Forest Ecosystems Forest Ecosystems International Journal of Forestry Research

International Journal of Forestry Research

This is a focus issue published in “International Journal of Forestry Research.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editorial Board

Han Chen, Canada Kurt Johnsen, USA Timo Pukkala, Finland Chris Cieszewski, USA Chandra Prakash Kala, India Robin Reich, USA Piermaria Corona, Italy Seppo Kellomaki,¨ Finland Scott D. Roberts, USA Qing-Lai Dang, Canada Kihachiro Kikuzawa, Japan Lisa Samuelson, USA Daniel C. Dey, USA Guy R. Larocque, Canada John Sessions, USA Yousry El-Kassaby, Canada Harri Makinen,¨ Finland Hubert Sterba, Austria Edward Farrell, Ireland Azim Mallik, Canada Andrew J. Storer, USA Mark Finney, USA Timothy Martin, USA Michael Tausz, Australia Jianbang Gan, USA Brian C. McCarthy, USA I. B. Vertinsky, Canada Frank Gilliam, USA Guillermo Mendoza, USA Kristiina Vogt, USA Andrew M. Gordon, Canada Ram Oren, USA Contents

Rainfall and Elevation Inﬂuence the Local-Scale Distribution of Tree Community in the Southern Region of Western Ghats Biodiversity Hotspot (India), Shijo Joseph, K. Anitha, V. K. Srivastava, Ch. Sudhakar Reddy, A. P. Thomas, and M. S. R. Murthy Volume 2012, Article ID 576502, 10 pages

Assessing Forest Production Using Terrestrial Monitoring Data, Hubert Hasenauer and Chris S. Eastaugh Volume 2012, Article ID 961576, 8 pages

Participatory Resource Mapping for Livelihood Values Derived from the Forest in Ekondo-Titi Subregion, Cameroon: A Gender Analysis, Daniel B. Etongo and Edinam K. Glover Volume 2012, Article ID 871068, 9 pages

Terrestrial Liming As a Restoration Technique for Acidiﬁed Forest Ecosystems, Sarah E. Pabian, Shawn M. Rummel, William E. Sharpe, and Margaret C. Brittingham Volume 2012, Article ID 976809, 10 pages

The Wood, the Trees, or the Forest? Carbon in Trees in Tasmanian State Forest: A Response to Comments,M.T.Moroni,T.H.Kelley,M.L.McLarin,andS.M.Read Volume 2012, Article ID 848502, 6 pages

Analysis of Soil-Vegetation Interrelationships in a South-Southern Secondary Forest of Nigeria, D. D. Eni, A. I. Iwara, and R. A. Oﬃong Volume 2012, Article ID 469326, 8 pages

An Ecological Comparison of Floristic Composition in Seasonal Semideciduous Forest in Southeast Brazil: Implications for Conservation,Sergio´ de Faria Lopes, Ivan Schiavini, Ana Paula Oliveira, and Vagner Santiago Vale Volume 2012, Article ID 537269, 14 pages Hindawi Publishing Corporation International Journal of Forestry Research Volume 2012, Article ID 576502, 10 pages doi:10.1155/2012/576502

Research Article Rainfall and Elevation Inﬂuence the Local-Scale Distribution of Tree Community in the Southern Region of Western Ghats Biodiversity Hotspot (India)

Shijo Joseph,1, 2, 3 K. Anitha,2, 4 V. K. Srivastava,1 Ch. Sudhakar Reddy,1 A. P. Thomas,4 andM.S.R.Murthy1

1 Forestry and Ecology Division, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad 500 625, India 2 Conservation Research Group (CRG), St. Albert’s College, Kochi 682 018, India 3 Forests and Environment Program, Center for International Forestry Research (CIFOR), Bogor 16115, Indonesia 4 Advanced Centre of Environmental Studies and Sustainable Development, School of Environmental Sciences, Mahatma Gandhi University, Kerala 686 560, India

Correspondence should be addressed to Shijo Joseph, [email protected]

Received 18 October 2011; Revised 22 February 2012; Accepted 23 February 2012

Academic Editor: Guofan Shao

Copyright © 2012 Shijo Joseph et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The present study characterises the tree communities with respect to topographic and climatic variables and identifies the most important environmental correlate of species richness in the southern region of Western Ghats Biodiversity Hotspot, India. Digitally derived environmental variables in combination with tree species richness information were analysed using Canonical Correspondence Analysis (CCA) to characterise the communities. Multiple regression technique based on stepwise backward elimination was used to identify the most important environment correlate of species richness. Canonical correspondence analysis results in six major tree communities along the first and second axes. Rainfall is the dominant environmental gradient influencing vegetation patterns on the first CCA axis while elevation showed the highest correlation with the second CCA axis. Backward elimination regression technique yielded rainfall as the most important environmental correlate of species richness. Results were in agreement with the observations in the Neotropics that rainier areas maintain high species diversity.

1. Introduction core of predictive geographical modelling in ecology [13]. Today’s concern about biodiversity losses and ecosystem Tropical forests are unique in many aspects such as high services suggest that forest classifications based on species diversity [1], high standing biomass and carbon storage composition are needed, along with the information on how [2], and global net primary productivity [3]. There have many species are shared between different forest types. This been many attempts to understand the structural complexity information in combination with the key environmental and species diversity of these ecosystems spanning over correlates is important for natural reserve area planning and three decades [1, 4–11]. While a complete perception of the management under global change scenarios. tropical forest still remains a challenge, the degradation and The importance of climate to map animal and plant deforestation is hastening on the other side. Information distribution was recognized during the early stages of on distribution of tree communities along environmental nineteenth century [14]. Climate in combination with other gradients has vital role in understanding their ecology as physical elements of the environment has been used to derive well as their conservation and management. Quantification vegetation patterns at the global scale [15, 16]. More recently, of such species-environment relationships yields valuable studies have revealed regional variability in vegetation types insights into ecological processes such as resource par- with respect to topography, seasonality, soil types, and water titioning and niche differentiation [12] and forms the and nutrient availability [17–22]. However, most of these 2 International Journal of Forestry Research studies were carried out in the Neotropics in comparison 2.2. Field Sampling and Environmental Data. Astratified with the Old World tropics [23].Notableexceptionsinclude transect survey was adopted for the study. The strata were studies conducted by Pascal [24], Gimaret-Carpentier et al. delineated considering the long-term climatic observations [25, 26], Davidar et al. [27, 28], and Joseph et al. [12]. and altitudinal variation in the study area. Similarly, most of the studies were conducted in the tropical Though there were a few weather stations scattered over humid forests ignoring the other tropical formations, partic- the area, the datasets available from these stations were ularly dry forests. Clearly, these patterns reflect the biases that for a maximum of five years [40]. Therefore the present characterize tropical biology [29]. study used a global database called Worldclim [41]. This is Western Ghats, located near to the western coastline of the station data during the period of 1950–2000 and was India, is one of the “Hottest Hotspots” of biological diversity interpolated to monthly climate surfaces at 1 km spatial res- in the Old World tropics [8]. It contains more than 30% olution using a thin-plate smoothing spline algorithm with of all plant and vertebrate species found in India, in less latitude, longitude, and elevation as independent variables than 6% of the country’s landmass. It is also recognized [41].Theannualaveragetemperature(◦C) and rainfall (mm) as one among the 200 globally most important ecoregions were calculated from the monthly datasets (Figure 2). The [30]. Of the four thousand species of flowering plants known topographic variables such as elevation (m), slope (angle from the Western Ghats, 1500 are endemic [31]. In spite of of inclination in degrees), and aspect (direction of slope these striking statistics, biodiversity in the Western Ghats is in degrees) were derived from Shuttle Radar Topographic threatened by a range of anthropogenic pressures. Menon Mission (SRTM) data (Figure 2). Further, the SRTM digital and Bawa [32] estimated that between 1920 and 1990 forest elevation model was placed over climatic data to study the cover in the Western Ghats declined by 40%, resulting in changes in climatic parameters with respect to elevation. a fourfold increase in the number of fragments and an Four strata were delineated and transects sampling was 83% reduction in size of forest patches. The yearly rate of conducted in these strata. Circular plots of 10 m radius forest loss has been estimated at 0.57% [32], 0.9% [33], and (plot size—314 m2) were laid on every 200 m interval along 1.16% [34] in unprotected areas while 0.04% in protected transects (Figure 1). In each plot, all woody plants with areas [35]. This highlights, the need for more studies on ≥10 cm DBH (Diameter at Breast Height, 1.3 m) were regional patterns of diversity and its drivers, as data on identified at species level, their individuals counted, and partitioning of tropical vegetation types under different DBH measured using a tape. A total of 19 transects (thirteen climatic and topographic gradients supply valuable inputs 2 km transects + six 1 km transects) with 179 sampling plots for conservation planning [36, 37]. On a macroscale (entire were surveyed. In addition, 27 sampling plots have been Western Ghats), the diversity and species richness are largely collected randomly especially in shola forest and evergreen attributed to seasonality [28] while the mesoscale assessment patches since one-or two-kilometer continuous stretch was showed that the diversity is a correlate of rainfall [37]. not available for these habitats. Thus, in total 206 sample Further to this, the present study is an attempt to identify the plots with an area of 65 ha (which is approximately 0.001 of highest environmental correlate species diversity at the local the total area) were surveyed. scale. 2.3. Data Analysis. In order to investigate the relationships between species richness and environmental variables, a 2. Materials and Methods Canonical Correspondence Analysis (CCA) was employed [42] using the software PC-ORD [43]. CCA is a direct 2.1. Study Area. Anamalai region located in the southern gradient ordination technique to cluster species abundance Western Ghats is known to be active zones of speciation with environmental variables [44]. The first step of CCA and localized centres of endemism [31, 38]duetoits was to summarize the main variation in the species data varied topography and microclimatic regimes (Figure 1). by ordination. This was done by constructing an axis such It is also considered as one of the 25 microcentres of that the species data optimally fit Gaussian response curves diversity in the Indian Subcontinent [39]. The overall terrain (Gaussian ordination) according to the following equation: is hilly with altitude ranging from 220 m at the foothills in the north-east to 2550 m (in the Grass Hills area) in 2 1 (Xi − Uk) the south-west. The annual rainfall varies from 500 mm in E(Yik) = Ck exp ,(1) the rain shadow eastern slopes to 5000 mm in the west. 2 tk2 Mean daily temperature varies from <5◦C in the winter ◦ at elevations above 2000 m to nearly 40 C in the eastern where Yik is the abundance of species k at site i and plain in the summer. Ecoclimatically, the area is considered E(Yik) denotes the expected (average) value of Yik at site i as a miniature of the Western Ghats biodiversity hotspots that has score Xi, on the ordination axis. The parameters and harbors all major forest types found in the Western for species k are Ck, the maximum of that species’ response Ghats [35]. The objectives of the present study are to curve; Uk, the mode or optimum (i.e., the value of x for outline the tree communities under diverse climatic and which the maximum is attained); tk, the tolerance, a measure topographic gradients in the Anamalai region and to identify of ecological amplitude. the important environmental correlate of the species richness The second step was to relate the ordination axis in the area. to the environmental variables by calculating correlation International Journal of Forestry Research 3

76◦500E76◦550E7777◦00E77◦50E ◦100E77◦150E 77◦200E

Anamalai wildlife sanctuary India

10◦350N + + + + + + + 10◦350N

10◦300N + + + + + + + 10◦300N

10◦250N + + + + + + + 10◦250N

10◦200N + + + + + + 10◦200N

10◦150N + + + + + + + 10◦150N (kms) N WE 036 12 S

76◦500E 76◦550E7777◦00E77◦50E ◦100E77◦150E 77◦200E

Figure 1: Location of the Anamalai wildlife sanctuary in the Western Ghats Biodiversity Hotspot (India). The dots represent the sampling locations. coefficients or by multiple regression [45] of the site scores The tree communities obtained after Canonical Cor- on the environmental variables by the following equation: respondence Analysis were assessed their compositional q attributes. Here, the compositional attributes refer to species richness, diversity, dominance, stand density, and average Xi = b + bj zij,(2) 0 basal area of each community. For calculating diversity, two j=1 indices were used in addition to simple species richness where b0 is the intercept and bj is the regression coefficient for (number of species). The first one was Shannon-Weiner environmental variable j. zij is the value of environmental diversity index (4)[46] and the second was Simpson diversity variable j at site i and q is the number of environmental index (6)[47]. The latter was calculated from Simpson variables. dominance index (5).Theaveragebasalareawascalculated Finally, the weighted average of a species distribution (k) from the diameter of the stem at breast height using (7): with respect to an environmental variable (j) was defined as Shannon-Weiner diversity index: the average of the values of that environmental variable at those sites at which that species occurs, the weighting of each H =− pi ln pi,(4) site being proportional to species abundance, that is,

n YikZij where H is the measure of diversity and pi is the proportion z = . kj (3) of the total sample belonging to the ith species. Y+k i=1 Simpson dominance index The weighted average indicates the “center” of a species’ ﬀ 2 distribution along an environmental variable, and di erences D = pi ,(5) in weighted averages between species indicate diﬀerences in their distributions along that environmental variable. where D is the Simpson index of dominance and pi is the As required by CCA, the data is set into two distinct proportional individuals of species i in the community. matrices: the species matrix and the matrix of environment Simpson diversity index: variables. The species matrix contained number of species per plot, and the environmental variables matrix consisted 2 elevation, slope, aspect, temperature, and rainfall. 1 − D = 1 − pi ,(6) 4 International Journal of Forestry Research

Elevation derived from SRTM data N

WE S

High: 2504 Slope derived from SRTM data Low: 253

Aspect derived from SRTM data

High: 69

Low: 0

High: 360 Temperature derived from WorldClim Low: 0 database

Precipitation derived from WorldClim database High: 32.42

Low: 0

High: 317

Low: 0 01020 40 (kms) Figure 2: Topographical (elevation (m), slope (degrees) and aspect (degrees)) and climatic gradients (temperature (◦C) and precipitation (cm)) in Anamalai wildlife sanctuary, India (topographical variables are derived from SRTM data and climatic variable are derived from 50 year average WorldClim datasets). where 1 − D is Simpson’s index of diversity. rainfall, and temperature were the independent variables. The analysis was carried out using the software SPSS [48]. (DBH)2 Basal area = ,(7) 4π 3. Results where DBH is diameter at breast height. Multiple linear regression analysis was conducted to Atotalof169treespecieswereobservedduringthepresent identify the most important environmental correlates of study. Among these, Maesa indica had the highest number species richness. A stepwise backward elimination approach of individuals (73) followed by Anogeissus latifolia (56) was adopted in which the analysis started with all the and Croton oblongifolius (55). The phytodiversity assessment continuous variables and eliminated the least signiﬁcant also showed that the sanctuary is a hub of many endemic variable in each progressive step. The variables were removed and threatened species which include Aglaia tamilnadensis, if the probability of “F” exceeds 0.05. The species richness Dysoxylum malabaricum, Myristica malabarica, Syzygium was the dependent variable and elevation, slope, aspect, malabaricum, and Vernonia travancorica. International Journal of Forestry Research 5

3.1. Tree Communities. Canonical correspondence analysis Ziziphus oenoplia and match up with Champion and Seth’s was performed for 169 species recorded on 206 plots with 5 southern moist mixed deciduous forest (3B/C2). This forest environmental variables to understand the tree community community occurs at altitudes of 500–700 msl with relatively composition of the study area. The ordination diagram low rainfall zones. obtained after Canonical correspondence analysis is shown in Figure 3. The eigenvalues for the first three CCA axes were 0.749, 0.523, and 0.304, respectively. The cumulative 3.1.5. Dry Deciduous Community. This group is a composi- percentage variance accounted for those axes was 16.0% tion of Anogeissus latifolia, Dalbergia latifolia, Albizia amara, (7.6, 5.2, and 3.2, resp.), indicating that a considerable Maesa indica, and Terminalia paniculata and corresponds to amount of “noise” still remained unexplained. In fact, southern dry mixed deciduous forest (5A/C3). This is found the CCA produced high correlations between species and on areas where altitude ranges from 400 to 600 m with very environmental variables for these axes (0.94, 0.88, and 0.74, low rainfall. resp.). The first ordination axis was highly correlated, in descending sequence, with rainfall, temperature, elevation, 3.1.6. Thorny Scrub Community. This community is formed and slope (Table 1). The second ordination axis has shown in areas where human disturbance is maxima and rain- high correlation with elevation and temperature while the fall is less. They occur at lower altitudes (<400 m) with third ordination axis is correlated with slope. The weighted thorny species such as Albizia amara, Gyrocarpus ameri- correlations between environmental variables showed strong canus, Catunaregam spinosa, Acacia planifrons, and Limonia interrelationships, especially between elevation and climatic crenulata. It corresponds to southern thorn scrub as per variables (temperature and rainfall). Segregation of plant Champion and Seth’s [51] classification of Indian forest. communities along the noted gradients was also observed. Canopy openings and open spaces are characteristics of the The left side of the ordination space was dominated with area. Cattle grazing and firewood collection is frequent in the communities which are primarily evergreen species whereas area. the right side was occupied by deciduous species (Figure 3). The details of the communities are further explained below. 3.2. Compositional Attributes of Tree Communities. The com- 3.1.1. Evergreen Community. This group is characterized positional attributes of tree communities in Anamalai region by the species such as Vateria indica, Persea macrantha, is given in Table 2. Evergreen community showed maximum Palaquium ellipticum, Eugenia calophyllifolia, and Macrantha species richness, that is, 81 tree species. Both the diversity roxburgii. This species assemblage more or less corresponds indices (Shannon and Simpson) showed that evergreen forest to west coast tropical evergreen forest (1A/C4) of Champion was highly diverse in comparison with other forest types, and Seths [51] classification of Indian forests. This forest whereas the dominance in the evergreen forest was lower. occurs in relatively undisturbed area between altitudes of The highest dominance was observed in dry deciduous 900–1500 msl. They are embarked by higher humidity, lower forest (Simpson dominance index—0.093) followed by moist temperature, lower understory, low canopy openings, and deciduous forest (0.075). The stand density was higher at high leaf litter. shola forest (446 individuals per hectare) while average basal area was higher in semi-evergreen forest (62.3 m2/ha). 3.1.2. Montane Shola Community. The major species in shola forests are Syzygium cumini, Vitex leucoxylon, Dysoxylum malabaricum, Cinnamomum sulphuratum, and Eugenia calo- 3.3. Environmental Correlates of Species Richness. The assess- phyllifolia. This group corresponds to southern montane ment of species richness from environmental variables wet temperate forest (11A/C1) as per Champion and Seth’s yielded the following model (Table 3). The model fit was 0.50 (F 39.82, P<0.05). Though the overall model was classification. It occurs at an elevation of more than 1500 m. t Annual rainfall in these areas is about 5000 mm while annual significant, the values of “ ” and its significance indicate that temperature is approximately 15◦C. Geographically the area each of these variables was not significantly contributing to the overall model. Therefore, the variable having the can be distinguished from the steep slopes and mountain ffi folding. least partial correlation coe cient (in this case aspect) was eliminated from the model. The model was then refitted with all other variables and this procedure was repeated until 3.1.3. Semi-Evergreen Community. These formations are dis- only statistically significant variables are left in the model. tinguished by species such as Nephelium longana, Palaquium In this case, only one variable was found to be a significant ellipticum, Vateria indica, Terminalia bellirica, and Vitex predictor of species number, that is, rainfall (8). Because of altissima and correspond to west coast tropical semi- the strong correlation among variables (Table 2), this was not evergreen forest (2A/C2). This is found at altitudinal ranges surprising, but it also means that it is difficult to disentangle of 700–900 m and relatively high rainfall areas. the separate effects of the independent variables. The model fit was 0.485 (F 192.91, P<0.05): 3.1.4. Moist Deciduous Community. They are characterized by the presence of species such as Anogeissus latifolia, Maesa indica, Albizia odoratissima, Terminalia crenulata, and Species Richness = 0.004 ∗ Rainfall − 1.311. (8) 6 International Journal of Forestry Research

Table 1: Canonical correspondence analysis of 169 species in 206 plots in Anamalai wildlife sanctuary. Matrix presents intraset correlation between environmental variables and ﬁrst three axes and weighted correlations between environmental variables.

Variable Axis 1 Axis 2 Axis 3 Elevation Slope Aspect Precipitation Temperature Elevation −0.662 0.734 0.063 1 0.422 0.181 0.757 −0.946 Slope −0.542 0.016 0.806 0.422 1 0.027 0.48 −0.436 Aspect −0.186 0.268 −0.123 0.181 0.027 1 0.186 −0.175 Rainfall −0.986 0.139 −0.052 0.757 0.48 0.186 1 −0.805 Temperature 0.715 −0.619 −0.131 −0.946 −0.436 −0.175 −0.805 1

Table 2: The diﬀerent compositional attributes such as species richness, diversity indices, dominance index, stand density per hectare, and average basal area per hectare with respect to tree communities in Anamalai wildlife sanctuary, India.

Tree community Parameters Evergreen Shola Semi evergreen MDF DDF Thorny scrub No. of species 81 17 55 32 18 27 Shannon diversity index 4.175 3.000 3.450 3.214 2.982 3.121 Simpson index of dominance 0.021 0.071 0.054 0.075 0.093 0.059 Simpson index of diversity 0.979 0.929 0.946 0.925 0.907 0.941 Stand density/ha. 347 446 276 192 212 69 Avg. Basal area/ha. 31.4 41.4 62.3 27.2 13.2 2.13 MDF: moist deciduous forest, DDF: dry deciduous forest.

Table 3: Overall regression model of species richness from areas (800–2000 mm/yr) with an altitude range of 500– environmental variables in Anamalai wildlife sanctuary (R2 − 0.50, 700 m by moist deciduous community; high rainfall areas F−39.82, df 1 − 5, df 2 − 199 and P − 2.98E − 28). (2000–3000 mm/yr) with medium altitude (700–900 m) by semievergreen community; high rainfall areas with high Variable Coefficient Std. error t Significance elevation range (900–1500) by evergreen community; very Intercept 3.354 4.857 0.690 0.491 high rainfall areas (>3000 mm/yr) with very high elevation Elevation −0.002 0.001 −1.737 0.084 (>1500 m) and slope by montane shola community. Slope 0.026 0.023 1.126 0.262 Though there is a clear distinction between evergreen Aspect 0.001 0.001 0.628 0.531 and deciduous communities in the ordination space, the Rainfall 0.004 0.000 8.248 0.000 boundary within each of these communities (e.g., the Temperature −0.135 0.134 −1.003 0.317 distinction among moist deciduous, dry deciduous and thorny scrub) is fuzzier. This may be because of the fact that the species composition is not significantly different among these communities. Moreover, the thorny scrub is considered 4. Discussion as a degraded state of deciduous system [51]. The gradual transition from one vegetation type to an other with respect 4.1. Tree Communities. The present study in Anamalai to changes in environmental gradients has been reported in region of southern part of Western Ghats identified six tree other parts of Western Ghats as well [37, 53]. communities, namely, tropical evergreen, montane shola, semi-evergreen, moist deciduous, dry deciduous, and thorny scrub along rainfall and elevation gradients (Table 4). Cumu- 4.2. Compositional Attributes. Shannon-Weiner and Simp- lative proportions of variance of the species data were quite son diversity indices indicated high diversity from the area low. However, ter Braak [52] considers low percentage of similar to previous observations from the Western Ghats unexplained variance as normal in vegetation data, and this [12, 49, 50, 54]. For example, Ayyappan and Parthasarathy fact does not weaken the significance of species-environment [54] reported Shannon diversity index of 3.926 in evergreen relationships. Our study revealed that rainfall has a profound habitats of Anamalai hills which was slightly lower than role in shaping up the vegetation of the area along with the present estimate (4.175). Tree density (stand density) topography. For instance, the low rainfall (<800 mm/yr) and in evergreen forest was 347 trees/ha which was slightly low elevation (<400 m) areas are characterized by thorny lower than the mean tree density (419 trees/ha) observed scrub community; low rainfall and medium elevation areas for Western Ghats closed-canopy evergreen forest [55]. The (400–600 m) by dry deciduous community; medium rainfall mean basal area in the wet forest was 45.03 m2/ha which International Journal of Forestry Research 7

Table 4: The tree communities and their distribution range with respect to environmental variables in Anamalai wildlife sanctuary, Western Ghats, India.

