the influence of climate on
17
decomposition. Adair and others (2008) recently tested a number of climate response functions
18
and found the empirically derived variable Q10 function of Lloyd and Taylor (1994) to be best
19
suited to capture the influence of temperature on decomposition. We thus adopt their function,
20
standardized to decomposition rates at 10˚C and optimum moisture, and implement it at a daily
21
time step in iLand. Water limitations on decomposition are calculated as monthly response to the
22
ratio between precipitation and potential evapotranspiration (Adair and others 2008). Both
23
limitations to decomposition were multiplicatively aggregated to allow for trade-offs between
24
temperature- and moisture limitation, and averaged to derive an annual climate modifier (re).
25
Humification, that is, the flow between the detritus pools and the soil organic matter pool, is
26
driven by a site-specific humification constant (hr). N pools are linked to C dynamics via C:N
1
27
ratios and the efficiency of the soil microbial community (Kätterer and Andrén 2001). Soil
28
nutrient feedback on plant growth is implemented via plant-available N (Nav) in iLand (see Seidl
29
and others 2012), which can be calculated from ICBM/2N by summing the net mineralization
30
rates from the litter, DWD, and SOM pools for a given time step, and accounting for a site-
31
specific leaching rate (Kätterer and Andrén 2001; Xenakis and others 2008). For the current
32
study, however, we used static, external data on Nav to drive the model (see Figure 1), to be able
33
to separate first order effects of nutrient availability from other drivers in the factorial design of
34
the study.
35
36
Snag dynamics
37
All mortality events (with the exception of harvest and windthrow) lead to the creation of snags
38
in the model, that is, the stem compartment is transferred to standing woody debris (SWD). At
39
the RU level SWD is tracked in three size cohorts based on diameter at breast height (dbh) at the
40
time of death. C in SWD pools undergoes decomposition following first order decay kinetics,
41
applying a species- and compartment-specific decay rate and accounting for the effect of re. The
42
annual probability for snags (and the respective C and N) to transfer to DWD is calculated from
43
snag half life data assuming a negative exponential decay process. This generic half life is
44
modified by re so that the snag lifetime is decreased under favorable climate for decomposition
45
(see Harmon and Marks 2002; Harmon and others 2009). Foliage and fine root biomass of dead
46
trees are transferred to the litter pool in the year of death. Branch and coarse root biomass is
47
routed to the DWD pool in the five years following tree death. A detailed sensitivity analysis of
48
the iLand soil module can be found at http://iland.boku.ac.at. The soil- and detritus-related model
49
parameters used for this study are summarized in Table A1.
50
51
Soil initialization and parameterization
2
52
Besides the main species-specific parameters of the iLand soil module (see Table A1) initial pool
53
sizes and site specific process parameters need to be estimated. We initialized soil pools from
54
observed empirical data (see main text) and conducted a Monte Carlo parameterization of the
55
56
57
58
59
60
Table A1: Parameters of the iLand Soil Module
symbol
description
level
estimate
source*
kSWD
SWD decomposition rate (dim.)
species
0.04
1
klitter
litter decomposition rate (dim.)
species
0.22
1
kDWD
DWD decomposition rate (dim.)
species
0.08
1
kSOM
SOM decomposition rate (dim.)
site
0.0343
see Table A2
CNf
C:N ratio foliage (dim.)
species
60.3
2
CNr
C:N ratio fine roots (dim.)
species
9.0
3
CNw
C:N ratio woody tissue (dim.)
species
452
4
CNe
C:N ratio of soil microbes (dim.)
general
5
5
CNSOM
C:N ratio of SOM (dim.)
site
33.3
6, 7
hl
snag half life (years)
species
20
1
le
leaching rate (dim.)
site
0.367
8, 9
hr
humification rate (dim.)
site
0.368
see Table A2
ke1
microbial efficiency, litter (dim.)
site
0.214
5, 10
ke2
microbial efficiency, DWD (dim.)
site
0.267
5, 10
61
* 1= Harmon and Domingo (2001); 2= Adair and others (2008); 3= Harmon (2005a); 4=
Harmon (2005b); 5= Kätterer and Andrén (2001); 6= Dyrness (2001); 7= Dyrness and others
(2005), 8= Lajtha and others (2005); 9= Vanderbilt and others (2003); 10= Xenakis and others
(2008)
Values for species-specific parameters are given exemplarily for Douglas-fir (Pseudotsuga
62
menziesii (Mirb.) Franco), values for site-specific parameters are averages over the 6364 ha HJA
63
landscape. Units are indicated in parenthesis. dim.= dimensionless, SWD= standing woody
64
debris, DWD= downed woody debris, SOM= soil organic matter.
