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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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