Foresttype Temperature(◦C) Rainfall (mm) Elevation (m) Evergreen forest 15–19 3000–4500 900–1500 Montane shola forest <15 >4500 >1500 Semi-evergreen forest 19–22 2200–3000 700–900 Moist Deciduous forest 22–27 1400–2200 500–700 Dry Deciduous forest 27–31 800–1400 400–600 Thorny scrub forest >31 <800 <400

P230

P229 P227 Montane shola forest communities

P222P225 P68 P228 P156 P51 P50 P43 P63 P49 P168 P46 P45 P44 P167 Moist deciduous communities P44 P48 P47 P46 P160P169 P171P170 P47 P159 P45 P165 P175 P164 P157 P163P155 P140 P161P154 P143 P153 Elevation P162 P53 P152 P150 P139 P54 P151 Dry deciduous communities P174 P203 P149P148 P114 P66 P67 P15 P192 P101 P117P107 P85P70 P64 P52 P172 P84P69P82 P206 P180P176 P145 P142 P71 P204 P129 P100 P141 P147 P146 P74 P202P201 Precipitation P56 P119 P120P126P123P122 P102P137 P135 P193 P16 P179P185 P121P113 P125P124 Scrub forest communities P196 P55 P186P178 P128P111 P138P138 P86 P195 P205 P209Slope P231 P182 P130 P109P106 P89 P198 P194 P57P181 P127 P93P136 P197 P189 P112 P103 P96 P134 P78 Axis 2 Axis P183P188 P115 P92 P133 P80 P200 P207 P241 P116 P79 P208 P233 P58 P94 P131 P81 P184 P110 P97 P91 P132 P75 P199 P72P76P77 P240 P232 P9 P59 P98 P105 P73 Evergreen communities P14 P8 P22 Temperature P7 P12P1 P3 P21 P2 P11 P4 P19 P23P20P24 P10 P18 P25P29 P239 P34

P237 Semievergreen communities P33P31 P27P35P30P238 P234 P26 P17 P235 P32 P36 P62 P40 P28 P236 P41 P37 P42 P13 P61 P39 P38 P60

Axis 1 Figure 3: CCA ordination diagram (Axis 1 by Axis 2) with plots (scattered points) and environmental variables (lines) in Anamalai wildlife sanctuary. Each circle represents partitioning of vegetation communities along environmental gradients. was higher than the previous reports (36.26 m2/ha) [54] [12]. Assessment of different compositional attributes of tree and Pantropical average (32 m2/ha) [56]. But it was not communities in the Anamalai region indicates that the area significantly different from other parts of the Western Ghats. is an essential element in the global biodiversity register in For example, Giriraj et al. [49] reported 47.01 m2/ha for general and the Western Ghats in particular. Kalakad-Mundanthurai Tiger Reserve, Ganesh et al. [57] reported 42.03 m2/ha at Kakachi, and Pascal and Pelissier [58] reported 39.7 m2/ha at Uppangala. The lowest basal 4.3. Environmental Correlates. Rainfall is found to be the area observed for thorny scrub forest was in correspondence most important environmental correlate of species richness with the similar trend in Mudumalai wildlife sanctuary in the Anamalai region. At macroscales, Davidar et al. [28] 8 International Journal of Forestry Research reported that length of dry season or seasonality drive wet [3] C. L. Sabine, M. Heimann, P. Artaxo et al., “Current status and evergreen forest beta diversity. Similarly, studies using pollen past trends of the global carbon cycle,” in The Global Carbon grains also pointed out that seasonality is the primary driver Cycle: Integrating Humans, Climate and the Natural World,C. that decides the composition of vegetation communities B. Field and M. R. Raupach, Eds., pp. 17–44, Island Press, throughout the Western Ghats [59]. However, the mesoscale Washington, DC, USA, 2004. [4] S. P. Hubbell, “Tree dispersion, abundance, and diversity in a assessment showed rainfall as the primary driver of species tropical dry forest,” Science, vol. 203, no. 4387, pp. 1299–1309, richness [37] in concordance with the findings of the present 1979. study at the local scale. Gentry [60, 61], Aplet et al. [62] [5] S. P. Hubbell, The Unified Neutral Theory of Biodiversity and andPitmanetal.[63] also made similar observations in the Biogeography, Princeton University Press, Princeton, NJ, USA, Neotropics that rainfall is a primary factor influencing trop- 2001. ical forest diversity. Though this generalization is applicable [6] R. Condit, R. Sukumar, S. P. Hubbell, and R. B. Foster, to Western Ghats Biodiversity Hotspot, it may not hold true “Predicting population trends from size distributions: a direct for all the Old World tropics. 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Research Article Assessing Forest Production Using Terrestrial Monitoring Data

Hubert Hasenauer and Chris S. Eastaugh

Department of Forest and Soil Sciences, Institute of Silviculture, BOKU University of Natural Resources and Life Sciences, Vienna, Peter Jordan Street 82, A-1190 Vienna, Austria

Correspondence should be addressed to Hubert Hasenauer, [email protected]

Received 20 October 2011; Revised 12 December 2011; Accepted 5 January 2012

Academic Editor: Timo Pukkala

Copyright © 2012 H. Hasenauer and C. S. Eastaugh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Accurate assessments of forest biomass are becoming an increasingly important aspect of natural resource management. Besides their use in sustainable resource usage decisions, a growing focus on the carbon sequestration potential of forests means that assessment issues are becoming important beyond the forest sector. Broad scale inventories provide much-needed information, but interpretation of growth from successive measurements is not trivial. Even using the same data, various interpretation methods are available. The mission of this paper is to compare the results of fixed-plot inventory designs and angle-count inventories with different interpretation methods. The inventory estimators that we compare are in common use in National Forest Inventories. No method should be described as “right” or “wrong”, but users of large-scale inventory data should be aware of the possible errors and biases that may be either compensated for or magnified by their choice of interpretation method. Wherever possible, several interpretation methods should be applied to the same dataset to assess the possibility of error.

1. Introduction (but substantial) financial penalties to countries signing up to successor agreements to the Kyoto protocol. Conversely, National forest inventories are an expensive and time- countries may claim carbon credits for a degree of forest consuming operation, particularly in large, remote, and sequestration that does not in fact exist. inhospitable regions. There is therefore great interest in Until recently, inventories were conducted solely as a alternative measures to estimate forest growth, such as means of measuring the timber resource present in a region, remote sensing [1] or mechanistic process modeling [2]. generally in order to determine its immediate extractive These methods, however, are not a direct measurement of capacity. The inventories were optimized to most efficiently the same physical characteristics as in an inventory, and it is reasonable to suggest that estimates made with these estimate a particular forest parameter, current standing alternatives should be shown to be unbiased with reference volume. This was and still is important, as it describes to ground data. a crucial aspect of forested landscapes. The carbon sink In the near future, national forest inventories will form strength of forests is not, however, directly related to standing an integral part of the way that many nations determine their volume, but is a function of forest increment. Mathemati- ff national carbon balance, and inventories are used to estimate cally, increment is simply the di erence in standing volume forest increment as a means of monitoring their value as between two periods, plus the volume of any removals from a carbon sink. Relatively minor errors in current standing the growing stock. In practice however this is not so simple, volume estimates may have little practical or policy impact, as sample designs or locations may vary between increments, but these may translate to substantial errors and biases in forest area may change and some inventory designs use a the estimate of forest increment. In some cases, these biases nonadditive method of increment estimation (where, at the may mean the difference between forest areas being assessed individual plot level, increment does not equal the difference asasinkorasourceofCO2 or could result in erroneous in standing volumes) [3]. 2 International Journal of Forestry Research

In recent decades, regular forest inventories have been 300 established in many countries using a permanent plot design to reduce the sampling error of the resulting increment 250 calculations [4]. Remeasurement intervals range from 5 to 10 years and either fixed area plots or angle-count sampling [5] may be used. In the latter case, three common methods may 200 be applied to repeated measurements in order to estimate forest increment: the difference, starting value or end-value 150 methods. Upscaling plot-level increments from fixed-plot ” coordinate (metres) coordinate ” y

inventories or using any three of the angle-count estimation “ methods produces mathematically unbiased estimates of 100 increment [6–8]. It has been shown, however, that mea- ff ff surement error a ects the various estimators di erently, and 50 thus different estimators will produce different results [6]. It has been suggested that comparing the results of different estimators can indicate the presence of measurement error 50 100 150 200 250 300 and thus be used as an inventory auditing tool [6, 9]. “x” coordinate (metres) In a theoretical simulation study using plausible esti- Figure 1: Stand layout at the Hirschlacke long-term forest moni- mates of measurement errors Eastaugh and Hasenauer [6] toring plot. Sizes of circles are proportional to diameter at breast reported possible biases in angle count-derived increment height, ranging from 5 to 75 cm. Axis coordinates are in metres. estimatesofupto±0.4–1.0 m3/ha/yr when averaged over 30 years, depending on the nature of the error and the estimation method used. The key findings of that study were that errors can result in different magnitudes of increment number of such sample plots necessary to support the bias and may manifest in the either the period in which concordance of different combinations of estimators. the error occurred or in subsequent periods, depending on the increment estimation method used. Thomas and Roesch [9]applieddifferent interpretation methods to large-scale 2. Data and Methods inventory data from the southern United States and found We obtain data from the 3.47 hectare Hirschlacke long- that an up to 48% difference in increment estimation was term forest growth monitoring site in northern Austria present between methods, which they attributed to trees [10]. When first measured in 1977, the stand was almost being missed (not counted) in the first inventory period. pure 110-year-old Norway spruce (Picea abies L. Karst) The Eastaugh and Hasenauer [6] study found that a 44.3% and has since then been managed under a target diameter increment difference could result from inventories errors that harvesting regime [11] designed to produce an equilibrium gave rise to an only 4.6% error in volume estimation. dbh distribution. All trees of over 5.0 cm dbh have been Applying several methods to the same dataset can, measured for both diameter and height at five yearly intervals ff however, give substantially di erent results due to sampling since 1977 and their location coordinates are precisely ff variation, even in the absence of error. The di erent results recorded. Figure 1 depicts the stand layout in 1977. are all equally valid estimators, but precision may be poor Since 1977, the stand structure has changed from having if relatively few plots are sampled. In such cases, it may 1510 trees with a mean dbh of 34.6 cm (standard deviation ffi be di cult to determine which (if either) of the estimators 14.3 cm) to 2820 trees with mean dbh 18.2 cm and standard ff may be error a ected and doubts can be raised over which deviation 17.5 cm in 2007. Mean tree heights have followed a estimated value should be accepted. Similarly, if inventory similar pattern, from 25.8 m (standard deviation 8.9 m) in data is to be used as a baseline to compare against modeled 1977 to 14.8 m (standard deviation 12.7 m) in 2007. This or remote-sensed estimates of forest characteristics, then it has been achieved by the removal of an average of 74 m3 of is important to first ensure the integrity of the inventory timber in each inventory period (timber volumes calculated estimate. This can be done by applying more than one according to the allometrics of Pollanschutz¨ [12]). Under this estimation method and ensuring that the final results are permanent-cover management system, the standing volume within a predetermined range of each other. on the site remains relatively constant over time, although In this study, we mimic a large scale forest inventory diameter distribution is currently quite different to that in through simulating 12 000 fixed area and angle count 1977, providing a diverse sampling space across time. samples inside a large long term forest monitoring plot We use the Hirschlacke dataset as a means of mimicking at Hirschlacke in northern Austria, over 7 measurement a large-scale national inventory. As all trees in the plot periods from 1977 to 2007. We are interested in how such are repeatedly remeasured, we are able to simulate what errors may be detectable in mean increment estimates with increments would be determined using angle counts or small high variance. Our primary purpose is to confirm the non- fixed-area sample plots assuming perfect measurements and biased nature of each of the increment estimators in the also with a range of simulated error conditions. The dataset absence of measurement error and determine the minimum is thus an ideal means of demonstrating the potential International Journal of Forestry Research 3

m nj ∗ m nj biases that may be apparent in angle-count-based inventories vji vji ZF = 10000 − 10000 containing error [6], or in this study, comparing the results m a m a ,(1) j= i= j j= i= j of different increment estimation methods. 1 1 1 1 We construct a pattern of 12 000 points at a 1 × 1meter n n n∗ K m j v∗ K m j v K m ji v∗ spacing within the Hirschlacke plot, such that no point is D ji ji ji Z = ∗ − + ∗ ,(2) m g m gji m g within 30 meters of the plot boundary. We then simulate a j=1 i=1 ji j=1 i=1 j=1 i=n+1 ji fixed area plot of 200 m2 and an angle-count sample with n∗ g < . a basal area factor of 4 at each point in each of the seven m nj v∗ m nj m j , ji19 63i v∗ S K ji K vji K ji measurement periods. This mimics the plot size used as part Z = − + ∗ , m gji m gji m gji of the Swiss National Forest Inventory [13] and the basal area j=1 i=1 j=1 i=1 j=1 i=nj +1 factor used in Austria [14]. (3) Increments are estimated four ways. n∗ m nj v∗ m nj m j v∗ E K ji K vji K ji (1) Differences between time 1 and time 2 volumes Z = ∗ − ∗ + ∗ m gji m gji m gji determined from fixed-area plots, plus removals j=1 i=1 j=1 i=1 j=1 i=nj +1 ∗ (4) (fixed-plot method). m nj K vji − . (2) Differences between time 1 and time 2 volumes ∗ m gji determined from angle-count plots, plus removals j=1 i=nj +1 ff (di erence method). ∗ nj The term i=n in (2)and(4) describes the “new” trees (3) The recorded increment of the trees within the angle- j +1 count sample multiplied by the estimated number entering a sample in the subsequent measurement period of trees of that size in the stand in time 1, plus the and includes nongrowth, ongrowth, and ingrowth. In this ff volume of the new trees entering the stand. (starting formulation of the di erence and end value methods, the value method). three types of new tree need not be distinguished. The starting value method does not include nongrowth, so only (4) The recorded increment of the trees within the angle- those new trees with a previous dbh of less than 5.0 cm (basal count sample multiplied by the estimated number area less than 19.63 cm2) are included in (3). of trees of that size in the stand in time 2. (end Mean per hectare volume and increment estimates value method). This method requires the estimation (without errors) for each plot across each and all time of prior dimensions of trees which (in a practical periods are compared with paired t-tests. In these examples, inventory situation) may not have been recorded at we use 84 000 paired data points for volume calculations time 1. and 72 000 for increment, thus even very small differences may be deemed to be statistically “significant”. In practical Much of the published literature on deriving incre- applications, however, given the large variances in increment ments from angle-count sampling distinguishes between estimation, the null hypothesis (that the means are the “survivors”, “ingrowth”, “ongrowth”, and “nongrowth” trees same) is difficult to reject even if the mean values appear (cf. Martin 1982 [15]), depending on whether they were quite different. If differences due to measurement errors above or below a particular diameter at breast height in the were present, we are interested to determine a minimum period preceding the current measuring period and whether number of samples necessary to detect that difference. To they were counted “in” or “out” of the angle count in the do this, we estimate the population mean increment and preceding period (Table 1). standard deviation through lumping data from all periods Defining the following variables. and applying standard statistical procedures [17] to find the t Z = volume increment per hectare, with superscripts F minimum number of paired samples that will give a -test for fixed are plots, D for difference method, S for with 90% confidence that the means are within 5%, with 95% starting value method, and E for end value method; power.

m = 2 Number of sample plots; sd 2 n = tα(2),v + tβ(1),v ,(5) nj = Number of trees in each sample j; δ2

vi = i 2 volume of individual tree ; where: n= required minimum sample size, sd = variance of ff δ = t = t aj = area of fixed area plot j; the di erences, maximum allowable error, α(2),v statistic at significance α, two tailed, and degrees of freedom K = basal area factor; v, tβ(1),v = t statistic for power 1-β, one tailed, and degrees of gi = basal area of individual tree i. and denoting measure- freedom v. ments made in a subsequent inventory period by (∗), These minimum sample sizes are then tested by drawing ignoring tree removals from the plots and following that many random plots from the data and comparing the Hradetzky (1995) [16] and Eastaugh and Hasenauer appropriate increment estimators with a paired t-test. This [6], the mathematical form of the four methods may is repeated 1000 times, and we report the number of times be presented as: where a statistically significant difference of greater than 5% 4 International Journal of Forestry Research

Table 1: Components of growth in angle-count sampling.

Presence in Presence in dbh in measurement dbh in measurement Tree classiﬁcation Angle-count sample, Angle-count sample, period 1 period 2 period 1 period 2 Survivors >threshold >threshold IN IN Ingrowth threshold IN IN Ongrowth threshold OUT IN Nongrowth >threshold >threshold OUT IN

appears to be present. These routines are implemented in the “sample size” package in the “R” programming environment [18]. Users of this package should note that it assumes one- 1500 sided tests, thus the value of α must be adapted to the two- tailed equivalent. /ha) 3

3. Results 1000 3.1. Standing Volume and Volume Increment. The mean volume estimated across all plots and time periods using fixed area methods was 775.5 m2/ha, with a standard deviation of 2 244.8 m /ha. Using angle counting, the mean was estimated 500 as 779.8 m2/ha, with a standard deviation of 168.2 m2/ha. The difference of 0.55% of mean volume is statistically significant at 95%, P = 3∗10−5. This statistical significance (m estimate volume Angle-count is a result of the very large number of data points (Figure 2). A breakdown of volumes estimated in each period is given in 0 Table 2. The mean increment estimated across all plots and 0 500 1000 1500 time periods with the four available estimation methods is Fixed-plot volume estimate (m3/ha) given in Table 3.Alldifferences were significant at P<.001. Figure 2: Comparison of fixed area and angle-count estimates of stand volume. Each point represents the sample measurement at 3.2. Minimum Number of Plots Required to Detect Differences. eachofthe12000simulatedsampleplotsinoneoftheseven The procedure for determining the minimum number of measurement years (84 000 data points). plots needed depends on the variance of the differences between the two methods to be compared, as per (5). This is given in the lower left portion of Table 4. The upper right portion of Table 4 gives the minimum number of plots represent the variability across the whole forest estate, ff required to detect a 5% di erence in increment estimates Figure 2 shows how much variation can be found between ff using di erent methods at 90% confidence, with 95% power plots even in such a small area. Examination of Table 2 t in a paired -test. Randomly selecting these numbers of plots shows that although the means are substantially different, the from the dataset and comparing increment estimates with a variation around the mean in our simulation is comparable t -test shows that in less than 0.1 percent of 1000 repetitions a to that from the real NFI. This suggests that a substantial ff spurious di erence of greater than 5% is found between the portion of the variability in an inventory is due to the estimates (bracketed values, Table 4). fine-scale estimation variability between plots, even plots within the same stand, rather than to the true broad-scale 4. Discussion differences between forest stands in different areas. The synthetic dataset that we construct mimics an NFI and The data we amass from the Hirschlacke stand provides allows the exploration of theoretical aspects of inventory us with 12 000 simulated plots over 7 years, which ranged sampling without the added complications of measurement from zero to 1800 m3/ha (Figure 2).Thesizeandspreadof error issues. this dataset is comparable to that of the Austrian National It is important to recognize that the dataset we con- Forest Inventory, comprising 9182 plots containing trees in struct is a hypothetical forest inventory and is not validly the 2007–2009 period with angle-count volume estimates comparable with any comprehensive calculations of volume ranging from 1 to 1806 m3/ha, with a mean of 344 m3/ha and or increment from the Hirschlacke stand. A valid sampling a standard deviation of 241.5 m3/ha (Eastaugh, unpublished design would require that all points in the stand have data). Even though a plot of only 3.47 ha cannot of course an equal possibility of sampling. Although in theory, it International Journal of Forestry Research 5

Table 2: Comparison of ﬁxed-area and angle-count estimates of stand volume, for the 12 000 plots simulated in each measurement year.

Fixed-area volume estimate Angle-count volume estimate Measurement year Mean (m3/ha) Std. dev. Mean (m3/ha) Std. dev. 1977 759.0 231.3 762.5 172.4 1982 769.3 226.3 774.6 161.9 1987 772.7 225.3 775.2 155.1 1992 818.2 241.2 822.4 163.9 1997 798.6 242.5 806.0 158.7 2002 775.5 259.0 780.8 168.0 2007 735.3 275.5 737.1 181.9

Table 3: Comparison of ﬁxed-area and angle-count estimates of stand increment per 5 years, for the 72 000 data points simulated in each measurement year.

Increment estimate, m3/ha/5 years Method Mean Std. dev. Fixed-area plots 67.88 28.14 Angle counts, difference method 69.01 56.97 Angle counts, starting Value method 68.23 21.89 Angle counts, end Value method 68.24 21.13 would be possible to extend our sample grid to encompass higher values than the angle counts in denser portions of the whole stand, problems then arise with the boundary the forest bur lower values in regions with lower density overlap problem, where some sample plots extend to outside (Figure 2). the stand. Solutions to this may exist [19]butwouldbe The difference method of increment estimation has very computationally extremely expensive to institute in our high variance in itself (ref. Table 3), as it is dependent simulation. Our purpose in this paper, however, is not to mostly on how many new trees (either ingrowth, ongrowth assess the accuracy of each inventory method in estimating or nongrowth) enter a sample in a remeasurement period. the values of a true stand, but to compare the estimates made Multimethod estimation comparisons involving either of with different methods from the same set of points. these two methods will thus require a large number of The apparent statistical significant differences in volume plots (Table 4). When comparing the starting value and results from different methods arise from the large number end value methods, most trees that make up the increment of plots. However, at 0.55% of volume and a maximum of estimate are the same trees, the only difference in the sample 1.66% of increment, these differences are not functionally is due to the nongrowth trees that enter the sample in significant [20]. This difference does not arise in a properly the remeasurement. This explains the plotwise intermethod conducted inventory, as both fixed-area plots and angle- variance of only 7.39 m3/ha/5 years. It is important, however, count plots are unbiased sampling procedures. In this to note that the end value method requires knowing or respect, our hypothetical inventory may slightly flawed, as estimating the dimensions of nongrowth trees in the period we effectively sample from a forest stand without edges and prior to when they entered the sample. In our example the population sampled with angle counts is slightly different studied here this information was known from the long- to that sampled with fixed-area plots, even though both use term monitoring records, but in real inventories it is usually the same plot centres. Nevertheless, significance testing with estimated with regression equations. This may change the sufficiently large samples will always find “some” significant variance in the estimates in comparison with other methods. difference, even if the null hypothesis that the sample means The inclusion of nongrowth trees enlarges the sample and so are equal is actually true [21]. Given that the effect size is so in principle the estimate should be more precise. This effect small, we believe that this difference does not invalidate our is, however, lessened by the fact that the covariance is greater sample collection for the purposes of this paper. Moreover, as the survivor estimates are calculated using their inclusion the (hypothetical) populations sampled for analysis with the probabilities at time 2, when the basal areas are larger [16]. three angle-count-based increment estimators are identical Hradetzky [16] derived equations showing that increments and thus validly comparable. derived from the end value method would have “slightly” A single fixed-plot will measure different trees to an angle less variance than the starting value method, if prior tree count with the same centre and thus high variance in the dimensions were known. Roesch et al. [22] and Heikkinen difference between each individual plot is to be expected. and Henttonen [23] however give details of empirical studies Interestingly, the behaviour at the extremes appears to be showing that the standing volume at the time of first quite different, as the fixed plot estimates appear to give measurement can be substantially more precisely estimated 6 International Journal of Forestry Research

Table 4: Minimum number of plots required to detect a 5% difference in increment estimates using different methods at 90% confidence, with 95% power in a paired t-test. Numbers in italics are the standard deviation of the differences between each pair of methods, numbers in normal text are the required numbers of plots. Values in brackets are the number of times that a t-test using the appropriate number of plots (randomly selected) incorrectly suggested that a significant difference of greater than 5% existed, from 1000 iterations.

