Simulating harvesting scenarios towards the ... - Springer Link

Wetlands Ecol Manage (2011) 19:397–407 DOI 10.1007/s11273-011-9224-4

ORIGINAL PAPER

Simulating harvesting scenarios towards the sustainable use of mangrove forest plantations M. L. Fontalvo-Herazo • C. Piou • J. Vogt U. Saint-Paul • U. Berger

•

Received: 17 January 2011 / Accepted: 11 July 2011 / Published online: 19 July 2011 Ó Springer Science+Business Media B.V. 2011

Abstract Mangrove forests appear among the most productive ecosystems on earth and provide important goods and services to tropical coastal populations. Thirty-five percent of mangrove forest areas have been lost worldwide in the last two decades. Management measures could be an option to combine human use and conservation of mangroves. These measures can be improved if their impacts are assessed before they are performed. By doing so, the best management option out of a set of all potential options can be selected in advance. The mangrove model—KiWi—has been proven to be suitable for analyzing mangrove forest dynamics in the neotropics. Here, the model was applied to

mangrove management scenarios. For this, the model was parameterized to Rhizophora apiculata, one of the most common mangrove species planted in Asia for timber production. It is thus the first simulation model describing Asian mangrove plantations. The recently developed Pattern Oriented Modelling approach was used to find those parameters fitting best density patterns and dbh (diameter at breast height) size classes reported in literature. The results demonstrated that the KiWi model was able to: (1) reproduce the growth patterns of a mono-specific plantation of R. apiculata in terms of forest density and size class distribution and (2) can provide criteria for the selection of a thinning strategy within a harvesting cycle.

M. L. Fontalvo-Herazo U. Saint-Paul Leibniz Center for Tropical Marine Ecology (ZMT), Fahrenheitstr. 1, 28359 Bremen, Germany

Keywords Rhizophora apiculata Individual-based modeling Pattern oriented modeling Management scenarios KiWi model

M. L. Fontalvo-Herazo (&) 4 rue de Fort, 34150 Puećhabon, France e-mail: [email protected] C. Piou CIRAD, Biological Systems Department, Locust Ecology and Control Research Unit, TA-A50/D, Campus International de Baillarguet, 34398 Montpellier Cedex 5, France J. Vogt U. Berger Department of Forest Biometry and Systems Analysis, Institute of Forest Growth and Forest Computer Sciences, Technische Universitaet Dresden, Postfach 1117, 01735 Tharandt, Germany

Introduction Mangroves appear among the most productive ecosystems on earth and provide important goods and services to coastal population such as filtering capacity of pollutants (Chu et al. 1998; Tam and Wong 1995) trap and stabilization of sediments (Fromard et al. 2004; Mazda et al. 2002; Nicholls and Ellis 2000), indicator of sea level rise

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(Blasco et al. 1996), storm protection (Doyle et al. 2003; Kathiresan and Rajendran 2005; Imbert et al. 2000), support of a diversity of animal species both within the forest and in offshore areas, direct benefits to local communities and indirect benefits to the economy of the region through the contribution to fisheries production and the development of the ecotourism industry (Primavera 2005; Saenger 2002; Vance et al. 2002; Acosta and Butler 1997 among others). Although mangroves show all these unique characteristics, they have been subject to massive destruction. In the past four decades, 35% of the area of mangrove forests has been lost globally (Valiela et al. 2001) due to human conversions of coastal wetlands to aquaculture, agriculture and urbanization (e.g. Nurzali 1987; Primavera 2000, 2005; Saenger 2002; Vannucci 1987). Therefore, mangrove forest protection and management have been proclaimed as an important activity in coastal environmental agendas (Saenger 2002). Mangrove management has been done in various countries of Asia (Bangladesh, Malaysia, Thailand, Indonesia, India and Vietnam) with the commercial purpose of extraction of charcoal, poles, fire-wood and pulp (Field 1996; Putz and Chan 1986; Watson 1928). Although, the management strategy in these countries have been recognized as a good one (e.g. Matang Forest in Malaysia, see Gong and Ong 1995; Ong 1995) the possibility of yield decrease in the second and third rotation has been stated by some authors (Gong and Ong 1995) reflecting a possible short-term financial return. Moreover, it does not prioritize the conservation of ecological properties of the ecosystem (Field 1996) which will compromise other ecosystem goods and services. Due to this, an alternative strategy that combines human use (timber exploitation) and conservation of mangroves might be considered as a better management approach. Simulation models have been proven to be good tools for both the study of forest dynamics and for testing silviculture management strategies. Two examples are the Silviculture and Yield Modelling for tropical Forests (SYMFOR, Phillips et al. 2004) and FORMIND (Ko¨hler and Huth 2004). The models simulate ecological processes such as growth, mortality, competition and recruitment on the base of species groups (SYMFOR) or functional groups (FORMIND). The models have been applied for analyzing wood harvesting management decisions