65
66
site specific parameters under a steady-state assumption (compare Thornton and Rosenbloom
67
2005). We assumed that the observed C pools for current old-growth forests (Dyrness 2001;
3
68
Dyrness and others 2005; Smithwick and others 2002) constitute the steady state for our study
69
landscape. We thus used the deviance of the modeled steady state SOM C to the observed SOM
70
C as convergence criterion for parameterization in our Monte Carlo analysis. We assumed an
71
uniform prior distribution over the site-specific parameters from literature sources (Table A2),
72
and conducted 10,000 Monte Carlo simulations to determine the optimal parameter set for every
73
100 m grid cell.
74
For 95% of the resulting parameter combinations the simulated steady state SOM was within
75
±10.5% of the observed values (Table A2). Furthermore, parameter estimates from the Monte
76
Carlo analysis were well in line with empirical observations and previous modeling studies in the
77
region (for example, Harmon and Domingo 2001).
78
79
80
Table A2: Prior and Posterior Distribution Parameters for the Site-specific Parameters Estimated
81
in a Monte Carlo Analysis
prior distribution
hr
kSOM
posterior distribution
range
source*
5th percentile
mean ± sd
95th percentile
0.1 – 0.4
1, 2
0.310
0.368 ± 0.029
0.398
0.001 – 0.05
1, 3
0.022
0.0343 ± 0.0079
0.0467
82
* 1= Xenakis and others (2008), 2= Peltoniemi and others (2004), 3= Harmon and Domingo
83
(2001)
84
Posterior distribution data are given for the entire 6364 ha HJA landscape. For parameter
85
descriptions see Table A1.
86
87
88
89
90
4
91
References
92
Adair EC, Parton WJ, Del Grosso SJ, Silver WL, Harmon ME, Hall SA, Buke IC, Hart SC.
93
2008. Simple three-pool model accurately describes patterns of long-term litter
94
decomposition in diverse climates. Global Change Biology 14: 2636-2660.
95
96
Andrén O, Kätterer T. 1997. ICBM: The introductory carbon balance model for exploration of
soil carbon balances. Ecological Applications 7: 1226-1236.
97
Dyrness C. 2001. Soil descriptions and data for soil profiles in the Andrews Experimental Forest,
98
selected reference stands, Research Natural Areas, and National Parks. Long-Term
99
Ecological
Research.
Forest
Science
Data
Bank,
Corvallis,
OR.
Available:
100
http://andrewsforest.oregonstate.edu/data/abstract.cfm?dbcode=SP001 (accessed 2011-
101
10-02)
102
Dyrness C, Norgren J, Lienkaemper G. 2005. Soil survey (1964, revised in 1994), Andrews
103
Experimental Forest. Long-Term Ecological Research. Forest Science Data Bank,
104
Corvallis,
105
dbcode=SP026 (accessed 2011-10-02)
OR.
Available:
http://andrewsforest.oregonstate.edu/data/abstract.cfm?
106
Harmon M. 2005a. Decomposition of Fine Woody Roots: a Time Series Approach. Long-Term
107
Ecological Research. Forest Science Data Bank, Corvallis, OR. http://andrewsforest.
108
oregonstate.edu/data/abstract.cfm?dbcode=TD031 (accessed 2011-10-02)
109
Harmon M. 2005b. Dimensions and volumes of bark and wood from logs, snags, and stumps
110
from multiple forests in the United States and Mexico. USFS PNW Ecosystem Processes
111
Research. Forest Science Data Bank, Corvallis, OR. http://andrewsforest.oregonstate.edu/
112
data/abstract.cfm?dbcode=TD012 (accessed 2011-10-02)
113
Harmon ME, Domingo JB. 2001. A Users Guide to STANDCARB version 2.0: A model to
114
simulate the carbon stores in forest stands. http://lterdev.fsl.orst.edu/lter/pubs/
115
webdocs/models/standcarb2.cfm (accessed 2011-10-02)
5
116
Harmon ME, Marks B. 2002. Effects of silvicultural practices on carbon stores in Douglas-fir –
117
western hemlock forests in the Pacific Northwest, U.S.A.: results from a simulation
118
model. Canadian Journal of Forest Research 32: 863-877.