Method Fixed plot Difference Starting value End value Fixed plot — 3204 (8) 491 (2) 490 (2) Difference 59.34 — 3032 (4) 2416 (6) Starting value 23.19 57.72 —52(2) End value 23.17 51.53 7.39 — by regressing information available only at time two. Our estimation, depending on what inventory interpretation results in Table 2 (with perfectly known tree volumes prior methods are used. If two different methods are applied to to their first-angle-count measurement) support Hradetzky’s the same dataset, over a sufficient number of samples, then view, leading to the conclusion that a modeled estimate of the integrity of the inventory can be proven. In the case of time one volume made with an angle count from a particular National Forest Inventories based on angle-count sampling, point may in fact be more precise than the actual observation the procedures in this paper can be easily applied and should made from that point. To the best of our knowledge, this be a precondition of using inventory data to validate other artificial reduction in sampling variability has not yet been approaches to forest-growth estimation. formally justified from a conceptual standpoint. At issue is which sampling method best represents the true variability of the stand, which would first require defining stand variability Appendix at a scale compatible with the scale-indeterminate samples. It has been suggested that the slight difference between the This, we suggest, is a problem for another day. volume estimates made with fixed-area plots or angle-count In real inventory situations, both fixed-area estimates and samples may be due to differences in stand density towards angle counts are not likely to be available for the same region the edges of the plot. The fixed area plots are limited to trees in one-time period. If our comparisons of increment esti- within a radius of 7.98 metres of the plot centres, but an angle mation here are to be applied to National Forest Inventories, count will count trees further away than this if they are over then this will be limited to those inventories based on angle 28.2 cm dbh. As the angle count method in our example gave counting (i.e., Finland, Germany, and Austria). results a little higher than the fixed area plots, this initially ff All e orts at assessing large-scale ecosystem productivity seems to imply that the density of the forest in the zone just will be estimates, whether they depend on “top down” outside the limitations of the fixed area plots must be higher approaches from satellite data [24]oruse“bottomup” and that this could easily be tested with our available data. methods from terrestrial samples (inventories). Although However, the edges of our edges of our study area were found advances have been made in linking these two approaches to be less dense than the inner parts. The explanation for [25, 26], success will depend on having a clear understanding the slightly higher volume estimates with angle-count sample of the variability, biases, and limitations of each method. plots becomes evident from the following example. As shown in this paper, confidence in the concordance Consider a stand of 4 trees, each of dbh 56.4 cm, in ff of estimates derived with di erent calculation methods is an area of 625 m2 (Figure 3(a)). A fixed-area plot of radius largely an issue of scale. 14.1 m and an angle-count plot of BAF = 4m2/ha are established in the centre of the stand. The true stand density 5. Conclusions is 16 m2/ha, and both the fixed area plot and the angle count will arrive at the same conclusion. Forest inventories are measured data and thus other forest If our stand was in fact a little larger (Figure 3(b)), it growth estimation methods such as remote sensing or might include one larger tree of dbh 80 cm and 2 small trees modeling must be shown to be consistent with accurate of dbh 10 cm. Assuming a radius of 18 m, the total stand inventories in order to claim to represent reality. Inventories, area is now 1017 m2. The angle count detects the larger tree, however, are not a full measurement of the whole forest but and so the density estimate is 20 m2/ha. The fixed-area plot are an estimate, a statistical model. We have shown here that estimate is the same 16 m2 as before, but the true stand different inventory interpretation methods can give different density is 14.9 m2/ha, with 16 m2/ha in the “inner” zone and results, even though all are mathematically unbiased. If 13.2 m2/ha in the outer boundary zone. Even though the sufficiently large numbers of samples are available then all outer zone is less dense than the inner and the angle count methods can be shown to agree, but with less than this “sees” further, the angle count appears to result in higher number, a range of equally plausible estimates could be basal area estimate than a fixed area plot with the same centre made. point. It is inevitable that any forest measurement program will The differences in volume estimates in the body of this contain some degree of error, hopefully (but not certainly) paper do not arise from differences in stand density in small. These errors have different effects on increment different zones of the forest, but are a result of the fact that the International Journal of Forestry Research 7

r = 18 m r = 14.1 m

(a) (b)

Figure 3: (a) Four trees each of basal area 2500 cm2 in an area of 625 m2. A fixed-area plot of 625 m2 or an angle count with BAF = 4 will agree that the stand density is 16 m2/ha. (b) A stand of 1018 m2, including the same four trees as (a) plus one tree of basal area 5027 cm2 and two of 78.5 cm2 each. The true stand density is now 14.9 m2/ha, but the fixed-area plot sees 16 m2/ha and the angle count 20 m2/ha. precise area being sampled has not been defined. It would be thank Hubert Sterba for providing the excellent Hirschlacke possible to obtain equal results from each sampling method dataset for this study. Insightful review comments were in our small example in this appendix if we adhered to the provided by Timo Pukkala and three anonymous reviewers. following strictures.

(i) The area to be sampled must be strictly defined in References space. (ii) A very large number of plots must be used and [1]H.K.Gibbs,S.Brown,J.O.Niles,andJ.A.Foley,“Monitoring aggregated. and estimating tropical forest carbon stocks: making REDD a reality,” Environmental Research Letters, vol. 2, no. 4, Article ID (iii) Plot locations must be random, with any point in the 045023, 2007. defined area having an equal or known probability of [2] G. M. J. Mohren, “Large-scale scenario analysis in forest selection (including near the edges). ecology and forest management,” Forest Policy and Economics, (iv) Estimates from plots where the plot boundary (or vol. 5, no. 2, pp. 103–110, 2003. the tree’s inclusion zone) overlaps the edge of the [3] C. T. Scott, “An overview of fixed versus variable-radius plots for successive inventories,” in State-of-the-Art Methodology of sampling area must be appropriately adjusted. For Forestry Inventory. A Symposium Proceedings. July 30-August 5, angle counts, this is not yet a fully solved problem 1989, V. J. LaBau and T. Cunia, Eds., Syracuse, NY, USA, 1990. [19]. [4] E. Tomppo, T. Gschwantner, M. Lawrence, and R.E. McRoberts, National Forest Inventories: Pathways for Common For perfect mathematical precision, future simulation ffi Reporting, Springer, Berlin, Germany, 2010. studies will need to either develop new, computationally e - [5] W. Bitterlich, “Die Winkelzahlprobe,”¨ Allgemeine Forst- und cient methods to deal with the boundary overlap problem or Holzwirtschaftliche Zeitung, vol. 59, no. 1-2, pp. 4–5, 1948. project their simulated forest onto a sphere thus eliminating [6] C.S. Eastaugh and H. Hasenauer, “Biases in volume increment boundaries. The purpose of this current paper, however, was estimates derived from successive angle count sampling,” to compare collections of point samples, not to assess their Forest Science. In press. accuracy in estimating stand densities in any defined area. [7] L. R. Grosenbaugh, “Point-sampling and line-sampling: probability theory, geometric implications, synthesis,” USDA Forest Service South Forest Experimental Station Occasional Acknowledgments Paper 160, pp. 34 , 1958. [8] F. A. Roesch, E. J. Green, and C. T. Scott, “New compatible This work was funded by the project “Comparing Satel- basal area and number of tree estimators from remeasured lite Versus Ground Driven Carbon Estimates for Austrian horizontal point samples,” Forest Science, vol. 35, pp. 281–293, Forests” (MOTI). The authors are grateful for the financial 1989. support provided by the Energy Fund of the Federal State [9] C. E. Thomas and F. A. Roesch, “Basal area growth estimators of Austria managed by Kommunalkredit Public Consulting for survivor component: a quality control application,” South- GmbH under contract number K10AC1K00050. The authors ern Journal of Applied Forestry, vol. 14, no. 1, pp. 12–18, 1990. 8 International Journal of Forestry Research

[10] H. Sterba, “20 Years target diameter thinning in the “Hirscblacke”, forest of the monastery of Schagl,”¨ Allgemeine Forst- und Jagdzeitung, vol. 170, no. 9, pp. 170–175, 1999. [11] H. Sterba and A. Zingg, “Target diameter harvesting—a strategy to convert even-aged forests,” Forest Ecology and Management, vol. 151, no. 1–3, pp. 95–105, 2001. [12] J. Pollanschutz,¨ “Formzahlfunktionen der Hauptbaumarten Osterreichs,”¨ Allgemeine Forstzeitung, vol. 85, pp. 341–343, 1974. [13] P. Brassel and H. Lischke, Swiss National Forest Inventory: Methods and Models of the Second Assessment, WSL Swiss Federal Research Institute, Birmansdorf, Switzerland, 2001. [14] K. Gabler and K. Schadauer, Methoden der Osterreichischen¨ Waldinventur 2000/02, vol. 135 of BFW Berichte, Bundesforschungs- und Ausbildungszentrum fur¨ Wald, Naturgefahren und Landschaft, Vienna, Austria, 2006. [15] G. L. Martin, “A method for estimating ingrowth on permanent horizontal sample points,” Forest Science, vol. 28, no. 1, pp. 110–114, 1982. [16] J. Hradetzky, “Concerning the precision of growth estimation using permanent horizontal point samples,” Forest Ecology and Management, vol. 71, no. 3, pp. 203–210, 1995. [17] J. H. Zar, Biostatistical Analysis, Prentice Hall, Englewood Cliffs, NJ, USA, 1999. [18] R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Com- puting, Vienna, Austria, 2011. [19] M. J. Ducey, J. H. Gove, and H. T. Valentine, “A walkthrough solution to the boundary overlap problem,” Forest Science, vol. 50, no. 4, pp. 427–435, 2004. [20] S. T. Ziliak and D. N. McCloskey, The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice and Lives, University of Michigan Press, Ann Arbor, Mich, USA, 2008. [21] J. Cohen, “The earth is round (P<0.05 ),” American Psychologist, vol. 49, no. 12, pp. 997–1003, 1994. [22] F. A. Roesch, E. J. Green, and C. T. Scott, “A test of alternative estimators for volume at time 1 from remeasured point samples,” Canadian Journal of Forest Research, vol. 23, pp. 598– 604, 1993. [23] J. Heikkinen and H. Henttonen, “Re-measured relascope plots in the assessment of inventory update methods,” in Nordic Trends in Forest Inventory, Management Planning and Mod- elling. Proceedings of SNS meeting in Solvalla, Finland. April 17–19, 2001, J. Heikkinen, K. T. Korhonen, M. Siitonen, M. Strandstrom,¨ and E. Tomppo, Eds., Finnish Forest Research Institute, 2001, Research Papers 860. [24] R. Paivinen¨ and P. Anttila, “How reliable is a satellite forest inventory?” Silva Fenica, vol. 35, no. 1, pp. 125–127, 1998. [25] R. Petritsch, C. Boisvenue, S. W. Running, and H. Hasenauer, “Assessing forest productivity: satellite versus terrestrial data- driven estimates in Austria,” in Forest Growth and Timber Quality: Crown Models and Simulation Methods for Sustainable Forest Management Proceedings of an International Conference, Portland, OR, USA, August 7–10, 2007, Dennis P. Dykstra and Robert A. Monserud, Eds., pp. 211–216, United States Department of Agriculture Forest Service Pacific Northwest Research Station, 2009, General Technical Report PNW-GTR- 791. [26] R. A. Houghton, D. Butman, A. G. Bunn, O. N. Krankina, P. Schlesinger, and T. A. Stone, “Mapping Russian forest biomass with data from satellites and forest inventories,” Environmental Research Letters, vol. 2, no. 4, Article ID 045032, 2007. Hindawi Publishing Corporation International Journal of Forestry Research Volume 2012, Article ID 871068, 9 pages doi:10.1155/2012/871068

Research Article Participatory Resource Mapping for Livelihood Values Derived from the Forest in Ekondo-Titi Subregion, Cameroon: A Gender Analysis

Daniel B. Etongo1 and Edinam K. Glover2

1 Department of Forest Sciences, Viikki Tropical Resources Institute (VITRI), University of Helsinki, P.O. Box 27, 00014 Helsinki, Finland 2 Faculty of Law, University of Helsinki, P.O. Box 4, 00014 Helsinki, Finland

Correspondence should be addressed to Daniel B. Etongo, daniel.etongobau@helsinki.ﬁ

Received 3 September 2011; Revised 15 December 2011; Accepted 27 December 2011

Academic Editor: John Sessions

Copyright © 2012 D. B. Etongo and E. K. Glover. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Increasingly, the multiplicity of products, services, and values, and the diversity of interests from diﬀerent resource users and groups, is being acknowledged as vital for sustainable use. This calls for a shift from protection to sustainable use and to resource- user focus. The aim of this study is to identify the spatial occurrence of livelihood values through participatory resource mapping, their changes over time and alternatives for sustainable management. A participatory resource mapping study was conducted with local community, including important stakeholders in Ekondo-Titi subregion of Cameroon. The research technique which focused on gender revealed diﬀerent patterns of forest resources and changes on the landscape. The study concludes that the importance of resources varies between men and women in Ekondo-Titi subregion of Cameroon, implying that resources may have multipurpose functions, but its exact role depends on the needs of the user groups that utilize them. The divergence of opinion on certain resources is a clear indication of preferences that are gender motivated. The study also revealed that the greatest impact of land use change is the conversion of forest land into agriculture.

1. Introduction have often been limited to a protected area of which that is not the case in reality because there is the need for conser- The forest ecosystem just like other ecosystem provides vation for development and not the other way round [13]. humanity with a wide range of resources and services which Those who advocate for conservation, designating parks are necessary for our survival. These services have been cate- under strict protection often accuse the local communities gorized as provisioning, regulating, cultural, and supporting as threat to biodiversity conservation [4, 11, 14, 15]. Such an services which interact with the direct and indirect drivers approach towards conservation explains why most conserva- of change to affect human well-being [1–4]. Though there is tion projects in West Africa have failed in the past [16]. The much awareness as to the potentials of the forest ecosystem, local population is aware and well connected with their sur- having a match between policies and practice is the root rounding landscapes thus being the best suited for shaping cause of poor management of resources. This erodes the the future of their landscapes and contributing meaningfully capacity of the forest to provide necessary services not only towards a successful management plan [12, 17]. to the local community but the nation as a whole [5–10]. The emphasis on a participatory approach arises from Much of the debate on how to conserve forest resources the wisdom that local communities not only understood while protecting biodiversity and meeting the demands for their problems best but also had solutions. This made community development at the same time has been very them be able to participate as beneficiaries and also as challenging [11, 12]. This is because conservation initiatives providers [18, 19]. Within the natural resource sector, the 2 International Journal of Forestry Research participatory approach argument became a focal point and The aim of this study was to conduct a participatory attracted a great deal of attention, following the Rio Earth resource mapping exercise with the local community of Summit, where it was accepted as an integral part of Ekondo-Titi in attempts to identify and locate the different the sustainable development process [20]. By involving all forest resources and uses in the area. Participatory Resource key stakeholders in the process of resource management, Mapping (PRM) is a tool used by practitioners of partic- understanding their needs and situations, allowing them ipatory methods to obtain a systematic and graphic [46] to influence decisions and receive benefits, and increasing overview [17] and understanding [46] of the resources avail- transparency [21], participatory research will go a long way able with the community in the form of social, economical, to secure local promote sustainable resource management demographical, and geographical details of the area where [22, 23]. Conservation and development have often been the community lives [17, 47, 48]. The aim of the mapping addressed as separate issues which are not the case in exercise with the villagers was to produce a resource map reality because a balance must be established to achieve both of the present situation and the situation in the past on a objectives [15, 24]. Building upon indigenous knowledge is landscape level. The issue of gender was of greater concern to no longer an option but imperative for sustainabl resource evaluate the different ways men and women can appreciate management [25]. The active involvement of the indigenous their resources on a landscape level. The investigators choose knowledge holders in the decision-making process in as far this method because of its many advantages (e.g., [19, 46, 48– as it affects them reinforces indigenous knowledge systems 51]). One argument is that the PRM tool serves as the for sustainable natural resource management and local liveli- base for discussions for the entire group of villagers. It also hoods [17]. Stakeholders are involved in a joint participation serves to elucidate important points and helps to verify and in which they share their problems and also share the check the accuracy and validity of information gathered benefits [26]. Collective analysis of problems will stimulate from household questionnaire, key informants’ interview, that sense of belonging which will contribute to sensitivity and literature review [17]. of the local community and also create collaboration in Another argument rests on the justification that partici- resource planning as power is shared among stakeholders patory methods and approaches with local people who utilize [22]. the resources on a daily basis, who are knowledgeable about Participatory rural appraisal (PRA) has emerged as its constraints and limits, its diversity and its opportunities, anewapproachthatisinvaluabletocommunitydevel- can increasingly be a practical and effective means of obtain- opment planning and resource management. This practice ing and managing people-centred natural resources manage- advocates for sustainable resource management through ment data [46]. Chambers enumerated wide range of uses the active participation of local communities with the aim of PRM: uses include land use and resource planning and of improving livelihood, empowerment (access rights and management, wildlife conservation, identifying tenure and security), and enhancing environmental conservation [27– rights, negotiating boundaries and resources uses, resolving 29]. PRA is multidisciplinary and multidimensional in its conflicts, and participatory monitoring and evaluation. approach and has gained attention in almost all fields of studies be it the humanities, sciences, arts, social sciences, health, and so forth [30, 31]. PRA has moved from a more 2. Study Site and Methods general to specific levels that focus on issues such as gender, ethnic diversity, age groups, and structure. Communities are 2.1. Study Site. Ekondo-Titi subdivision consists of thirty- not homogenous but rather heterogeneous in nature [32] four villages and one urban center which are all located in which creates problems for resource management as different Ndian division in the South West province of Cameroon. ethnic groups have different cultural values that affect access This area was carved out from the Kumba division in and control rights to forest resources [33–35]. 1966 by presidential decree No. 66/08/66. It is bounded by Hardin [37] puts forward the following views that “the Mundemba to the north, to the west by the rural district of users of a commons were trapped in inexorable tragedy and Kumbo Etindi, to the south by Bamusso subdivision, and unable to engage in sufficient collective action to extract to the east by Meme division (Figure 1). It is situated in themselves from drastic overuse and destruction”. This view the equatorial rainforest zone of Cameroon and experiences has received a lot criticism from scholars since the 80s [38– both the rainy and dry season. This area is characterized by 41] on the grounds that it is too general and some local lowlands of vast extent with fairly undulating hills that rises examples have proven otherwise. Notwithstanding these to the Rumpi range of mountains with the highest peak at the criticisms, Hardin’s view still holds true in some regions Rata Mountain in Dikome Balue (1900 m). The high lands ◦ ◦ where resource management has not been sustainable. It of this region are located between latitudes 4 and 7 north has been estimated that approximately 200,000 ha of forests of the equator. The estimated population of this region is are being lost to agriculture each year in Cameroon [42]. 55,000 people distributed over a surface area of 5.602 km2 Other studies give similar estimates which reveals that in and projected at 100,000 people by 2015 [36]. a ten-year-period (1990–2000) in Cameroon, forest loss to The geology of the area shows precambrian basement other land uses per annum was approximately 221,763 ha gneisses overlain unconformably by cretaceous shaly sand- [43]. Devolution of forest rights will have an effect on access stones. Volcanic extrusions are said to have formed the and users right which will improve on forest management Rumpi range of Mountains that constitutes a series of approaches and local livelihoods [44, 45]. plateaus separated by gorges and narrow valleys [52]. The International Journal of Forestry Research 3

Dikome balue

Mundemba subdivision Bossunga

Funge Bafaka Loe Kitta Makobe

Mokono Ekondo Nene Ekondo-Titi

Beach Bamuso Meme division subdivision N

0510 km Figure 1: Administrative map of Ekondo-Titi sub-Region [36].

soil types that dominate this region are sandy and clay The period of data collection was in the month of June soils. Clay soils have developed from the volcanic ashes that to August 2009 [55] and the month of December 2010 were deposited during periods of active sequence of volcanic in which the mapping exercise was conducted with both eruptions that took place in the past [53]. men and women from the local communities in this region. Located in the equatorial forest zone of Cameroon, The mapping exercise was conducted in two phases: at the this area is very rich in floristic composition with varieties landscape level which was for the whole region (this was done of plant species that range from simple fungi to complex by a member of the Ekondo-titi traditional council) and at angiosperms found in the terrestrial ecosystem. Notwith- the village level. The villages considered for the mapping standing, significant variation do occur in the distribution exercise were Ekondo-Titi and Bafaka Balue that are located of the forest ecosystems in this area which is a true reflection in the center and north-east of the region. The reasons for in the changing agricultural landscape. Oil palm plantation selecting the above villages are that, Ekondo-Titi is located on forms a new vegetation type though there are tree species gentle undulating land with high population density, cultural such as Rhizophora racemosa, Nypa frauticans, Guibourtia diversity and surrounded by oil palm plantations. Bafaka on demensei Acrostidum aureum, Hibiscus tiliaceus, Guibourtia the other hand, is mountainous, dominated by indigenous demensei, Drepanocarpus lunatus, Pandanus candelabrum, population and the physical environment has favored the and Phoenix reclinata [5, 54]. creation of protected areas (site with the highest biodiversity Subsistence food cultivation is the main source of in the study area), it is the main water catchment of the livelihood to approximately 85% of the population in 33 region and supplies food crops to the former and other out of the 36 villages in the study area. Food crops such villages.InEkondo-Titi,10menand8womenwereinvolved as cassava, yams, cocoa yams, maize, groundnuts, plantains, in the mapping exercise while in Bafaka Balue, 8 men and 6 vegetables, and other spices are cultivated extensively in this women were involved. area. The collection of nontimber forest products (NTFPs) During the mapping exercise that lasted for 2-3 hours, is also widely practiced in this area which is considered the participants were given a brief lecture about what as an offseason job when pressure on crop cultivation is is required of them in this exercise. Samples of related reduced. Some of the villagers are employed as labourers workssuchasGlover,“Tropical dryland rehabilitation: case by Pamol Plantations Plc (PPP) which is the second largest study on participatory forest management in Gedaref,”Sudan employer after the government in the South West region of and Kalibo and Medley, “Participatory resource mapping for Cameroon. Locally made craft with NTFPs from the forest adaptive collaborative management at Mt. Kasigau, Kenya” such as loose fruit baskets are of high demand by PPP and [17, 19]providedaguideforthesubjectmatter.Thevillagers other common initiative groups (CIG) that are involved in were given the opportunity to construct a resource map on oil palm cultivation. the open ground using local materials such as stones, leaves, and back of trees. After this phase, the participants were given 2.2. Methodology a cardboard paper with pencils and erasers to transfer the map drown on the ground. They were later given a hundred 2.2.1. Participatory Resource Mapping with Men and Women. seeds each per group (men and women’s group) to place on Participatory resource approach in form of participatory the cardboard where they obtain water, where they practice resource mapping was employed in collecting information. agriculture, where they collect forest products such as fish, 4 International Journal of Forestry Research bush meat, building materials, and fuel wood. The maps were is used all-year-round for vegetable cultivation. The women later recopied on an A4 paper by the investigators, scanned did not consider the rivers just for fishing but as a means of and reduced for better presentation. transport of fuelwood from the mangrove forest. Field visit was also carried out in some villages such as AstudybyGlover[17] incorporated this finding that the Masore, Lobe, and Lipenja that are located at the outskirts importance of various functions of natural resources may of Ekondo-Titi village. During such visit that received a lot vary between different user groups. This study also found of support from forestry and agricultural extension workers, in Elrawashda area in the Sudan that women were more some photographs were taken and farmers were able to interested in the role of trees for domestic fuelwood supply or express their views in terms of resource management in the food production while men regard commercial gum arabic area. production as major role of trees. Related works by Campbell [56]; Dei [57]; Falconer [58]; Rocheleau [59]; Nabanoga; Gombya-Sembajjwe [60] and Mogaka et al. [61] also found 2.2.2. Historical Resource Mapping and Analyses. This exer- that trees and forests in subsaharan Africa constitute a major cise was conducted with the help of 15 men drawn from the source of income and livelihoods. A study by Kaimowitz Ekondo-Titi traditional council, including the paramount [62] found that losing access to these forest products and chief for the whole region. This council is madeup of men markets can have negative impacts on rural households and only but with a brief discussion the investigators had with even threaten their survival. Other studies recorded by FAO the paramount chief, three women were asked to join the [63] found out that focusing on one aspect of forest products mapping exercise. The three women that were included and benefits, even on such a major one as fuelwood, at just like the members of the traditional council were all the expense of others can be gravely misleading. Trees are indigenous and have lived in this village for all their lives (the considered a major source of support for rural community youngest amongst them was 62 years old). at the times of vulnerability; therefore, analysis of the main This exercise was able to produce two historical maps causes of depletion of the forest resources must be based on that covered the period 1940–1960 and 1960–1980. The three these needs of rural communities. Extension programmes women gave valuable contributions since they have more may bring men who perceive rivers as a source of only one access to the forest especially for fuel wood collection and benefit towards accepting the rivers for more than one benefit food crop cultivation. Questions such as where was the and towards believing that the river is for multiple uses. The first settlement in this region and why? What were sites more the people perceive resources (also trees and forests) for different forest products? Have the sites changed? What as for multiple uses, the better can be the acceptance of a about the water resources? where questions were asked. The multiple land use system [17]. elders were asked of some of the significant events that have While the women considered the homes and resources to taken place with much impact on the landscape and also how be concentrated in smaller areas, the men on the other hand the availability of resources has changed. considered it to be more expanded (Figure 2(b)). Women were interested in resources such as water for domestic use, 3. Results and Discussions fuelwood, home garden, and fruit trees. The concentration of these resources by the women is to reduce travel time but 3.1. Participatory Resource Mapping with Men and Women in culture plays a major role. Cash crop cultivation and hunting Ekondo-Titi Village. The mapping of resources concentrated of games are considered as activities for men according to on two villages in which men and women mapped their the tradition and customs of the land. This explains some of resources based on the values attached to them and also by the reasons why the men had an extended view of resources using their local knowledge (Figure 2:Ekondo-Titivillage on the landscape when compared with the women. The men and Figure 3: Bafaka village). In the case of Ekondo-Titi, were able to locate their cocoa farms and hunting sites which both men and women mapped a narrow forest zone to the are considered as men’s job. In the men’s map, the water north. The difference in the women’s map is that the forest catchment sites were identified while the agroforestry farms covers the boundary of the entire village except a narrow were located to the south, west, and center. The men paid strip of land towards the north-west which is considered as little attention to areas used for food crop but more to cash oil palm plantations (Figure 2(a)). The women also indicated crops and agroforestry (trees on farms). According to the the sites for fuelwood collection which is given a higher value men building materials came from the north and western bywomensinceitisconsideredasawoman’sdutytocollect part of the village and that the forest to southen west was wood for the household. Areas of subsistence crop cultivation the major supplier until the 80s. were identified close to the homes and major roads while When Cameroon gained her independence in 1960, agroforestry is said to be fast developing towards the south resource exploitation took a different dimension as the forest due to land scarcity. Both men and women identified almost was seen as the only mean to consolidate the fragile state. similar area occupied Pamol Plantations with its oil palm The number of villages increased from 6 to 36 while the plantations but the women went further to identify the area under agriculture continued to increase. The present- buffer zones of the company which they use for food crop day Ekondo-Titi region is dominated by oil palm plantation cultivations. The women view the landscape to be more while subsistence agriculture, relying on slash-and-burn expanded in terms of the resources that it provides. They also brought about some changes in the forest ecosystem went further to identify the areas very close to the river that (Figure 5). The slash-and-burn agriculture coupled with oil International Journal of Forestry Research 5

(a) (b)

Figure 2: (a) Participatory resource maps of the present landscape drawn by women of Ekondo-Titi village showing the arrangement of the various resources in the area. (b) Participatory resource maps of the present landscape drawn by men of Ekondo-Titi village showing the arrangement of the various resources in the area.