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Wetlands Ecol Manage (2011) 19:397–407

showing changes on the forest structure over time due to repeated logging events among others. Until now, no individual based models have been applied for logging management of mangrove forests. Three models parameterized for the same three neotropic mangrove species, Rhizophora mangle, Avicennia germinans and Laguncularia racemosa, are currently available for the simulation of mangrove dynamics: MANGRO (Doyle et al. 1997), FORMAN (Chen and Twilley 1998) and KiWi (Berger and Hildenbrandt 2000). The MANGRO model was developed for assessing the impact of hydrologic restoration of freshwater flow in the Everglades (Berger et al. 2008 and citations within). FORMAN is used to simulate long-term dynamics of mangrove development based on soil nutrient conditions and salinity. A prominent example is the simulation of possible restoration trajectories of the former degraded mangrove estuary Cienaga Grande de Santa Marta in Colombia (Twilley et al. 1998). The KiWi model was particularly developed for analyzing the influence of local tree-to-tree interactions on mangrove forest dynamics in the Neotropics (Berger and Hildenbrandt 2000). The model has been successfully used for studying trajectories of secondary mangrove succession (Berger et al. 2006) and recovery after hurricane occurrence (Piou et al. 2008). One reason for not using these models for silviculture practices so far is the absence of silviculture practices in neotropic mangroves, the focused area of both models. Nearly all plantations are located in the Indo-West-Pacific (e.g. Field 1998; Gong and Ong 1995; Gong and Ong 1995, 1995, 1995; Srivastava and Harnarinder 1984; Saenger 2002). Another aspect is the lack of field data needed to parameterize the models. These data are usually not in the focus of the timber industry. In an attempt to deal with the latter problem, the application of the pattern oriented modelling (POM) approach, developed as a strategy for any kind of individual based-models (IBMs), is promising. One of the POM advantages is that different kinds of data and information obtained in literature and field studies can be synthesized in order to define models’ structure, parameterize them and evaluate their suitability for describing an ecological system in a particular situation (Grimm et al. 1996; Grimm et al. 2005).


With POM, we used field patterns cited in mangrove silviculture literature to pre-define values and value ranges of model parameters. The best combination of parameter values is then determined by systematic comparison between simulations results and so called filter patterns, which are defined from those used for the pre-definition of the parameters (for further reading see Wiegand et al. 2003; Grimm and Railsback 2005). In this context, the general objective of this study is to contribute to the ongoing research for mangrove silviculture management alternatives. More specifically, this study aims at testing different harvesting cycles of mono-cultural plantations of Rhizophora apiculata,. For this purpose, the KiWi model needed to be adapted to R. apiculata. Therefore, two steps were performed in this study: The first one was the parameterization of the KiWi model to the mangrove species R. apiculata basing on data available in literature. In the second step, harvesting cycles suggested by Gong and Ong (1995) for the Matang Forest were simulated in order to evaluate their potential ecological and economic impacts. This species and specific forest were considered in the present study because R. apiculata is one of the most important mangrove species for silviculture in SouthEast Asia and the Matang Mangrove Forest Reserve is recognized as one of the best managed silviculture area (Walters et al. 2008).

Methodology Available empirical data used The empirical data used in this study correspond to plantation areas located near Kuala Sepetang (4°50 N, 100°37E) in the Matang Mangrove Forest Reserve (center around 4°45 N, 100°35E) which covers an area of about 40000 ha over 52 km of coast (Chan 1996; Gong and Ong 1995; Ong 1995). This forest has been managed for timber production (charcoal and firewood) for more than a century. The main species in the planted forest is R. apiculata. The management strategy is based on an annually clear-felling of small patches among the whole forest (about 1000 ha per year, Ong 1995). The harvesting process is based on a 30 years rotation cycle with two thinning activities at 15 and 20 years. After the final

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clear-felling, sites are evaluated for natural establishment of seedlings. In case natural recruitment remains low (less than 90% seedlings with roots) planting activities support the recolonization (Chan 1996).

