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Patterns of stocks of aboveground tree biomass, dynamics, and their

determinants in secondary Andean forests

Miguel A. Pea a,b, Alvaro Duque c,

a Doctorado en Ecologa, Universidad Nacional de Colombia Sede Medelln, Calle 59A # 63-20, Medelln, Colombiab Instituto de Hidrologa, Meteorologa y Estudios Ambientales de Colombia (IDEAM), Bogot D.C., Colombiac Departamento de Ciencias Forestales, Universidad Nacional de Colombia Sede Medelln, Calle 59A # 63-20, Medelln, Colombia

a r t i c l e i n f o

Article history:

Received 30 December 2012Received in revised form 17 March 2013Accepted 19 March 2013

Keywords:

FacilitationFunctional traitsSoil fertilityVariance partitioningVegetation dynamicsGlobal change

a b s t r a c t

In this study, we aimed to identify the main factors that affect the dynamics and rates of abovegroundbiomass (AGB) accumulation in secondary forests located in the interandean valleys of Colombia. Weused two censuses performed on trees with a diameter at breast height ofP10cm in 10 1-ha plots toanswer the following questions: (1) Does prior use of the land for raising crops (instead of pasturelandfor cattle ranching) promote an increase in the rate of AGB accumulation? (2) To what extent did climate,soil characteristics, the functional traits of resident species, and/or stand age affect the stocks and dynam-ics of the AGB in these secondary forests? We did not find any evidence that prior land use for cattleranchi ng had a negative effect on the tree AGB-accumulat ion rate, when compared with land previouslyused for raising crops. Greater AGB stocks at an intermediate fertility point may be associated with ahigher abundance of tall, short-lived, fast-growth pioneer species. Tree mortality rates in secondary for-ests were primarily determined by the forests composition and the variation in the amount of rainfallbetween sites. Soil fertility, which is known to accelerate the growt h rate of plant species, was signifi-

cantly and positively associated with the rate of AGB recruitment. We did not find any evidenc e for a neg-ative correlation between wood density and growth, as has been reported for primary tropical forests.The net AGB change (%) was primarily associated wit h the low soil-fertility characteristics of sandywell-drained soils that could, in the presence of steep slopes, increase forest dynamics. Land restorationthat restores the natural cycle of secondary forests could assist in the mitigation of global warming bypromoting key environmental functions, such as carbon sequestration.

2013 Elsevier B.V. All rights reserved.

1. Introduction

Secondary succession can be defined as the long-term, direc-tional change in both forest structure and species composition that

follows a large-scale disturbance event (Chazdon, 2008). Fosteringthe management and development of secondary forests in tropicalregions is a promising approach for the reduction of greenhousegas concentrations and thus could be an important means of mit-igating climate change (Brown and Lugo, 1990; Chazdon and Coe,1999; Grace, 2004; Sierra et al., 2007; Silver et al., 2000). However,the underlying causes of the high variability in the capability ofsecondary forests to recover aboveground biomass (AGB) are notfully understood. Variation in the rate of AGB accumulation havecommonly been associated with land use history, soil fertility, cli-matic characteristics, and the structure and degree of forest con-

servation within the landscape matrix (Chazdon, 2008;Guariguata and Ostertag, 2001; Kauffman et al., 2009; Silveret al., 2000). Prior use of land for raising crops rather than for pas-turing cattle seems to speed up the rate of growth and the yield of

the natural succession (Ferguson et al., 2003; Zimmerman et al.,2007). Likewise, soil fertility and higher temperatures may facili-tate the recovery of AGB, and recovery may also accelerate withproximity to seed sources (Chazdon, 2008; Guariguata andOstertag, 2001; Lu et al., 2002).

The use of chronosequences to characterize the patterns of suc-cession in stands of different ages is one of the most widely em-ployed tools for assessing the rate of AGB accumulation insecondary forests (Letcher and Chazdon, 2009). However, the lin-ear approach of changes in the AGB accumulation rate assessedusing chronosequences does not enable researchers to identifythe ecological processes that cause the observed, non-linear, incre-mental changes in the AGB over time (Chazdon, 2008; Chazdonet al., 2007, 2010; Silver et al., 2000). Because a fundamental pre-

mise of functional ecology concerns the level of relationship

0378-1127/$ - see front matter 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.foreco.2013.03.025

Corresponding author. Tel.: +57 4 4309089; fax: +57 4 4309079.