(a) (b)

Figure 3: (a) Participatory resource map of the present landscape drawn by men of Bafaka showing the arrangement of the various resources in the area. (b) Participatory resource map of the present landscape drawn by women of Bafaka showing the arrangement of the various resources in the area. palm plantations has resulted in a number of environmental the forest still intact in the north, west, and south. According impacts such as fast conversion of the forest lands in this to Figure 3(a), the center is dominated by agroforestry with region to grasslands. This situation has resulted in the loss a mixture of cocoa, cocoyam, plantains, banana, maize, and of livelihoods for local people and the deterioration of cassava. The south-west is the protected area under the the environment in which they live. There is need to find Rumpi Hills conservation project while the rivers take their alternatives to monocultural plantations that reflect more rise from the Rata Mountain in the north and flow over the environmentally friendly approaches to land use patterns in entire village and the region as a whole. Hunting, fishing, and the region [64]. the collection of nontimber forest products such as medicinal plants, fruits, and thatches for building are dominated but 3.2. Participatory Resource Mapping with Men and Women not restricted to men. in Bafaka Village. The situation in Bafaka (Figures 3(a) and The women were able to identify similar features just as 3(b))portraysadifferent situation because this area is moun- themenbutmoreattentionwasgiventositesforfuelwood tainous and highly inaccessible until 1995 when a secondary collection and food crop cultivation. The major difference road was constructed by the government. This situation in these maps is that, the area identified as protected areas explains why the influence of oil palm plantation that by men (Figure 3(a)) was identified as agroforestry farms dominates this region is not favorable in this area because of by the women (Figure 3(b)). The women gave the protected the topography. The men’s map identified the homes to the area a restricted range to the east while food crop cultivation south east which is similar to that of the women with much of was located to the center, south, and south east. In tropical 6 International Journal of Forestry Research

(a) (b)

Figure 4: (a) Historical map of the past landscape showing the Ekondo-Titi region between 1940–1960. (b) Historical map of the past landscape showing the Ekondo-Titi region between 1960–1980. The maps (a) and (b) were drawn by members of the Ekondo-Titi traditional council with contributions from three elderly women in the village.

divisions of labor, decision making, the allocation of profits and incentives and the potential for change in these dynamics [Ibid.]. Similar findings have been reported by Seniloli et al. [67], regarding gender differences in patterns of resource use. They observed that lack of adequate knowledge or consideration with regard to gender issues can hamper development efforts. The foregoing statements have confirmed that involving gender issues in environmental programmes and the various ways in which men and women utilize their natural resources is crucial if development programmes are to be relevant and sustainable. Thus, formulating policy or Figure 5: Forest ecosystems are converted to savannah due to project interventions from a gender perspective coupled with frequent use of fire along the Ekondo-titi-Mundemba major road. collaborative resource management; “a partnership by which various stakeholders agree on sharing among themselves the management functions, rights and responsibilities for forest areas, people normally apply specific practices for a territory or a set of resources” [68], have implications utilization and management of forest and/or trees [65]. In in environmental and poverty reduction strategies and the study area, women are responsible for collecting firewood imperative for sustainable resources management. and fruits, but men own property and are in charge of agricultural production. Meninbothvillagesareunlikewomenwhoweremore 3.3. Changes in the Landscape Over Time (1940–1960 and concerned with food crops, fuelwood, water, and so forth, 1960–1980). According to the members of the Ekondo-Titi the men were concerned with the forest (hunting of games traditional council, the very first settlers in this region were and collecting NTFPs), cash crops, and building materials in Ekondo-nene which lies approximately 25 km away from from the forest. In Ekondo-Titi the men consider the trees Ekondo-Titi. Figure 4 shows the historical maps of the past on farm in agroforestry system as fodder for animals. The landscape of the Ekondo-Titi subregion between 1940–1960 situation in Bafaka tells a different story as both men (a) and 1960–1980 (b). When the Germans established the and women consider agroforestry as an adaptation to the banana plantation in the 1920s in Lobe, job opportunities difficult environment. Wiersum [66] incorporated the above were available to the people of Ekondo-nene as labourers in facts and confirmed recognition of gender issues as an the plantation. The banana plantation was later replaced with important aspect in resource management and implications oil palm (Elaeis guineensis) plantation by 1930. Ekondo-Titi for planning of interventions in resource use. It should which literally means “small ekondo” started as a dormitory be recognized that the inclusion of both men and women village. This was due to its suitable location which was is vital in analyzing the feasibility of a social forestry closer to the road and closer to the plantation in Lobe intervention in a specific situation, in order to be able (2.5 km) Ekondo-nene on the otherhand, which means “big to assess the dynamic factors that determine the existing ekondo” gave birth to the former due to its distance from the International Journal of Forestry Research 7 plantation site in Lobe (27.5 km). The forest was still intact region and from neighbouring villages. The participants with footpaths being the main means of transportation since also complaint that the forest policies by the Ministry the crops were transported through the sea to the present-day of Forestry and Wildlife donot meet the demands of the economic capital of the country, Douala. The council reveals local communities. Resource management must always take that streams and rivers were flowing across the village and a community-based approach if it has to succeed which wildlife was abundant. Most people depended on the forest must also take into account conservation and development and collected NTFPs from the forest with the authority of the objectives. We therefore recommend that this region should traditional ruler. When the secondary road was constructed design its own resource map based on local knowledge on in the 1960s, the forest began to disappear and oil palm how the community perceives its resources can be managed. plantation was dominating as a form of land security to the This is because the landscape is changing all the time and indigenous from outsiders. The council stated that is the a human-resource relation that is flexible which involves reason why almost all the native own hectares of oil palm cooperation of the government with the local communities plantations because the seedlings were available and at a will cope with the changing landscape. cheaper rate. References

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Research Article Terrestrial Liming As a Restoration Technique for Acidiﬁed Forest Ecosystems

Sarah E. Pabian,1, 2 Shawn M. Rummel,1 William E. Sharpe,1 and Margaret C. Brittingham1

1 School of Forest Resources, The Pennsylvania State University, University Park, PA 16802, USA 2 Department of Biology, Colorado State University, Campus Delivery 1878, Fort Collins, CO 80523, USA

Correspondence should be addressed to Sarah E. Pabian, [email protected]

Received 1 September 2011; Revised 4 November 2011; Accepted 7 November 2011

Academic Editor: Kristiina Vogt

Copyright © 2012 Sarah E. Pabian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

We studied the effects of liming on soils and forest songbirds as well as vegetation and calcium-rich invertebrate prey variables that were predicted to link birds to changes in soil conditions. We observed increases in soil pH, calcium, and magnesium, as well as in songbird abundances in response to lime application, with continuing increases through five years after liming. We observed an overall increase in snail abundance on limed sites, but an initial peak of a 23 fold increase three years after liming was reduced to an 11 fold increase five years after liming. We observed an increase in forb ground cover on limed sites, but liming had no effect on millipede abundance or other vegetation measures. Of the variables we measured, snail abundance was the most likely mechanism for the response in bird abundances. Because we observed continued benefits of liming up to five years post treatment, we concluded that liming is a very promising technique for restoring forest ecosystems impacted by acidic deposition.

1. Introduction research has taken place in Europe. Researchers have found many positive results of liming on soils, forest vegetation Anthropogenic acidic deposition, as well as forest harvesting health and regeneration, and snail abundances [9–14]. How- and aging, has depleted forest soils of calcium and other ever, very little is known about how liming affects terrestrial nutrients and made them more acidic [1–4]. In Pennsylvania, vertebrates, and most evaluations of the effectiveness of an area that receives very high levels of acidic deposition, liming are focused on soil and water quality. An approach forest soils have experienced significant declines in soil pH, that includes multiple ecosystem levels would give a more calcium, and magnesium over the past 40 years [5]. This complete picture of the forest ecosystem response to liming. soil acidification and concurrent depletion of base cations, As a part of a larger project to restore an acidified stream especially calcium, and increases in availability of toxic to historic water-quality levels in central Pennsylvania, metals have the potential to negatively affect the entire forest two, 100 ha subwatersheds were limed [15]. The primary ecosystems. Acidic deposition and soil acidification have goal of the project was to improve stream water quality, been linked to changes in many levels of the forest ecosystem, but secondary goals included revitalizing forest health and including tree diebacks, suppressed tree regeneration, fish improving forest habitat for wildlife. Liming was completed mortality, reduced snail abundances, and reduced avian using methods and materials that are available to land reproductive success [2, 6]. managers, including using a modified log skidder to spread One method used to mitigate the effects of acidic deposi- the lime and using dolomitic limestone sand. tion on forest soils is to apply limestone sand. This technique We focused our evaluation of the liming project on was traditionally used in acidified agricultural areas, and was terrestrial ecosystems, from soils to terrestrial vertebrates. later applied as a mitigation technique to restore acidified We selected songbirds as our focal vertebrate group because bodies of water [7, 8]. Only in the past decades has liming they may be particularly sensitive to soil acidification [6]. been used to restore acidified forests, and most of the We then focused our efforts on links between soils and 2 International Journal of Forestry Research birds, including food and habitat variables. Birds require 2.3. Soils. We collected Oa-horizon soil samples at the 68 large amounts of calcium to reproduce, which is less available survey points in all years. We collected soils in 2003, 2004, in acidified soils [6]. Therefore, we focused on calcium- 2006, and 2008 during the first two weeks in July, and rich invertebrates, including snails and millipedes, because collected soils in 2005 during the last week in May. Soils were both contain higher levels of calcium than other forest analyzed at the Pennsylvania State University Agricultural invertebrates, and are documented to be important calcium Analytical Services Lab for exchangeable calcium (Ca), sources for songbirds [6, 16]. We also included vegetation magnesium (Mg), potassium (K), and phosphorus (P) using measurements because bird habitat quality is associated with the Mehlich 3 (ICP) method [22] and pH in water paste [23]. specific vegetative characteristics required for nesting and In 2003–2006, we additionally analyzed soil samples for plant foraging, and vegetative characteristics can change with soil available aluminum and calcium in the A-horizon using a acidification [17, 18]. 0.01 M SrCl2 saturated paste extract to calculate the calcium In Pabian and Brittingham [19], we reported some of to aluminum ratio. our preliminary findings on soil calcium, snail abundance, and songbird abundance. We observed that before liming 2.4. Snails and Millipedes. We sampled both snails and milli- the study sites were of poor quality for forest songbirds, as pedes at the survey points using timed-area searches centered measured by a low abundance of birds and large territory at each of the 68 survey points each year. Plot locations size of our focal species. Liming resulted in increased song- were shifted each year to avoid resampling the same areas. bird abundances and increased territory density. However, We completed all surveys between the last week in June and our findings were somewhat inconclusive and incomplete second week in July. Snails and millipedes were surveyed by because we did not have enough information to observe hand searching the leaf litter, down to the Oa-horizon, in a effects of liming on individual bird species, and we did 25 m2 area for 20 minutes [24–26]. Searches were completed not evaluate the effects of liming on many important between 0700 and 1200 hours on days without rain 24 hours soil, invertebrate, and vegetation variables. To give a more prior to searching. complete picture of the effects of liming on forest ecosystems andtobetterevaluatetheeffectiveness of terrestrial liming 2.5. Vegetation. We quantified vegetation at each of the 68 for restoring acidified forests, we continued the study for survey points using methods from Martin et al. [27]. We an additional two years and incorporated more ecosystem measured vegetation from the last week in June through variables into our analysis and report. the second week in August. We measured vegetation within four plots located within a 50 m radius of each survey point. Each survey plot had a 5 m radius, for counting shrubs and 2. Methods saplings. We counted all shrubs and saplings by species, estimated ground cover of forbs, ferns, shrubs, and grass, 2.1. Study Sites. This study was conducted in the Mosquito and measured leaf litter depth within 5 m radius plots, and Creek Watershed located in Clearfield, Cameron, and Elk we measured canopy cover from the center of each plot. counties in central Pennsylvania over the summers of 2003– Leaf litter depth was measured by using hard plastic rulers 2008. This study was focused on four, 100 ha watersheds with no boarders to dig small holes to where individual (sites) in the Gifford Run drainage to Mosquito Creek in ◦ ◦ leaf parts were no longer visible. We used a spherical crown Clearfield County (41 11 Nand7817 W). For more site densiometer to take four measurements (in each cardinal details, see Pabian and Brittingham [19]. direction) of canopy cover from the center of each plot. Vegetation measurements were made in 2003, 2004, 2005, 2.2. Study Design. We conducted this study over a six-year and 2008. We used the means from the four vegetation plots period (2003–2008) using a Before-After-Control-Impact at each survey point in our data analysis. (BACI) study design [20].TheBACIdesigncontrolsfor natural environmental variation that affects all sites and 2.6. Bird Abundance. We monitored bird abundance using controls for any initial site differences [21]. During year point counts at the 68 survey points every year. The points one, we collected data at all four sites before liming. Before were separated by 200 m to prevent repeat counts of the same the beginning of the bird-breeding season in year two, birds [27]. Upon arrival at a survey point, we waited one we randomly selected two of the four sites and applied minute before beginning the count. We recorded any bird approximately 4500 kg/ha of dolomitic limestone sand using seen or heard for 5 min. within a 50 m fixed-radius circle. a modified log skidder. For more liming details see Pabian Each year, we completed point counts three times at each and Brittingham [19]. We collected data on the limed and site between 1 June and 15 July. To avoid any time-of-day control sites over the following three years and again in the effects, we surveyed points in different orders each time. We fifth year after liming. Preliming conditions and some of the conducted surveys between 0600 and 1000 on days without results through 2006 have been published, so this paper will strong wind, rain, or dense fog. We used the maximum focus on what was not covered in that paper [19]. Within number of each species recorded from the three surveys each of the four sites, we established 17 survey points per site completed each year for each point for analysis. (68 points total) for bird point counts, invertebrate counts, soil samples, and vegetation measurements. The points were 2.7. Analysis. All statistical analyses were completed in R located 200 m apart on a grid. [28]. We used mixed-effects models to conduct repeated International Journal of Forestry Research 3 measures generalized linear models on the data collected 3. Results at the point level for soils, invertebrates, vegetation, and bird abundances. We used the lmer function from the lme4 3.1. Soils. We observed increases in soil calcium, magnesium, package to analyze the data [29]. The models included fixed and pH in response to liming (Figure 1). From the first treatment and time effects, fixed treatment by time inter- year of the study, before liming, to five years after liming, action effect, random site effect, and random point within soil exchangeable calcium did not substantially change on site effect. Random effects allowed us to structure the error control sites (1.19 + 1.14 cmol/kg change) and increased by to account for repeated samples within sites and repeated 8.00 + 1.61 cmol/kg on limed sites (a 4.1 fold increase from measures over time without committing pseudoreplication before liming; Figure 1). Soil exchangeable magnesium did [30]. We used the time by treatment interaction to evaluate not change on control sites (0.61 + 0.39 cmol/kg change) the effect of liming, which evaluates if the limed sites changed and increased by 6.76 + 1.10 cmol/kg on limed sites (a 5.5 differently than the control sites from before liming to fold increase from before liming; Figure 1). Soil pH also after. did not change on control sites (0.03 + 0.13 change in pH) We log-transformed most count and soil variables and increased by a mean pH of 0.86 + 0.17 on limed sites to make them approximately normal, and we arcsine- (Figure 1). We detected no significant effect of liming on soil transformed all percentages. If any visual examination of data exchangeable potassium and phosphorus (Figure 1). indicated a peak, we modeled it with a quadratic term for We also observed an increase in the soil calcium to time, and used the ANOVA function in R to perform a drop aluminum ratio in response to liming (Figure 1). From 2003 χ in deviance -squared test to determine if the quadratic term to 2006, Ca:Al increased on limed sites by 2.41 + 1.20 and improved the model [28]. remained the same on control sites (0.99 + 1.36). Before We assessed the magnitude and uncertainty in the time liming, 66% of the survey points had Ca:Al below one, and by treatment interaction parameter estimates using 95% after liming, only 24% of the points in the limed sites had confidence limits generated using Markov Chain Monte Ca:Al below one. Carlo (MCMC) samples from the posterior distribution of each parameter estimate using the mcmcsamp function 3.2. Snails and Millipedes. We observed an increase in snail in the R package lme4 [29] and computing the Bayesian abundances and no change in millipede abundances in highest posterior density (HPD) 95% confidence limits of response to liming (Figure 2). Snail abundance peaked the MCMC samples using the HPDinterval function in the in 2006 and decreased in 2008; therefore, we evaluated a Rpackagecoda [31]. We considered confidence limits that quadratic term in our statistical model to describe changes excluded zero to indicate a time-by-treatment interaction ff with time. Adding a quadratic year term to the model and an e ect of liming on the variable measured. We χ2 = . ff significantly improved the model fit to the data ( 2df 42 00, reported interaction e ect parameter estimates with their P<0.001). From the year before liming to three years confidence intervals (CIs) and the changes that occurred after (when snail abundance on limed sites peaked) snail on control and treatment sites with their standard errors abundance did not change on control sites (0.29 + 0.17 to indicate the direction and magnitude of the liming ff change in snails per point) and increased by 23 fold on limed e ect. sites (1.26 + 0.24 snails per point). From the year before For bird abundance data, we divided the songbirds liming to five years after, snail abundance did not change into three foraging categories: ground-, understory-, and on control sites (0.21 + 0.15 change in snails per point) and canopy-foraging species. Because the lime was applied to increased by 11 fold on limed sites (0.59 + 0.14 change in the ground, we predicted the invertebrates on the ground snails per point; Figure 2). and the understory vegetation would be affected first and thus the ground-foraging birds would be the first to respond 3.3. General Vegetation. We observed an increase in forb to liming. Ground-foraging birds included veery (Catharus ff fuscescens), hermit thrush (C. guttatus), ovenbird (Seiu- ground cover in response to liming (Figure 3) and no e ect rus aurocapilla), common yellowthroat (Geothlypis trichas), of liming on the other vegetation variables we measured eastern towhee (Pipilo erythrophthalmus), chipping spar- (Table 1). Absolute forb ground cover increased on control row (Spizella passerina), dark-eyed junco (Junco hyemalis), sites by 6.67 + 1.13% and increased on limed sites by 12.67 + and indigo bunting (Passerina cyanea). Understory-foraging 1.98% from before liming to five years after liming. birds included blue-headed vireo (Vireo solitarius), chestnut- sided warbler (Setophaga pensylvanica), black-throated blue 3.4. Bird Abundance. We counted 43 bird species over the warbler (S. caerulescens), and American redstart (Setophaga course of the study. We observed an increase in overall bird ruticilla). Canopy-foraging birds included eastern wood- abundance in response to liming (Figure 4). The number of pewee (Contopus virens), least flycatcher (Empidonax min- birds per point did not change from before liming to five imus), eastern phoebe (Sayornis phoebe), red-eyed vireo years after liming on control sites (0.97 + 0.56 change in (Vireo olivaceus), black-throated green warbler (S. virens), number of birds per point) and increased by 1.88 fold on blackburnian warbler (S. fusca), scarlet tanager (Piranga oli- limed sites (3.09 + 0.53 change in number of birds per point; vacea), and rose-breasted grosbeak (Pheucticus ludovicianus). Figure 4). Classifications were based on foraging information included When songbirds were grouped in ground-, understory-, in the birds of North America species accounts [32]. and canopy-foraging categories, we observed increased 4 International Journal of Forestry Research

5 14 8 12 4.5 6 10 pH Ca 4 4 8 Mg 6 2 ∗ ∗ 3.5 0.153 (0.083, 0.208) 4 0.120 (0.054, 0.189) 0 0.203 (0.126, 0.281)∗

2 140 10 1.8 8 120 1.6 6 K P 100

1.4 Ca : Al 4

1.2 80 2 0.012 (−0.012, 0.074) −0.036 (−0.089, 0.025) 0.216 (0.017, 0.427)∗ 1 60 0 2003 2004 2005 2006 2007 2008 2003 2004 2005 2006 2007 2008 2003 2004 2005 2006 2007 2008 year year year

Control Control fitted Limed Limed fitted Figure 1: Mean soil pH, soil exchangeable calcium (Ca), magnesium (Mg), potassium (K), and phosphorus (P) in cmol/kg and soil Ca:Al ratio with SE bars on lime-treated and control sites with model lines. Lime treatment was applied between 2003 and 2004 in central Pennsylvania. ∗Confidence intervals of the time-by-treatment interaction excluded zero, indicating a significant effect of liming on that variable.