First step: KiWi model description and parameterization The KiWi model was developed with the purpose of analyzing neotropical mangrove forest dynamics affected by environmental settings, specific competition and natural and/or anthropogenic disturbances (Berger and Hildenbrandt 2000; Berger et al. 2008). In order to accomplish this task the model uses two hierarchical levels: individual trees of three different species (Avicennia germinans, Rhizophora mangle, Laguncularia racemosa) and simulated area. An individual tree is described by its stem position (x,y), age, stem diameter (dbh) and annual stem increment (Ddbh). The simulated area is characterized by maps that define abiotic conditions that can interact with individual tree development. The trees life cycle is by different biological processes operating at a yearly time step. To simulate interactions among trees, the KiWi model incorporates a competition sub-model defined by the field of neighborhood (FON). In this approach a tree is described by its stem position and a circular zone of influence (ZOI) around the stem with a size-dependent radius. Within the ZOI, a scalar field, the FON, is calculated to determine the competition strength that the tree extent to their neighbors. The FON radius R of a tree depends on its size: R ¼ a rbhb

ð1Þ

where rbh is the stem radius of the tree, a and b are scaling parameters based on allometric relations of the trees measured in the field (for further details see Berger and Hildenbrandt 2000, 2003 and the overview, define concepts and details (ODD) protocol of the model provided by Grimm et al. 2006, Appendix or Piou et al. 2008, Appendix). The parameterization procedure of the FON consisted on the elaboration of a linear regression model among the logarithms of monospecific stands data of dbh and density. The slope of the regression (K) and the intercept (r) value are used for the calculation

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of a and b parameters according to the following formulas: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 21500 ð2Þ a¼ 1 K r e p 200 b ¼ K=2

ð3Þ

Literature data of mean density and mean dbh reported by Chan (1996) and Srivastava and Harnarinder (1984, in Saenger 2002) were used for this regression. The growth of a mangrove tree is described, in the KiWi growth sub-model, as the stem diameter increase over time. It applies a formula introduced by Botkin et al. (1972) which was also used by the FORMAN model (Chen and Twilley 1998) with the assumption of optimal growth conditions: G dbh 1 dbhdbhH max Hmax Ddbh ¼ ð4Þ 274 þ 3b2 dbh 4b3 dbh2 where dbh refers to the diameter at breast height of the tree (cm), H is the tree height (cm), dbhmax and Hmax are maximum values of diameter and height for a given tree species. G, b2 and b3 are species-specific growth parameters that can be calculated by the following equations (Botkin et al. 1972): b2 ¼

2ðHmax 137Þ dbhmax

ð5Þ

b3 ¼

ðHmax 137Þ dbh2max

ð6Þ

G¼

Ddbhmax Hmax 0:2 dbhmax

ð7Þ

where Ddbhmax is the maximal annual stem diameter increment of the tree (Botkin et al. 1972). For representing situations of not optimal conditions, correction factors between 0 (bad conditions) and 1 (optimal conditions) are multiplied to the optimal growth value given by Eq. 2. The correction factor C(FA) determines a competition effect on tree growth of neighboring trees (for more details see Berger and Hildenbrandt 2000, 2003). C(FA) takes a value of 1 if a tree does not have any competing neighbor and decreases when competition strength increase (FA). C(FA) is calculated as: C ðFA Þ ¼ ð1 u FA Þ

ð8Þ

where FA is the integration of the neighbors FON intensities over the FON area overlap with the focal

