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Threshold change in forest understory vegetation as a result of selective fuelwood extraction in Nairobi, Kenya
Forest Ecology and Management 262 (2011) 962–969
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Threshold change in forest understory vegetation as a result of selective fuelwood extraction in Nairobi, Kenya Takuya Furukawa a,⇑, Kazue Fujiwara b, Samuel K. Kiboi c, Patrick B. Chalo Mutiso c a
Graduate School of Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogayaku, Yokohama 240-8501, Japan Graduate School in Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama 236-0027, Japan c School of Biological Sciences, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya b
a r t i c l e
i n f o
Article history: Received 5 January 2011 Received in revised form 16 May 2011 Accepted 18 May 2011 Available online 12 June 2011 Keywords: Ecological threshold Fuelwood use Species preference Species diversity pattern Invasive alien plant Understory vegetation
a b s t r a c t We examined whether heavy fuelwood collection can cause threshold change in understory forest community and evaluated how selective wood extraction might lead to delayed forest recovery in an urban forest of Nairobi, Kenya. Piecewise regression which represents strongest support for threshold change provided the best fit for the relationships between understory floristic composition (i.e. DCA axis 1) and human disturbance gradients (i.e. canopy cover, and distance from the slum), where threshold changes were detected at c.a. 350 m from the slum and c.a. 30% canopy cover. Only one tree species significantly indicated communities beyond the threshold while an aggressive invasive alien plant (IAP) Lantana camara was strongly represented. Total species diversity along the two human disturbance gradients peaked before the threshold was reached, suggesting that decline in species diversity along the prevailing disturbance gradient might be able to forecast threshold change. Tree species richness in the understory rapidly declined as the threshold was surpassed while other growth forms (i.e. shrubs, herbs and climbers) were relatively unaffected. The effect of selective tree cutting was indirectly impacting the forest understory as species richness pattern of preferred and non-preferred species paralleled that of trees and shrubs, respectively. Thickets of L. camara can negatively affect indigenous flora and its establishment was favored under selective fuelwood extraction removing certain tree species while leaving the IAP untouched. Shading can readily eliminate the IAP, but weak tree regeneration beyond the threshold suggested forest recovery might be delayed for longer than expected because of the interaction between selective fuelwood use and the IAP. Ó 2011 Elsevier B.V. All rights reserved.
1. Introduction Theoretical work and recent empirical evidence in a variety of ecosystems have demonstrated that ecosystems can exhibit abrupt and nonlinear responses to human disturbance (Scheffer et al., 2001; Folke et al., 2004). The level of disturbance at which ecological conditions change abruptly and nonlinearly is called ‘ecological threshold’ (Groffman et al., 2006). Substantial loss of ecosystem function and biodiversity can occur when thresholds are surpassed, while change in the opposite direction can lead to Abbreviations: IAP, invasive alien plant; DCA, detrended correspondence analysis; AIC, Akaike’s information criterion; INSPAN, indicator species analysis; RF, relative frequency; GAM, generalized additive model. ⇑ Corresponding author. Present address: United Nations University Institute of Advanced Studies, 6F International Organizations Center, Pacifico-Yokohama, 1-1-1 Minato-Mirai, Nishiku, Yokohama 220-8502, Japan. Tel.: +81 45 221 2320; fax: +81 45 221 2302. E-mail addresses: [email protected] (T. Furukawa), [email protected] (K. Fujiwara), [email protected] (S.K. Kiboi), [email protected] (Patrick B. Chalo Mutiso). 0378-1127/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2011.05.030
