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Forest Ecology and Management 247 (2007) 80–90
Regional scale variation in forest structure and biomass in the
Yucatan Peninsula, Mexico: Effects of forest disturbance
Tania Urquiza-Haas *, Paul M. Dolman, Carlos A. Peres
Centre for Ecology, Evolution and Conservation, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Received 3 March 2006; received in revised form 5 April 2007; accepted 6 April 2007
Abstract
Aboveground biomass is a key variable in understanding the role of tropical forests in the global carbon cycle. The forests of the Yucatan
Peninsula form part of the largest remaining tract of Mesoamerican forests, where the predominant land use is still slash-and-burn agriculture.
Previous estimates of aboveground live phytomass of late-successional forests in this region vary almost twofold, but are derived from relatively
few forest plots. We estimate aboveground forest biomass using data from 243 inventoried forest plots (totalling 58.5 ha), ranging across a
disturbance gradient from nearly intact to severely degraded forests. We assess the effects of environmental and disturbance variables on forest
basal area, stand-level wood specific gravity and aboveground biomass. Major differences in basal area and aboveground biomass were explained
by levels of human disturbance (clear-cutting, logging, and fire disturbance), whereas edaphic factors played only a minor role. Total mean
phytomass density estimates ranged from 28.8 � 3.8 mg ha�1 in plots aged 10–15 years to 191.9 � 9.5 mg ha�1 in undisturbed old-growth forest
plots (>50 years). Severe logging and fire disturbance reduced AGB in late-successional plots (30–50 years) by 36% and 37%, respectively. Stand-
level wood specific gravity increased with succession, due to an increase in the proportion of total basal area contributed by high wood density
genera. Logging intensity had a small additional effect on stand-level wood specific gravity. Incorporating stand-level wood specific gravity into
the algorithm explained only an additional 4% of the variation in estimates of aboveground biomass, which was largely determined by basal area.
However, ignoring plot-level variation in wood specific gravity resulted in overestimates of 19% in plots aged 10–15 years. Our aboveground
biomass estimates were highly comparable with previous studies using the same allometric equations, and fell within the highest range of estimates
reported for tropical dry forests. Forest of this region still retains a significant carbon stock, but rates of biomass recovery
(2.8 � 0.2S.E. mg ha�1 year�1) were low compared to other neotropical forests.
# 2007 Elsevier B.V. All rights reserved.
Keywords: Biomass accumulation rate; Fire; Forest perturbation; Forest structure; Wood specific gravity
1. Introduction
Aboveground biomass (hereafter, AGB) is a key variable in
understanding the role of forests in the global carbon cycle, as it
represents the potential for carbon emissions to the atmosphere
through biomass burning and decay when forests are degraded
or converted to other land uses (Malhi and Grace, 2000). A third
of the increase in CO2 concentrations in the atmosphere over
the last two centuries is estimated to have come from
deforestation; with the annual rate of CO2 released due to
global changes in land use during the 1980s estimated at
* Corresponding author. Present address: CONABIO (Comision nacional para
el conocimiento y uso de la biodiversidad), Liga Periferico-Insurgentes Sur No.
4903, Parques del Pedregal, Tlalpan, C.P. 14010 Mexico, D.F., Mexico.
Tel.: +52 55 5004 5016; fax: +52 55 5004 4931.
E-mail address: turquiza@conabio.gob.mx (T. Urquiza-Haas).
0378-1127/$ – see front matter # 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2007.04.015
between 1.2 and 2.8 gigatons of C (Houghton, 2003). The range
reflects uncertainties in both the rates of land-use change and in
carbon fluxes and stocks remaining in ecosystems affected by
anthropogenic activities (Houghton, 2003). For example, forest
AGB is gradually lost through domestic pole and fuelwood
harvest, selective logging, and increased susceptibility to wind
damage (Dixon et al., 1994; Laurance et al., 1997; Noble and
Dirzo, 1997; Houghton, 2003). Conversely, forest biomass can
recover at different rates through forest succession (Silver et al.,
2000). Only half of the world’s remaining closed-canopy
forests remains intact (UNEP, 2001), and 60% of all tropical
forests are secondary or degraded (ITTO, 2002). Hence it
becomes increasingly important to evaluate the carbon storage
capacity of disturbed forests.
Estimates of AGB are an important source of uncertainty in
tropical forest carbon stocks (Houghton et al., 2001). There is a
need to improve understanding of how anthropogenic and
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–90 81
environmental factors affect AGB estimates through their
impacts on important stand parameters. Forest biomass
equations are usually derived from allometric relationships
based on measurements of the dimensions and mass of
destructively sampled trees (Martinez-Yrizar et al., 1992;
Baker et al., 2004). Stand basal area and wood specific gravity
account for a large proportion of the variation in AGB estimates
(Baker et al., 2004; Nogueira et al., 2005). Wood specific
gravity is also a particularly useful way of characterising tree
types because it is correlated with several important plant traits
such as tree growth rate, which is one of the main criteria used
to differentiate between early (pioneer) and late-successional
species (Suzuki, 1999; Slik, 2005).
Tropical dry forests are the most extensively distributed land
cover type in the tropics (Murphy and Lugo, 1986). Dry forests
have been greatly exploited and disturbed by human activities;
land conversion has been higher in tropical dry forests than in
any other tropical forest type (Millennium Ecosystem Assess-
ment, 2005). In comparison to wet tropical forest, relatively few
studies have been conducted in tropical dry forests and carbon
stocks remain largely unquantified (Tiessen et al., 1998; Read
and Lawrence, 2003). Given their broad geographic extent it is
essential to quantify their potential for carbon sequestration and
storage.