119
120
Harmon ME, Moreno A, Domingo JB. 2009. Effects of partial harvest on the carbon stores in
Douglas-fir/ Western Hemlock forests: A simulation study. Ecosystems 12: 777-791.
121
Kätterer T, Andren O. 2001. The ICBM family of analytically solved models of soil carbon,
122
nitrogen and microbial biomass dynamics — descriptions and application examples.
123
Ecological Modelling 136: 191-207.
124
Lajtha K, Crow SE, Yano Y, Kaushal SS, Sulzman E, Sollins P, Spears JDH. 2005. Detrital
125
controls on soil solution N and dissolved organic matter in soils: a field experiment.
126
Biogeochemistry 76: 261-281.
127
128
Lloyd J, Taylor JA. 1994 On the temperature-dependence of soil respiration. Functional Ecology
8: 315–323.
129
Peltoniemi M, Mäkipää R, Liski J, Tamminen P. 2004. Changes in soil carbon with stand age—
130
an evaluation of a modeling method with empirical data. Global Change Biology 10:
131
2078–2091.
132
Seidl R, Rammer W, Lexer MJ. 2009. Schätzung von Bodenmerkmalen und Modellparametern
133
für die Waldökosystemsimulation auf Basis einer Großrauminventur. Allgemeine Forst-
134
und Jagdzeitung 180: 35–44
135
136
Seidl R, Rammer W, Scheller RM, Spies TA. 2012. An individual-based process model to
simulate landscape-scale forest ecosystem dynamics. Ecological Modelling 231: 87-100.
137
Smithwick EAH, Harmon ME, Remillard SM, Acker SA, Franklin JF. 2002. Potential upper
138
bounds of carbon stores in forests of the Pacific Northwest. Ecological Applications 12:
139
1303-1317.
6
140
Thornton PE, Rosenbloom NA. 2005. Ecosystem model spin-up: Estimating steady state
141
conditions in a coupled terrestrial carbon and nitrogen cycle model. Ecological Modelling
142
189: 25-48.
143
Vanderbilt KL, Lajtha K, Swanson FJ. 2003. Biogeochemistry of unpolluted forested watersheds
144
in the Oregon Cascades: temporal patterns of precipitation and stream nitrogen fluxes.
145
Biogeochemistry 62: 87-117.
146
Xenakis G, Ray D, Mencuccini M. 2008. Sensitivity and uncertainty analysis from a coupled 3-
147
PG and soil organic matter decomposition model. Ecological Modelling 219: 1-16.
7
148
Appendix B: iLand regeneration module
149
The iLand regeneration module explicitly addresses the processes seed dispersal, establishment,
150
as well as sapling growth and competition up to the stem exclusion stage (that is, where trees are
151
recruited into the iLand individual-based competition module, Seidl and others 2012).
152
153
Dispersal
154
The dispersal approach taken in iLand closely follows established landscape models (for
155
example, Scheller and Domingo 2006), that is, it doesn't keep track of individual seeds of trees
156
explicitly but assumes probabilistic dispersal kernels around mature trees. The shape of the
157
dispersal kernel is highly influential on migration speed and occupation success of tree species,
158
and has been intensively discussed in ecology (see for example, Greene and others 2004).
159
Following Lischke and Löffler (2006) we chose a two-part exponential dispersal kernel in iLand
160
(Eq. B1), allowing the separate parameterization of wind and zoochorous dispersal for every
161
species.
pseed
e  d / kK1
e  d / kK2
 1  kK3  
 kK3 
kK1
kK2
Eq. B1
162
with pseed the seed probability (with pseed  0, 1), d the distance from the source, and kK1, kK2, and
163
kK3 empirical parameters (Table B1). Dispersal probabilities are further modified to account for
164
seed years. Occurrence of seed years is simulated by randomly drawing seed years to match a
165
mean seed year return interval (see Lexer and Hönninger 2001). In non-seed years the dispersal
166
kernel is reduced by a species-specific factor. Landscape scale synchrony in seed years is
167
assumed, that is, all individuals of a given species experience the same patterns of seed years
168
over time (see Koenig and Knops 2000).