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tree; and u is an arbitrary maximum value of effect of competition, simulating resource sharing capacity (Piou et al. 2008). The scale of the FON intensity over the ZOI, as defined by Berger and Hildenbrandt (2000), takes values between Fmin = 0 (boundary of the ZOI) and 1 (position of the focal tree stem). Fmin is assumed as a species-specific parameter. The tree mortality sub-model describes mortality of each tree by a ‘‘memory effect’’ which registered the growth increment (Ddbh) over a time range of 5 years. A tree dies if the mean growth increment during the 5 years is less than a threshold (CritDX) defined as half the average stem diameter increment under optimal conditions (Berger and Hildenbrandt 2000). This provides an increase of the probability that a tree dies with unfavorable environmental conditions and for trees approaching maximum size used as an indicator of the unknown age (Berger et al. 2006). Due to the ‘‘memory effect’’, however, the probability that a vuDSlnerable tree survives increases as soon as an improvement of the local situation results in an increasing growth rate. The use of the pattern oriented modelling (POM) approach For the performance of the model parameterization, species-specific parameters were modified in order to reproduce the available patterns (forest density and dbh size classes, see also empirical data section). The purpose of this variation was to identify the best combination of parameters that adjust the model to these patterns. The parameters considered from the growth equation were: maximal height (Hmax), maximal diameter at breast height (dbhmax) and maximal increment of the dbh (Ddbhmax) (Table 1). Additionally, three other parameters were taken into account: (1) the constant value u (Eq. 8) which influences the growth threshold of R. apiculata; (2) the Fmin parameter that determine the shape of the FON and thus the type of competition (asymmetric and symmetric) among neighbor trees and (3) the CritDX parameter which determines directly the mortality due to reduced growth. Since no data on the growth equation parameters were found for R. apiculata in literature, a range of values for each parameter (Hmax and dbhmax) was selected taking into account the parameterization done by Chen and Twilley (1998) and Berger and Hildenbrandt (2000) for Rhizophora


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Table 1 Species-specific parameters used for the parameterization Parameter

Description

R. mangle literature values

Values tested

Hmax

Maximum height (cm)

4000a

2500, 3000, 3500, 4000, 4500, 5000

dbhmax

Maximum diameter at breast height (cm)

100a

60, 80, 100, 120, 140, 160

Ddbhmax U

Maximum increment dbh (cm/year) Resource sharing capacity

_ 2.0b

0.25, 0.5, 0.75, 1.0, 1.25, 1.50 0.2, 0.25, 0.5, 0.75, 1.0, 1.25

Fmin

Minimum value of the FON

0.1b

0.01, 0.1, 0.3, 0.5, 0.7, 0.9

CritDX

Constant for tree mortality threshold

0.2b

0.05, 0.1, 0.15, 0.2, 0.25, 0.3

a

Literature values are taken from Chen and Twilley (1998) and

mangle, a mangrove tree of the same genus (Table 1). For the Ddbhmax parameter the range of values was selected based on the highest value found in literature for R. apiculata 0.7 cm year-1 (Saenger 2002). The same procedure was applied for the other three parameters. In order to test the parameterization, KiWi model simulations were performed in a hectare describing a R. apiculata forest plantation. The initial establishment was 6000–7000 R. apiculata trees (1.3 m height) at a distance of 1.2 m. The number of recruitment was assumed high for the first 5 years, stochastically between 0 and 3000 new trees per year depending on the FON intensity. After the fifth year the number of recruits decreased to stochastically between 0 and 100 new trees per year. The simulated harvesting cycle included two cutting events at 15 and 20 years corresponding to the field data (see following section). The parameter space of the model was systematically explored by covering the whole set of parameter combination (six values per parameters, six parameters, 6^6 = 46656 combinations). For each combination a number of 10 replicates were performed. The obtained results included the total number of trees per year (density) and the dbh size classes distribution for simulated time-steps after 5, 8, 13, 18, 23 and 28 years. The results were compared with the patterns extracted from the literature (Gong and Ong 1995). This literature data given in girth in breast height (gbh) was converted into dbh (dbh = gbh/p). For a systematic selection of the best model parameterization, the method of mean sum of squares deviation (MSD, e.g. Hilborn and Mangel 1997) was applied. The distance between model data and literature data was estimated by the following equation:

b

Berger and Hildenbrandt (2000)