The forests of the Yucatan Peninsula have experienced a
long history of Mayan occupation with intense habitat
alteration and recovery over the past few thousand years
(Edwards, 1986; Turner et al., 2001). The present landscape of
the Yucatan Peninsula ranges from heavily used vegetation
cover to relatively preserved forest stands. In the northwest
portion of the peninsula (Yucatan state), slash-and-burn
agriculture has taken place for over 2000 years, leading to
repeated short cycles of forest succession, where only small
remnants of old-growth can be found (Wilson, 1980; Rico-Gray
and Garcia-Franco, 1992). In contrast, eastern Yucatan (south-
eastern Quintana Roo state and south-western Campeche) has
the second highest percentage of forest cover in Mexico and one
of the lowest annual deforestation rates in Mesoamerica. These
forests form part of one of the last major tracts of tropical forest
remaining in Central America and have been incorporated
within the Mesoamerican Biological Corridor initiative (Miller
et al., 2001; Bray et al., 2004).
Current estimates of aboveground live biomass in late-
successional forests of the Yucatan vary almost twofold but are
derived from a small number of plots (Harmon et al., 1995;
Cairns et al., 2003; Read and Lawrence, 2003). Qualitative
improvements in the allometric equations that can predict AGB
may be less gainful at this stage than a more robust and
extensive sampling effort involving a substantially larger
number of plots (Keller et al., 2001), particularly as forest
disturbance varies markedly across the region. Therefore, the
objectives of this study were to (1) provide additional estimates
of above ground forest biomass across the Yucatan Peninsula;
(2) estimate biomass recovery rates; (3) assess the relative
importance of these forests for carbon storage; (4) understand
the effects of environmental and disturbance variables on the
structural and compositional components of aboveground
biomass; (5) compare our values with previous estimates using
the same allometric equations; and (6) assess the relative
importance of incorporating species information in AGB
estimates.
2. Methods
2.1. Study area
This study was conducted in the states of Quintana Roo and
Yucatan within the Mexico’s Yucatan Peninsula (Fig. 1), a flat
limestone platform of karst topography located between the
Gulf of Mexico and the Caribbean Sea (Wilson, 1980). The
predominant soil types are well-drained rendzinas and shallow
and rocky lithosols, but areas of chromic luvisols, pellic
vertisols and gleysols also occur (Barrera et al., 1977; INEGI,
1994). The climate of the Yucatan Peninsula is tropical sub-
humid with a dry winter and rains in the summer. Mean annual
temperature varies around 26 8C. Annual rainfall increases
from north to south from 1000 to 1500 mm, April–March being
the driest months of the year with <60 mm of rainfall (Garcıa
and CONABIO, 1998). On average, one hurricane or tropical
storm hits the Yucatan Peninsula each year (Wilson, 1980).
The most extensive vegetation types in the Peninsula can be
described as semi-evergreen medium-statured forest, semi-
evergreen low-statured forest, semi-deciduous medium-sta-
tured forest and deciduous low or medium-statured forest
(Miranda, 1964; Pennington and Sarukhan, 1998) comprising
dry tropical forest (<2000 mm/year) sensu Holdridge (1967).
2.2. Study sites and vegetation sampling
We sampled 243 quarter-hectare (10 m � 250 m) forest
plots (totalling 58.5 ha) distributed throughout the northern and
eastern Yucatan Peninsula (Fig. 1) during two periods. The first
period (September 2002–February 2003) included floristic
surveys within 148 plots distributed throughout the study
region. Vegetation sampling within 95 plots during the second
period (July 2003–March 2004) took place along transects
within communal tenure landholding units [hereafter, ejidos] in
a gradient of landscape disturbance (from north to south:
Tezoco Nuevo, Yodzonot Laguna, X-Conha and Tierra Negra),
protected forest reserves (two sites within the Sian Kaan
biosphere reserve and a private reserve, El Zapotal, in the
northern part of the Peninsula), and an additional private
property (Palmas). At each site during the second period, 12
plots were placed along transects of 3.0–4.5 km each, with plots
separated from one another by 500 m and treated as
independent sample units in analysis of forest structure.
2.3. Forest structure
Neotropical tree identification at the genus level captures
80% of the between-site similarity matrix at the level of species
(Higgins and Ruokolainen, 2004), and congeners tend to be
similar in many life history traits (ter Steege and Hammond,
2001; Higgins and Ruokolainen, 2004). Wood specific gravity
Fig. 1. Map of the study region encompassing two states of south-eastern Mexico showing the location of all forest plots sampled.
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–9082
is closely constrained by phylogeny and differences between
genera account for the largest proportion of the variation in
wood specific gravity (Baker et al., 2004). Taxonomic
resolution at the generic level was, therefore, considered to
be sufficiently robust for this study.
A total of 31,224 woody stems�10 cm in diameter at breast
height (DBH), belonging to 120 genera and 42 families, were
measured and identified using Mayan vernacular name supplied
by a knowledgeable local field assistant. Mayan names were
attributed to genera following several bibliographic sources for
the regional flora (Pennington and Sarukhan, 1998; Ogata et al.,
1999; Arellano-Rodrıguez et al., 2003). This methodology
permitted sampling in numerous study sites and has been
previously used by Hernandez-Stefanoni and Ponce-Hernandez
(2004). Local identifications of 85.3% of all stems belonging to
66 genera (55% of all genera) were independently verified
using a taxonomic identification software (Ogata et al., 1999;
Perez-Salicrup, 2001) and a plant field guide (Pennington and
Sarukhan, 1998).