recovery if threshold changes are reversible (Bestelmeyer, 2006). Such notion is changing the conceptual underpinning of habitat management (Suding and Hobbs, 2009) and applications to forest management are increasing (Craig and Macdonald, 2009; Gooden et al., 2009; Digiovinazzo et al., 2010). The notion of ecological thresholds applied to vegetation management can be classified into two concepts: one that investigates the pattern of change (i.e. whether an abrupt change occurs and at what level of disturbance), and the other that investigates both the pattern and irreversibility of change (Bestelmeyer, 2006). The former is used as ‘preventive threshold’ that represents the point at which disturbance should be controlled to prevent drastic changes in ecological conditions. It is defined using pattern threshold based on community structure (e.g. community composition, species diversity) under a prevailing disturbance regime (Bestelmeyer, 2006). It is readily translated to management guidelines (Radford et al., 2005; Suding and Hobbs, 2009) as it does not require extensive examination of process rates (e.g. erosion rate, dispersal/colonization rate) and degree of degradation (e.g. nutrient availability,
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habitat occupancy) that determines the irreversibility of change (Bestelmeyer, 2006). However, efforts to evaluate likelihoods of irreversibility by inferring the mechanism of threshold change are necessary for successful forest management even when dealing with preventive threshold (Suding et al., 2004; Suding and Hobbs, 2009). Identification of pattern threshold has often been based on changes in floristic composition, plant growth form, and species diversity along the prevailing disturbance gradient (Sasaki et al., 2008; Craig and Macdonald, 2009; Digiovinazzo et al., 2010). Additionally, in some forest ecosystems, invasive alien plants (IAPs) have played a deterministic role in threshold response (Gooden et al., 2009). Severity of disturbance determines whether communities surpass the threshold, but whether they experience delayed or altered forest succession is determined by feedback mechanisms (e.g. novel community structure, trophic interactions, landscape connectivity and seed source) that maintain the alternative stable state (Suding et al., 2004; Suding and Hobbs, 2009). In fact, delayed forest recovery has been reported in some East African forests where creation of large gaps (i.e. clearcuts) led to arrested succession due to changes in feedback mechanisms, such as altered understory species composition, limited seed dispersal and trampling by elephants (Chapman and Chapman, 1997; Bonnell et al., 2011). In Africa, both rural and urban poor households rely heavily on local and regional forests as source of energy (i.e. fuelwood and charcoal) (Abbot and Homewood, 1999; Brouwer and Falcão, 2004). Even without changes in forest area (Foley et al., 2005), intensive extraction of fuelwood can have significant effects on forest structure and biodiversity (Christensen and Heilmann-Clausen, 2009). Selective species extraction has been widely reported in fuelwood collection activities (Tabuti et al., 2003; Ramos et al., 2008), resulting in heavier impacts imposed on certain woody species (Pote et al., 2006). Forest management in developing countries thus needs to find a way to combine local sustainable use of natural resources and nature conservation (Millennium Ecosystem Assessment, 2005) by grasping how disturbance peculiar to human use can affect forest communities in particular ways. In this study, we examined whether heavy fuelwood collection can cause threshold change in understory forest communities and investigated whether selective wood extraction can create feedback mechanisms that might lead to delayed forest recovery in an urban forest of Nairobi, Kenya. We focused our survey on the forest understory, which is not directly affected by fuelwood extraction but plays an important role in forest recovery that influences forest dynamics, development, and productivity in both the short and long term (Burke et al., 2008). Pattern threshold was examined for change in understory floristic composition along the tree cutting gradient, and was compared with species diversity patterns of all species and of different growth forms. We evaluated the effect of selective wood extraction by comparing species diversity patterns of preferred and non-preferred species to the identified threshold and other species diversity patterns. Information on species preference was based on another study investigating wood use preference of the fuelwood collectors in the same study site (Furukawa et al., in revision). Through the study, we discussed how selective wood use might underlie threshold change in forest understory and possibly cause delayed tree regeneration.