P MSD ¼

ðsimi liti Þ2 n

ð9Þ

where sim and lit are the simulated and literature data respectively, and n refers to the total number of observations. A relative index of goodness of fit was calculated by adding the sum of squares of each parameter combination for each pattern: density and girth distribution. The best fit was considered as the minimum relative index value (GFj). MSDdbhj 1 MSDdensityj GFj ¼ þ ð10Þ 2 MSDdensitymin MSDdbhmin Second step: simulations of different thinning strategies within the 30 years harvesting cycle According to Gong and Ong (1995), a yield decline of about 22 ton ha-1 was observed between the second (1967–1969) and third (1970–1977) generations of the Matang forest plantation. In order to cope with the lost of useful biomass due to self-thinning and increase the probability of remaining trees to get a better commercial size, Gong and Ong (1995) suggested the reduction of the thinning management time and an increased frequency. Inspired by these suggestions, four different types of thinning activities were created and simulated: (1) the current management plan implemented at the Matang plantation. It corresponds to a 30 years log cycle; with the first thinning practice at 15 years using a 1.2 m stick circumference radius to determine the cutting area around a selected remaining tree. A second one is performed at 20 years with a 1.8 m stick method (Gong and Ong 1995). (2) A 30 years management plan with thinning at 7, 13 and 20 years. The harvesting procedure

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was the same as described above, except that the 7 years cutting eliminated 25% of the smallest trees considering dbh. (3) The last tested management plan contains cutting events at 7, 15 and 20 years and thus combines the establishment management plan with the one suggested as (2). The idea with this cycle is to reduce the self-thinning effect but also to maximize the size of trees that can be harvested for pole use by increasing growth time. (4) Moreover, we simulated the situation with no thinning activities. The first wood removal took place during the clear-felling after 30 years. For evaluating which one of the suggested harvesting cycles is best, we used two types of indicators: (1) tree density and the distribution of size classes as ecological-oriented indicators, and (2) biomass production over dbh classes in the respective cycle as economic-oriented indicator. We performed a Pearson Chi-Square test in order to evaluate whether different management treatment scenarios result in significant differences of biomass production in particular size classes. Standardized residuals were evaluated as a Post hoc test. All statistical analyses have been carried out with the statistical software R (R Development Core Team 2008). Results First step: KiWi model parameterization FON selected scaling parameters The linear regression of the logarithms of the density and dbh (Fig. 1) allowed the calculation of the FON

Fig. 1 Linear regression for the calculation of the FON scaling parameters a and b

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scaling parameters a and b corresponding to 13.019 and 0.665 respectively.

Tree growth parameters Table 2 shows the best six combinations of parameters according to the calculation of the MSD and the minimum relative index value. The values in the first row of the table correspond to the ‘‘best parameter combination set’’ (used afterward) reproducing growth pattern, forest density and dbh distribution, found in literature for R. apiculata forest plantation (Gong and Ong 1995).

Forest density pattern As shown in Fig. 2, the model reproduced well the forest density tendency. The density of trees above 2 m height showed a progressive and rapid increase during the first 6 years, followed by a decline until the first cutting time at 15 years. Forest density was reduced after the first thinning at 15 years to 3219 trees ha-1 (52%) and did not vary significantly until the second thinning at 20 years, when the density was reduced to 1649 trees ha-1 (52%). The constant densities after the two thinning events indicated that the spatial configuration of the remaining trees in the model did not allow natural recruitment or selfthinning to happen.

Size distribution pattern Figure 3 shows the dbh size distribution obtained with the ‘‘best’’ model parameterization for 6 consecutive years for which literature data were available (Gong and Ong 1995). The comparison shows qualitatively similar patterns. However, model results during the first times (at 5 and 8 years) have a tendency to present more individuals of small size classes than the field observations. Like in the field, the simulated size distributions become broader in the later time steps. They show, nevertheless, a pronounced homogeneity in tree size. In comparison to the literature data given for the plantations, the numbers of both the small and large trees are too small in the model.


403

Table 2 Six best parameter combination sets selected according to the calculation of the minimum relative index value Parameter Hmax

dbhmax

Ddbhmax

u

Fmin

CritDX

Minimum relative index value (GFj)

3000

80

1.5

1.25

0.5

0.05

4.220 (1)

3000

80

1.5

1.0

0.7

0.05

4.329 (2)

2500

60

1.5

1.25

0.5

0.05

4.453 (3)

2500

60

1.5

1.0

0.7

0.05

4.482 (4)

4500

120

1.5

1.0

0.7

0.05

4.498 (5)

4500

120

1.5

1.25

0.5

0.05

4.501 (6)