E-mail addresses: mapenah@unal.edu.co (M.A. Pea), ajduque09@gmail.com,ajduque@unal.edu.co (A. Duque).

Forest Ecology and Management 302 (2013) 5461

Contents lists available at SciVerse ScienceDirect

Forest Ecology and Managemen t

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / f o r e c o

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Fig. 1. Geographical location of the 10 1-ha plots established for the study of forest secondary succession inthe Colombian interandean valleys. Names and characteristics ofthe plots are described in Table 1.

Table 1

Geographical and environmenta l description of the 10 1-ha plots surveyed in secondary forests in the interandean valleys of Colombia. NC: plots located at the Miel (north of

Caldas, land formerly used for crops); EA: plots located in eastern Antioquia (land formerly used for pasture). AGB: aboveground biomass.

Site Plot code (Standage)

Coordinates Altitude(masl)

Precipitation(mm)

Individuals 2007(2010)

Species 2007(2010)

AGB (Mg ha1) 2007(2010)

Miel NC1 (15) 533048.100N 600 5500 688 (743) 105 (117) 105.5 (115.1)7454015.300W

Miel NC2 (15) 533013.700N 525 5500 707 (723) 57 (68) 74.3 (85.5)7455038.800W

Miel NC3 (25) 533057.400N 625 5500 671 (673) 83 (86) 81.5 (87.3)7455038.800W

Miel NC4 (25) 533041.900N 570 5500 713 (709) 83 (87) 133.9 (133.4)7454048.700W

Miel NC5 (25) 533014.600N 530 5500 702 (681) 65 (70) 112.5 (115.1)7453035.000W

SanRafael

EA1(18) 622022.300N 1300 3540 542 (554) 55 (64) 85.2 (94.0)7501030.500W

SanRafael

EA2(18) 623031.000N 1280 3540 686 (723) 67 (79) 87.1 (93.9)7502020.300W

SanRafael

EA3(18) 0621031.6300N 1330 3540 658 (677) 44 (56) 86.7 (96.1)7500027.01400W

SanCarlos

EA4(28) 612016.200N 955 3000 676 (695) 126 (142) 96.1 (105.0)7451005.400W

SanCarlos

EA5(28) 613015.300N 790 3000 845 (928) 120 (140) 108.9 (123.3)74510000W

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defense and longevity versus growth, while height has been asso-ciated with competitive ability and light capture (Westoby, 1998).The maximum height (Hmax) of the individuals ineach plot was cal-culated as the average of the 10 tallest trees, and average height(Hmean) was defined as the mean height of the trees found in each

plot. We assigned the WD values available in the literature (Chaveet al., 2006; Zanne et al., 2009) to each species found in each plot.In cases in which we could not assign a WD value at the species le-vel, we used the average value at the genus or family level. Forindividuals without a botanical identification, we used the averageWD value of all other individuals found in the plot. Average WD(WDmean) was defined as the mean of the trees found in each plot.All of the WD data were pooled and divided into two extreme func-tional groups, which were defined by the first (WD0.25) and fourthquartiles (WD0.75), respectively. Then, for each plot, we calculatedthe total number of individuals with a WD0.25 below 0.45 g cm

3

and the number of individuals with a WD0.75 equal or higher than0.61 g cm3.

We performed a one-way analysis of variance (ANOVA) to test

for significant differences in soil properties and functional traitsbetween regions. When significant differences were found, we car-ried out a Tukeys honestly significant difference test. Prior to cal-culations, values in percentage form were arc-sin transformed. Theremaining variables, except for pH, were log-transformed.

2.3. Above-ground biomass stocks

We used the following allometric equation developed to esti-mate the AGB of secondary forests established in the surroundingsof a dam in the region of Porce, Antioquia (Sierra et al., 2007):lnAGB 2:232 2:2422 lnDBH. The Porce region is sepa-rated from EA and NC, respectively, by approximately 80 and200 km. This allometric equation was built based on 152 trees thatwere harvested in a geographic area covering approximately4400 ha, with an altitudinal range between 900 and 1500 m asl(J.I. del Vallepers. comm. February 15 2013).