2. Materials and methods 2.1. Study area Research was conducted in Ngong Road Forest Reserve (1°190 S, 36°450 E; 1750–1830 m a.s.l.), Nairobi, Kenya. The reserve is located
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about 7 km southwest of the city center and borders to the east onto Kibera, the largest slum in Africa (Fig. 1). It consists of approximately 600 ha of semi-deciduous indigenous forest, 500 ha of plantation (mostly Eucalyptus spp.), along with some small patches of grassland. The survey was conducted in the largest contiguous indigenous forest patch of approximately 500 ha within the reserve (Fig. 1). The soils are mainly red friable clays (latosolic soils) and dark-grayish brown mottled clays. The mean annual temperature is 17.6 °C and the mean annual precipitation is approximately 1000 mm, most of which occurring from April to May and November to December. Although the reserve had experienced selective logging in the past, it still harbors one of the few and largest remnants of the ‘plateau forest’ (or Brachylaena–Croton forest) that once covered a vast area of the lower dry slopes of the central highlands (Lind and Morrison, 1974). The indigenous forest is dominated by Brachylaena huillensis, and Croton megalocarpus in the canopy layer of 25 m or more in height, Teclea simplicifolia, Teclea trichocarpa and others in the subcanopy (Kigomo et al., 1990; Hayashi et al., 2006). Forest understory found under closed canopy is relatively sparse and graminoids and woody/semi-woody herbs are common (Hayashi et al., 2006). Study on species composition has indicated low similarity with other forests in the country (Kigomo et al., 1990), suggesting the importance of the reserve in conserving regional biodiversity. Many inhabitants of the slum both with and without permits visited the reserve daily to collect fuelwood. Forest rangers have advised fuelwood collectors to collect dead and fallen wood only, but the large number of fresh stumps indicated active harvesting of live trees. Comparison of stumps and remaining trees indicated strong species preference for fuelwood use, and tree cutting increased towards the slum though minor cutting was found throughout the reserve (Furukawa et al., in revision). Despite daily patrols, declining national woodfuel supply and rising fuel demand in the city (Kituyi et al., 2001) are probably making fuelwood collection an easy means of earning cash and meeting daily consumption needs of populations living adjacent to the reserve. Occasional cattle grazing has been permitted inside the forest during severe droughts, but the main anthropogenic disturbance for the past several decades is focused on tree clearing and fuelwood collection.
2.2. Data collection In order to effectively capture understory vegetation change along the human disturbance gradient, we limited our survey along paths extending from the slum towards the forest interior (from east to west). Four paths originated from the slum which diverged and converged complexly and eventually disappeared in the forest (Fig. 1). In August 2008 and April 2009, understory vegetation and canopy cover were sampled along these paths using transects placed along the human disturbance gradient. Since we anticipated that vegetation change is more rapid near the gradient source (i.e. the slum), we increased sampling resolution toward the slum. Two transects were placed on each path running through the shrubbery outside the reserve between the slum and the forest fence, while in the forest, transects were placed every 100 m up to 1 km from the fence and every 200 m thereafter (Fig. 1). Transect belts of 20 m by 2 m (40 m2) were placed parallel to the paths, 0.5–1 m from the path edge. Transects occurring in eucalyptus plantations and open grasslands with shallow soil were omitted from the analysis. In each transect, vegetation was sampled by recording the occurrence of plants lower than 3 m in height. We took three hemispherical digital photos (at the center and ends of each belt) at 2 m from the ground and calculated percent canopy cover, which was later averaged to represent each transect. In the
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Fig. 1. Map of study area, showing Ngong Road Forest Reserve and Kibera slum. The survey was conducted in the largest contiguous indigenous forest patch (dark gray) surrounded by the fence, and the location of the belt transects are indicated by the dots.