Total forest basal area (BA) was calculated as BA =Pp(DBHi/2)2, where DBHi is the diameter at breast height of
the ith tree, and expressed as m2 ha�1.
2.4. Forest disturbance variables
The forest stands surveyed included a wide range of
successional stages, from relatively undisturbed old-growth
forest in the Sian Kaan reserve to heavily disturbed and open
stands in communally managed ejidos. The severity of logging
disturbance within each plot was assessed by the number of
stumps per hectare and classed as none (0), moderate (4–20),
and severe (>20). Field assessments of logging severity were
corroborated by interviewing key local informants at each
forest site. Of the 23 sites classified as severely disturbed, 14
were known to have been intermittently logged within the
previous 5 years for the extraction of construction materials
(poles, railroad ties). Although no stumps were found in those
plots classified as unlogged, logging and other disturbance
cannot be ruled out in the Yucatan Peninsula (Turner et al.,
2001; White and Hood, 2004).
Classification of previous fire disturbance was based on the
history of wildfires for each plot, but information on local fire
intensity was unavailable. Information was obtained by
interviewing key local informants. Most fires reported for tree
plots had not occurred recently (within 10 years of sampling)
which hindered direct assessments of fire-induced tree
mortality or burn severity.
Fallow age was obtained by interviewing long-term residents
of the area who were familiar with the study plots, following
(Williams-Linera, 1990). Successional categories, defined as the
time since plots were last affected by cultivation, follow the
Mayan forest succession nomenclature (Gomez-Pompa, 1987):
10–15 years (early-successional), 16–30 years (mid-succes-
sional) and 30–50 years (late-successional forest stands).
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–90 83
2.5. Landscape scale variables
Georeferenced forest plots were overlaid with several
layers of information including forest type, soil type, and two
classes of soil humidity: xeric [90–180 days of humidity] and
ustic [180–270 days] (Maples-Vermeersch, 1992; INEGI,
1994; Garcıa and CONABIO, 1998; CONABIO, 1999). Data
sets were acquired in digital format from the Institute of
Geography at the Universidad Nacional Autonoma de Mexico
(UNAM) and from the Comision Nacional para el Con-
ocimiento y Uso de la Biodiversidad (CONABIO) web site.
Highways, paved, unpaved and dirt roads, were extracted
from georeferenced digital maps (1:250,000) of the region
from the Instituto Nacional de Estadıstica, Geografıa e
Informatica (INEGI) and distance from each plot to the
nearest road was calculated. Georeferenced population
clusters (households, ranches, towns and cities, to a
resolution of one inhabitant) from the 2000 population
census (INEGI, 2002) were processed to derive several
variables: distance to population clusters >1, >50, >100 and
>500 inhabitants; human population size within buffers of 1,
2.5 and 5 km. Geographic information was processed using
ArcView GIS 3.2, supplemented by ESRI extensions (X-
Tools, Buffer Theme Builder, Nearest Features v3.6 and
Distance Matrix).
Areas that had been affected by hurricanes were identified
using hurricane track information from the NOAA’s National
Hurricane Center database (NOAA, 2005). Following Boose
et al. (1994) and Ayala-Silva and Twumasi (2004), buffer
distances (d) along the track points were calculated as:
d = 14.48 � [log(100 km h�1/VS)/log(0.639)], where VS is the
Saffir/Simpson scale wind velocity and d is the maximum
distance within which wind speeds are �100 km h�1. This cut-
off value was obtained by calculating the sustained wind
velocity at a location known to be affected by the passage of
Hurricane Gilbert in 1988 (Sanchez-Sanchez and Islebe, 1999).
Buffers around track points were joined to create a polygonal
buffer around each hurricane track.
2.6. Wood specific gravity
Mean wood specific gravity values for tree genera were
obtained by averaging reported values in the neotropical forest
literature (Reyes et al., 1992; Hidayat and Simpson, 1994;
Tamarit-Urias, 1996; Fearnside, 1997; Chave et al., 2003;
Baker et al., 2004; Nogueira et al., 2005). These sources
provide values for basic specific gravity, which is obtained as
the ratio between the dry weight and fresh volume of wood
(Fearnside, 1997). Whenever available, we considered wood
specific gravity values for species that occur in the Yucatan
Peninsula. Our comprehensive search resulted in a compilation
of wood specific gravity values for 74 (out of 120) genera that
contributed 91.7% and 94.3% of all measured stems and basal
area, respectively. For those genera lacking reported values, and
for unidentified stems (only 0.5% of all stems), we used the
overall mean for all tree genera in our region (0.66 g cm3,
n = 74). Stand-level wood specific gravity was calculated from
the mean wood specific gravity of the tree genera present,
weighted by their basal area contribution.
To examine further the changes in mean stand-level wood
specific gravity with succession, genera were divided among
three wood specific gravity categories: low (<0.49 g cm�3),
intermediate (0.49–0.66 g cm�3) and high (>0.67 g cm�3),
which represent a transition from pioneer to climax species
(Slik, 2005).