169
Seed dispersal calculations are implemented at a 20 m grid resolution in iLand. For every grid
170
cell holding at least one 1 mature trees of a species (that is, a source cell) the dispersal
171
calculations described above are carried out. Probabilities from different source cells are
8
172
aggregated to derive the overall probability of seed availability for every cell in the landscape,
173
whereas source cells themselves are assumed to have maximum seed availability (He and
174
Mladenoff 1999).
175
The technical implementation of seed dispersal employs a pattern-based approach that uses pre-
176
calculated dispersal patterns, similar in concept to the spatially explicit light competition
177
calculations in iLand (Seidl and others 2012).
178
179
Establishment
180
Establishment is driven by three factors in iLand, the availability of seeds (pseed, described
181
above), abiotic environmental factors (that is, thermal environment, water and nutrient
182
availability, penv), and light availability (that is, shading from surrounding trees, plight). The
183
phenology-based, mechanistic model TACA (Nitschke and Innes 2008) is adopted to account for
184
the thermal limitations for tree establishment. After the winter chilling requirement has been
185
lifted (that is, a number of days with temperatures between +5˚C and -5˚C, counted from the end
186
of the previous vegetation period) growing degree days (GDD) above a species-specific
187
threshold are accumulated. In addition to a general GDD envelope – a first coarse filter for the
188
ability of a species to establish – the GDD required for bud burst are calculated and compared to
189
a species-specific threshold value. Acknowledging the importance of frost for tree establishment
190
and considering that frost-hardiness of plants varies throughout the year the calculation of frost
191
effects differs with season. A threshold for minimum winter temperature must not be exceeded
192
for a species to establish at a given site. The effect of spring frost is considered by resetting the
193
bud burst GDD counter if the daily mean temperature falls below zero. During the growing
194
season (that is, after bud burst), a minimum number of frost-free days are required for
195
establishment. All the above described thresholds are implemented as binary exclusion filters
196
(that is, if exceeded penv= 0 else penv= 1).
9
197
To account for the effect of growing season frost a continuous response function (fF) is applied to
198
allow for a hardening effect, that is, the first growing season frost event has the biggest impact
199
while additional events decrease in effect (Eq. B2).
f F  kEFT
frost0.5
Eq. B2
200
with frost the number of frost days in the vegetation period and kEFT an empirical parameter. In
201
addition to this regeneration-specific establishment filters related to the thermal environment
202
(Nitschke and Innes 2008) also effects of water and nutrient availability are accounted for in the
203
iLand regeneration module. To that end we harness the detailed physiological calculations
204
conducted for adult trees in iLand (see Seidl and others 2012) to derive a process-based modifier
205
of annual physiological limitations to tree growth (Eq. B3).
 uAPAR   eff

f env  min 
, 1
 APAR    f

0
ref


Eq. B3
206
with APAR the absorbed photosynthetically active radiation of a given species, uAPAR the used
207
APAR, ε0 the potential light use efficiency, εeff the effective light use efficiency (see Seidl and
208
others 2012 for details), and fref an empirical scaling factor. Eq. B3 assumes that the
209
physiological status of a species under given conditions can be expressed by relating the
210
theoretical maximum light use to the actual light use for a given leaf area. The addition of fref,
211
which is the fenv derived for the most productive sites within a species range, scales fenv to a unit
212
scale for application as scalar modifier. This index has the advantage that it is based on
213
physiological principles, and grants consistency between saplings and adult trees with regard to
214
environmental responses. If the binary thermal requirements for GDD limits, winter frost, and
215
bud burst are fulfilled, penv is calculated as Eq. B4:
penv  f F  f env
Eq. B4
216
The effect of light availability on establishment is derived from executing iLands detailed light
217
calculations for the regeneration layer (Seidl and others 2012). Tree establishment probability is
10
218
finally calculated according to Eq. B5, assuming that all three factors (seeds, favorable abiotic
219
environment, light) are required for a species to establish (that is, est  seed  env  light ).
pest  pseed  penv  plight
Eq. B5
220
This calculation is conducted at a spatial resolution of 2 m grid cells (note that the involved
221
processes have different spatial resolutions, that is, pseed 20 m, penv 100 m, and plight 2 m) and, a
222
random number is drawn to determine whether a species establishes in a given year and cell.