end, we obtained 116 transects (4640 m2) of understory vegetation and canopy cover data. 2.3. Data analysis In the following analyses, we used both distance from the slum and understory light environment to represent the human disturbance (i.e. tree cutting) gradient. Because distance from human settlements often reflects the intensity of fuelwood collection (i.e. the closer the stronger) and often strongly correlates to vegetation change and diversity patterns of forests under fuelwood use (Pote et al., 2006; Christensen and Heilmann-Clausen, 2009), we considered distance from the slum to represent fuelwood collection intensity. In contrast, canopy cover was considered as a direct indicator of the understory light environment reflecting both anthropogenic and any natural disturbances. In order to extract the change in understory vegetation along the human disturbance gradient, understory species composition data (occurrence of each species) for each transect were simplified using detrended correspondence analysis (DCA). The first axis of the ordination explained more than 60% of total variance far exceeding that of the subsequent axes, and showed significant correlations with the two disturbance gradients (Supplementary Appendix A) due to our survey design in which we attempted to eliminate extraneous influences other than tree cutting (i.e. eucalyptus plantation and grassland). Thus, we used the first axis of DCA ordination to represent understory vegetation change along the human disturbance gradient. As a preliminary search for threshold change in understory floristic composition, we fitted a locally weighted, nonparametric regression (LOESS) model (Cleveland, 1979) to each scatterplot of understory floristic composition (i.e. the score of DCA axis 1) plotted against the two disturbance gradients (i.e. distance and canopy cover) and found nonlinear relationships. Similarly, we examined the scatterplot between the two disturbance gradients in order to investigate the relationship between the fuelwood collection gradient and the understory light environment, where again the
LOESS fitting suggested a non-linear relationship. We did not use a multiple regression model to investigate the relationships among the three variables as the two human disturbance gradients (i.e. distance and canopy cover) had a strong correlation with each other (rp = 0.769, P < 0.0001) leading to multicollinearity (Silvey, 1969). Instead, we simply compared their relationships in detail one at a time. Evidence for threshold change was then examined by two linear regression models (i.e. explanatory variable untransformed and log10 transformed) and three non-linear regression models: inverse curve [yi = b0 + (b1/xi)], exponential curve [yi = b0 + b1 exp(b2 xi)], and piecewise regression [yi = b0 + b1 xi (for xi 6 a1), yi = b0 + b1 xi + b2(xi a1) (for a1 6 xi 6 a2), and yi = b0 + b1 xi + b2(xi a1) + b3(xi a2) (for xi > a2); sensu Toms and Lesperance (2003)]. Here, yi is the value for the ith observation (e.g. canopy openness or the score of DCA axis 1), and xi is the corresponding value for the explanatory variable (e.g. distance from slum or canopy cover). In piecewise regression, a1 and a2 are the estimated breakpoints, and the slopes of the lines are b1, b1 + b2, and b1 + b2 + b3, so b2 and b3 can be interpreted as the difference from the preceding slopes. When we assumed one breakpoint, the third equation was not used. When understory light environment was used as the response variable, canopy openness (i.e. 100 – canopy cover) was used so that the pattern shown in the diagram parallels the relationship between DCA axis 1 and distance. Model selection (i.e. determining the number and value of breakpoints) was based on Akaike’s information criterion (AIC) and AIC differences (i.e. DAIC). AIC differences of between 0 and 2 indicate substantial support for a model, whereas differences greater than 4 indicate low or no support for the model (Burnham and Anderson, 2002). Sound evidence for a threshold response (i.e. a discontinuity) requires that one of the piecewise regression models provides the best fit to the data, followed by the exponential, inverse, log10 -transformed, and untransformed linear models (Radford et al., 2005). Subsequently, in order to interpret species turnover along the disturbance gradients, we examined which species were representing different vegetation groups separated by the detected
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shrubs (48), trees (46), and climbers (30). The most frequent were Setaria plicalitis (grass), Clausena anisata (shrub), Meineckia phyllanthoides ssp. capillariformis (woody herb), Vernonia holstii (semiwoody herb), and Schoenoxiphium lehmannii (sedge) found in more than 75% of the transects (Supplementary Appendix C).
breakpoints. Since our dataset consisted of only binary data (presence/absence) and not abundance, we simplified indicator species analysis (INSPAN; Dufrêne and Legendre, 1997) to calculate and test the significance of relative frequency (RF) values only. The original INSPAN combines relative frequency and relative abundance to obtain indicator value of each species (Dufrêne and Legendre, 1997), but a simplified analysis using binary data can give similar results (Bakker, 2008). Thus, we calculated RF values (ranging from 0 to 1) for each species, and tested their significance using a Monte Carlo technique (McCune and Mefford, 1999) as in the original INSPAN. We used generalized additive models (GAMs), a nonparametric smoothing technique suited for more complex non-linear relationships, to examine the relationship between understory diversity (species richness) of different growth forms and of preference and disturbance (i.e. distance and canopy cover). Growth form classification followed that of Raunkiaer (1934) where we used four groups: trees (H P 8 m), shrubs (2 6 H < 8 m), herbs (H < 2 m), and climbers (both herbaceous and woody vines). We used the most and least preferred 25 woody species estimated in a different study in the same survey site (Furukawa et al., in revision) as preferred and non-preferred species, respectively (Supplementary Appendix B). Here, species preference reflected the probability of a stem of a given species being cut when the effect of stem size, accessibility, and patrolling were statistically excluded (Furukawa et al., in revision). In GAMs, response curves with increased complexity and nonlinearity have larger values of effective degrees of freedom (edf) (Wood, 2006), and we considered models with edf P 2 as evidence of nonlinear relationship. We assumed a Poisson error distribution for all GAMs since it is best suited for modeling species richness (Quinn and Keough, 2002). For nonlinear curves (edf P 2), we visually searched for peaks and declines and compared them with the breakpoints detected in the piecewise regression models. Statistical analyses were conducted using PC-ORD (MjM Software, Oregon) and R 2.10.1 (R Development Core Team, 2009).