2.7. Aboveground biomass estimates
Of all equations previously used to calculate AGB in the
Yucatan Peninsula (Brown et al., 1989; Martinez-Yrizar et al.,
1992; Cairns et al., 2000; Cairns et al., 2003; Read and
Lawrence, 2003), only one was developed from destructive
sampling within the Peninsula. This equation [AGB =
exp{�2.12605 + 0.868 ln(DBH2ht)}, where AGB is given in
total dry weight (kg), DBH is the diameter at breast height (cm)
and ht is tree height (m), Cairns et al., 2003] was therefore
thought to be most applicable to our study area. However, it was
derived from an old-growth forest plot that contained a high
proportion of high-density timber species and so may system-
atically overestimate AGB in plots with lower stand-level wood
specific gravity. This equation was therefore modified to
account for the variation in stand-level wood specific gravity
according to Baker et al. (2004). Variation in wood specific
gravity (r) is incorporated by multiplying the AGB estimate by
ri/rm, where ri is the wood specific gravity value for each tree
and rm is the mean wood specific gravity of the trees harvested
to calculate the biomass equation. rm was estimated as
0.72 g cm�3 (on a basal area basis) from species composition
reported in Cairns et al. (2003). Tree height was estimated from
a regression equation developed by Read and Lawrence (2003)
for the southern Yucatan Peninsula, that predicts tree height as a
function of diameter at breast height (DBH) [ln(ht) =
0.93687 + 0.55204 ln(DBH)]. Our estimates were then com-
pared to previous estimates for the Yucatan Peninsula using the
equation employed in each of the previous studies (Cairns et al.,
2000, 2003; Read and Lawrence, 2003).
The rate of aboveground biomass accumulation was
obtained from regression analysis of plots of known age (4–
33 years) (n = 41, excluding four heavily disturbed plots).
2.8. Data analysis
Statistical analyses were conducted in SPSS v. 12.0. All
dependent variables were tested for normality (Kolmogorov–
Smirnov test). Both basal area and aboveground biomass
estimates were square root transformed to attain normality and/
or to improve variance homogeneity and linearity in the
regression analyses. Data were analysed using univariate
general linear models (GLMs) with Hochberg’s GT2 tests for
post hoc pair-wise comparisons, recommended in cases of
unequal sample size (Field, 2000). Robust ANOVA (Welch test)
was used when variances were heterogeneous (Quinn and
Keough, 2002). Square root transformed times since fire and
clear-cutting (Hyears) were entered as covariates; other
Fig. 2. Mean AGB (mg ha�1) for four classes of forest recovery time since the
plot had last been clear-cut.
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–9084
variables were entered as fixed factors. Residuals from the final
fitted models were normally distributed and had variance
homogeneity (Levene test). Associations among pairs of
independent variables were assessed by Cramer’s V correlation
(Siegel and Castellan, 1988) when one or more variables were
nominal, and by Spearman rank correlation when both were
ordinal. Significance level was set at a = 0.05. Unless stated
otherwise, we provide summary values as means � S.E.
3. Results
3.1. Forest structure
Mean DBH was negatively correlated with the density of
stems �10 cm (rs = �0.30, P < 0.001), but positively corre-
lated with both basal area (rs = 0.61, P < 0.001) and the density
of stems �30 cm (rs = 0.82, P < 0.001). In the absence of
recent and severe forest disturbance events, semi-evergreen
low-statured forest plots had lower mean basal areas than other
forest types (mean � S.E.: 14.8 � 3.7 m2 ha�1; F2,56 = 6.3,
P = 0.003, n = 59; Hochberg post hoc test, P = 0.002). As data
were available from only a few semi-evergreen low-statured
forest plots (n = 14), these were eliminated from all subsequent
analyses.
3.2. Relationship between explanatory variables
Of the landscape variables related to potential human
impact, distance to towns >100 inhabitants provided the
strongest correlations with forest basal area (rs = 0.34,
P < 0.0001, n = 229) and logging severity (rs = �0.35,
P < 0.0001). There were no relationships between distance
to towns and successional stage of forest plots, or with time
since fire or hurricane disturbance (P > 0.05). Importantly,
successional stage (cultivation history) was also independent of
soil type, soil humidity class and time since last fire (P > 0.05).
Dry biomass fuel loads are greater following hurricane
impact, thus explaining the positive correlation between time
since last fire and hurricane disturbance (rs = 0.21, P = 0.002,
n = 229). Logging severity was correlated with successional
stage (rs = 0.20, P = 0.002, n = 229). Both logging severity and
history of hurricanes were each associated with soil type and
soil humidity class (Cramer’s V � 0.33, P < 0.05). Soil type
and class of soil humidity were also associated (C = 0.32,
P = 0.001).
3.3. Environmental and disturbance determinants of basal
area
Time since clear-cutting (years) was the strongest predictor
of forest basal area. Forest basal area in early to mid-
successional stands (<30 years) ranged from 1.6 m2 ha�1 in a
4-year-old stand to 23.8 m2 ha�1 in a 29-year-old stand
(mean � S.E.: 9.4 � 0.7 m2 ha�1, n = 45), whereas basal area
in late-successional stands (30–50 years) ranged from 7.8 to
46.3 m2 ha�1 (mean 20.6 � 0.6 m2 ha�1, n = 184). In contrast,
old-growth forest plots that had not been clear-cut or affected
by disturbance events for at least 50 years had consistently
higher basal area, ranging from 23.2 to 46.3 m2 ha�1 (mean
30.4 � 1.1 m2 ha�1, n = 28). Average AGB estimates in
relation to age classes of forest plots are presented in Fig. 2.
As most late-successional plots could not be dated, the effects
of environmental and disturbance variables were examined
separately for late-successional (30–50 years) and early to mid-
successional plots (<30 years).
Over the early to mid-successional gradient, basal area
increased with time since clear-cutting (TSCC) (main effects
minimal GLM model: R2 = 0.49, F1,43 = 43.5, P < 0.0001),
with no additive effects of soil humidity, soil type, logging
intensity, hurricane history or time since last fire (P > 0.05).
However, the number of heavily disturbed plots, one in the
severely logged category and three along the path of the last
hurricane (hurricane Roxanne in 1995), was too limited (n = 4)
to properly evaluate the influence of these disturbance factors in
secondary forest plots.