2.4. Above-ground biomass dynamics

The annual AGB mortality rate (m) between 2007 and 2010 wasestimated as m = (log (AGB 2007/(AGB 2007D))/time) 100. Theannual AGB recruitment rate (r) was estimated as r= (log ((AGB2007D + R)/(AGB 2007D))/time) 100. Relative growth rate (g)between 2007 and 2010 was estimated as g= (log ((AGB 2010R)/(AGB 2007D))/time) 100. The net AGB change (n) in eachplot was then estimated as n = (log (AGB 2010/AGB 2007)/time) 100, where AGB 2007 and AGB 2010 are the AGB in eachcensus, D is the AGB of dead trees, and R is the AGB of new recruits

(Valencia et al., 2009).

2.5. Does a prior use of land for raising crops promote an increase in

the rate of AGB accumulation?

We used ANOVA to test for significant differences in the AGBstocks, m, r,g, and n between regions. When significant differenceswere found, we carried out a Tukeys honestly significant differ-ence test.

2.6. To what extent do climate, soil characteristics, species functional

traits, and/or stand-age determine the stocks and dynamics of the AGB

in these secondary forests?

Multiple regression analysis based on ordinary least squares(OLSs) was performed to analyze the influence of climate, physicaland chemical properties of soils, species functional traits, and standage on the AGB stocks in 2010 in the 10 1-ha plots. The climatic

variables were represented by the estimated annual, mean precip-itation and the altitudinal position of each plot. The altitudinal po-sition was used as a proxy for temperature variation. The physicalproperties of soils were assessed by the square root of the arc-sin-transformed percentages of the slope of the terrain, and the clay,

loam, and sand contents. The chemical properties of soils (soil fer-tility) were evaluated by means of the soil pH and the log-trans-formed Ca, Mg, K, Al, CEC, and P content and the square root ofthe arc-sin-transformed percentages of organic matter (OM). Thesquare root of the arc-sin-transformed slope was included to char-acterize the form of the terrain. The log-transformed WDmean, Hmax,Hmean, WD0.25, and WD0.75 were employed to evaluate functionallydriven processes at the forest stand scale. The log-transformedstand-age was used to evaluate the influence of forest age afterabandonment on the AGB accumulation rate.

Each set of climatic, edaphic, functional and stand-age variableswere independently subjected to the stepwise procedure, based onthe Akaike Information Criterion (AIC), to select the significantvari-ables that best explained the variation of the AGB stocks (Burnham

and Anderson, 1998). Soil chemistry variability was reduced bymeans of Principal Component Analysis (PCA) and divided intotwo independent datasets. Each soil fertility data set was composedof the variables with weightsP 0.65 in the first two PCA axes. Thequadratic terms of each of the selected edaphic variables were alsoincluded in the independent stepwise selection procedure.

We then re-applied the stepwise procedure based on AIC to theall-joined, independent set of variables previously selected as sig-nificant from each climatic, edaphic, functional and stand-age setof variables to define the overall best (and most parsimonious)explanatory model (Burnham and Anderson, 1998). All variableswith a significance level (a)6 0.05 were retained in the final mod-els. We used this stepwise procedure because we had a largernumber of explanatory variables than plots. This analysis allowedfor a further partitioning of the AGB variation explained by the dif-ferent sets of variables.

With all variables remaining in the final model, we performed avariance partitioning analysis (Legendre and Legendre, 1998) toevaluate the extent to which the selected climatic, edaphic, func-tional or stand-age factors explained the pattern of the AGB varia-tion observed in the 1-ha plots. To correct for statistical bias, weadjusted the R2 values to account for the number of plots andexplanatory variables (Peres-Neto et al., 2006).

Toquantitate the extent to which climate, soil,species functionaltraits, and stand-age controlled the pattern of AGB dynamics (m, r,g,and n) in the 1-ha plots, we applied a stepwise procedure based onthe OLS and AIC to select the most significant explanatory variablesin the same way as described above, but we used m, r,g, and n in-stead of the AGB stocks as the dependent variables. As before, a var-

iance-partitioning analysis based on the adjusted R2 value wascarried out on the most parsimonious model selected for eachdependent variable (Peres-Neto et al., 2006). In this case, the func-tional factors WDmean, Hmax, Hmean, WD0.25, and WD0.75 were calcu-lated onthe dead (for m), recruited (r) or live (g) individuals in eachplot, and the functional factors were then log-transformed as well.All statistical analyses were performed using the R package version2.15 (R Development Core Team, 2012). Variance partitioning wasperformed with Vegan 17.2 in R (Oksanen, 2010).