223
224
Sapling growth and competition
225
After successful establishment, stand initiation stage vegetation development is modeled using a
226
mean tree approach at a 2 m cell horizontal resolution (see Rammig and others 2006). The main
227
tree attribute modeled is height growth, using a two-parameter Bertalanffy function to define
228
species-specific height growth potential (Eq. B6),
ht 1  hmax
 
   h
 1  1   t
   hmax
 



1
3


 kg 
e 




3
Eq. B6
229
with ht the tree height at time step t, hmax the maximum attainable tree height, and kg an empirical
230
parameter (Table B1). Rammig and others (2007) showed that this formulation, based on general
231
physiological principles, is a good middle ground between accuracy and parsimony.
232
Furthermore, they documented that parameter estimates were consistent over a wide range of
233
tree heights, which allows the parameter kg to be estimated from widely available yield table
234
growth curves for maximum site index. Potential height growth (ihpot) is derived from
235
differentiating Eq. B6, and actual height growth is calculated via the aggregated environmental
236
modifier of physiological growth limitations (Eq. B3). Furthermore, light availability is
237
accounted for via the species-specific light-use potential (that is, the light utilization index LUI
238
of Seidl and others 2012) to arrive at the realized height growth ih (Eq. B7).
11
ih  ih pot  f env  LUI
Eq. B7
239
In the mean tree sapling model intra-specific tree competition is not simulated explicitly, that is,
240
competitive pressure is only exerted by surrounding overstorey trees via LUI. For every species,
241
the 2 m cell is simulated as structurally homogeneous species cohort, and a fixed height-diameter
242
ratio for saplings is assumed. For accounting purposes and biogeochemical budgets sapling
243
numbers can be calculated from tree dimensions using Reinekes rule (Reineke 1933). We
244
currently don’t simulate effects of ungulate herbivory explicitly, but implicitly account for a
245
density effect via reducing Reinekes maximum stand density (SDIsap, see Table B1). If more than
246
one species establishes per 2 m cell, mean tree cohorts for every species are simulated as
247
described above. Inter-specific competition is an emerging property of different height growth
248
potentials (early vs. late seral species), different light-use potentials (LUI), as well as
249
environmental responses (fenv). If a species cohort fails to realize a specific minimum percentage
250
of its height growth potential for a subsequent number of years, mortality removes the species
251
from the respective cell (Keane and others 2001). Trees are recruited from the mean tree model
252
of the regeneration layer into the individual tree structure of iLand once they enter the stem
253
exclusion stage (currently defined generically as reaching a tree height of ≥ 4 m). For every cell
254
the species that first exceeds this threshold, that is, the species that is the most competitive in the
255
regeneration layer, is recruited while all other species cohorts on the same cell are discarded.
256
257
Biogeochemical cycling in the regeneration layer
258
The C and N budgets of the regeneration layer are calculated applying the species-specific
259
allometric equations used for adult trees in iLand (Seidl and others 2012). To that end an average
260
tree per species and resource unit (RU) is calculated as arithmetic mean over all tree cohorts on
261
regenerated 2 m cells (note that we deliberately ignore Jensens inequality here in order to
262
increase the computational performance in simulating large landscapes). Its’ C compartments are
12
263
derived from allometries and scaled to RU level by accounting for sapling stem numbers derived
264
from Reinekes rule. The C uptake of the regeneration layer can be calculated from translating the
265
annual height increment of all individual cohorts to changes in C compartments at the RU level.
266
Turnover of foliage and fine roots and their corresponding fluxes into the litter pool are handled
267
in a way similar to that used for adult individuals. Mortality fluxes from self-thinning (derived
268
from Reinekes rule as an effect of dbh growth) are routed directly to the respective litter and
269
downed deadwood pools (that is, saplings are not assumed to create snags). The model
270
parameters for the regeneration module are summarized in Table B1, and a technical
271
documentation can be found at http://iland.boku.ac.at.
272
273
13
274
275
276
277
Table B1: Parameters of the iLand Regeneration Module
symbol
description
estimate
source*
amat
maturity age (years)
20
1
aseed
seed year interval (years)
7
1
pnsy
fraction of seeds in non-seed years (dim.)
0.25
1
kK1
seed kernel parameter (dim.)
30
1, 2
kK2
seed kernel parameter (dim.)
200
1, 2
kK3
seed kernel parameter (dim.)
0.2
1, 2
kEtmin
minimum winter temperature for establishment (°C)
-37
1, 3
kEchill
chilling requirement to break dormancy (days)
30
1, 3
kEGDDbase base temperature for establishment GDD (°C)
3.4
1, 3
kEGDDmin
minimum GDD for establishment (GDD)
177
1, 3
kEGDDmax
maximum GDD for establishment (GDD)
3261
1, 3
kEGDDbb
GDD for bud burst (GDD)
255
1, 3
kEFF
frost-free days required during the veg. period (days)
65
1, 3
kEFT
tolerance to frost during the veg. period (dim.)