Considering only late-successional (30–50 years) and old-
growth (>50 years) forest plots, soil type, soil humidity class,
logging severity and recovery time since the last fire
disturbance affected both basal area and AGB, both of which
were greater on rendzinas than on lithosols, and on ustic than on
xeric soils. Basal area and AGB were gradually reduced in
moderately and severely logged sites, and both increased with
time since the last fire disturbance (Fig. 3), whereas the effect of
hurricane disturbance was not significant. Thirty-nine percent
of the variance in basal area of late-successional plots was
explained by a main effects minimal GLM (Table 1). Time
since the last fire and logging severity explained most of the
variance, followed by class of soil humidity and soil type.
Factors affecting AGB were similar, although here soil
humidity was not retained in the minimal model (Table 1).
3.4. Effects of environmental and disturbance variables on
stand-level wood specific gravity
Wood specific gravity values for 74 tree genera ranged from
0.25 to 1.12 g/cm3 (mean � S.E.: 0.66 � 0.02 g/cm3) and mean
Fig. 3. (a) Forest basal area as a function of forest age (time since clear-cutting: TSCC) for semi-evergreen and semi-deciduous forest plots. Includes early and mid
successional plots (<30 years) [linear regression: HBA = �0.004 (�0.46S.E.) + 0.68 (�0.10S.E.) HTSCC; R2 = 0.49, F1,43 = 43.5, P < 0.0001, n = 45]; (b) forest
basal area as a function of time since the last fire for late-successional semi-evergreen and semi-deciduous medium-statured forest plots (linear regression: HBA = 3.0
(�0.36) + 0.19 (�0.08) H(time since fire); R2 = 0.90, F1,54 = 6.3, P < 0.015, n = 56; (c) stand-level wood specific gravity as a function of forest age for semi-
evergreen and semi-deciduous forest plots [linear regression: (wood specific gravity) = 0.49 (�0.04S.E.) + 0.03 (�0.009S.E.) HTSCC; R2 = 0.12, F1,43 = 7.1,
P = 0.01, n = 45]; (d) stand-level wood specific gravity plotted against time since the last fire for late-successional semi-evergreen and semi-deciduous forest plots
(R2 = 0.02, F1,54 = 0.07, P = 0.8, n = 56).
Fig. 4. Contribution of tree genera to mean forest basal area according to their
wood specific gravity categories in relation to time of recovery (years) since
clear-cutting.
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–90 85
stand-level wood specific gravity in our 229 plots ranged from
0.40 to 0.83 g/cm3 (mean 0.65 � 0.005 g/cm3).
Stand-level wood specific gravity increased as succession
progressed over the early to mid-successional gradient
(R2 = 0.12, TSCC: F1,43 = 7.1, P = 0.01, n = 45) (Fig. 3). This
was primarily due to an increase in the proportion of total stand-
level basal area contributed by high wood specific gravity
genera (Kruskal–Wallis test: x2 = 44.7, d.f. = 2, P < 0.0001,
n = 229) and a decline in the contribution of intermediate wood
specific gravity genera (Kruskal–Wallis test: x2 = 36.6, d.f. = 2,
P < 0.0001) as succession progressed. In contrast, the
contribution of low wood specific gravity genera was similar
throughout the succession (Kruskal–Wallis test: x2 = 5.8,
d.f. = 2, P = 0.06) (Fig. 4).
Again, given that successional stage was the strongest
predictor of wood specific gravity but most late-successional
plots could not be dated, analyses of the effects of edaphic
and disturbance variables (logging, hurricane and fire
disturbance) excluded early to mid successional plots.
Although stand-level wood specific gravity was slightly
greater in heavily logged stands, this explained very little of
the overall variation in wood specific gravity, while no other
disturbance or edaphic variables were retained in the minimal
model (Table 1).
Table 1
Minimal general linear models of effects of edaphic, anthropogenic and
environmental disturbance variables on basal area, aboveground forest biomass
(AGB) and stand-level wood specific gravity of late-successional (30–50 years)
and old-growth forest plots (>50 years) semi-evergreen and semi-deciduous
medium-statured forest plots
Basal areaa,b AGBa,b Stand-level wood
specific gravity
Corrected model F7,170 = 16.8 F6,171 = 13.2 F2,181 = 3.5
P < 0.0001 P < 0.0001 P = 0.034
Intercept F1,170 = 71.9 F1,171 = 37.3 F1,181 = 14242
P < 0.0001 P < 0.0001 P < 0.001
2.95 � 0.43 5.99 � 1.17 0.69 � 0.01
Soil type F3,170 = 7.9 F3,171 = 7.2
P < 0.0001 P = 0.0001
Redzina �0.26 � 0.25 �0.59 � 0.71
Lithosol �0.78 � 0.25 �2.17 � 0.75
Pellic vertisol �0.06 � 0.33 �0.15 � 1.00
Chromic luvisol 0 0
Soil humidity F1.170 = 9.0
P = 0.003
Xeric �0.34 � 0.12
Ustic
Time since fire F1.170 = 22.4 F1.171 = 28.1
P < 0.0001 P < 0.0001
0.27 � 0.06 0.89 � 0.17
Logging F2,170 = 13.9 F2,171 = 8.1 F2,181 = 3.4
P < 0.0001 P < 0.0001 P < 0.034
None 0.83 � 0.16 1.84 � 0.48 �0.04 � 0.02
Moderate 0.04 � 0.15 0.87 � 0.45 �0.03 � 0.02
Severe 0 0 0
Adjusted R2 0.386 0.293 0.028
Levene’s test F18,159 = 1.4 F11,166 = 1.4 F2,181 = 0.1
P = 0.150 P = 0.175 P = 0.898
a Other soils categories (n = 3) were not included in analysis because of low
sample size (n = 6 plots).b General linear models (GLM) were conducted on square root transformed
data.