3. Results

3.1. Environmental and functional variation between regions

Between regions, the only two environmental variables thatshowed significant differences were Pconcentrations and the slopeof the terrain (Table 2). The soil fertility variation did not show any

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systematic trend associated with either the region or the stand-age(Fig. 2). The functional variables did not show any significant dif-ferences between regions (Table 2).

3.2. Aboveground biomass stocks

The secondary forests in the interandean valleys and mountains

of Colombia showed an average AGB of 104.85 16.20 Mg ha1 in2010. We found no significant differences in the average AGB stockbetween regions (Table 2). Overall, 82.1% of the variation in AGBstock over the entire region studied was determined by the soil fer-tility and the functional properties of the species present (Table 3,Fig. 3a and b).

3.3. Aboveground biomass changes

Overall, the studied forests showed an average annual AGBmortality rate of 3.32 1.27% y1 between 2007 and 2010. Wefound significant differences in the average mortality rate between

regions (Table 2). A high percentage (86.3%) of the AGB mortalityrate variation in the entire region was primarily determined bythe climatic variability and the functional properties of species (Ta-ble 3, Fig. 3c and d). The average annual AGB recruitment rate was1.31 0.44% y1. We found no significant differences in the averageAGB recruitment between regions (Table 2). Nonetheless, 88.1% ofthe variation in the AGB recruitment rate for the entire study areawas primarily determined by the soil fertility and the functionalproperties of species (Table 3, Fig. 3e and f). The average AGBgrowth rate was 5.02 1.22% y1. We found no significant differ-ences in the average AGB growth between regions (Table 2). Noneof the selected variables could significantly explain the relativevariation of the AGB growth in the 10 plots. The average annualnet AGB change was 2.84 1.55% y1. At the plot level, all plots ex-

cept the NC4 plot gained AGB (Fig. 4). We found no significant dif-ferences in the average net AGB change between regions (Table 2).Nonetheless, 56.6%of the net AGB change variation in the study re-gion was primarily determined by stand-age and soil fertility (Ta-ble 3, Fig. 3g and h).

4. Discussion

4.1. Influence of prior land use on the aboveground biomass

accumulation rate

In this study, we did not find any evidence that the prior use ofland for cattle ranching had any negative effect on the tree AGBaccumulation rate in comparison with land formerly used for rais-

ing crops (Ferguson et al., 2003; Zimmerman et al., 2007). In thewet lowlands of the Colombian Andean mountains, local factorswere shown to overcome the influence of both stand-age and re-gional features in determining the rate of forest recovery after ma-jor disturbances. For example, one of the youngest plots (NC1) hada higher AGB than an older plot in the same region (NC3), and theAGB of NC1 was even higher than that of the oldest plots consid-ered in this study (EA4). The high AGB found in the NC1 plot ap-peared to be a consequence of the presence of a larger number ofremaining resident trees, such as Ficus insipida, rather than of thetype of land use before abandonment. Although the remainingtrees were excluded from the AGB assessment in the plots, theirpresence in the forests may have facilitated the establishment ofwoody and shade-tolerant species (Schlawin and Zahawi, 2008).

This phenomenon, which forms the basis of the nucleation model(Peterson and Carson, 2008), promotes a quicker recovery of foreststructure (Guariguata and Ostertag, 2001; Kauffman et al., 2009)and helps increase local AGB stocks. However, other non-assessedprocesses that operates at a regional scale, such as the likely effectof the surrounding landscape on the individual forest stands(Chazdon et al., 2007), could not be discarded as a determinantforce that obscured the expected negative influence of former landuses.