3.1. Threshold analysis Based on the analysis of AIC, the relationship between distance from the slum and canopy openness (i.e. 100 – canopy cover) was best represented by piecewise regression model with two breakpoints, first at 304.7 m and the second at 1711.0 m (Table 1a; Fig. 2a). The slope was quite steep up until the second breakpoint, and canopy openness did not show significant change thereafter (Fig. 2a). Similarly, the relationship between DCA axis 1 and distance from the slum was best predicted by piecewise regression with two breakpoints (355.5 m and 2166.0 m) over other linear and nonlinear models (Table 1b; Fig. 2b). Vegetation change was quite steep up to the first breakpoint, changed gently until the second breakpoint, and did not show significant change thereafter (Table 1b; Fig. 2b). Comparison of AIC values suggested one breakpoint for the relationship between the DCA axis 1 and canopy cover (Table 1c). Floristic composition changed rapidly up to 29.4% of canopy cover and the change became gradual as the canopy closed (Table 1c; Fig. 2c). Canopy cover was better in explaining change in understory vegetation (i.e. DCA axis 1) compared to distance from the slum as indicated by the generally lower AIC values (Table 1b and c). Our simplified INSPAN based on groups separated by the detected breakpoints showed distinct difference in floristic composition along the disturbance gradients (Fig. 3; Supplementary Appendix C). Transects closest to the slum (<355.5 m) were indicated by many shrubs and herbs (Fig. 3) and among the most frequent was an aggressive invasive alien shrub Lantana camara (Supplementary Appendix C). Herbs such as Cynodon dactylon and Commelina benghalensis were also frequent, and only one tree species Acokanthera schimperi had a significant RF value (Supplementary Appendix C). In the mid section (355.5 m to 2166 m), Uvaria scheffleri a shrub commonly found in forest gaps was most frequent, and many tree species such as B. huillensis, Scutia myrtina, and T. trichocarpa also had significant RF values (Fig. 3;
3. Results We recorded a total of 183 plant species in the 116 belt transects. Herbs were the top consisting of 59 species, followed by
Table 1 AIC values of linear regression models (i.e. explanatory variable untransformed and log10 transformed) and non-linear regression models (inverse curve, exponential curve, and piecewise regression) on the relationship between (a) distance from the slum and canopy openness, (b) distance and DCA axis 1, and (c) canopy cover (i.e. 100 – canopy openness%) and DCA axis 1. Up to two breakpoints were tested in piecewise regressions for relationships (a) and (b). The best fit models (DAIC = 0) are indicated by the lowest AIC value (bold) and are shown in Fig. 2. Model
No. of parameters
(a) Canopy openness vs. Distance Untransformed linear regression Log10 transformed linear regression Inverse curve Exponential curve Piecewise regression (single) Piecewise regression (double)
2 2 2 3 4 6
(b) DCA1 vs. Distance Untransformed linear regression Log10 transformed linear regression Inverse curve Exponential curve Piecewise regression (single) Piecewise regression (double) (c) DCA1 vs. Canopy cover Untransformed linear regression Log10 transformed linear regression Inverse curve Exponential curve Piecewise regression (single)
AIC
DAIC
Deviance
Breakpoints
948.5 887.1 969.7 879.6 879.6 870.8
77.7 16.3 99.0 8.8 8.8 0
22933.1 13511.9 27551.5 12448.5 12238.5 10956.3
NA NA NA NA 1527.0 m 304.7 and 1711.0 m
2 2 2 3 4 6
1236.3 1161.8 1188.6 1144.7 1141.1 1134.1
102.2 27.7 54.5 10.6 7.0 0
274249.8 144227.6 181701.7 122350.0 116656.9 106058.4
NA NA NA NA 389.2 m 355.5 and 2166.0 m
2 2 2 3 4
1141.8 1099.3 1157.5 1097.2 1080.4
61.4 19.0 77.2 16.8 0
121398.0 84213.9 139080.8 81249.1 69074.4
NA NA NA NA 29.4%
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Fig. 2. Piecewise regression models on the relationships between (a) distance from the slum and canopy openness, (b) distance and DCA axis 1, and (c) canopy cover (i.e. 100 – canopy openness%) and DCA axis 1. The best fit models were selected based on the analysis of AIC (Table 1).