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–9086
3.5. Aboveground biomass estimates
AGB estimates were primarily determined by forest basal
area (R2 = 0.95, P < 0.0001, n = 229). Incorporating stand-
level wood specific gravity slightly improved this model
(P < 0.0001) explaining an additional 4% of the variation in
AGB (R2 = 0.99, P < 0.0001, n = 229), but considered alone
this explained only 14% of the variation in AGB (P < 0.0001,
n = 229). However, when calculating AGB, incorporating wood
specific gravity reduced mean estimates by 8.3% in late-
successional plots (30–50 years) and by nearly a fifth (19.2%)
in early-successional plots (10–15 years).
3.6. Aboveground biomass recovery rates
Forest biomass was related to stand age as HAGB = �1.53
(�1.17S.E.) + 1.97 (�0.27S.E.) Hage [R2 = 0.58, F1,39 =
P < 0.0001, n = 41] in forest plots of known age (4–33 years).
Calculated growth rates from this regression analysis suggest an
annual increment of aboveground live biomass of 2.78
(�0.18S.E.) mg ha�1 year�1 in secondary forests over a 30-
year period.
4. Discussion
4.1. Effects of forest disturbance on basal area and stand-
level wood specific gravity
Our study used 243 plots ranging across a wide spectrum of
forest disturbance and provides the most comprehensive survey
yet for semi-evergreen and semi-deciduous medium-statured
forest structure in the Yucatan Peninsula. The most important
determinant of both forest basal area and stand-level wood
specific gravity was time since disturbance. In the absence of
additional severe disturbance events, basal area and AGB
continued to accumulate beyond 30 years after clear-felling,
and high AGB was attained in undisturbed old-growth forests
plots. However, basal area and AGB in late-successional plots
(30–50 years) was reduced with increased logging intensity and
shorter time since fire. Disturbance factors have been
recognised to be the most important determinants of spatial
variability of biomass at small spatial scales (Chave et al.,
2001).
A reduction in forest basal area and AGB can be explained
by higher levels of tree mortality as a direct consequence of
logging (Pinard and Putz, 1996), fire disturbance (Barlow et al.,
2003; Kauffman et al., 2003; Van Nieuwstadt and Sheil, 2005)
or both. Mean basal area was greatly reduced in moderately
(24.1%) and severely (41.5%) logged stands. Logging in the
Yucatan Peninsula is extremely selective (1–3 m3/ha of timber
removal) resulting in overall low canopy distrubance (Dick-
inson et al., 2001). However, damage of trees >10 cm in DBH
within felling gaps can be high [65%] during logging
(Whitman et al., 1997; Dickinson et al., 2000). Pulses of
selective timber harvest that have occurred throughout the
Peninsula since the early 20th century (Turner et al., 2001) and
continuous extraction of building poles and fuelwood
contribute to the reduction in mean basal area found in this
study.
Lower stand-level wood specific gravity was expected in
logged sites, but the results were quite the opposite, with higher
stand-level wood specific gravity found in the most severely
logged sites. Continuous wood extraction may fail to create
sufficiently large canopy gaps for pioneer establishment, while
the density and size of canopy gaps can be low even in
commercially logged stands (Dickinson et al., 2001). A higher
stand-level wood specific gravity in logged sites may, reflect a
preference for timber extraction in older stands as we found
greater logging intensity in later successional stages. On the
other hand, a higher proportion of heavy-wooded taxa may have
resulted from a preference for light wood extraction in late-
successional stands. The region’s timber industry demands both
high-density species for strength and durability (e.g. Metopium
and, Manilkara), as well as more pliable, lower-density timber
species (e.g. Cedrela, Swietenia and Dendropanax), yet local
inhabitants may prefer to extract light-wooded species which
involves less work.
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–90 87
Stand-level wood specific gravity was also unrelated to fire
disturbance. If fire related mortality were high, an increase in
density of light-wooded pioneer taxa may be expected.
However, in tropical dry forests in Nicaragua, fire resulted in
low mortality and minimal effects on the relative tree species
composition due to the ability of most species to survive the fire
or persist in the forest community through resprouting
(Otterstrom et al., 2006).
Subtle differences in forest basal area and forest biomass
between soil types (rendzina and lithosols) and soil humidity
(ustic and xeric), were only apparent in late-successional plots.
Although soil nutrients and drainage influence tropical forest
biomass (Clark and Clark, 2000; Malhi et al., 2004), the
variance explained by edaphic variables in this study was minor
compared to disturbance factors. However, the AGB estimates
were derived from a single allometric scaling between tree
basal area and tree height, derived in the southern part of the
Yucatan Peninsula. If the relationship between tree stature and
basal area differs among soil types or regional gradients of
rainfall, this variation would not have been incorporated in our
estimates. Furthermore, soil characteristics of individual plots
may differ from those predicted by the relatively coarse-scaled
maps used, due to local topographic effects. The apparently
weak importance of soil type and humidity found in our study
should therefore be regarded as preliminary.
The history of hurricane disturbance did not explain
variation in forest basal area or stand-level wood specific
gravity. Hurricanes can cause a relatively small reduction in
forest basal area, as small trees appear to be more vulnerable to
this type of disturbance (Sanchez-Sanchez and Islebe, 1999).
Both human population size and distance to towns were
related to logging intensity and degree of forest degradation.