0.5
1, 3
kg
sapling height growth parameter (dim.)
-0.0427
4, 5
kMrs
relative stress threshold for sapling growth (dim.)
0.1
6, 7
kMra
stress hardiness of saplings (years)
2
6, 7
hdsap
height to diameter ratio of saplings (dim.)
112
4
SDIsap
stand density index for the sapling layer (trees ha-1)
655
4, 8
fref
annual physiological response at maximum growth (dim.)
0.503
9, 10
278
* 1= Burns and Honkala (1990), 2= Garman (2004), 3= Nitschke and Innes (2008), 4= McArdle
and others (1961), 5= Rammig and others (2007), 6= Woltjer and others (2008), 7= Price and
others (2001), 8= Reineke (1933), 9= Seidl and others (2012), 10= this study.
All parameters are species-specific and are indicated here exemplarily for Douglas-fir
279
(Pseudotsuga menziesii (Mirb.) Franco). Units are given in parenthesis. dim.= dimensionless,
280
GDD= growing degree days.
281
282
References
283
Burns RM, Honkala BH. 1990. Silvics of North America, Vol. 1, Conifers. Washington DC:
284
U.S.D.A.
285
http://www.na.fs.fed.us/pubs/silvics_manual/table_of_contents.shtm (2011-10-03)
Forest
Service
Agriculture
14
Handbook
654.
286
287
288
289
290
291
292
293
294
295
Garman SL. 2004. Design and evaluation of a forest landscape change model for western
Oregon. Ecological Modelling 175: 319-337.
Greene DF, Canham CD, Coates KD, LePage PT. 2004. An evaluation of alternative dispersal
functions for trees. Journal of Ecology 92: 758-766.
He HS, Mladenoff DJ. 1999. The effects of seed dispersal on the simulation of long-term forest
landscape change. Ecosystems 2: 308-319.
Keane RE, Austin M, Field C, Huth A, Lexer MJ, Peters D, Solomon A, Wyckoff P. 2001. Tree
mortality in gap models: Application to climate change. Climatic Change 51: 509-540.
Koenig WD, Knops JMH. 2000. Patterns of Annual Seed Production by Northern Hemisphere
Trees: A Global Perspective. American Naturalist 155: 59-69.
296
Lexer MJ, Hönninger K. 2001. A modi®ed 3D-patch model for spatially explicit simulation of
297
vegetation composition in heterogeneous landscapes. Forest Ecology and Management
298
144: 43-65.
299
Lischke H, Löffler TJ. 2006. Intra-specific density dependence is required to maintain species
300
diversity in spatio-temporal forest simulations with reproduction. Ecological Modelling
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198: 341-361.
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303
304
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McArdle RE, Meyer WH, Bruce D. 1961. The yield of Dougals fir in the Pacific Northwest.
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Appendix C: iLand evaluation
326
iLands competition modeling algorithms and production physiology have been thoroughly
327
evaluated previously (Seidl and others 2012). Here, we focused on evaluating the models ability
328
to simulate landscape scale forest dynamics and C cycling. At the stand level, we compared
329
simulated C compartments against the detailed data reported by Smithwick and others (2002) for
330
13 one hectare old-growth stands. Furthermore, we compared simulated aboveground live carbon
331
(ALC) at the landscape scale against the independent estimates based on Lidar data (see Table
332
3). In addition, variables of stand structure and composition were compared to landscape-scale
333
estimates of gradient nearest neighbor (GNN) imputation (Ohmann and others 2011).
334
335
Stand level
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Comparing undisturbed 500 year model simulations against the upper bounds of C storage
337
estimated empirically by Smithwick and others (2002) resulted in good correspondence of iLand
338
simulations to observations (Figure C1). For the 13 stands investigated by Smithwick and others
339
(2002) in our study area the mean difference between predicted and observed total ecosystem C
340
was -30.3 Mg ha-1 (that is, -3.6%). Model and observations were in agreement with regard to C
341
stocks being highest in the mid-elevation zone of the HJA landscape. Soil and litter pools
342
simulated with the newly developed iLand soil module differed only moderately from
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observations (on average -10.8 Mg ha-1 (-8.8%) and +1.2 Mg ha-1 (+6.3%), respectively),
344
whereas woody detritus was underestimated by the model (-24.8 Mg ha-1 (-18.3%) over all 13
345
stands). Overall, model performance with regard to stand level C pools was well in the range of
346
previous results with detailed stand level C models for the region (for example, Harmon and
347
Marks 2002; Harmon and others 2009).