Fig. 3. Number of significant indicator species by growth forms in each group differentiated by the detected breakpoints identified in the corresponding piecewise regression in Table 1 and Fig. 2.
Supplementary Appendix C). In the farthest section (>2166 m), the most frequent indicators were herbs common in relatively undisturbed forest understory (e.g. S. lehmannii, Acalypha racemosa, and M. phyllanthoides ssp. capillariformis), along with some trees such as Elaeodendron buchananii and Drypetes gerrardii (Fig. 3; Supplementary Appendix C). The simplified INSPAN based on the canopy cover model (groups separated at 29.4% canopy cover) produced similar results where the open group (629.4%) resembled the group closest to the slum, and the other group (>29.4%) resembled the other two distance groups (the mid and farthest sections) combined (Fig. 3; Supplementary Appendix C). 3.2. Species diversity analysis The generalized additive models (GAMs) on the relationships between species richness and distance from the slum showed variable relationships among different growth forms (Fig. 4a and c). Total species richness changed nonlinearly along the spatial gradient (v2 = 44.62, edf = 3.3, P < 0.0001) peaking around 1400 m from the slum (Fig. 4a). Tree species richness also changed nonlinearly (v2 = 50.98, edf = 6.6, P < 0.0001) with a more complex curve where drastic decline was predicted around 700 m from the slum,
and relatively high values were predicted between the two breakpoints (Fig. 4c). The responses of other growth forms (shrubs, herbs and climbers) were rather linear (v2 = 17.39, edf = 1.8, P < 0.001; v2 = 7.47, edf = 1.0, P < 0.01; and v2 = 6.815, edf = 1.98, P = 0.052, respectively), and they generally increased as disturbance intensified (Fig. 4c). In the GAMs on the relationships between species richness and canopy cover, not only total and tree species richness (v2 = 31.54, edf = 2.9, P < 0.0001 and v2 = 62.85, edf = 3.4, P < 0.0001, respectively), but also shrubs and climbers (v2 = 19.03, edf = 2.6, P < 0.001 and v2 = 8.686, edf = 2.6, P < 0.05, respectively) responded nonlinearly. Species richness peaked around 50% for total and tree species richness and around 40% for shrubs and climbers, and all declined as disturbance intensified where trees decreased most abruptly (Fig. 4b and d). The response of herbs was linear (v2 = 8.165, edf = 1.1, P < 0.01) and species richness increased as the canopy opened (Fig. 4d). The GAMs on the relationship between species richness of preferred and non-preferred woody species and each disturbance gradient suggested that all relationships were non-linear. Preferred species richness peaked around 1700 m from the slum and decreased rapidly near the disturbance source (v2 = 30.52, edf = 4.8,
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Fig. 4. Generalized additive models (GAMs) showing the relationships between species richness and distance from the slum on the left hand side (a, c and e) and canopy cover on the right (b, d, and f). The top two (a and b) show the patterns of total species richness, where the solid lines represent the estimated models and the broken lines the upper and lower 95% confidence intervals. The middle (c and d) show the patterns of different growth forms: tree (T), shrubs (S), climbers (C) and herbs (H). The bottom two (e and f) show the patterns of preferred (bold solid) and non-preferred (bold broken) woody species with their upper and lower 95% confidence intervals (broken lines). The gray vertical lines represent the breakpoints detected in the corresponding piecewise regression models (Table 1; Fig. 2).