This has important implications for both land-use planning and
the calibration of biomass distribution models at large spatial
scales (e.g. Brown and Gaston, 1995). In contrast, no
relationship was found between distance to towns and
successional stage of forest plots. Late-successional forest
patches (30–50 years old) occurred in remote sites, but also near
towns where they had been protected for fuelwood, as seed
reserves of mahogany and cedar, or because of the presence of
archaeological ruins (Urquiza-Haas, unpublished data).
4.2. Aboveground biomass estimates
Stand-level wood specific gravity explained only a small
proportion of the variation in AGB estimates, in contrast to its
importance in tree plots spanning the Amazon region
[�6,000,000 km2] (cf. Baker et al., 2004). Stand-level wood
specific gravity is closely related to forest species composition
and thus may be more variable at much larger spatial scales,
than at that of our study (�60,000 km2). Variation in AGB in
relation to edaphic variables was driven by differences in basal
area. However, stand-level wood specific gravity was important
in explaining patterns of forest succession, and the exclusion of
this parameter would cause AGB to be overestimated in early
and mid successional forests.
Our mean stand-level wood specific gravity value (0.66 g/
cm3) for late-successional plots is equivalent to values reported
for central and eastern Amazonian forests (0.66 g/cm3: Baker
et al., 2004), higher than values reported for lowland tropical
moist forests at La Selva, Costa Rica (0.47 g/cm3) and Barro
Colorado, Panama (0.51 g/cm3) (Muller-Landau, 2004), and
similar to the mean value (0.69 g/cm3) reported by Read and
Lawrence (2003) for mature forest stands in the southern part of
the Yucatan Peninsula.
All aboveground biomass estimates derived from allometric
equations may be prone to error (Clark and Clark, 2000). We
have used the equation derived by Cairns et al. (2003) based on
sampling a large number of tree plots within the same forest
type in the Yucatan region. The Martinez-Yrizar et al. (1992)
equation employed by Read and Lawrence (2003) to estimate
AGB in Yucatan plots, was developed for a drier life zone
(770 mm year�1) and would have yielded low biomass
estimates (Brown, 1997), thus underestimating AGB in our
study region (where rainfall ranges 1000–1500 mm year�1).
Another common uncertainty in biomass estimations is the
application of an equation outside the range of DBH for which
it was developed (Brown, 1997). This is unlikely to have been a
problem in our study, as the Cairns et al. (2003) equation was
developed on the basis of trees ranging up to 63.4 cm DBH, and
only 0.8% of our stems were >60 cm DBH. However, in
estimating tree height we used the equation of Read and
Lawrence (2003) derived from sampling of mature forest plots
(>25 years). If we had instead used that for estimating tree
height in younger plots (aged 2–25 years) this would reduce our
biomass estimates for early (10–15 years) and mid-successional
(16–29 years) forest plots by 21% and 22%, respectively.
Therefore, we have probably overestimated biomass in younger
plots.
4.3. Comparisons with previous aboveground biomass
estimates in the Yucatan Peninsula
Our estimate of mean AGB in old-growth forest plots,
accounting for stand-level wood specific gravity
(191.9 mg ha�1 � 9.5 SE), was virtually identical to that
measured by Cairns et al. (2003) in a single old-growth forest
plot in central Quintana Roo (191.5 mg ha�1) (Table 2). Our
mean AGB estimate for late-successional forest plots (30–50
years) was only 4.9% lower than that obtained by Cairns et al.
(2000) using national forest inventories, for plots of unknown
age, in the Yucatan Peninsula (111.2 mg ha�1) and using the
Brown et al. (1989) equation. The greatest difference in AGB
estimates (43.3%) is between our late-successional plots and
the mature forests surveyed by Read and Lawrence (2003) in
the southern portion of the Yucatan Peninsula [126.1 �4.5 mg ha�1, n = 8] on the basis of the Martinez-Yrizar et al.
(1992) allometric equation; the difference was still consider-
able (17.9%) when we considered only our old-growth forest
plots (Table 2). This discrepancy may partly arise because
stems <10 cm DBH in Read and Lawrence’s (2003) study
contributed as much as 30% of the forest biomass in mature
forest plots. In addition, differences in AGB estimates may be
Table 2
Between-site comparisons of mean aboveground biomass (AGB) estimates within the Yucatan Peninsula, Mexico
AGB mg ha�1
(mean � S.E.)
n Equation used Study
Old-growth forest plots 126.1 � 4.5 8 log10ðAGBÞ ¼ �0:7590þ 0:9011 log10ðBAÞþ0:5715 log10ðWSGÞ þ 0:5654 log10ðhtÞ (1)
Read and Lawrence (2003)
Old-growth forest plots (>50 years) 103.5 � 4.4 28 Eq. (1) This study
Late-successional forest plots (30–50 years) 71.5 � 2.1 184 Eq. (1) This study
Single old-growth forest plot 191.5 1 AGB ¼ expf�2:12605þ 0:868 lnðDBH2htÞg(2) Cairns et al. (2003)
Old-growth forest plots (>50 years) 210.2 � 9.0 28 Eq. (2) This study
Old-growth forest plots (>50 years) 191.9 � 9.5 28 AGB ¼ expf�2:12605
þ0:868 lnðDBH2htÞgri=0:72 (3)
This study
Forest plots from national forest inventories 111.2 NRa AGB ¼ expf�1:996þ 2:32 lnðDBHÞg(4) Cairns et al. (2000) b
Late-successional forest plots (30–50 years) 105.8 � 3.5 184 Eq. (4) This study
Notes: AGB = aboveground biomass in dry mass (kg); BA = basal area (cm2); WSG = wood specific gravity (g/cm3); DBH = diameter at breast height (cm);
ht = height (m); Eq. = equation.a NR = not reported.b Value cited in Cairns et al. (2003). Eq. (1), Martinez-Yrizar et al. (1992); Eq. (2), Cairns et al. (2003); Eq. (3), Cairns et al. (2003) modified equation to account for
variation in wood specific gravity (see methods section); Eq. (4), Brown (1997), cited originally in Brown et al. (1989).