Fig. 2 Comparison of Rhizophora apiculata forest density (trees ha-1) with a plantation management plan of 30 years rotation. Thinning events take place at 15 and 20 years. Model results are shown as gray squares and literature field data as black dots

Second step: simulation of different thinning strategies within the 30 years harvesting cycle In this step, the best model parameterization was used to simulate the four thinning activities mentioned above and to assess their potential outcome (Fig. 4). Considering forest density, it is important to underline that the unmanaged forest simulations showed a higher density at year 30 in comparison with the forest with thinning management (Fig. 4a). Nevertheless, trees in unmanaged plantation are smaller than in managed plantations according to the dbh curve (Fig. 4b). After 7 years, a decrease in density is observed even in the unmanaged plantation due to natural mortality. All thinning activities had a decreasing effect on density, favoring the growth in size of the remaining trees. The self-thinning mortality can be also observed in all three management scenarios. Surprisingly, the thinning event at 7 years

implemented in order to reduce competition among trees does not have a significant effect. It does not show much difference in terms of density in comparison to the 15–20 year thinning or to the simulation without thinning management. The 7–13– 20 thinning practice showed the largest mean dbh results. The other two management types (15–20 and 7–15–20 years) showed almost the same dbh distribution. The three managed plantations showed significantly higher values after the cutting event around 13 or 15 years than the unmanaged situation. These results suggest that the harvesting of trees at this age help to increase the growth rates of remaining trees rapidly. Regarding the cut biomass (Fig. 5), the 7–13–20 thinning practice seems to be the most interesting management type. The higher amount of trees cut with this cycle correspond to the dbh class between 10 and 13 cm (57.38 ton ha-1) and compensate a loss in the class of dbh higher than 13 cm (Fig. 5). The Chi-Square test showed the existence of a relationship between the distribution of the harvested biomass and the particular thinning practice applied (X-squared = 15.5776, df = 6, P-value = 0.01621). The Post hoc test highlights that the increase in biomass in second dbh class ([10 to \13) and the decrease in the largest dbh class of the 7–13–20 years thinning practice are the significant contributors (with P \ 0.05) to the relationship between the harvested biomass and the management thinning practices proposed (Fig. 5).

Discussion Our study is a first attempt in using an IBM to assess different management practices in Asian mangroves. We adapted an existent model to the special case of

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Fig. 3 Comparison of the dbh size distribution data in terms of the percentage of numbers of Rhizophora apiculata trees at six different plantation ages. Model results are labeled as bars and literature field data as lines

R. apiculata at the Matang Mangrove Forest in Malaysia. Once parameterized to R. apiculata, the Kiwi model indicated that the management practice with thinning at 7, 13 and 20 years was the most productive in interesting medium dbh trees. First step: KiWi model parameterization The KiWi model reproduced well the tree density literature data. Gong and Ong (1995) reported a high natural recruitment in the early years at the Matang forest plantation that influences the decline of density after the first 5 years. They suggested this was a selfthinning phase with a high percentage of dead stems (over 40%), that caused a high waste of wood. In concordance, our simulations reproduced the high natural recruitment by the increase of the number of trees from an initial density of about 6000 trees/ha (1.3 m height) to a density of 15050 trees ha-1 (more than 2 m height) in the 5th year, representing an increment of about 25%. After 6 years, the model also showed a strong decline in density due to competition for space, resulting in a self-thinning phase. The model also gave a good reproduction of the literature densities at 18, 23 and 28 years, which

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means that thinning events at 15 and 20 years were well mimicked by the simulations. In relation to the dbh distribution patterns the model results showed differences in the number of size classes presented in each time distribution. There are different sources of uncertainties contributing to this deviation. First of all, the growth function used in the KiWi model (and in all other available mangrove simulators) is known to be rather inflexible (Berger et al. 2008). However, the Botkin function (Botkin et al. 1972) can be parameterized from relatively small data sets and this advantage is not negligible. The discrepancy might also come from the scarce field data collected from the literature and the resulting uncertainties in model parametrization. The different dbh distributions from the literature correspond to different stands of different ages at a same sampling time (Gong and Ong 1995). These different stands could have experienced different mean tree growth rates or natural recolonization events. This reminds that precaution should be taken when comparing these historical patterns since they might not be the same for all the stands. The initial densities might have been different on each stand and as a result also the dbh distribution.