P < 0.001; Fig. 4e) whereas it did not show a distinct peak and decreased rapidly as the canopy opened (v2 = 34.67, edf = 3.0,
P < 0.001; Fig. 4f). Non-preferred species richness changed rather linearly along the distance gradient increasing towards the slum
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(v2 = 19.21, edf = 1.6, P < 0.001; Fig. 4e), but peaked around 50% canopy openness (v2 = 26.17, edf = 2.8, P < 0.001; Fig. 4f). Preferred species richness was generally higher than non-preferred species under low disturbance, but the relationship reversed under severe disturbance (Fig. 4e and f). 4. Discussion 4.1. Threshold change and species diversity patterns Threshold response in understory floristic composition (at c.a. 350 m from the slum and c.a. 30% canopy cover) was clearly demonstrated by the piecewise regression models selected over other linear and non-linear regression models (Table 1; Fig. 2). Although the pattern of understory vegetation change along the distance gradient somewhat paralleled the change in canopy openness, the locations of largest change in slope values were quite far from each other (c.a. 350 m and c.a. 1700 m, respectively) (Table 1a and b: Fig. 2a and b). Moreover, the response of understory vegetation to light availability suggested that severe disturbance (i.e. almost total clearance of the canopy) triggered the threshold response (Table 1c; Fig. 2c) rather than a simple reflection of the spatial distribution of disturbance as also indicated by the lower AIC value of the canopy cover model (Table 1). Once the threshold was surpassed, the vegetation changed from a gap community phase indicated by many indigenous tree seedlings/saplings to a Lantana thicket with almost no trees (Fig. 3; Supplementary Appendix C). Total species richness exhibited unimodal patterns along the two disturbance gradients and started to decline before the threshold in understory floristic composition was reached (Fig. 4a and b), which relationship has been demonstrated in many Mongolian rangelands under relatively benign environments (Sasaki et al., 2009). Forest managers might therefore use species diversity patterns along the prevailing disturbance gradient to forecast threshold change in understory species composition (Sasaki et al., 2009), but further work in forest ecosystems would be required to confirm its generality. As the threshold was surpassed, replacement of growth forms occurred in the understory as tree species diversity decreased dramatically while other growth forms were relatively unaffected (Fig. 4c and d), which was also reflected in the change in understory species composition (Fig. 3). Trees were most contributing to the pattern of total understory species richness, which suggested their importance in not only maintaining forest structure but also diversity. The abrupt decline in trees might be due to the occurrence of L. camara as negative effects to the indigenous flora has been reported throughout the world (Sharma et al., 2005) and in East Africa (Totland et al., 2005). Once the IAP is established, allelopathy and shading reinforce its dominance (Sharma and Raghubanshi, 2010), and establishment success and impacts to tree regeneration usually increases with larger gap size (Totland et al., 2005; Sharma and Raghubanshi, 2010). Therefore, L. camara might have played an important role in the observed threshold change by adding a shift in biological interactions underlying the threshold. 4.2. Selective wood extraction and likelihood of delayed forest recovery Human disturbance in the form of fuelwood extraction is known to impose heavier impacts on adult individuals of preferred woody species (Pote et al., 2006). In this study, we examined whether the effect of selective species extraction (information based on fuelwood species preference estimated in another study in the same study site (Furukawa et al., in revision)) extends to regenerating woody species, and investigated its relationship with
threshold change. Richness of preferred woody species in the understory rapidly declined before the threshold (Fig. 4e and f), while that of non-preferred species increased (Fig. 4e) or gently declined (Fig. 4f). The pattern of preferred and non-preferred species corresponded to the pattern of trees and shrubs, respectively (Fig. 4c–f), as preferred species mostly consisted of trees compared to non-preferred species with more shrubs and climbers (Supplementary Appendix B). Thus, selective fuelwood extraction was strongly related to understory diversity patterns and change in floristic composition (Fig. 3), and was negatively impacting the regeneration of trees, especially those preferred. Thus, communities beyond the threshold might experience long-term reduction in ecosystem services in terms of decreased overall resource value even if the tree stratum recovers. Because people preferred to cut tree species than shrubs or woody climbers and also avoided the prickly L. camara (Supplementary Appendix B; Furukawa et al., in revision), the