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–9088
partly related to forest successional status, which is unknown
beyond 25–30 years in both studies. Furthermore, past human
perturbation can also account for the differences between these
sites; our late-successional plots range across a marked
disturbance gradient, whereas Read and Lawrence (2003) do
not report disturbance by fire or hurricanes in their plots,
although their sites had probably experienced occasional
logging.
Mean basal area estimates reported to date for the Yucatan
Peninsula range from 11.9 to 45.0 m2 ha�1 (Dickinson et al.,
2001; Ceccon et al., 2002; Gonzalez-Iturbe et al., 2002;
Lawrence and Foster, 2002; Cairns et al., 2003; La Torre-
Cuadros and Islebe, 2003; White and Hood, 2004). The large
variation in history of forest disturbance and methods employed
in these studies prevents straightforward comparisons with our
basal area estimates. However, AGB estimates in our stands are
comparable to the range of values found in forests across the
Yucatan Peninsula, whenever the allometric equation used to
derive the estimates was the same. This suggests that
differences between previous AGB estimates in the Peninsula
were primarily due to the use of different equations.
4.4. Aboveground recovery rates
Calculated growth rates in this study (2.8 mg ha�1 year�1)
fell within the values estimated by Read and Lawrence (2003)
for the southern Yucatan Peninsula (2.3–3.4 mg ha�1 year�1;
2–25 years), but was almost half that of tropical secondary
forests (up to 30-year-old) worldwide (5.0 mg ha�1 year�1;
Brown and Lugo, 1990) and about two thirds of the growth rate
reported for 20-year-old secondary forests in the Amazon
(4.1 mg ha�1 year�1; (Brown and Lugo, 1990; Fearnside and
Guimaraes, 1996). In global scale analyses differences in
climate and moisture holding capacity are the main factors
influencing aboveground biomass accumulation in post-
disturbance secondary forests (Johnson et al., 2000). A more
rapid recovery was expected in the seasonal forests of the
Yucatan Peninsula due to their greater resprouting capability
following clear-cutting (Negreros-Castillo and Hall, 2000).
According to Silver et al. (2000), during the first 20 years of
regrowth, forests within different life zones accumulate
biomass at approximately the same rate, even though during
the following 20–80 years, wet forests (>2500 mm/year)
accumulate biomass at a higher rate than moist forests (1000–
2500 mm/year). On the other hand, faster recovery in terms of
basal area has been reported for other dry and wet forests
(Kennard, 2002; Pena-Claros, 2003). Turner et al. (2001)
reported a higher recovery rate for the southern peninsular
region, with forest basal area increasing within 25 years to 80%
of that of mature forest. This is at odds with our study in which
basal area of stands aged 25–29 years (n = 14) had reached only
62% and 42% of that of late-successional (30–50 years) and
old-growth (>50 years) stands, respectively. However, our
results are comparable to those of Lawrence and Foster (2002)
who reported that live biomass of 25-year-old secondary forests
was only 40% of that of mature forests. In our study, AGB in
stands aged 25–29 years had reached 54.4% and 38.0% of that
of late-successional (30–50 years) and old-growth (>50 years)
stands, respectively.
5. Conclusions and management implications
Forests of this region are part of one of the largest remaining
tracts of Central American forests, where current land use is
still dominated by slash-and-burn agriculture (De Clerck and
Negreros-Castillo, 2000). Our study shows that the forest of the
Yucatan region still retain a significant carbon stock, but major
differences in basal area and AGB result from the history of
forest disturbance. Recovery time since last clear-cutting
explained much of the variation in basal area and was a key
determinant of AGB. Basal area and AGB were also reduced in
heavily logged sites and sites disturbed by fire events. Inferred
biomass recovery trajectories were lower than those in many
other tropical forest types, emphasising the importance of
retaining old-growth forest stands. We propose that conserva-
tion strategies should (1) explicitly incorporate programs aimed
T. Urquiza-Haas et al. / Forest Ecology and Management 247 (2007) 80–90 89
to protect remaining old-growth forest stands, as they still retain
large biomass stocks, and (2) support ejido management
including fire-suppression practices that can protect remaining
forest fragments by cutting fire-breaks along the borders of
agricultural land. Finally, implementing global carbon market-
ing has the potential to benefit both carbon stocks and
biodiversity conservation within the Mesoamerican Biological
Corridor Project (Miller et al., 2001), by providing economic
alternatives to forest conversion.
Acknowledgements
This work was supported by a grant from the Wildlife
Conservation Society and a CONACYT (Consejo Nacional de
Ciencia y Tecnologıa) fellowship to T.U.H. We are grateful to
Alejandro Tuz, Arsenio Xool and Cosme Caamal for assistance
in the field, and to Pronatura Penınsula de Yucatan, Juan Carlos
Faller and Gabriel Ramos-Fernandez for logistical support in El
Zapotal and Punta Laguna respectively. Many thanks to the
anonymous referees for commenting on earlier drafts of the
manuscript. T.U.H is indebted to all Mayan communities from
the study area for their help and hospitality and wants to thank
Cecilia Elizondo, David Lopez and in particular Bernardo Urbani
and family Urquiza-Haas for their love and continuing support.
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