348
17
predicted
n=7
live C
800
0
200
400
Mg C ha
400
200
0
observed
600
1
800
600
1
Mg C ha
600
400
0
200
Mg C ha
1
800
1000
(c)
1000
(b)
1000
(a)
observed
predicted
n=3
woody detritus C
observed
predicted
n=3
litter C
soil C
349
350
Figure C1: Carbon compartments in old-growth forests at the HJ Andrews experimental forest in
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three vegetation zones (from low to high elevation): (a) Tsuga heterophylla zone, (b) transition
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zone, (c) Abies amabilis zone. Observed data are taken from Smithwick and others (2002),
353
predicted data are results of 500 year simulations with iLand. n: number of 1 ha reference stands
354
analyzed per vegetation zone.
355
356
357
Landscape level
358
Structure
359
At the landscape level, iLand was generally able to reproduce structure and composition of old-
360
growth forests at HJA compared to GNN- imputed inventory data (Ohmann and others 2011).
361
Over the 2191 ha study region the distribution of simulated stand structure, that is, basal area
362
(BA), quadratic mean diameter (QMD), standard deviation of diameter distribution (SD dbh), and
363
number of trees at least 100 cm dbh (N100), fell well within the bounds of their empirical
18
364
distributions (Figure C2). Both the central tendency and extremes of the simulated distributions
365
corresponded well to the GNN-derived distribution of inventory data. In a cell to cell comparison
366
simulated stand structure matched GNN data well (Table C1). Because iLand simulates
367
individual trees explicitly, also the simulated canopy structure could be evaluated, using Lidar
368
95th percentile return heights as reference data. Simulated rumple indices matched Lidar-derived
369
data well, supporting iLands ability to simulate the complex 3D structure of old-growth forest
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ecosystems (Figure C3).
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372
373
Table C1: Comparison of Simulation Results to Independent Reference Values from Lidar
374
(Rumple Index) and GNN (All Other Variables) for Old-growth Forests at the HJ Andrews
375
Experimental Forest
structure
basal area (BA, m2·ha-1)
quadratic mean dbh (QMD, cm)
sd of dbh distribution2 (SDdbh, cm)
trees >100cm dbh (N100, n·ha-1)
rumple index (rumple, dim.)
composition
376
Psme (% basal area)
Tshe (% basal area)
Thpl (% basal area)
Abam (% basal area)
Abpr (% basal area)
Acma (% basal area)
Alru (% basal area)
Tsme (% basal area)
For abbreviations see Table 1.
reference
simulation
59.9
35.2
24.3
14.8
2.32
52.9
33.9
22.4
18.2
2.24
mean pixel
deviance
-7.0
-1.3
-1.9
+3.4
-0.08
62.4
22.0
10.0
3.0
1.3
0.6
0.4
0.3
58.1
26.4
8.8
4.3
1.4
0.1
<0.1
0.4
-4.3
+4.4
-1.2
+1.3
+0.1
-0.5
-0.4
+0.1
377
378
Composition
379
iLand was well able to reproduce the species composition in old-growth forests of the western
380
Cascades. Overall, the HJA is a landscape strongly dominated by Pseudotsuga menziesii ((Mirb.)
381
Franco), which is well captured by the simulations (Table C1). The true fir species Abies
19
382
amabilis (Dougl. ex Forbes) and Abies procera (Rehd.) as well as Tsuga mertensiana ((Bong.)
383
Carr.) are specialists found in the mid to high elevation portions of our study landscape, a pattern
384
that is also well reproduced by iLand (Figure C4). In general the distributions of simulated
385
species shares fell well within the boundaries of the GNN-imputed empirical data (Figure C5).