4.2. Determinants of the variation in the aboveground biomass stocks

Consistent with our first expectation of the second researchquestion, the only environmental factor that was shown to be

significant in explaining the differences in the AGB stocks pres-ent in the secondary forests evaluated in this study was the con-tent of Ca, which, due to its significant correlation with Mg andK content, represents soil fertility (Fig. 2). Nonetheless, the

Table 2

Mean and standard deviation of the explanato ry variables by region. NC: north of

Caldas (Miel); EA: eastern Antioquia (San Carlos and San Rafael). See Table 1 for

symbols and details. Letters indicate significant differences ( P6 0.05, P6 0.01,

P6 0.001) between means using the Tukeys honestly significant difference test.

Variable NC OA Fvalue

Sand (%) 57.74 3.01 55.18 8.35 0.42Silt (%) 14.26 2.03 14.46 3.13 0.02Clay (%) 28.00 3.22 30.37 5.71 0.65pH 4.39 0.17 4.39 0.19 0.00OM (%) 6.94 0.90 5.61 1.37 3.29Al (meq/100g de suelo) 2.71 0.61 2.80 0.37 0.08Ca (meq/100 g de suelo) 0.39 0.31 0.28 0.10 0.69Mg (meq/100 g de suelo) 0.21 0.15 0.14 0.04 0.90K (meq/100g de suelo) 0.14 0.05 0.11 0.02 0.74CEC 3.44 0.95 3.32 0.40 0.08P (ppm) 3.40 0.57b 2.36 0.38a 11.70

Slope (%) 60.29 6.22b 41.35 13.71a 7.91

WD (gcm3) 0.51 0.02 0.53 0.02 2.79Hmax (m) 24.79 1.61 22.69 2.14 3.08Hmean (m) 14.50 0.48 14.23 0.28 1.23WD0.25 237.20 65.14 173.40 29.05 4.00WD0.75 199.40 60.17 200.80 115.09 0.00AGB (Mg ha1) 107.24 20.48 102.47 12.50 0.20Mortality rate (%) 4.32 0.94 2.32 0.47 18.10

Recruitment rate (%) 1.31 0.65 1.32 0.10 0.00Growth rate (%) 5.60 1.51 4.44 0.47 2.71Net change (%) 2.40 2.14 3.28 0.58 0.78

3 2 1 0 1

2

1

0

1

2

PCA 1

PCA2

pH

OM

Al

Ca

Mg

K

CEC

P

NC15

NC25

EA18

EA28

Fig. 2. Principal Component Analysis that illustrates the soil fertility variation in 101-ha plots located in the Colombian interandean valleys. Names and characteristicsof the plots are described in Table 1.

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relationship between the AGB and soil fertility was quadraticrather than monotonic, implying the existence of an optimumpoint at an intermediate fertility that maximizes the AGBthroughout the successional path. In addition to soil fertility,and as was expected, tree height was positively correlated withincreased AGB stock. Therefore, greater AGB stocks at an inter-mediate fertility point may be associated with a higher abun-dance of tall, short-lived, fast-growth pioneer species. Becauseshort- and medium-lived pioneer species seem to be more effi-

cient in allocating resources after the abandonment of pasturesor fields than long-lived, slow-growth, shade-tolerant species(Chapin et al., 1986; Rees et al., 2001), a differential species re-sponse to the spatial variation in the availability of nutrients in

soils may be a key determinant of forest recovery and the asso-ciated increase in the AGB accumulation rate in tropical, second-ary forests (Chazdon et al., 2010; Rees et al., 2001; Saldarriagaet al. , 1988).

The lack of relevance of the altitudinal range covered by the for-est plots included in this study might be due to the lack of changesin temperature substantial enough to affect the AGB accumulationrate. Likewise, the plot stand-age range considered may have beentoo small to differentiate AGB stocks. Therefore, the absence of dif-

ferences in AGB stocks between plots of different ages illustratesthe inappropriateness of using chronosequences for assessingand comparing AGB accumulation rates in environmentally heter-ogeneous ecosystems (Walker et al., 2010).

Table 3

Final regression models defining the determin ants of above ground biomass stocks and dynamics in the interandean valleys of Colombia. AGB: aboveground biomass stocks by

2010; m: AGB annual mortality rate; r: AGB annual recruitment rate; n: AGB annual net change.