Table 1: Effect of liming on vegetation, with parameter estimates for the year-treatment interactions with 95% highest posterior density (HPD) intervals from Markov Chain Monte Carlo samples from repeated measures analysis of data collected in PA before and after lime application on two of four forest sites. All of the confidence intervals (CIs) include zero, indicating no significant effect of liming on those variables. Control Limed 2003 (SE) 2008 (SE) 2003 (SE) 2008 (SE) Interaction (CIs) Litter depth (mm) 20.94 (0.81) 27.50 (0.71) 22.78 (1.15) 27.07 (0.90) −0.017 (−0.043, 0.008) Canopy cover (%) 85.78 (1.30) 91.88 (1.18) 85.60 (1.82) 91.15 (1.32) −0.002 (−0.013, 0.008) Sapling density (count/site) 897 (8) 1161 (6) 297 (8) 587 (5) 0.115 (−0.014, 0.252) Shrub density (count/site) 602 (9) 851 (9) 1447 (9) 2073 (12) −0.007 (−0.227, 0.214) Grass cover (%) 9.17 (2.09) 3.90 (1.38) 6.71 (1.47) 3.81 (0.86) 0.013 (−0.008, 0.036) Fern cover (%) 51.54 (4.69) 55.75 (4.97) 39.10 (4.19) 31.54 (3.86) −0.028 (−0.066, 0.009) Shrub cover (%) 17.98 (3.15) 25.67 (3.65) 28.13 (4.15) 49.66 (3.96) 0.027 (−0.009, 0.062)

abundances of ground- and understory-foraging bird abun- control sites (−0.12 + 0.07 change in abundance) and dances in response to liming, but no change in canopy- increased on limed sites (0.21 + 0.09 change in abundance; foraging bird abundances (Figure 4). Ground-foraging song- Figure 5). Eastern towhee abundance did not change on bird abundance, from before liming to five years after liming, control sites (−0.09 + 0.13 change in abundance) and did not change on control sites (0.24 + 0.32 change in birds increased on limed sites (0.26 + 0.12 change in abundance; per point) and increased by 2.6 fold on limed sites (1.50 + Figure 5). Understory-foraging songbird abundance, from 0.26 change in birds per point; Figure 4). Ground-foraging before liming to five years after liming, did not change on bird species that most strongly followed this pattern were control sites (0.235 + 0.224 change in birds per point) and ovenbird, veery, and eastern towhee. Ovenbird abundance increased on limed sites (0.706 + 0.217 change in birds per did not change on control sites from before liming to five point; Figure 4). The chestnut-sided warbler followed the years after liming (0.18 + 0.13 change in abundance) and same trend and did not change on control sites (−0.029 + increased on limed sites (0.56 + 0.13 change in abundance; 0.075 change in birds per point) and increased on limed sites Figure 5). Veery abundance had a decreasing trend on (0.324 + 0.123 change in birds per point; Figure 5). International Journal of Forestry Research 5

10 20

8 15

6 10 Millipedes Forb cover

4 5

0.004 (−0.069, 0.096) 2 0 0.021 (0.005, 0.039)∗

2003 2004 2005 2006 2007 2008 year 1.5 Control Control fitted Limed Limed fitted Figure 3: Mean percent understory coverage of forbs with SE bars 1 and model lines on lime-treated and control sites. Lime treatment was applied between 2003 and 2004 in central Pennsylvania. ∗Confidence intervals of the time-by-treatment interaction ex- Snails cluded zero, indicating a significant effect of liming on that variable. 0.5

years. These combined responses to liming indicate a strong 0.444 (0.223, 0.683)∗ bottom-up influence of soil nutrients in an acidified forest 0 and a link between soil conditions and songbird abundance. 2003 2004 2005 2006 2007 2008 year 4.1. Soils. The continued increases observed in soil pH, cal- Control Control fitted cium, and magnesium five years after liming are promising Limed Limed fitted and agree with other liming studies that one application of lime can continue to improve and maintain improved Figure 2: Mean millipede and snail abundance (number per 25 m2) with SE bars on lime-treated and control sites with model lines. soil condition for a long time. In other studies, continued Lime treatment was applied between 2003 and 2004 in central increases in these soil parameters have been observed beyond Pennsylvania. ∗Confidence intervals of the time-by-treatment inter- five years after dolomitic limestone application [10]. Also, action excluded zero, indicating a significant effect of liming on that based on models, dolomitic lime application is predicted to variable. elevate soil-base cation levels for up to 50 years, with continued increases in the first 15 to 20 years, followed by a gradual decline [12]. The increases in soil pH brought average soil 4. Discussion condition from below 4.0, which is considered too low to maintain healthy oak forests, to 4.7 and never exceeded We observed beneficial effects of liming at multiple ecosys- normal soil conditions for forest soils in the area (no soils tem levels five years after lime was applied to study areas with pH > 7.0) [33]. Potential negative effects of liming within and acidified forest and gained a better understanding on soils include decreases in potassium, resulting from of the links between soil conditions and other components of displacement by Mg and Ca, and decreased phosphorus, the forest ecosystem. With data from an additional two years resulting from the formation of insoluble Ca phosphates or after liming since our preliminary report [19], we continued uptake by plants [34–36]. Unlike in other liming studies, we to observe increases in soil pH, calcium availability, and observed no substantial effects of dolomitic lime application bird abundance. We also observed unexpected results, with onsoilPorK[10, 37]. snail abundance initially increasing and then decreasing, Soil calcium/aluminum ratios below one are related to although still remaining significantly more abundant than a high risk of adverse tree growth or nutrition [38]. Before prior to liming. Some components of the forest ecosystem liming, we observed ratios as low as 0.35, and the majority of (e.g., millipede abundance and vegetation) did not respond soil samples were below one. After liming, many soil samples or responded too slowly to liming to be observed in five remained below one, but the majority became above one, 6 International Journal of Forestry Research

8 2.5 7 2 6 1.5 5 Ground Abundance 4 1

∗ ∗ 3 0.082 (0.043, 0.126) 0.5 0.103 (0.053, 0.154)

1.5 2.5

1 2 Canopy Understory 0.5 1.5

0.058 (0.009, 0.112)∗ 0 1 0.011 (−0.042, 0.068)

2003 2004 2005 2006 2007 2008 2003 2004 2005 2006 2007 2008 year year

Control Control fitted Control Control fitted Limed Limed fitted Limed Limed fitted Figure 4: Mean number of all birds (abundance) and of ground-, understory-, and canopy-foraging birds (birds per point) with SE bars and model lines on lime-treated and control sites. Lime treatment was applied between 2003 and 2004 in central Pennsylvania. ∗Confidence intervals of the time-by-treatment interaction excluded zero, indicating a significant effect of liming on that variable. indicating substantial improvement of soil conditions for abundance did not respond to liming. Although millipedes tree health. contain relatively high amounts of calcium, their abundances seem more sensitive to leaf litter depth and moisture, 4.2. Invertebrates. Although both snails and millipedes con- and in other research, we found that abundances were tain high levels of calcium, only snail abundances increased negatively related to soil pH [42]. Researchers in the past have after liming. Snail abundances peaked three years after liming observed mixed results when investigated the relationship with a 23 fold increase, then unexpectedly decreased. Despite between diplopods and soil conditions. Kalisz and Powell a 50% decline in snail abundance during the fourth and [43] observed higher millipede biomass on soils with higher fifth years of the study, snail abundance was still 11 times soil calcium and pH resulting from proximity to limestone higher on limed sites than before liming. The decline in roads, while Wasson [44]foundastrongnegativetrend snail abundance was unlikely a result of diminishing effects between millipede abundance and soil calcium availability. of lime application. We observed increases and elevated Because millipede abundances did not increase after liming, levels of calcium and pH in the soils five years after liming, we believe that they were not limited by soil pH or calcium and the effects of liming are predicted to last much longer levels. [12]. Birds may play a large role in trophic cascades and have great potential to control populations of invertebrate 4.3. Vegetation. We observed very little evidence that liming prey items [39, 40] and, potentially, the increase in bird had any large effects on vegetation structure. Potentially, sub- abundance in response to liming resulted in an increased tle changes in canopy cover and understory vegetation could rate of snail predation, especially if snails are indeed a critical have occurred, which our methods were not designed to calcium source in the study area. Other factors unrelated to detect. However, our methods did a sufficient job measuring liming, such as soil and litter moisture, can also affect snail the potential for larger changes that would affect bird habitat abundances [41] however; we did not record any differences quality. in litter moisture in the final year compared to other years or The only vegetation response to liming was an increase control sites. in forb cover. In other research, we looked more closely at We also analyzed the effects of liming on millipedes be- the response of understory vegetation biomass to liming and cause they are a potential source of calcium for breed- also only found a positive response of forb biomass to liming, ing songbirds [16]. Contrary to our prediction, millipede similar to these results [42]. International Journal of Forestry Research 7

0.3 0.8

0.2

0.4 Ovenbird Veer y 0.1

0 0

0.6

0.4 0.4

0.2 0.2 Eastern towhee Eastern Chestnut-sided warblerChestnut-sided

0 0

2003 2004 2005 2006 2007 2008 2003 2004 2005 2006 2007 2008 year year

Control Control fitted Control Control fitted Limed Limed fitted Limed Limed fitted Figure 5: Mean abundance (count per point) of Ovenbird, Veery, Eastern Towhee, and Chestnut-sided Warbler with SE bars and model lines on lime-treated and control sites. Lime treatment was applied between 2003 and 2004 in central Pennsylvania.

We expected the greatest changes in vegetation to occur effects of liming on individual species, and no species in canopy cover, leaf litter depth, understory vegetation declined in abundance after liming. Many of the species that cover, and sapling density. We predicted increased canopy benefited from liming, such as veery, chestnut-sided warbler, cover after liming because other researchers found increases American redstart, common yellowthroat, eastern towhee, in canopy health and cover after liming [9, 13, 14]; however, and indigo bunting are currently declining either regionally these studies focused on declining sugar maple stands, and or nationally [47], suggesting that this technique could be we found no evidence of diebacks or decline in the trees in beneficial for restoring bird populations at least locally. our study, nor did we have any sugar maples on our study We predicted that forest songbirds could respond to sites. We predicted that understory vegetation would respond changes in soil conditions caused by liming through vege- to liming; however, other potentially limiting factors, such as tation changes or changes in invertebrate prey availability. light, competition with hay-scented fern, or other nutrients Because we observed little change in vegetation structure, could have prevented a response to liming [45]. Many we strongly suspect that increased calcium availability in researchers observed positive responses of mature tree basal snails was the most likely mechanism (of the variables we area growth to liming, but our methods would not have been measured) for increases in bird abundance. Snails are an able to detect such changes [9, 10, 13]. However, Donaldson important calcium source for many forest songbirds because [46] measured radial growth of trees at the same study sites they store little to no calcium for reproduction and their diets and found only a slight increasing trend in growth after do not normally contain enough [6, 48, 49]. By increasing liming. snails for great tits (Parus major), Graveland et al. [6] reduced the occurrence of thin eggshells that were common 4.4. Bird Abundance. We continued to observe increases in in birds breeding in highly acidified areas. Increased calcium- bird abundance through five years after liming, with nearly rich prey availability could improve reproductive output, double the number of birds per point count since the potentially resulting in higher return rates of adults and more year before liming. The increase in abundance between young birds returning to the same area to reproduce. In years was also fairly constant, with an average increase addition, snails could be a strong indicator of habitat quality of 0.6 birds per point per year. With the additional two and increase immigration rates. Because snail abundances years, we were able to support our hypothesis that ground- were reduced in the final year of the study, bird abundances and understory-foraging birds would benefit more than could also eventually decrease, but continued data collection canopy-foraging birds. We were also able to detect positive would be required to examine this possibility. 8 International Journal of Forestry Research

We also recognize that changes in other invertebrate Conservation and Natural Resources and the Pennsylvania species, which we did not measure, could also be responsible Game Commission. for increases in bird abundances, but in past liming studies, snails have been the only invertebrate to regularly benefit from base cation additions, with mixed results in other References invertebrate groups [50–53]. Liming has been shown to increase earthworm abundances, but earthworms were not [1] M. B. Adams, J. A. Burger, A. B. Jenkins, and L. Zelazny, present at our study sites [54]. Caterpillars are a major “Impact of harvesting and atmospheric pollution on nutrient food source for many forest songbirds, but no studies depletion of eastern US hardwood forests,” Forest Ecology and have found changes in caterpillar abundances with liming, Management, vol. 138, no. 1–3, pp. 301–319, 2000. as most liming studies have focused on aquatic or litter [2] C. T. Driscoll, G. B. Lawrence, A. J. Bulger et al., “Acidic deposition in the northeastern United States: sources and inputs, invertebrates [51–53, 55]. However, Haack [56]observed ff greater caterpillar abundances in more acidified areas, likely ecosystem e ects, and management strategies,” BioScience, vol. 51, no. 3, pp. 180–198, 2001. resulting from increased palatability of tree foliage when trees were under mild-to-moderate pollution stress from acidic [3]S.P.Hamburg,R.D.Yanai,M.A.Arthur,J.D.Blum,and T. G. Siccama, “Biotic control of calcium cycling in Northern deposition, and Butler [57] observed higher abundances of Hardwood forests: acid rain and aging forests,” Ecosystems, vol. caterpillars in an experimentally acidified site compared to 6, no. 4, pp. 399–406, 2003. control. [4] R. A. F. Warby, C. E. Johnson, and C. T. Driscoll, “Continuing acidification of organic soils across the northeastern USA: 4.5. Evaluation. 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J. observed increases in forb ground cover on both control Van Noordwijk, “Poor reproduction in forest passerines from and limed sites from before to after liming, indicating that decline of snail abundance on acidified soils,” Nature, vol. 368, an environmental factor common to all four study sites was no. 6470, pp. 446–448, 1994. responsible. Using the BACI design, we were able to detect an [7] T. S. Traaen, T. Frogner, A. Hindar, E. Kleiven, A. Lande, and R. effect of liming on forb cover because the change was much F. Wright, “Whole-catchment liming at Tjonnstrond, Norway: greater on limed sites compared to control sites. an 11-year record,” Water, Air, and Soil Pollution, vol. 94, no. Terrestrial application of dolomitic limestone sand was 1-2, pp. 163–180, 1997. successful at improving soil condition to the point of being [8] J. Gunn, R. Sein, B. Keller, and P. Beckett, “Liming of acid and near preacidic deposition levels. 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Letter to the Editor The Wood, the Trees, or the Forest? Carbon in Trees in Tasmanian State Forest: A Response to Comments

M. T. Moroni, T. H. Kelley, M. L. McLarin, and S. M. Read

Forestry Tasmania, 79 Melville Street, Hobart, TAS 7000, Australia

Correspondence should be addressed to M. T. Moroni, [email protected]

Received 10 August 2011; Accepted 3 October 2011 Copyright © 2012 M. T. Moroni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Recently we presented data on the amount and distribution of standing-tree carbon on 1.5 M ha of Tasmanian State forest (Moroni et al. 2010) and deduced that the concept of carbon-carrying capacity (CCC) could usefully be applied at the site level but not at the landscape level in disturbance-driven wet eucalypt forest ecosystems. The recent response in this journal of Dean (2011) perpetu- ates the confusion between site-level CCC and attainable landscape-level C stocks, but mostly comprises material not at all relevant to the data and concepts presented by Moroni et al. (2010). Here, we rebut the response of Dean (2011) to the substance of our original analysis, in regard to the CCC of forests subject to disturbance, processes of ecological succession with and without disturbance, the use of Forest Class inventory datasets, and old-growth forest as a reference state. We respond to the wider issues raised by Dean (2011) by noting that the carbon footprint of active forest management needs to consider not the long-term landscape- average C stocks attainable under natural or anthropogenic disturbance regimes, but also the carbon stock in wood products, and furthermore the carbon emissions mitigation resulting from the use of timber in place of resources with higher greenhouse-gas emissions.

1. Introduction mature, then prevention of the subsequent progression of eucalypt forests to less C-dense rainforest. Moroni et al. [1] used a large inventory dataset from 1.5 M ha Few of the comments of Dean [2] were in fact related to of Tasmanian State forests to determine the distribution of the above content, with the author instead presenting his standing-tree carbon (C) stocks across forest types and age views on a range of different and often contentious matters classes. The total mass of C on this estate was estimated at in regard to forestry and climate change. In particular, Dean 163 Tg. Although the highest C densities (Mg ha−1)occurred [2] assumes that changes in forest C storage resulting from in the tallest mature wet eucalypt forest of highest crown forest management represent the only C footprint of the cover, this forest type represents only 0.2% by area of state forest industry, and overlooks the role of wood products in forest—more typical, and containing the majority of the total greenhouse-gas mitigation through C storage and through C stock, were shorter mature forests with lower crown cover, reducing emissions from fossil fuel usage (using wood to re- rainforest, and forests containing regrowth or silvicultural place other resources), as well as the impact of the global regeneration. Moroni et al. [1] used the range of forest class- trade in wood products on forest management decisions. es, types, and ages in this dataset to illustrate the ecological These latter roles are widely recognized as having important impossibility of attaining or maintaining theoretical C sat- roles in greenhouse gas mitigation [3–5]. uration in landscapes containing wildfire-generated forests: Here we refocus the discussion on the actual objec- conversion of an entire landscape permanently to mature tives, data, and analyses of Moroni et al. [1], and respond eucalypt forest (that stores the most C) would first require to the relevant comments and assertions of Dean [2]. We fire to convert existing mixed forest and rainforest back to also briefly give context to the role of forest management eucalypt forest, then exclusion of fire while eucalypt forests in greenhouse-gas mitigation and respond to some of the 2 International Journal of Forestry Research comments of Dean [2] related to the Tasmanian forest in- phrase “and natural disturbance regimes” suggests that time dustry. since fire is accounted for in some way, but instead CCC has regularly been calculated as the maximum potential C that a 2. Carbon Storage in Forests forest can store, that is, solely in relation to mature forests. Estimates of the additional potential C storage capacity of The Australian public discourse around forest C storage often forested landscapes are then presented as the difference bet- invokes forest types associated with very large trees and C ween current forest C stocks and those anticipated if all sites stocks [6] but which are atypical across the landscape, and in in the landscape were simultaneously occupied by mature, any case are predominantly in reserves [7, 8]. Generalising C-rich forests. This approach might work in drier or more from these very tall, wet, mature eucalypt forests overesti- open eucalypt forests not subject to stand-replacing wildfire mates actual and potential forest C storage across the land- and that would always naturally support a proportion of scape, with most forests in fact able to store substantially older trees, but does not capture the impact of disturbance less C [9]. The first objective of Moroni et al. [1]wasthus history on the landscape-level age-class patterns of wetter stated as “to improve understanding of forest C stocks at the eucalypt forests [17]. Large wildfires, as typified by those on landscape level in Tasmanian State forest using an inventory- the south-east Australian mainland in 2003, 2006, and 2009 based analysis of C in standing live and dead trees, and exa- [18], always keep a portion of the wet eucalypt forest area in mine carbon in forest by Forest Class, forest type, elevation, younger age classes and below maximum potential C storage, and land-use classes.” In contradiction to the assertion of and it is not realistic for the total area of these forests to be Dean [2], we do accept the value of previous measurements simultaneously mature and at their CCC. CCC thus cannot in tall, wet, C-rich forest, but our analysis showed that addi- be both values simultaneously—a landscape or temporal tional data is required from a wider variety of productivity average including fire effects (as the definition might suggest) and cover types for estimation of landscape-level forest C- and a local and temporary stand maximum (as CCC is gene- stocks and the actual potential of forests to store C. rally calculated). Even though Dean [2] acknowledges wild- The second, and related, objective of Moroni et al. [1] fire may be “intrinsic to both the standard definition of CCC arose from the difficulty in applying the parameter carbon- and to the time-based average method of determining carbon carrying capacity (CCC) across landscapes containing euca- budgets for forest activities,” this is then ignored in the in- lypts at various stages of their fire disturbance cycle, and correct generalisation of CCC values across the landscape. was stated as to “estimate the carbon carrying capacity of In his concluding remarks, Dean [2]appearstoagree Tasmanian State forest and explore the methods required to when he criticises Moroni et al. [1] for “their unnecessary re- achieve and maintain carbon carrying capacity in Tasmanian definition of CCC and their use of C saturation at a land- State forest.” In spite of its original definition, the CCC con- scape-scale rather than its usual usage over a specific dura- cept is typically associated with stands of old, C-rich forests, tion for a specific forest stand.” However, only 2 sentences and thus overestimates potential C stocks when applied to later Dean himself applies CCC at the landscape scale when landscapes containing forests of a range of ages following he writes “the estimated C deficit in Tasmanian State forests natural or anthropogenic disturbance. Specifically, CCC is (the amount below its CCC) due to commercial forestry is of dubious relevance to management where the forest with currently 29 (±4) Tg.” This internal contradiction is the best highest C stocks is not ecologically stable: wet eucalypt forests example of the flaw in the logic. Dean [2] needs to decide are susceptible either to fire or to succession to rainforests whether he is using CCC as a stand parameter or a landscape with lower C density. parameter, because in disturbance-driven ecosystems—and especially in extreme cases of these such as wet eucalypt for- 2.1. Carbon-Carrying Capacity of Disturbance Forests. Euca- ests—CCC differs substantially between the stand level and lypt regeneration strategies vary considerably [10], including the landscape level. requirements for disturbance of various degrees of severity. We thus do not disagree with the definition or applica- Tall, wet eucalypt forest systems are among the most production of CCC for individual forest sites and have not attem- tive, high-quality forests in south-eastern Australia, extend- pted to redefine this term, as asserted by Dean [2], but we do ing in a discontinuous arc from southern Queensland to disagree with how CCC has been applied at a landscape level Tasmania, and many require severe site disturbance to regen- in disturbance forests, and its use to calculate some theore- erate [10–12]. Drier eucalypt forests show less reliance on tical deficit in C stocks due to management at the estate level. severe site disturbance for regeneration. Disturbance in wet The dynamic nature of these ecosystems and their C stocks in sclerophyll forests is often but by no means always stand-re- time and space prevents all stands across a landscape reach- placing [11, 13], whereas disturbance dynamics in drier scle- ing stand-level CCC at the same moment, prevents any stand rophyll forests are generally gap-driven and not stand-replac- from remaining at its CCC indefinitely, and makes a non- ing. sense of the goal of managing for stand-level CCC at the Dean [2] unfortunately reinforces the confusing relation- landscape scale [19]. It would be preferable to use the term ship of CCC to wildfire. Roxburgh et al. [14], Mackey et al. theoretical carbon saturation sensu Nabuurs et al. [20] at the [15], and Keith et al. [16] define CCC as “the mass of C landscape level, restricting CCC to the stand or site level. This able to be stored in a forest ecosystem under prevailing envi- would be ecologically sensible, and also would prevent the ronmental conditions and natural disturbance regimes, but illogical situation where mature forest sites could carry more excluding anthropogenic disturbance.” The inclusion of the C than their calculated CCC. International Journal of Forestry Research 3