405

parameter is relatively high and according to Bauer et al. (2004) should already produce asymmetric competition in the simulated stands. However, this parameterization or the FON approach in general might not produce asymmetry as strong as what is happening in reality and thus lead to these differences in number of size classes between the observed data and the simulation results. Second step: simulation of different thinning strategies within the 30 years harvesting cycle

Fig. 4 Results of the a density and b dbh of Rhizophora apiculata simulated plantation forest managed with different thinning practices (no treatment (squares), 15–20 years (dots), 7–15–20 years (triangles), 7–13–20 years (crosses)) within a 30 years rotation cycle

Fig. 5 Total harvested trunk biomass of Rhizophora apiculata classified on four different dbh classes (\6 cm, [6 to \10 cm, [10 to \13 cm, [13 cm) for each one of the selected thinning practices (no treatment, 15–20 years, 7–15–20 years and 7–13–20 years). *Trunk biomass corresponds to the clear thinning of the forest at 30 years. **The Chi-Square test shows a relationship between the thinning practices and harvested biomass distribution

Finally, asymmetry in plant competition is known to widen size distributions (Schwinning and Weiner 1998). In our modeling exercise, the selected Fmin

According to our simulation results the self-thinning phase observed in density distribution of the ‘‘unmanaged’’ scenario is not entirely compensated with an early cutting at year 7. This result differs from the suggestions of Gong and Ong (1995). They supposed that a high natural tree mortality triggered by a high initial recruitment, could be prevented with an additional silvicultural thinning of smaller trees around 8–9 years with a remaining density of about 8000 trees ha-1. One probable reason is that the amount of trees cut at 7 years was not sufficient enough to reduce natural mortality. During the simulations the amount of cut trees was the equivalent of 25% of the smallest trees in a hectare. This proportion was selected in order to maintain the suggested remaining density of 8000 trees ha-1 (Gong and Ong 1995). The percentage of space released by this thinning might not be sufficient to compensate the high recruitment and competition among remaining individuals. For this reason, natural mortality is still occurring until the first harvesting event at 13 or 15 years. Another possible explanation might be related to the model parameterization. The low value (0.05) of the constant for tree mortality threshold might be giving a very small range for tree growth in unfavorable conditions (from environmental or competition stress). In this case, a tree would die too early in our simulations instead of staying at constant size during the years 7–13. This comes back to an eventual problem in producing wide enough size distributions in the simulations as explained in the section above. The harvesting thinning at 13 years seems to be better than at 15 years because it reduces wood waste due to natural mortality (as pointed out by Gong and Ong, 1995), density is maintained until the following thinning and remaining trees grow faster as pointed

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out with the increment of dbh (Fig. 4). The following thinning (year 20) does not seem to change the density distribution nor the dbh increment after 13 years, which result in the slightly higher mean dbh values of the trees at year 30 (Fig. 4). The results of the total amount of trunk biomass harvested after an entire cycle of 30 years showed a statistical significant relationship between the distribution of the harvested biomass and the management scenario. From an economic point of view, all management scenarios provides the same possibility of making profits from smaller dbh classes (\6 cm and [6 to \10 cm, wood used usually in the charcoal production). But, the 7–13–20 management scenario also gives the best profit possibility for the medium dbh classes ([10 to \13 cm), which are considered as the ideal sizes for poles utilization. Further studies including economic modeling of labor cost, pole prices and cost of extraction depending on sizes could be conducted to demonstrate which of these management scenario could be considered as the ‘‘best ecological and economic sound’’ thinning practice.

Conclusion This study has illustrated how mangrove simulation models can become a helpful tool for management forest practices. Further management scenarios could be implemented with the aim of analyzing the best management options for the identification of compromising strategies between the use and conservation of mangroves forests. The potentiality of the KiWi simulation model as a tool for management might be further confirmed by the gathering of field data (i.e. species-specific data, monitoring of recruitment rates, forest plantation growth parameters, etc.) that permit a more precise parameterization and validation of model scenarios. Finally, the integration of this model into a mangrove economic model would help to evaluate scenarios benefits and decide the best strategy for economic and ecological sustainability. Acknowledgements This study was carried out as a part of the German-Vietnamese collaboration project ‘‘Can Gio’’ and was funded by the Deutsche ForshungsGemeinschaft (DFG).

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