Model Adjusted R2 (%) P-value AIC

ln(AGB) =4.92 + 0.48 ln(Ca) + 0.27 ln(Ca2) + 3.63 ln(Hmean) 82.05 0.004 21.61

m = 27.78 + 2.66 ln(Precipitation) + 2.60 ln(WD0.25) 20.12 ln(Hmean) 86.32 0.002 18.09r= 32.75 + 0.58 ln(Ca) + 2.26 sin1 (p

(OM)) 7.28 ln(WDmean) 14.85 ln(Hmean) 88.12 0.004 4.30n = 13.58 6.50 ln(stand-age) + 12.32 sin1 (

p(OM)) 56.56 0.022 33.27

4.2 4.4 4.6 4.8 5.0 5.2

4.5

4.7

4.9

Predicted

NC15

NC25

EA18

EA28

(a)

Functional

traits

0.522

0.164Soils

0.463

Residuals = 0.179

(b)

1 2 3 4 5 6

2

3

4

5

Predicted

(c)

Functional

traits

0.281

0.306Climate

0.276

Residuals = 0.137

(d)

0 1 2 3

1.0

1.5

2.0

Predicted

(e)

Functional

traits

0.852

0.356Soils

0.385

Residuals = 0.119

(f)

1 0 1 2 3 4 5 6

2

3

4

5

Observed

Predicted

(g)

Stand age

0.6710.471

Soils

0.365

Residuals = 0.434

(h)

Fig. 3. Observed values versus values predicted by the regression models and their respective variance partitioning (Venn diagram) for the AGB stocks in 2010 (aand b); AGBannual mortality rate (c and d); AGB annual recruitment rate (e and f); and AGB annual net change (g and h). The observed negative values in the shared component of thevariance partitioning means that the set of variables together explain the variation better than the sum of the individual effects of each variable (Legendre and Legendre,1998).

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4.3.4. Aboveground biomass net change

Our fifth prediction of the second research question, that netAGB change (%) would correlate positively with soil fertility andnegatively with stand-age, was partially confirmed by the regres-sion model. The OM contents were positively correlated with sand

(R = 0.6), but negative and significantly correlated with soil fertil-ity. Therefore, the net AGB change was primarily associated withthe low soil-fertility associated with sandy well-drained soils that,in presence of steep slopes, could increase forest dynamics. The de-crease in the rate of net AGB associated with stand-age could beexplained by a lower proportional increase in the AGB (%) due toa higher a AGB mortality rate in the older plots of NC. In EA, in con-trast, the net AGB change was not related to stand-age and m, r,andgwere quite uniform among plots of different age (Fig. 4).

Our study focused on the accumulation rate of AGB in second-ary forests after large-scale disturbances within a relatively shortperiod of the natural succession (1530 years) in an environmen-tally complex geographic region. Overall, local edaphic conditionsand functional diversity have been shown toplay key roles in influ-

encing the rate and dynamics of tree AGB accumulation. This resultemphasizes the fact that land restoration that restores the naturalcycle of secondary forests could assist in the mitigation of globalwarming by promoting key environmental functions, such as car-bon sequestration.

Acknowledgments

The authors of this study extend our special thanks to all thestaff members of the Equipo Ambiental of ISAGEN, especially to Ju-lian Carmona, Huber Vanegas, Claudia lvarez, and Ana Gmez. Weare very much indebted to the students, forest engineers and fieldassistants who participated in the fieldwork and the processing ofinformation. The authors acknowledge the valuable collaborationand comments of Juan Saldarriaga, Natalia Norden and three anon-ymous reviewers. English editing of the first versin submitted wascovered by the rea curricular en bosques y conservacin ambien-tal de la Universidad Nacional de Colombia. This work was madepossible by funding received from ISAGEN (Interinstitutional Coop-eration Agreement # 46-3127). Alvaro Duque was partially sup-ported by the Convocatoria Nacional de Investigacin y decreacin artstica de la Universidad Nacional de Colombia 20102012. Alvaro Duque (through the Contract No. IDEAM II-CPS-001de 2013) and Miguel A. Pea received funding from the projectConsolidacin de un Sistema de Monitoreo de Bosques y Carbono,

como soporte a la Poltica Ambiental y de Manejo en Colombia,which is supported by the Gordon and Betty Moore Foundation.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.foreco.2013.03.025.

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