2.2. Ecological Succession to Rainforest. Cool-temperate rain- appropriate proportion of very old stands, and an area- forests of SE Australia are comprised of considerably smaller weighted C stock. Sample plots are randomly located in State trees than the wet eucalypt forest they replace [21–23], and forest [27], rather than the “regular grid array” assumed by this transition from eucalypt forest to rainforest is associated Dean [2]. with a loss of standing-tree C [1]: forests containing trees of As Dean quotes from Roxburgh [17], consideration of large dimensions are the key to large forest-C stocks [24]. forest C stocks therefore “requires knowledge of the actual This transition can occur without the “higher stand densi- spatiotemporal variation in natural disturbance history of ties” surmised by Dean, and instead it often involves pre- the forest estate, the spatiotemporal variation in other attri- mature senescence of the eucalypt overstorey. Thus, Dean is butes relating to site quality, and the site histories from where not necessarily correct when he writes “the biomass of old- the data were collected.” Moroni et al. [1] provide this infor- growth mixed-forests, being a superposition of two forest mation for Tasmanian State forest. However, these real, on- types, is likely to be higher than that of each component sep- ground data appear to be at variance with earlier assump- arately”; instead, the wet sclerophyll-mixed forest-rainforest tions as to the area of tall wet forest, which leads Dean [2]to successional pathway is associated with progressive loss of the try to locate “missing high-biomass stands,” with suggested tallest and largest trees on the site (and loss of C), not simply reasons including “European colonisation” or “clearing for the addition of a rainforest understorey. agriculture” as much as harvesting and regeneration. Cer- Dean [2] is also incorrect in asserting that we argue that tainly, reafforestation of the rich soils of much Tasmanian a reduction in tree dimensions with ecological succession agricultural land in the valleys of southern, north-eastern, only occurs in Tasmania: it in fact occurs in more instances and north-western Tasmania would increase landscape C than Dean [2] provides, but nevertheless these relatively few stocks over the next couple of centuries, as forest grows to instances are atypical of more common ecological devel- various stages of maturity, and provided wildfire does not in- opment where later successional species attain dimensions tervene. However, Moroni et al. [1] did not aim to compare equivalent to or larger than early successional species [25, extant C stocks across Tasmania to some pre-European 26]. Dean [2] is also incorrect in asserting that Moroni et al. (ideal) state, restricting instead their analysis to the current [1] indicate wet eucalypt forest will transition to rainforest situation on existing state forest. We agree with the descrip- only with the cessation of timber harvesting: this transition tion by Dean [2]oftheneedforbetterforestCaccounting occurs in the absence of disturbance, including wildfire [21– but the logic is even more applicable to agricultural and 23]. urban land, where the C deficit compared to mature forest is much greater. 2.3. Inventory Datasets. Dean [2] provides a CCC estimate The signal of past disturbances is thus present in current for the eucalypt component of Tasmanian State forest that inventory datasets [1]. While the CCC of individual sites does attempts (commendably), but fails, to account for landscape not change (being the maximum value that site could carry), age-class structure. Dean [2] overestimates CCC primarily by attainable landscape-level C stocks are substantially lower basing his estimate on Forest Classes 1–17. These classes are and cannot be estimated by the theoretical exercise of extend- dominated by mature trees, with or without some regrowth, ing site CCC values across a complete forest estate that in- while Forest Classes 22–45 omitted from his calculations are correctly assumes eucalypt forests can be held at maturity dominated by immature regrowth resulting from wildfire. indefinitely. It is thus also quite inappropriate, as others have Comparing classes of a given height potential, Forest Classes done [2, 15], to subtract the current C content of wet euca- 22–45 of course have lower C densities than Forest Classes lypt forests from an estimated CCC derived from mature- 12–17 [19] but only the area-weighted combination of all forest site values, and present the answer as a putative land- these forest classes will give an unbiased estimate of the C scape C loss from forest harvesting that could be sequestered stock of natural forests. Dean then falls into the trap of back into the forest. comparing C densities of current silvicultural regeneration Lastly, Dean refers to an unpublished presentation [28]to (Classes 50–74) only to the densities of Forest Classes at least justify his claim of little difference in long-term average C 110 years after disturbance (Classes 1–17), which of course stocks depending on the initial state (age) of the forest. How- will overestimate C loss due to human activities; he admits ever, slide 51 of 62 in that presentation indicates that this “the areas of regeneration would not all achieve maximum result is driven by assumed large, progressive decreases in soil C stock simultaneously,” but then continues as though the organic C on each harvesting cycle, as well as much more numerical comparisons are valid. frequent logging than is actually planned. Losses of 30% of Dean [2] also suggests that Moroni et al. [1] assume for- soil organic C per rotation were assumed in this work and ests at 110 years of age are C-saturated and ecologically main Dean et al. [29], but amended downward over 10-fold, to ture. This is clearly not the case. The 110-year age threshold 2.5%, in Dean and Wardell-Johnson [30]. Unduly pessimistic that differentiates regrowth and mature forests across forest assumptions in regard to forest management will lead, of classes is inherent to the inventory dataset used, as it indicates course, to lower modelled carbon stores in managed forests. when tree form begins to take on mature characteristics. As indicated in Moroni et al. [1], most mature forests will be 2.4. Old Growth as a Reference State. Wildfire is not evenly significantly older than 110 years, and the inventory cohort distributed across time. Tasmania has experienced relatively of forests over 110 years old thus contains an area-weighted low levels of wildfire since the 1960s, and no megafire of mixture of forests of all ages greater than this age, with an 1 M ha or more since 1934 [31], partly because active fire 4 International Journal of Forestry Research management has reduced the impact of the 90 average annual balanced by C sequestration in growing trees, unlike the case unplanned wildfires (to an average of ∼10 000 ha burnt per with fossil fuels. The exclusion of further Australian native year over the period 2003–2009: [7]). Even though as Dean forests from productive usage would also lead to increased [2] suggests many of the recent wildfires in Tasmanian forests imports to supply Australian wood demands, consequently are likely of European origin [31], Tasmania’s natural forests increasing emissions from increased transportation and are currently relatively old and C-rich, but this simply means more intensive harvesting internationally. that they will lose large amounts of C in the next landscape- The misrepresentation of the Tasmanian forest industry level wildfire. by Dean [2] does need to be corrected for the wider audience The age-class structure of pre-European wet eucalypt for- of this journal. Dean states the Tasmanian forest industry ests is unknown, although it has been suggested that repeated hasreceivedrepeatedeconomicsupportandsuggestsitis low-intensity aboriginal burning of moorland reduced the financially unviable. The Tasmanian forest sector has recei- likelihood of moorland fires escaping into eucalypt forest and ved $AUD 211 million in State and Commonwealth funding potentially contributed to the infrequent but high-intensity to compensate the industry for transferring production for- wildfire pattern in wet eucalypt forests [31]. The total area ests to reserves [34]. The compensation was largely used to of old growth also likely varied widely over millennia due to establish plantations to offset production losses from newly variation within the fire regime; similar natural, stochastic variation in the proportion of forests that are old-growth reserved native forest. Income is expected from this invest- has been modelled elsewhere [32]. It is thus perverse to use ment as the plantation estate matures to harvest. However, old-growth stands as the reference points for carbon storage more than half of the people employed in the Tasmanian as though all forests were originally old-growth. The pre- timber industry depend on native forest management [35], European age-class structure of eucalypt forests will have and in 2009/2010 the estimated final value of wood products contained a range of ages, possibly including much open for- from logs sourced from native Tasmanian State forests was est or young forest resulting from both wildfire and Abo- $AUD 563 million [7]. Monash University estimates that Tas- riginal burning, with the overall extent of eucalypt forests mania is $AUD 111 million a year better off with Forestry being a subset of the extent of pre-European wildfire [1]. Tasmania than it would be otherwise [34]. Since corporatisa- Additionally, fire danger in Australia is increasing [33], and tion 16 years ago, Forestry Tasmania has earned $AUD 200 hence with time Tasmania’s forest age-class structure will million in profit and has paid taxes, rates, and dividends likely decrease. exceeding $AUD 99 million [34]. Lastly, Dean unreasonably criticises Forestry Tasmania 3. Forest Politics for not making its data and systems freely publically available. Forestry Tasmania is a Government Business Enterprise Much of Dean [2] comprises comments on climate change, engaged in commercial activities, and as for any company the climate science, the role of forests and forestry in the global release of datasets and systems would expose it to competitive carbon cycle, forest carbon flux measurement, historical tree disadvantage. One outcome would be a disincentive to con- biomass measurements, forest allometry, the economic via- tinue investing in data collection and technical development, bility of the Tasmanian forest industry, forest politics, and and the loss of data sets for research and collaborative re- Forestry Tasmania (the Tasmanian State Government Busi- search programs. ness Enterprise charged with managing State Forests). These comments are interesting, some are contentious, but are unrelated to the datasets analysed by Moroni et al. [1], so we 4. Conclusions will mostly not deal with them. However, it is worth noting that the wider response of We have restricted our response to that part of Dean [2] Dean [2] is predicated on the erroneous assumption that relevant to the substance of our research article [1]. In es- maximising landscape C storage is the sole role of forest man- sence, Dean [2] overestimates the amount of C that Tasma- agement in greenhouse gas mitigation. The C footprint of nian wet eucalypt forests can store by assuming all stands can active forest management in Tasmania and elsewhere needs reach their carbon-carrying capacity (CCC) simultaneously, to consider not just the long-term landscape-average C stocks thereby ignoring the proportion of forests that will at any attainable under the natural or anthropogenic disturbance point in time be immature regrowth following wildfire. This regimes (wildfire or timber harvesting), but also the C stock in turn leads to an overestimation of the impact of forest in wood products, and further the C emissions mitigation management and timber harvesting on landscape C stocks. resulting from use of timber in place of resources with higher greenhouse-gas emissions [19]. If wood was unavailable, Dean [2] also asserts that changes in landscape C stocks alternative products (steel, aluminium, glass, or concrete) as- constitute the C footprint of forest industries, whereas a life- sociated with greater greenhouse-gas emissions would be cycle analysis of forest and wood products shows a different used for structural and appearance products, increasing picture [19]. Accounting for the direct and substitutional the burning of fossil fuels both directly and indirectly: roles of wood products as well as landscape C stocks is nece- wood usage is widely recognized as being associated with ssary to determine the impact of forest management in Tas- lower emissions [3–5]. Wood can also be used directly as mania and elsewhere on the atmospheric levels of greenhouse a C-neutral biofuel, with the associated C emission being gases. International Journal of Forestry Research 5

Acknowledgment [16] H. Keith, B. Mackey, S. Berry, D. Lindenmayer, and P. Gib- bons, “Estimating carbon carrying capacity in natural for- The authers would like to thank the staff from Forestry Tas- est ecosystems across heterogeneous landscapes: addressing mania who collected and compiled inventory data and help- sources of error,” Global Change Biology, vol. 16, no. 11, pp. ed locate other data related to forests and forest management 2971–2989, 2010. in Tasmania. [17] S. H. Roxburgh, “Increase in carbon stocks in pre-1990 eucalypt forests,” in An Analysis of Greenhouse Gas Mitigation and Carbon Biosequestration Opportunities from Rural Land References Use, S. Eady, M. Grundy, M. Battaglia, and B. Keating, Eds., [1] M. T. Moroni, T. H. Kelley, and M. L. 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Lindenmayer, “Re-evaluation trytas.com.au/uploads/File/pdf/pdf2010/stewardship report of forest biomass carbon stocks and lessons from the world’s 2010.pdf. most carbon-dense forests,” Proceedings of the National Aca- [8] Forestry Tasmania 2011, Giant Trees. http://gianttrees.com demy of Sciences of the United States of America, vol. 106, no. .au/. 28, pp. 11635–11640, 2009. [9] J. Norris, S. Arnold, and T. Fairman, “An indicative estimate of [25] F. H. Bormann and G. E. Likens, PatternandProcessinaFores- carbon stocks on Victoria’s publically managed land using the ted Ecosystem, Springer, New York, NY, USA, 1979. FullCAM carbon accounting model,” Australian Forestry, vol. [26] C. J. Burrows, ProcessesofVegetationChange, Unwin Hyman, 73, no. 4, pp. 209–219, 2010. London, UK, 1990. [10] R. G. Florence, Ecology and Silviculture of Eucalypt Forests, [27] M. G. Stone, “Forest-type mapping by photo-interpretation: CSIRO Publishing, Collingwood, VIC, Australia, 1996. a multi-purpose base for Tasmania’s forest management,” Tas- [11] D. H. Ashton, “Fire in tall open-forests (wet sclerophyll fore- forests, vol. 10, pp. 15–32, 1998. sts),” in Fire and the Australian Biota,A.M.Gill,R.H.Groves, [28] C. Dean and G. Wardell-Johnson, “Effective carbon manage- and I. R. Noble, Eds., Australian Academy of Science, Can- ment of forests requires recognition of major pools and time- berra, Australia, 1981. lines,” in Proceedings of the the Adapting Forest Management [12] P. M. Attiwill, “Ecological disturbance and the conservative to Maintain the Environmental Services: Carbon Sequestration, management of eucalypt forests in Australia,” Forest Ecology Biodiversity and Water, Koli National Park, North Karelia, and Management, vol. 63, no. 2-3, pp. 301–346, 1994. Finland, September 2009. [13] P. A. M. Turner, J. Balmer, and J. B. 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[32] M. C. Wimberly, T. A. Spies, C. J. Long, and C. Whitlock, “Simulating historical variability in the amount of old forests in the Oregon Coast Range,” Conservation Biology, vol. 14, no. 1, pp. 167–180, 2000. [33] C. Lucas, K. Hennessy, G. Mills, and J. Bathols, 2007, Report to the Climate Institute of Australia. http://www.bushﬁrecrc .com/research/downloads/climate-institute-report-september- 2007.pdf. [34] State of Tasmania, “Auditor general,” Tech. Rep. 100, Financial and Economic Performance of Forestry Tasmania, Hobart, TAS, Australia, 2011. [35] J. Schirmer, Tasmania’s Forest Industry: Trends in Forest Indus- try Employment and Turnover, 2006–2010,CRCforForestry, Hobart, TAS, Australia, 2010. Hindawi Publishing Corporation International Journal of Forestry Research Volume 2012, Article ID 469326, 8 pages doi:10.1155/2012/469326

Research Article Analysis of Soil-Vegetation Interrelationships in a South-Southern Secondary Forest of Nigeria

D. D. Eni,1 A. I. Iwara,2 andR.A.Ofﬁong1

1 Department of Geography and Regional Planning, University of Calabar, P. M. B. 1115, Nigeria 2 Department of Geography, University of Ibadan, Ibadan, Nigeria

CorrespondenceshouldbeaddressedtoA.I.Iwara,[email protected]

Received 14 December 2010; Revised 23 April 2011; Accepted 16 May 2011

Academic Editor: Piermaria Corona

Copyright © 2012 D. D. Eni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Soil-vegetation interrelationships in a secondary forest of South-Southern Nigeria were studied using principal component analysis (PCA) and canonical correlation analysis (CCA). The grid system of vegetation sampling was employed to randomly collect vegetation and soil data from ﬁfteen quadrats of 10 m × 10 m. PCA result showed that exchangeable sodium, organic matter, cation exchange capacity, exchangeable calcium, and sand content were the major soil properties sustaining the regenerative capacity and luxuriant characteristics of the secondary forest, while tree size and tree density constituted the main vegetation parameters protecting and enriching the soil for its continuous support to the vegetation after decades of anthropogenic disturbance (food crop cultivation and illegal logging activities) before its acquisition and subsequent preservation by the Cross River State government in 2003. In addition, canonical correlation analysis showed result similar to PCA, as it indicated a pattern of relationship between soil and vegetation. The only retained canonical variate revealed a positive interrelationship between organic matter and tree size as well as an inverse relationship between organic matter and tree density. These extracted soil and vegetation variables are indeed signiﬁcantly important in explaining soil-vegetation interrelationships in the highly regenerative secondary forest.

1. Introduction providing nutrients for plant to grow as well as habitat for millions of micro- and macro-organisms. Healthy soil Soil and vegetation exhibit an integral relationship, in that enables vegetation to flourish, releases oxygen, holds water soil gives support (moisture, nutrient, and anchorage) to and diminishes destructive storm runoff, breaks down waste ff vegetation to grow e ectively on the one hand, and on the materials, binds and breaks down pollutants, and serves as other hand, vegetation provides protective cover for soil, the first course in the larger food chain [4, 5]. The distur- suppresses soil erosion, and helps to maintain soil nutrient bance, compaction, and degradation of soils impact the soil through litter accumulation and subsequent decay (nutrient structure and reduce its ability to provide these functions. cycling). Hence, vegetation and soil are interrelated and The lowland secondary forest of Tinapa Resort is a re- ff provide reciprocal e ects on each other. Vegetation supports generative forest approaching climax, after series of anthro- ff critical functions in an ecosystem at di erent spatial scales. pogenic disturbances notably food crop cultivation, fuel- ff Vegetation strongly a ects soil characteristics, including soil wood gathering, and illegal logging activities. The forest volume, chemistry, and texture, which feed-back to affect vegetation over nine years of abandonment following its various vegetation characteristics, including productivity, acquisition by the Cross River State government is charac- structure, and floristic composition [1]. terized by a luxuriant forest canopy and diverse tree species. Soil nevertheless is fundamental to ecosystem and agri- There is therefore a need to identify the basic soil and cultural sustainability and production because it supplies vegetation parameters encouraging the regenerative capacity many of the essential requirements for plant growth like of this once degraded forest and impoverished soil [6]. water, nutrients, anchorage, oxygen for roots, and moderated This reciprocal relationship between soil and vegetation temperature [2, 3]. Soil serves a vital function in nature, demands a multivariate approach in order to determine 2 International Journal of Forestry Research critical vegetation and soil properties that sustain this inte- and then 15 grids were randomly selected. This approach grative association. On this premise, multivariate analytical was used to establish fifteen quadrats of 10 m × 10 m in techniques (principal component analysis, canonical corre- dimension across the area; and in each quadrat, vegetation lation analysis, factor analysis, and canonical correspondence and soil samples were collected. The floristic and structural analysis among others) are very useful in the analysis of soil vegetation samples determined included tree density, species and vegetation as each consists of data corresponding to a composition, tree height, tree size/girth, vegetation/crown large number of variables. Thus, analysis via these techniques cover, species diversity index, and aboveground biomass. produces easily interpretable results [7]. Data on vegetation/crown cover was obtained using the Studies explaining the reciprocal effect of soil and line-intercept method [21–23]. Tree size/girth was taken at vegetation have been conducted in the past by scholars in 1.37 m DBH. Species diversity index was determined using the fields of ecology, geography, forestry, and soil science Shannon-Wiener’s approaches [24, 25]. Tree height was using varying multivariate approaches. For instance, studies determined using the trigonometry method [26, 27]. The on soil-vegetation relationships of saline localities have been above-ground biomass was estimated using the allometric documented [8–14]; studies on soil-vegetation relationships formula given by FAO (1989) for tropical areas as cited by in tropical rainforests have also been conducted [15–17]. Woomer [28]asy = exp(−2.134+2.53 InD),wherey = above- However, in Nigeria, studies on soil-vegetation interrelation- ground biomass in kilogrammes, exp = 2.71828, and D is the ships in the rainforest zones show locational bias, as most of measured diameter at breast height in cm. However, during the studies were carried out in the South-Western ecological the collection of vegetation parameters, only mature trees zone [18]. with ≥ 0.30 m girth were enumerated and analyzed. Only perhaps the study by Ukpong [19] used multivari- In the same way, fifteen (15) soil samples were collected ate analyses to determine soil-vegetation interrelationships using a soil auger at rooting depth of 30 cm. The soils were in the coastal mangrove swamps of Cross River, however, put in polythene bags with label; they were thereafter air multivariate analysis of soil-vegetation interrelationships dried and taken to the laboratory at the Department of using PCA in the rainforest belt of Cross River State has Agronomy, University of Ibadan, Ibadan for analysis of soil not been fully documented in the literature. It is on this physical and chemical properties. Particle size composition background that the present study attempts to analyse was determined using the hydrometer method [29]; organic soil-vegetation interrelationships in the secondary forest of carbon by the Walkley-Black method [30], after which Tinapa Resort, Cross River State. The aim is to identify sig- values obtained were multiplied by 1.72 [31]toconvertto nificant soil properties that influence vegetation regeneration organic matter; total nitrogen by the Kjeldahl method [32]; and productivity as well as vegetation parameters that help to available phosphorus was determined by the method of Bray protect and nourish the soil to sustain its luxuriant outlook. and Kurtz [33]. The soils were leached with 1 N neutral ammonium acetate to obtain leachates used to determine exchangeable bases adapted from the method described 2. Materials and Methods by Daly et al., [34]. Soil cation exchange capacity was determined by the summation method, while pH values were 2.1. Study Area. Tinapa is located on latitude 05◦ 02 and determined using a glass electrode testronic digital pH meter 05◦ 04 N and on longitude 08◦ 07 and 08◦ 22 E. The area with a soil : water ratio of 1 : 2. falls along the coastal fringes of Cross River State where raining season lasts for about 10 months. The proximity of 2.3. Data Analysis. Two data matrices representing soil and the Atlantic Ocean has a moderating effect on temperature vegetation characteristics were constructed and the SPSS for with highest average daily maximum of 35◦C and recorded windows (Ver. 17.0) and SAS (ver. 9.0) software packages mean actual temperature of 26◦C. The area has an average were used for performing principal component analysis relative humidity of 80–90% at 10 am during the wet season (PCA) and canonical correlation analysis (CCA), respec- [20]. The vegetation of the area is a mixture of mangrove tively. PCA was performed to find the main factors deter- and rainforest. The rainforest is further subdivided into the mining the reciprocal effects of soil and vegetation. Principal lowland rainforest and the freshwater swamp forest. The components according to Li et al., [14], are considered mangrove swamp is found in the southern fringe of the area useful if their cumulative percentage of variance approached and stretches from the freshwater limits to the ocean beaches 80%. The scores of rotated component loadings (correlation [6]. The rich and luxuriant vegetation of the area has been coefficients) from the PCA output were used to determine subjected to severe degradation in the past before the advent the main soil and vegetation components sustaining the of Tinapa; as such, the vegetation comprised secondary forest regeneration, enrichment, moisture content, and produc- approaching climax. The soils are generally deep, porous and tivity of forest vegetation. The rotated component loadings weakly structured, and well drained with low moderate status for the variables were determined using Varimax rotation [20]. (variance maximization); this method was applied as it helps to minimize the complexity of the components by making 2.2. Sampling Procedure and Analysis. The grid system of large loadings larger and smaller loadings smaller within vegetation sampling was used to superimpose grids of each component [7, 35, 36]. The idea of Varimax rotation 2cm × 2 cm on the lowland forest using the vegetation is that each variable should load heavily on few components map of the area, the grid intersections were numbered, as possible to make interpretation easier [37, 38]. Variables International Journal of Forestry Research 3 were also rotated to obtain new significant and uncorrelated Table 1: Ordinary component matrix of vegetation parametersa. variables called principal components or principal axes, and thereafter, the number of principal components was reduced Principal components by eliminating relatively unimportant components [7]. Vegetation parameters 1 2 In order to determine main components, only principal Tree size .884 .417 components with eigenvalues greater than 1 were selected; Above-ground biomass .848 .455 components with an eigenvalue of less than 1 accounted for Vegetation cover .813 .000 less variance than did the original variable (which had a Tree height .645 .611 variance of 1), and so were of little use, as such were not Tree density −.656 .712 extracted. From each extracted component, variables with Species diversity −.662 .678 ffi ≥± coe cients 0.70 were selected and considered significant Species composition −.376 .619 (Johntson, 1980 and Wotling et al., 2003 quoted in [39]). Eigenvalues 3.59 2.10 However, in order to determine the basic soil and vegetation variables sustaining these interrelationships, the component % Variance 51.28 30.05 defining variables (CDVs), that is, those variables with the Cumulative explanation 51.28 81.33 highest loadings (correlation coefficients) on each extracted aVariables underlined with eigenvectors (coefficients) ≥±0.70 are consid- principal components for soil and vegetation, were selected ered significant. to represent the extracted components because they provide the best relationship [40]. What this means is that on every extracted component components and also the overwhelming concentration of ff for soil and vegetation, variable with the highest coefficient, significant variables in component 1 a ected interpretation for example, on component 1, tree size was selected as as well as understanding of the vegetation data structure. In the most significant variable (for either soil or vegetation order to have a fair distribution of variables and to discover variable) to represent that component. In addition, canonical the set of vegetation parameters that helps to protect the soil correlation analysis (CCA) was performed to examine the for its continuous nutrient enrichment, the two extracted main ways in which the properties of soil were related to components were rotated using Varimax method (Table 2). ff those of vegetation. The extracted components of soil and However, the rotation did not a ect the sum of eigenvalue vegetation were used to form pairs of linear combinations of (cumulative explanation) but altered the distribution of the two variables in such a way as to maximize the correlation eigenvalue as well as assigned variable loadings to higher between each pair. This analysis provides a clearer picture of components (Tables 1 and 2). the complex interrelationships between soil and vegetation The loadings of rotated components on vegetation variables. Theoretically, canonical correlation does not dis- parameters are depicted in Table 2. From the table, two tinguish between predictor and criterion variables, but for components were extracted, and they accounted for 81.3% this study, it was done to enhance interpretation. The soil of the total variance in vegetation data set. Three vegetation variables were used as predictor variables, while vegetation parameters loaded heavily on component 1; they included parameters as criterion variables. The essence of canonical tree size (0.97), above-ground biomass (0.96), and tree analysis is the formation of pairs of linear combinations height (0.88). This component was regarded as measuring of two sets of variables in such a way as to maximize the vegetation structure and accounted for 3.11 of the total correlation between each pair. eigenvalue loading and 44.5% variance in the linear combination of vegetation parameters; while in component II, three parameters also loaded heavily on it, these variables included tree density (0.96), species diversity (0.93), and 3. Results species composition (0.72). This component exemplified 3.1. PCA Result on Vegetation Parameters. PCA was per- floristic attributes, and it accounted for 2.58 total eigenvalue formed for seven (7) vegetation parameters across the fifteen loading and 36.9% variance in vegetation dimension. These ffi (15) sampled quadrats in order to identify critical vegetation results based on the criteria of component defining coe - factors that protect and enrich the soil. Component loadings cients (CDV) implied that the main vegetation parameters (correlation coefficients) and the variances (eigenvalues) for influencing and protecting the soil included tree size and tree the various vegetation parameters were computed. Table 1 density (Table 2). shows results of the ordinary component matrix of vegetation parameters with eigenvalues ≥1. It shows that three 3.2. PCA Result on Soil Parameters. In the same manner, (3) vegetation parameters loaded heavily on component 1, PCA was performed for thirteen (13) soil properties across the parameters/variables included tree size (0.88), above- fifteen (15) sampled quadrats to determine the main soil ground biomass (0.85), and vegetation cover (081). This factors/variables that facilitate the regenerative capacity of component accounted for 51.3% of the total variance in the once disturbed forest to the state of climax. The ordinary the vegetation parameters. On component II, only one componentmatrixofsoilpropertiesisshowninTable 3. variable, tree density (0.71) loaded heavily; this component Based on the significant threshold for variables, only one accounted for 30.1% of the variation in the data set. The soil property loaded on component I, the variable was lack of spread of variable loadings across the two extracted exchangeable potassium (0.71); this component accounted 4 International Journal of Forestry Research