386
40
60
80
100 120
10
0.8
0.4
0.0
20
40
60
(c)
(d)
20
30
40
50
0.0
0.4
0.8
quadratic mean diameter (cm)
0.4
0
0
basal alrea (m²)
cummulative probability density
20
0.8
0
cummulative probability density
0.0
0.4
0.8
(b)
0.0
cummulative probability density
cummulative probability density
(a)
cm
0
10
20
30
40
50
60
trees per hectare
387
388
Figure C2: Distribution of stand structure indicators in old-growth forests at the HJ Andrews
389
watershed (at 100 m grid resolution). (a) basal area, (b) quadratic mean diameter, (c) standard
390
deviation of the diameter distribution, (d) number of trees with a dbh greater than 100cm. The
391
panels show a comparison of simulated stand structure (red) against mean gradient nearest
392
neighbor (GNN) estimates (black) and the empirical 2.5th and 97.5th percentile (grey) calculated
393
from within grid cell variation, based on 30 m GNN data.
394
395
20
0.8
0.4
0.0
cummulative probability density
1.0
1.5
2.0
2.5
3.0
3.5
rumple index
396
397
Figure C3: Landscape scale distribution of canopy heterogeneity as represented by the rumple
398
index, calculated for 100 m grid cells. Black: Lidar; red= iLand.
399
400
401
Carbon density
402
Simulated mean ALC was slightly lower than Lidar-derived ALC (- 8.88%, see Table 3),
403
resulting mainly from moderately underestimated basal area levels in the simulations. However,
404
the simulated ALC distribution over the landscape fell well within the confidence bounds of the
405
empirical Lidar model (Figure C6). Some disagreement between the two estimates could be
406
detected for the spatial location of ALC hotspots estimated from Lidar and those derived from a
407
500-year model simulation with iLand (Figure C7).
408
21
409
410
Figure C4: Simulated and GNN-imputed distribution and abundance of the high elevation tree
411
species Abies amabilis (lower panels), Abies procera (middle panels), and Tsuga mertensiana
412
(top panels). Data are species shares in percent basal area, and the results are masked to the old-
413
growth forest area of the landscape. iLand data are results after a 500 year simulation.
414
22
0.4
0.6
0.8
0.4
0.6
0.8
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
1.0
0.0
0.0
0.8
0.6
0.4
0.2
1.0
1.0
0.0
0.2
0.4
0.6
(f)
(g)
(h)
0.6
basal alrea share
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
basal alrea share
1.0
0.2
0.4
0.6
0.8
1.0
0.8
1.0
basal alrea share
0.8
0.6
0.4
0.2
0.0
cummulative probability density
0.8
0.6
0.4
0.2
0.0
cummulative probability density
0.8
0.6
0.4
0.2
0.0
cummulative probability density
0.8
0.6
0.4
0.8
1.0
(e)
1.0
basal alrea share
1.0
basal alrea share
0.4
0.2
cummulative probability density
1.0
0.2
0.0
cummulative probability density
0.8
0.6
0.4
0.0
basal alrea share
0.2
0.0
(d)
basal alrea share
0.0
cummulative probability density
0.2
1.0
1.0
0.2
0.0
cummulative probability density
0.8
0.6
0.4
0.2
0.0
cummulative probability density
0.0
415
(c)
1.0
(b)
1.0
(a)
0.0
0.2
0.4
0.6
basal alrea share
416
Figure C5: Species distribution in old-growth forests (2191 ha, 100 m resolution) at the HJ Andrews watershed. (a) Pseudotsuga menziesii, (b)
417
Tsuga heterophylla, (c) Thuja plicata, (d) Abies amabilis, (e) Abies procera, (f) Tsuga mertensiana, (g) Acer macrophyllum, (h) Alnus rubra. The
418
panels show a species-wise comparison of simulated species share (red) against mean gradient nearest neighbor (GNN) estimates (black) and the
419
empirical 2.5th and 97.5th percentile (grey) calculated from within grid cell variation based on 30 m GNN results.
23
420
421
Figure C6: (a) Cumulative density distribution of aboveground live carbon (ALC) in old-growth
422
forests at HJ Andrews according to Lidar (black) and simulation modeling (red). The 5-95%
423
confidence interval of the empirical Lidar - ALC model is indicated in grey. (b) Pixel-wise (100 m
424
resolution) difference between aboveground live biomass estimated by Lidar and iLand.
425
24
426
427
Figure C7: Spatial distribution of simulated (iLand) and Lidar-based (Lidar) aboveground live C
428
(ALC) in old-growth forests at the HJ Andrews. Values are in Mg C·ha-1 and maps are masked to
429
the old-growth forest area within the HJ Andrews watershed (bold black line).
430
431
25
432
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434
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