Table 2: Rotated components matrix of vegetation parametersa. 14.4% of the total variation in the linear combination of soil variable. Based on this result and the criteria of CDV, the Principal components basic soil factors that influenced vegetation productivity and Vegetation parameters 1 2 sustainability included exchangeable sodium, organic matter, Tree size .965 −.158 cation exchange capacity, exchangeable calcium, and sand Above-ground biomass .956 −.105 content. Tree height .878 .138 Vegetation cover .670 −.461 3.3. Canonical Correlation Analysis. Canonical correlation Tree density −.137 .958 analysis (CCA) is one of the most general of the multivariate Species diversity −.162 .934 techniques that is used to investigate the overall correlation Species composition .040 .723 between two sets of variables. It examines the main ways in which two multivariate measures are related as well as the Eigenvalues 3.11 2.58 strength and nature of the interrelationships [18, 41]. The % Variance 44.47 36.86 basic principle behind canonical correlation is determining Cumulative explanation 44.47 81.33 how much variance in one set of variables is accounted aVariables underlined with eigenvectors (coefficients) ≥±0.70 are consid- for by the other set along one or more axes, which are ered significant. orthogonal (uncorrelated). Unlike many other techniques, in CCA, there is no designation that one set of variables is independent and the other set is dependent, but for clarity, for 3.34 of eigenvalue loading and 25.7% of the variance the predictor-criterion language is used. However, from the in the soil data. Components II and III equally had single two variables, a linear combination is derived such that the soil property; these included available phosphorus (0.77) association/relationship between them is maximum; these and silt content (0.73), and they accounted for 18.6% and pairs of maximally correlated linear combinations are called 14.7% of the variation in soil data, respectively. However, canonical variates, [18, 42]. the remaining components (i.e., IV and V) had no soil The results of correlating the five soil properties with property loaded on them based on the threshold that only the two dimensions of vegetation characteristics are shown component loadings ≥±0.70 are significant, but they in Table 5. The canonical correlations for the first and accounted for 20.5% of the combined variation in the data second canonical functions (or variates) were 0.99 and set. Again, the lack of spread of component loadings across 0.97, respectively, which were significant using the Bartlett’s the five extracted components affected interpretation as well [43] test at 5 percent significance level. However, in the as understanding of the dimension in the soil data. For better literature, the significance of the canonical correlation is distribution of component loadings and interpretation of soil believed to be insufficient in making valid conclusions, as structure, the five components were rotated (Table 4). there are contentious arguments on using the significance Table 4 depicts loadings of rotated components on soil of canonical correlation to make conclusion as well as to properties. It shows that five components with eigenvalue determine the number of canonical variates or functions to loadings of ≥1 and above were extracted, and they accounted retain for the purpose of making inference. The reason is for 79.5% of the total variance in the original data set. that significance test tells us absolutely nothing about the On component 1, two soil properties, exchangeable sodium magnitude of the relationship (i.e., it does not reveal the (0.90) and exchangeable magnesium (0.85), loaded heavily; amount of variance shared by the two sets of variables), this component measured soil cation concentration and as and its statistical significance is heavily influenced by sample such accounted for 2.18 eigenvalue loading and 16.8% total size; as it is possible for the test to be statistically significant variance in soil data set. On component II, two soil properties with large sample sizes (see [41, 44, 45]). On this note, loaded heavily on it; these included organic matter (0.90) and the use of redundancy coefficientwassuggestedasit total nitrogen (0.79); this component represented organic reveals the amount of variance shared by the two sets of accumulationandassuchaccountedfor2.18eigenvalue variables. Redundancy coefficient or index is an asymmetric loading and 16.7% total variance in the linear combination of index that measures how much variance in one set of soil variable. Component III had also two soil properties that variables (say soil properties) is shared by the variability in loaded heavily on it; this component accounted for 16.0% the other set of variables (vegetation characteristics) [46]. total explanation in the soil data. However, the redundancy result in Table 5 shows that the The two soil properties identified on this component redundancy coefficient for first canonical variate for soil were cation exchange capacity (−0.84) and base saturation properties indicated that 19 percent of the variance in (0.81); this component in essence measured the effect of vegetation characteristics on the first canonical variate was CEC content in the soil. Also, on component IV, only accounted for by the variability in soil properties; likewise, exchangeable calcium loaded heavily with coefficient value of the redundancy coefficient for the second canonical variate 0.90. This component measured the effect of calcium content for soil properties indicated that 12 percent of the variance in and accounted for 15.5% of the variance in soil data. In vegetation characteristics was accounted for by the variability addition, two soil properties loaded heavily on component; in soil properties. The redundancy result for vegetation they included sand content (−0.87) and clay content (0.82). characteristics equally showed that 83 and 16 percent of the This component exemplified soil texture and accounted for variance in soil properties on the first and second canonical International Journal of Forestry Research 5

Table 3: Ordinary components matrix of soil propertiesa.

Principal components Soilproperties12345 Exch. K .709 .146 .354 .263 −.026 Base saturation .672 −.417 −.240 .363 −.070 Exch. Na .672 .508 −.213 .368 .220 Organic matter .659 .229 .153 −.231 −.597 Total nitrogen .616 .361 −.029 −.522 −.118 pH −.507 −.025 −.446 .419 .018 Av. P −.327 .767 −.053 −.204 −.159 Exch. Mg .364 .633 −.036 .102 .504 Sand content .256 −.618 −.147 −.532 .306 Silt content .105 .317 −.728 .018 .115 Exch. Ca .427 −.293 .639 .170 .364 Clay content −.469 .310 .615 .360 −.198 CEC −.381 .353 .343 −.358 .473 Eigenvalues 3.34 2.14 1.91 1.46 1.21 % Variance 25.70 18.57 14.70 11.22 9.28 Cumulative explanation 25.70 44.27 58.97 70.19 79.46 aVariables underlined with eigenvectors (coeﬃcients) ≥±0.70 are considered signiﬁcant.

Table 4: Rotated components of soil propertiesa.

Principal components Soilproperties12345 Exch. Na .900 .205 .282 −.012 .079 Exch. Mg .852 .103 −.242 .038 .028 Organic matter .035 .903 .295 .000 .128 Total nitrogen .292 .798 −.082 −.101 −.241 pH −.028 −.639 .131 −.418 .178 CEC .086 −.082 −.843 .124 .015 Base saturation .212 .062 .811 .204 −.269 Av. P .151 .207 −.516 −.507 .419 Exch. Ca .149 .048 .064 .896 −.087 Silt content .456 −.073 .113 −.622 −.203 Exch. K .424 .421 .318 .483 .166 Sand content −.207 .065 .021 .187 −.873 Clay content −.171 −.139 −.283 .237 .823 Eigenvalues 2.18 2.18 2.08 2.01 1.88 % Variance 16.78 16.73 16.01 15.50 14.44 Cumulative explanation 16.78 33.51 49.52 65.02 79.46 aVariables underlined with eigenvectors (coefficients) ≥±0.70 are considered significant. variates were accounted for by the variability in vegetation the first linear combination of vegetation characteristics characteristics. Based on the magnitude of relationships loaded positively and heavily on tree size and negatively shared by the two sets of variables across the two canonical on tree density. This therefore implied that strong positive variates (considering the redundancy coefficient), the first correlation existed between organic matter concentration canonical variate was chosen for further explanation, because and tree size, while the linear association between organic it explained a large proportion of the variation in soil and matter and tree density depicted an inverse relationship. vegetation dimensions. The results in Table 5 also showed In essence, the result of the first canonical function/axis that two canonical variates were extracted, and each is showed that organic matter concentration and tree size were identified by soil and vegetation components with loadings positively and directly related; implying that an increase in exceeding 0.60. The first canonical variate of soil properties organic matter concentration in the soil would result in a loaded positively and heavily on organic matter, while corresponding increase in tree size and vice versa, while tree 6 International Journal of Forestry Research

Table 5: Result of canonical correlation analysis of relationships between soil and vegetationa.

Canonical variates Variables 12 Soil properties Exchangeable sodium 0.58 0.38 Organic matter 0.71 0.15 CEC 0.04 0.20 Exchangeable calcium 0.04 0.16 Sand content −0.30 0.64 Redundancy coeﬃcient 0.19 0.12

Vegetation characteristics Tree size 0.82 0.57 Tree density −0.99 −0.15 Redundancy coefficient 0.83 0.16 Canonical correlation coefficient 0.99 0.97 Squared canonical correlation 0.98 0.94 X2 39.17∗ 28.28∗ Degree of freedom (df) 10 4 P values < 0.05 < 0.05 aVariables underlined with canonical loadings ≥±0.70 are considered significant. ∗ Significant at 5% significance level. density and organic matter concentration showed an inverse Also, the PCA result showed that five fundamental soil relationship, meaning that increase in the density of trees and properties sustained the regrowth and luxuriant character- the continuous addition of nutrient through decomposition istics of the secondary forest; they included exchangeable of large tree residue would reduce the amount of OM in the sodium, organic matter, cation exchange capacity, exchange- soil. able calcium, and sand content. The findings of this study somehow corroborated that of Aweto [18] who identified, based on the threshold of ≥±0.70 organic matter status, 4. Discussion and Conclusion pH, sand proportion, total nitrogen content, clay, silt, bulk Indeed, with the aid of varimax-rotated principal compo- density/porosity, and potassium as paramount soil component analysis, the large, intercorrelated soil and vegetation nents; and tree density, tree size/vegetal cover, nanophanero- properties initially thought to determine soil-vegetation phytes, and species composition as vegetation components interrelationships had been reduced to fewer, uncorrelated influencing soil-vegetation interrelationships. In addition, and more important variables. This is because PCA is a the result of the canonical correlation analysis indicated a fact-finding tool that reduces measurement problems, such pattern of relationship between soil and vegetation. The as bias, and reduces the complexity of correlated data, as only retained canonical variate (the first canonical function) it extracts only variables that have significant contributions revealed a positive interrelationship between organic matter among a set of variables or principal components which concentration and tree size as well as an inverse relationship account for most of the variance in the observed variables between organic matter concentration and tree density. [47, 48]. However, the PCA result for this study showed that This meant that organic matter and tree size were the reciprocal effects of soil and vegetation were influenced interrelated; the importance of tree size in this association is by seven sets of soil-vegetation variables. PCA identified two obvious, as an increase in tree size would lead to an increase basic vegetation parameters that sustained and enriched the in nutrient accumulation in the forest by increasing litter soil for its continuous support to vegetation regenerative production and protecting the soil against accelerated nutri- capacity; these included tree size and tree density. These ent (OM) destruction and subsequent loss through erosion. variables stood out as critical vegetation parameters pro- This implied that tree size helped to improve the content tecting the soil from varying climatic conditions like heavy of organic matter in the soil by the addition of nutrient rainstorm, regulating soil erosion and maintenance of soil in solution form through stem flow as well as through moisture among others. These parameters also helped to the accumulation and decomposition of biomass (plant conserve soil fertility through biomass accumulation and residue). Nevertheless, the inverse relationship between subsequent decay; their combined effects to the soil system organic matter and tree density was expected as simple were significant through variation in the mineral contents of Pearson’s correlation indicated a negative correlation. The biomass or litter and the canopy hydrological effects. negative relationship was attributed to the high rate of OM International Journal of Forestry Research 7 addition through the accumulation of large plant residue. “Soil-vegetation in Hon-e-Soltan region of Qom province, According to Foth [49], high organic matter contents in soils Iran,” Pakistan Journal of Nutrition, vol. 2, pp. 329–334, 2003. are the result of slow decomposition rates rather than high [13] J. A.´ Rogel, F. A. Ariza, and R. O. Silla, “Soil salinity and rates of organic matter addition. The present area enjoys high moisture gradients and plant zonation in Mediterranean salt precipitation and temperature, which facilitates the quick marshes of Southeast Spain,” Wetlands, vol. 20, no. 2, pp. 357– decomposition of biomass, thereby increasing the rates of 372, 2000. organic matter addition. The results of canonical analysis [14] W. Li, ,L. Xiao-Jing, M. A. Khan, and B. Gul, “Relationship therefore indicated that organic matter and tree size were between soil characteristics and halophytic vegetation in positively interrelated, while organic matter and tree density coastal region of north China,” Pakistan Journal of Botany, vol. 40, no. 3, pp. 1081–1090, 2008. were inversely related. It therefore implied that organic [15] S. V. Koptsik, G. N. Koptsik, S. Y. Livantsova, N. A. Berezina, matter and tree size were the major soil and vegetation and M. G. 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Research Article An Ecological Comparison of Floristic Composition in Seasonal Semideciduous Forest in Southeast Brazil: Implications for Conservation

Sergio´ de Faria Lopes, Ivan Schiavini, Ana Paula Oliveira, and Vagner Santiago Vale

Post-Graduation in Ecology and Conservation of Natural Resources, Federal University of Uberlandia, P. O. Box 593, 38400-902 Uberlandia, MG, Brazil

Correspondence should be addressed to Sergio´ de Faria Lopes, [email protected]

Received 30 March 2011; Revised 6 May 2011; Accepted 9 May 2011

Academic Editor: Frank Gilliam

Copyright © 2012 Sergio´ de Faria Lopes et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

We examined floristic patterns of ten seasonal semideciduous forest sites in southeastern Brazil and conducted a central sampling of one hectare for each site, where we took samples and identified all individual living trees with DBH (diameter at breast height, 1.30 m) ≥ 4.8 cm. Arboreal flora totaled 242 species, 163 genera, and 58 families. Fabaceae (38 species) and Myrtaceae (20 species) were families with the largest number of species. Only Copaifera langsdorffii and Hymenaea courbaril occurred at all sites. Multivariate analysis (detrended correspondence analysis and cluster analysis) using two-way indicator species analysis (TWINSPAN) indicated the formation of a group containing seven fragments in which Siparuna guianensis was the indicator species. This analysis revealed that similarities between studied fragments were due mainly to the successional stage of the community.

1. Introduction In southeast Brazil, SSFs are distributed widely in sites with a seasonal rainfall regime, characteristic of the Atlantic The extent of seasonal semideciduous forests (SSFs) in forest and Cerrado domains. In the Atlantic forest domain, Brazil is underestimated because of its naturally fragmented SSF is the predominant typology, and in the Cerrado distribution [1, 2]. Semideciduous seasonal forests occur domain, SSF occurs in enclaves, associated with permanent along the contact zone between Atlantic forest and the or intermittent watercourses [12] and should be regarded diagonal of opened formations [3–5], comprising three as lato sensu Atlantic forest, since it presents a floristic- different scenarios: (1) in northeastern Brazil, the SSFs form structural identity similar to forests of the Atlantic forest amarkedbelt(<50 km) in transition between the coastal domain [12]. rainforest and semiarid formations (Caatinga), but also ff occur in enclaves of montane forests, the altitude marsh Semideciduous seasonal forests su ered the same degra- [6, 7]; (2) the transition between the Cerrado and the coastal dation process as other Brazilian ecosystems. Since the 1970s, Atlantic forest in southeastern Brazil involves an extensive there was an accelerated replacement of natural vegetal occurrence of SSFs to the south, up to the east of Paraguay formations to pasture and use for agriculture, transforming and northeast of Argentina, forming a complex mosaic with extensive areas into an important agriculture area for grain the Cerrado vegetation in the west; (3) in southeastern and fruit production and livestock [13]. This rural reorga- Brazil, a large Araucaria forest confronts subtropical coastal nization was determined by the II National Development Atlantic forest, and the SSF appear to the west and south as Plan (NDP), which collaborated for a modern agricultural a transition to the Chaco forests, and to the southeast with deployment. With increasingly intense adoption of mecha- fields or the southern pampas [8, 9], and beyond disjunct nization and land use, SSFs were drastically reduced, with areas located in Mato Grosso and Tocantins states [10]. only a few remaining in Southeast Brazil. 2 International Journal of Forestry Research

Table 1: Information of ten seasonal semideciduous forest fragments in southeastern Brazil analyzed regarding regional tree ﬂora composition.

Site Counties Area (ha) Latitude (S) Longitude (W) Altitude (m.a.s.l.) 1 Araguari 200 18◦ 29 50 48◦ 23 03 680 2 Ipiac¸u 40 18◦ 43 39 49◦ 56 22 530 3 Monte Carmelo 119 18◦ 44 59 47◦ 30 56 910 4 Uberaba 70 19◦ 40 35 48◦ 02 12 790 5Uberlandiaˆ 17,5 18◦ 40 26 48◦ 24 32 600 6Uberlandiaˆ 30 18◦ 57 03 48◦ 12 22 880 7Uberlandiaˆ 22,3 19◦ 08 39 48◦ 08 46 930 8Uberlandiaˆ 16 19◦ 10 04 48◦ 23 41 800 9Uberlandiaˆ 35 18◦ 55 40 48◦ 03 51 890 10 Uberlandiaˆ 20 18◦ 51 35 48◦ 13 53 890

Table 2: Number of families and species, Shannon diversity index Brazil (Table 1). Studied sites are located in the extreme west (H), and Pielou evenness index (J) from ten sites of seasonal of Minas Gerais state, defined by geographic coordinates 18◦ semideciduous forest in southeastern Brazil. 29–19◦40 Sand47◦30–49◦53 W (Figure 1). Site Families Species H J The studied region is part of a relief global set, named the “Domain of Central Brazil Tropical Plateaus” by Ab’Saber 1 32 78 3,442 0,79 [19], and the “Plateaus and elevated plains of the Parana´ 2 38 98 3,97 0,866 sedimentary basin” by the RADAM project [20]. The soils in 3 27 50 2,924 0,747 the studied areas are predominantly red nonferric latossols 4 34 88 3,275 0,733 (LV) [21]. The predominant climate in the studied region is 5 33 79 3,366 0,768 tropical savanna (Aw Megathermic), characterized, accord- 6 37 86 3,705 0,832 ing to Koppen¨ classification [22], by rainy summers and dry 7 37 73 3,471 0,809 winters. The clearly seasonal climate has two well-defined seasons, where the winter season (April to September) has 8 36 98 3,778 0,824 approximately six months of drought, and the summer 9 38 103 3,868 0,836 season (October to March) is warm and rainy. The average 10 41 88 3,509 0,784 annual temperature lies between 23◦Cand25◦C, with July Total 58 242 4,616 0,840 the month with the lowest average temperature (16◦C). The annual rainfall ranges from 1160 to 1460 mm [23]. Habitat fragmentation transforms the original landscape into different dynamic units that continually modify its structure [14]. Furthermore, the occurrence of disturbance 2.2. Data Collection and Analysis. We conducted a central ff sampling of one hectare from each fragment of SSF, in histories on di erent scales and heterogeneity environmental ff landscapes can influence the species composition of forest an attempt to exclude edge e ect and to obtain uniform fragments [15] and form species richness patterns according samples, avoiding the ecotones of the adjacent formations. to the fragment successional stage [16, 17]. Central sampling gave priority to sites without the influence Therefore, owing to the similar process of disturbance in of watercourses (gallery forest), savanna formations (cerrado southeastern Brazilian forest formations and being located sensu stricto), and others forestry formations (dry seasonal in the same catchment area, it is expected that the remaining forest and cerradao˜ ), since these vegetation contact areas vary SSF will have the same floristic pattern. considerably between fragments and would lead to changes This work aims to analyze the floristic composition and in the species listing of each area. In each central sampling, all individual living trees with a DBH (diameter at breast height, regional richness in the arboreal species of ten fragments of ≥ SSF in southeastern Brazil, in order to answer the following 1.30 m) of 4.8 cm were recorded and identified. questions: (1) what is the alpha diversity in sampled We identified the species using literature, queries in fragments? (2) is there a floristic pattern that represents the herbarium, and specialists. For specific binomial formation, SSF in the studied region? (3) is the beta diversity in SSF we employed the w3 Tropicos database [24]. We deposited similar to patterns found in other tropical forests? fertile material to the Uberlandia Federal University herbaria (Herbarium Uberlandensis: HUFU). Vegetative samples of all 2. Material and Methods species were deposited at the Laboratory of Plant Ecology at the same university. Species were classified into families, 2.1. Floristic, Geographic, and Climate Data. We elaborate a according to the Angiosperm Phylogeny Group III system floristic list from the tree species compilation in ten semide- [25]. ciduous seasonal forest fragments (according to Veloso clas- We compiled all species present in the inventories to sification [18]), distributed in five counties of southeastern undertake the floristic composition analysis. We carried International Journal of Forestry Research 3

Brazil

1 2 5 10 3 6 9 8 7

4 South America

2550 100 200 (Km)

Cerrado domain Atlantic forest domain Figure 1: Location of the ten sites of seasonal semideciduous forest studied in the region of Trianguloˆ Mineiro, Minas Gerais state, southeastern Brazil. out the analysis by preparing a binary database (pres- detrended correspondence analysis (DCA, [30]). In addition, ence/absence), containing the tree species listed in each to define predominantly the indicator and preferred species fragment. We only considered individual trees identified at of the floristic groups, based on frequency and density the species level in this database composition. For the Alpha (species with n ≥ 5 individuals), we used dichotomous diversity evaluation, we used the Shannon diversity index hierarchical division using TWINSPAN (two-way indicator (H) and the Pielou evenness index (J). species analysis, [31]), from an absolute data matrix and its We used the binary database for the measurement of frequency at the 10 sites, with a cut level of 0, 2, 5, and 10. richness analysis, and also for the total number of species These analyses were made by PC-ord for Windows program projection, we used first- and second-order, nonparametric version 4.0 [32]. jackknife estimators, in addition to Chao and Chao 2 estimators, which were capable of projecting total species richness from the samples of species richness [26], with 3. Results 5,000 randomizations. We conducted the analysis using the EstimateS 8.0 program [27]. A total of 242 species, distributed in 165 genera and 58 families, were found in the SSF (Table 2). Families with a higher species richness were Fabaceae (sensu lato)(38 2.3. Multivariate Analyses. We did similarity and sorting species), subdivided into Fabaceae Faboideae (20), Fabaceae analysis utilizing the FITOPAC SHELL 1.6.4 program [28], Mimosoideae (11), Fabaceae Caesalpinioideae (5) and using an absolute density matrix for the species of ten sites, Fabaceae Cercideae (2), Myrtaceae (20 species), Rubiaceae considering only those species with two or more occurrences, (13), Annonaceae (11), Moraceae (10), Lauraceae (9), Meli- since species that had just one occurrence did not contribute aceae (9), and Malvaceae (8). These eight families showed to the floristic site ordering for floristic similarity. For a great contribution to the regional tree diversity, covering categorical floristic data (presence and absence), we analyzed 48.8% of the species. Twenty-six families were represented by the similarity between sites, using the same absolute density only one species. Annonaceae, Apocynaceae, Combretaceae, matrix, transformed into presence/absence. We used the Fabaceae, Mimosoideae, Fabaceae, Faboideae, Lauraceae, Sørensen similarity coefficient [29] and the unweighted Myrtaceae, Meliaceae, Rubiaceae, Salicaceae, Sapindaceae, pair group method with arithmetic mean (UPGMA) for a and Sapotaceae were found at all ten studied sites. dendrogram graphical representation. Most well-represented genera are in Fabaceae Faboideae We applied multivariate analysis to quantitative tree (13), Rubiaceae (13), Myrtaceae (10), Annonaceae (8), and species floristic data (abundance) present in all the compared Euphorbiaceae, Fabaceae Mimosoideae, and Malvaceae, with fragments. For this, we realized a data ordination by using the seven genera each. Approximately 30% of families were 4 International Journal of Forestry Research

300 Area 2

250 Area 4 200

150 Area 1

Number of species 100 Area 5 50

Area 8 1 2 3 4 5 6 7 8 9 10 Area (ha) Area 3 Figure 2: Sampling sufficiency designed by “jackknife” estimator for the ten sites of seasonal semideciduous forest in southeastern Brazil. Dashed line: standard deviation. Area 7 represented by only one genus. The best represented genera Area 9 in terms of species number were Aspidosperma, Machaerium, and Ficus, with seven species each, followed by Ocotea, Cordia, Inga, Trichilia, and Casearia with four species each. Area 10 The total species richness, designed by jackknife estimators 1 and 2 and Chao 1 and 2 estimators, showed a similar pattern (273, 284, 285, and 268 species, resp.). Results Area 6 suggest high regional tree species richness. The 242 species 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 found approximates of richness designed for estimators, Sørensen (UPGMA) demonstrating sampling sufficiency among the ten hectares ffi studied (Figure 2). Figure 3: Dendrogram of similarity (Sørensen coe cient) pro- The Shannon’s diversity index (H) for each site showed duced by cluster analysis (UPGMA connection method) of tree species composition among the ten sites of seasonal semideciduous diversity values between 2.92 and 3.97, with the Pielou’s forest in southeastern Brazil. evenness index (J) between 0.73 and 0.87 (Table 2). However, when we considered the ten sites as a single sample, Shannon’s diversity index (H) was 4.62, and Pielou’s explaining about 85% of the total data variation (Figure 4). evenness index was 0.84. This value can be attributed to The diagram demonstrated a large group formed from seven the sampling covering quite heterogeneous sites, allowing sites. Structural ordination, on the left, contemplates the differences in remnant structure and floristic composition same sites forming Group 1 by grouping analysis. These and, consequently, increasing beta diversity. seven sites indicate the floristic pattern representative of the Only Copaifera langsdorffii and Hymenaea courbaril studied SSF. The second DCA axis was associated strongly were presented with the highest frequency in distribution, with the sampled fragments’ conservation degree. The group reaching 100% of occurrence at the sites (Table 3). However, formed on the center left is characterized by intermediate 75 species occurred in at least five studied fragments, conservation stages, while other sites on the top right are highlighting Cordiera sessilis, Apuleia leiocarpa, Casearia characterized by more advanced conservation stages (Group gossypiosperma, Cheiloclinium cognatum, Ixora brevifolia, 2). On the lower right is the initial stage (site 2). Therefore, Luehea grandiflora, Protium heptaphyllum, Sweetia fruticosa, the three fragments on the right do not form a cohesive and Terminalia glabrescens which occurred in nine sites, and group. In addition, these three sites are those that are can be indicated as characteristic SSF species (Table 3). On more distant from each other. Thus, the structural floristic the other hand, 31.8% (77) of species occurred at only one similarity of the seven areas above may have been established site. on a geographical position basis. The division made by The floristic similarity analysis between sites showed a TWINSPAN corroborated the ordination results by DCA and similar pattern in seven of the ten sites examined (Figure 3). the Sørensen coefficient (Figure 3). Accordingly, there was the formation of a floristically The first division by TWINSPAN separated the seven cohesive group (Group 1), where sites 3, 6, 7, 8, 9, and 10 SSF representative sites in this region from the three other were presented with values exceeding 0.5, indicating sites that fragments, owing to the absence of Siparuna guianensis,a were floristically similar. Also, a second group was formed species regarded as an indicator, which only occurs in the with sites 1 and 4, whereas site 2 was floristically different group formed on the other side of the dichotomy (Figure 5). from all the other sites (Figure 3). In the second division, sites 3 and 8 were separated from the Detrended correspondence analysis, DCA, presented other fragments (Figure 5). The lower levels of division by eigenvalues of 0.61 and 0.25 in the two first ordination axes, TWINSPAN do not reveal new groups that make sense. International Journal of Forestry Research 5

Table 3: Species list sampled in ten sites of seasonal semideciduous forest of southeastern Brazil. NI = number of individuals; RF = frequency in the ten studied fragments; ∗First record for the Triangulo Mineiro region, ∗∗First record for the Minas Gerais state in seasonal semideciduous forests, according [11].

Families/Species NI RF (%) Anacardiaceae Astronium fraxinifolium Schott ex Spreng. 18 40 Astronium nelson-rosae Santin 257 60 Lithraea molleoides (Vell.) Engl. 9 20 Myracrodruon urundeuva Allemao˜ 42 40 Tapirira guianensis Aubl. 37 20 Tapirira obtusa (Benth.) J.D.Mitch. 85 50 Annonaceae Annona cacans Warm. 29 50 Annona montana Macfad.∗ 110 Cardiopetalum calophyllum Schltdl. 6 40 Duguetia lanceolata A. St.-Hil. 222 60 Guatteria australis A. St.-Hil.∗ 28 10 Porcelia macrocarpa (Warm.) R. E. Fr.∗ 110 Rollinia sylvatica (A. St.-Hil.) Mart. 5 30 Unonopsis lindmanii R. E. Fr. 102 40 Xylopia aromatica (Lam.) Mart. 31 60 Xylopia brasiliensis Spreng. 37 40 Xylopia sericea A. St.-Hil. 1 10 Apocynaceae Aspidosperma cuspa (Kunth) S. F. Blake ex Pittier 10 10 Aspidosperma cylindrocarpon Mull.¨ Arg. 25 40 Aspidosperma discolor A. DC.∗ 294 50 Aspidosperma olivaceum Mull.¨ Arg. 8 10 Aspidosperma parvifolium A. DC.∗ 28 50 Aspidosperma polyneuron Mull.¨ Arg. 8 30 Aspidosperma subincanum Mart. ex A. DC. 19 50 Aquifoliaceae Ilex cerasifolia Reissek 3 10 Araliaceae Aralia warmingiana (Marchal) J. Wen 12 30 Dendropanax cuneatus (DC.) Decne. & Planch. 14 20 Scheﬄera morototoni (Aubl.) Maguire et al. 29 80 Arecaceae Acrocomia aculeata (Jacq.) Lodd. ex Mart. 5 10 Asteraceae Piptocarpha macropoda Baker 6 30 Bignoniaceae Handroanthus serratifolia (Vahl) Nicholson 22 70 Jacaranda cuspidifolia Mart. ex A. DC. 2 20 Jacaranda macrantha Cham.∗ 420 Tabebuia impetiginosus (Mart. ex DC.) Mattus 2 20 Tabebuia roseoalba (Ridl.) Sandwith 13 20 Boraginaceae Cordia alliodora (Ruiz & Pav.) Oken∗∗ 110 Cordia sellowiana Cham. 52 50 Cordia superba Cham. 13 10 Cordia trichotoma (Vell.) Arrab. ex Steud. 5 20 6 International Journal of Forestry Research

Table 3: Continued. Families/Species NI RF (%) Burseraceae Protium heptaphyllum (Aubl.) Marchand 282 90 Cannabaceae Celtis iguanaea (Jacq.) Sarg. 30 60 Trema micrantha (L.) Blume 3 20 Cardiopteridaceae Citronella paniculata (Mart.) R.A.Howard 4 10 Caricaceae Jacaratia spinosa (Aubl.) A. DC. 4 20 Celastraceae Cheiloclinium cognatum (Miers.) A. C. Sm. 407 90 Maytenus floribunda Reissek∗ 71 60 Maytenus robusta Reissek 4 10 Maytenus sp. 5 10 Salacia elliptica (Mart. ex Schult.) G. Don 1 10 Chrysobalanaceae Hirtella glandulosa Spreng. 51 40 Hirtella gracilipes (Hook. f.) Prance 72 50 Hirtella racemosa Lam. 11 10 Clusiaceae Calophyllum brasiliense Cambess. 1 10 Garcinia brasiliensis Mart. 64 60 Combretaceae Terminalia argentea (Cambess.) Mart. 2 10 Terminalia glabrescens Mart. 202 90 Terminalia phaeocarpa Eichler 56 60 Cunoniaceae Lamanonia ternata Vell. 12 20 Ebenaceae Diospyros hispida A. DC. 108 50 Elaeocarpaceae Sloanea monosperma Vell. 19 40 Erythroxylaceae Erythroxylum daphnites Mart. 6 10 Erythroxylum deciduum A. St.-Hil. 3 10 Euphorbiaceae Acalypha gracilis (Spreng.) Mull.¨ Arg.∗∗ 720 Alchornea glandulosa Poepp. & Endl. 20 30 Mabea fistulifera Mart. 25 10 Maprounea guianensis Aubl. 32 40 Micrandra elata Mull.¨ Arg. 118 10 Pera glabrata (Schott) Poepp. ex Baill. 12 20 Sapium glandulosum (L.) Morong 5 30 Fabaceae caesalpinoideae Apuleia leiocarpa (Vogel) J. F. Macbr. 114 90 Cassia ferruginea (Schrad.) Schrad. ex DC. 9 30 Copaifera langsdorffii Desf. 155 100 Hymenaea courbaril L. 119 100 Peltophorum dubium (Spreng.) Taub.∗ 220 Sclerolobium paniculatum Benth. 3 10 International Journal of Forestry Research 7

Table 3: Continued. Families/Species NI RF (%) Fabaceae cercideae Bauhinia rufa (Bong.) Steud. 9 40 Bauhinia ungulata L.∗ 20 50 Fabaceae faboideae Andira fraxinifolia Benth. 7 20 Andira ormosioides Benth.∗ 110 Centrolobium tomentosum Guillem. ex Benth. 1 10 Dipteryx alata Vogel 5 20 Lonchocarpus cultratus (Vell.) Az.-Tozzi & H. C. Lima 11 30 Machaerium acutifolium Vogel 7 30 Machaerium brasiliense Vogel 69 70 Machaerium hirtum (Vell.) Stellfeld 25 30 Machaerium nyctitans (Vell.) Benth. 2 10 Machaerium opacum Vogel 1 10 Machaerium stipitatum (DC.) Vogel 20 60 Machaerium villosum Vogel 62 60 Myroxylon peruiferum L. f. 1 10 Ormosia arborea (Vell.) Harms 23 70 Platycyamus regnellii Benth. 50 30 Platypodium elegans Vogel 32 70 Pterodon emarginatus Vogel 2 10 Sweetia fruticosa Spreng. 69 90 Vatairea macrocarpa (Benth.) Ducke 1 10 Zollernia ilicifolia (Brongn.) Vogel 21 40 Fabaceae Mimosoideae Acacia polyphylla DC. 37 70 Albizia niopoides (Spruce ex Benth.) Burkart 23 30 Albizia polycephala (Benth.) Killip ex Record 5 10 Anadenanthera colubrina (Vell.) Brenan 67 20 Calliandra foliolosa Benth. 5 10 Enterolobium contortisiliquum (Vell.) Morong 9 40 Inga laurina (Sw.) Willd. 9 50 Inga marginata Willd. 10 10 Inga sessilis (Vell.) Mart.∗ 44 60 Inga vera Willd. 84 40 Piptadenia gonoacantha (Mart.) J. F. Macbr. 167 50 Lacistemataceae Lacistema aggregatum (P.J.Bergius)Rusby∗∗ 320 Lamiaceae Aegiphila sellowiana Cham. 11 50 Vitex polygama Cham. 6 20 Lauraceae Cryptocarya aschersoniana Mez 212 70 Endlicheria paniculata (Spreng.) J. F. Macbr. 3 10 Nectandra cissiﬂora Nees 44 30 Nectandra megapotamica (Spreng.) Mez 29 50 Nectandra membranacea (Sw.) Griseb.∗ 81 40 Ocotea corymbosa (Meisn.) Mez 136 70 Ocotea minarum (Nees) Mez 4 10 Ocotea pulchella Mart. 8 10 Ocotea spixiana (Nees) Mez 51 40 8 International Journal of Forestry Research

Table 3: Continued. Families/Species NI RF (%) Lecytidaceae Cariniana estrellensis (Raddi) Kuntze 51 70 Lythraceae Lafoensia densiflora Pohl 1 10 Malpiguiaceae Byrsonima laxiflora Griseb. 12 40 Malvaceae Apeiba tibourbou Aubl. 4 30 Ceiba speciosa (A. St.-Hil.) Ravenna 19 40 Eriotheca candolleana (K. Schum.) A. Robyns 28 50 Guazuma ulmifolia Lam. 49 50 Luehea divaricata Mart. 6 20 Luehea grandiflora Mart. & Zucc. 199 90 Pseudobombax tomentosum (Mart. & Zucc.) A. Robyns 3 20 Quararibea turbinata (Sw.) Poir.∗ 510 Melastomataceae Miconia cuspidata Mart. ex Naudin 1 10 Miconia latecrenata (DC.) Naudin 10 30 Miconia minutiflora (Bonpl.) DC.∗ 710 Meliaceae Cabralea canjerana (Vell.) Mart. 7 40 Cedrela fissilis Vell. 16 40 Guarea guidonia (L.) Sleumer 23 50 Guarea kunthiana A. Juss. 19 20 Trichilia catigua A. Juss. 165 80 Trichilia claussenii C. DC. 130 20 Trichilia elegans A. Juss. 55 70 Trichilia pallida Sw. 28 70 Monimiaceae Mollinedia widgrenii A. DC. 6 30 Moraceae Ficus clusiifolia Schott∗ 210 Ficus guaranitica Chodat 6 40 Ficus obtusiuscula (Miq.) Miq. 1 10 Ficus pertusa L. f.∗ 110 Ficus trigona L. f. 3 20 Ficus sp1 110 Ficus sp 2 110 Maclura tinctoria (L.) Steud. 4 30 Pseudolmedia laevigata Trecul´ 5 20 Sorocea bonplandii (Baill.) W. C. Burger et al. 9 40 Myristicaceae Virola sebifera Aubl. 127 80 Myrtaceae Calyptranthes clusiifolia O. Berg 5 30 Calyptranthes widgreniana O. Berg 7 20 Campomanesia guazumifolia (Cambess.) O. Berg 1 10 Campomanesia velutina (Cambess.) O. Berg 93 70 Eugenia florida DC. 190 50 Eugenia involucrata DC. 42 40 International Journal of Forestry Research 9

Table 3: Continued. Families/Species NI RF (%) Eugenia ligustrina (Sw.) Willd. 27 30 Eugenia subterminalis DC.∗ 20 20 Gomidesia lindeniana O. Berg 1 10 Myrcia splendens (Sw.) DC. 11 60 Myrcia tomentosa (Aubl.) DC. 9 40 Myrciaria glanduliﬂora (Kiaersk.) Mattos & D. Legrand∗∗ 106 40 Myrciaria tenella (DC.) O. Berg 2 10 Psidium longipetiolatum D. Legrand∗∗ 210 Psidium rufum DC. 12 40 Psidium sartorianum (O. Berg) Nied. 41 50 Siphoneugena densiﬂora O. Berg 133 60 Syzygium jambos (L.) Aston.∗ 110 Myrtaceae 1 110 Myrtaceae 2 110 Nyctaginaceae Guapira opposita (Vell.) Reitz 28 10 Guapira venosa (Choisy) Lundell 54 40 Neea hermaphrodita S. Moore∗∗ 12 10 Ochnaceae Ouratea castaneifolia (DC.) Engl. 12 50 Olacaceae Heisteria ovata Benth. 113 60 Oleaceae Chionanthus trichotomus (Vell.) P. S. Green 3 10 Opiliaceae Agonandra brasiliensis Miers ex Benth. & Hook. 24 50 Phyllanthaceae Margaritaria nobilis L. f. 27 70 Phyllanthus acuminatus Vahl 2 10 Piperaceae Piper amalago L. 4 10 Piper arboreum Aubl. 2 10 Polygonaceae Coccoloba mollis Casar. 9 40 Primulaceae Ardisia ambigua Mez 25 30 Myrsine coriacea (Sw.) Roem. & Schult. 2 10 Myrsine leuconeura Mart. 2 20 Myrsine umbellata Mart. 15 20 Proteaceae Roupala brasiliensis Klotzsch 31 80 Rhaminaceae Rhamnidium elaeocarpum Reissek 34 50 Rubiaceae Amaioua guianensis Aubl. 35 40 Chomelia pohliana Mull. Arg. 14 40 Cordiera sessilis (Vell.) Kuntze 404 90 Coussarea hydrangeifolia (Benth.) Mull.¨ Arg. 32 60 Coutarea hexandra (Jacq.) K. Schum. 21 70 Faramea hyacinthina Mart. 43 50 Genipa americana L. 1 10 10 International Journal of Forestry Research

Table 3: Continued. Families/Species NI RF (%) Guettarda viburnoides Cham. & Schltdl. 29 50 Ixora brevifolia Benth. 131 90 Machaonia brasiliensis (Hoffmanss. ex Humb.) Cham. & Schltdl.∗ 210 Rudgea viburnoides (Cham.) Benth. 11 50 Simira sampaioana (Standl.) Steyerm.∗ 47 70 Tocoyena formosa (Cham. & Schltdl.) K.Schum. 1 10 Rutaceae Galipea jasminiflora (A. St.-Hil.) Engl.∗ 142 10 Metrodorea nigra A. St.-Hil. 10 20 Metrodorea stipularis Mart. 6 10 Pilocarpus spicatus A. St.-Hil.∗ 110 Zanthoxylum riedelianum Engl. 11 30 Salicaceae Casearia gossypiosperma Briq. 172 90 Casearia grandiflora Cambess. 218 40 Casearia rupestris Eichler 8 10 Casearia sylvestris Sw. 65 60 Prockia crucis P. Browne ex L. 5 20 Xylosma prockia (Turcz.) Turcz.∗ 110 Sapindaceae Allophylus edulis (A. St.-Hil., Cambess. & A.Juss.) Radlk. 1 10 Allophylus racemosus Sw. 8 30 Cupania vernalis Cambess. 78 80 Dilodendron bipinnatum Radlk. 10 20 Magonia pubescens A. St.-Hil. 2 10 Matayba elaeagnoides Radlk.∗∗ 39 40 Matayba guianensis Aubl. 105 70 Sapotaceae Chrysophyllum gonocarpum (Mart. & Eichler) Engl. 69 40 Chrysophyllum marginatum (Hook. & Arn.) Radlk. 161 20 Micropholis venulosa (Mart. & Eichler) Pierre 61 30 Pouteria gardneri (Mart. & Miq.) Baehni 42 70 Pouteria torta (Mart.) Radlk. 128 80 Siparunaceae Siparuna guianensis Aubl. 407 70 Styracaceae Styrax camporum Pohl 35 50 Symplocaceae Symplocos pubescens Klotzsch ex Benth.∗ 810 Urticaceae Cecropia pachystachya Trecul´ 11 30 Urera baccifera (L.) Gaudich. ex Wedd. 10 10 Verbenaceae Aloysia virgata (Ruiz & Pav.) A. Juss. 3 20 Vochysiacaeae Callisthene major Mart. 62 40 Qualea dichotoma (Mart.) Warm. 5 30 Qualea jundiahy Warm. ∗ 39 60 Vochysia magnifica Warm. 114 30 Vochysia tucanorum Mart. 5 10 International Journal of Forestry Research 11

Area 4 serious difficulties for their preservation. However, some 300 species, such as Galipea jasminiflora and Micrandra elata, Area 10 250 Area 1 occurred in only one fragment, but with a high density. This Area 9 0.21) Area 6 observation acquires relevance in terms of plant population = 200 Area 7 Area 3 conservation. The conservation of the remaining forest, with Area 8 ff 150 di erent structures and consequent higher beta diversity, can Area 5 be a relevant parameter to the decision for choosing priority 100 areas for conservation in SSF [44]. Diversity values are among those frequently found for Axis 2 (eigenvalue 50 SSF and Atlantic rainforest, which generally vary between Area 2 3.2 and 4.2 [45]. However, they are lower than those found 0 60 120 180 240 300 360 in other tropical forests [46]. The lower values for evenness Axis 1 (eigenvalue = 0.63) found in fragments indicate a relatively high concentration Figure 4: Ordination diagram in first two axes of detrended cor- in density of a small number of species, which dominate respondence analysis (DCA) of ten sites of seasonal semideciduous the tree community. The predominance of a few species, forest in southeastern Brazil. Symbols correspond to floristic groups in number or biomass in a community (also known as 1(), 2 (•), and 3 (o). ecological dominance), is not uncommon in tropical forests [47]. 4. Discussion A variation in similarity values occurs as a result of different conditions in the use and historic occupation of Studies conducted in areas of SSF in Brazil presented each fragment and acts as an important force capable of Fabaceae, Myrtaceae, Rubiaceae, Annonaceae, Lauraceae, modifying plant communities through spatial and temporal and Meliaceae as the families with greater species richness heterogeneity, thus determining the community composi- [33–36]. In Sao˜ Paulo state SSF, as well as the greater tion and structure [16, 48]. The separation of site 2 from richness of families found in this study, there is also an other areas, along with Group 2, can be related to different emphasis on Melastomataceae and Solanaceae [37, 38]. In stages of forest succession. Site 2 presents in an initial northeastern Brazil, the most prominent families in terms successional stage owing to an intense human processes. of species number in SSF sites are Fabaceae Mimosoideae, This fragment, although situated in the countryside, reveals Euphorbiaceae, Fabaceae Faboideae, Myrtaceae, and Rubi- recent historical disturbance as a result of selective logging aceae [39]. The pattern in families and genera richness and the presence of cattle, and the surrounding matrix is found here corroborates that described by Oliveira Filho and formed entirely by agricultural sites [49]. Selective logging Fontes [40] for SSF domains in southeastern Brazil, with enables the creation of gaps within the fragment and the significant tree species richness in Fabaceae (all subfamilies), establishment of pioneering species. As Whitmore states Lauraceae, Myrtaceae, Moraceae, and Rubiaceae and genres [47, 50], pioneer trees and shrubs need high light and Aspidosperma, Ficus, Machaerium, and Ocotea. temperature intensities for seed germination and seedling Copaifera langsdorffii and H. courbaril are wide-ranging establishment and growth. Group 2 (sites 1 and 4) is adaptive species, occurring in various forest formations of formed by the more well-preserved fragments among those the Cerrado region, as well as in other geographical provinces studied and is characterized by the presence and dominance in Brazil and are, therefore, considered to be habitat of individuals with a large basal area. In fragment 4, for generalists [41]. Apuleia leiocarpa and Luehea grandiflora example, Micrandra elata was sampled with a density of are also among the most frequent species of SSF, according 118 ind·ha−1 and a basal area of 24.51 m2·ha−1 [51], whereas to a study presented by Ferreira Junior´ et al. [42], which in fragment 1, Psidium sartorianum was the species with the brought together 15 studies in southeastern Brazil. The tree highest dominance [52]. species composition studied in SSF showed a relationship Although the floristic similarity between the fragments with lowland seasonal forests of Brazil’s eastern Minas can be related to the geographical proximity, as already Gerais state, based on the indicator species analysis made by reported by MaCdonald et al. [53], we verified that similarity Oliveira-Filho and Fontes [40]. Of the 57 species identified relations between studied sites are established mainly as by these authors as eastern Minas Gerais region indicators, a result of the community successional stage. This same 36 (63.1%) were recorded in the sample sites presented here. pattern was also found by Durigan et al. [17]; that is, within This data corroborates the idea that interior SSF fragments the same vegetal formation, communities at similar stages of may have a floristic connection with the Atlantic forest that is the successional process tend to have similar flora. able to expand its distribution in sites with strong seasonality This analysis showed high beta diversity of the studied by means of gallery forests [43]. SSF arboreal flora, mainly as a result of the coverage of The presence of unique species to each location high- heterogeneous areas in relation to fragments at a successional lights the heterogeneity between samples (sites), reflected stage. The largest SSF sampling in the Cerrado domain led to in increased beta diversity. We should point out that the the registration and distribution of several plant populations, limited number of individuals in some populations may which had previously only been described for the Atlantic impair the biological conservation of many species, causing forests domain, demonstrating not only a lack of studies 12 International Journal of Forestry Research

10 areas

Eigenvalue = 0.39

Areas 3, 5, 6, 7, 8, 9 and 10 Indicator species Area 1 Area 4 Area 2

Siparuna guianensis Eigenvalue = 0.38

Areas, 5, 6, 7, 9 and 10 Areas 3 and 8 Preferential species Preferential species

Duguetia lanceolata Anadenanthera colubrina Aegiphila sellowiana Chrysophyllum marginatum Maprounea guianensis Dendropanax cuneatus Ocotea spixiana Lithraea molleiodes Aspidosperma discolor Myrsine umbellata Astronium nelsonrosae Tabebuia roseoalba Myrciaria gladuliflora Tapirira guianensis

Figure 5: Classiﬁcation by TWINSPAN of ten sites of seasonal semideciduous forest in southeastern Brazil.

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