establishment of the thicket was probably directly associated with selective species use. Studies have suggested that L. camara can be readily eliminated if canopy cover can be restored (Totland et al., 2005), but our data suggested weak regeneration of tree species under the IAP beyond the threshold (Figs. 3 and 4c and d). This in part can be due to active regeneration of the IAP under frequent disturbance providing no gaps for trees to regenerate. Selective fuelwood use might have facilitated weak forest recovery through indirect effects, such as limited seed dispersal. In fact, poor representation of trees in the soil seed bank have been reported in some African dry forests (Skoglund, 1992; Teketay and Granström, 1995). Even if trees establish under the Lantana thicket, it is most likely removed by the people before shading the IAP. Therefore, not only the strong pressure of fuelwood extraction, but also uneven impacts characteristic to human resource use play an important role in surpassing the threshold. 4.3. Management implications of threshold change Identification of pattern threshold is a key step for preventive forest management especially when management goals are set to prevent significant divergence in forest conditions (e.g. species diversity, community composition) from the reference state (Craig and Macdonald, 2009; Gooden et al., 2009; Digiovinazzo et al., 2010). However, in order to manage communities beyond the threshold (i.e. restoration), feedback mechanisms, if not a full examination of irreversibility through long-term monitoring, should be evaluated in order to assess likelihoods of delayed forest recovery and needs of active intervention (Bestelmeyer, 2006; Suding and Hobbs, 2009). Our results and literature review suggested selective wood extraction imposing differential impacts on woody species both directly and indirectly, and altered biological mechanisms by the IAP were underlying threshold change and leading to delayed recovery. Although controlling fuelwood collection pressure is a difficult socioeconomic task involving poverty alleviation in the slum and is beyond the scope of this study, our results suggested that reducing tree cutting is necessary for both preventive management and restoration. The use of Eucalyptus plantation for fuelwood might diverge some of the pressure imposed on the indigenous forest as long as timber production does not conflict with local wood use (Mead, 2005). Removal of L. camara might be promoted by introducing handicraft techniques to use the species to produce furniture (i.e. rattan) (Kannan et al., 2008) which can also provide alternative sources of income to the urban poor. Once tree cutting pressure is successfully reduced, tree planting might be conducted in heavily degraded areas as planted trees can increase seed dispersal by birds in East African forests and facilitate restoration (Omeja et al., 2011).
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The lack of replications in other forest ecosystems limits the generality of our results as different species compositions and interactions might result in different vegetation responses considering the fact that an IAP played an important role in threshold response. Also, human disturbance may interact with forest species in different ways because species preference might be associated with different species characteristics depending on the way people use the forest (Ramos et al., 2008). Threshold distance can also vary depending on the intensity and longevity of disturbance as well as site conditions (Sasaki et al., 2008). We used canopy openness as an independent measure of disturbance directly representing the understory environment, in order to validate our results and reduce the uncertainty in predicted outcomes. The need of further research is high in developing countries, especially in Africa, where most of the fuel demand is met by supplies from local and regional forests. Acknowledgements This research was financially supported by Hioki E.E. Corporation, Global COE Program of ‘‘Global Eco-Risk Management from Asian Viewpoints’’ at Yokohama National University, and the Joint Research Program C of the Research Institute of Environment and Information Sciences, Yokohama National University. We also thank Akira S. Mori, Tomoyo Koyanagi and Akiko Sakai for their comments on earlier drafts of the manuscript, and Fabrice A.J. DeClerck for english editing. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.foreco.2011.05.030. References Abbot, J.I.O., Homewood, K., 1999. A history of change: causes of miombo woodland decline in a protected area in Malawi. J. Appl. Ecol. 36, 422–433. Bakker, J.D., 2008. Increasing the utility of indicator species analysis. J. Appl. Ecol. 45, 1829–1835. Bestelmeyer, B.T., 2006. Threshold concepts and their use in rangeland management and restoration: the good, the bad, and the insidious. Restor. Ecol. 14, 325–329. Bonnell, T.R., Reyna-Hurtado, R., Chapman, C.A., 2011. Post-logging recovery time is longer than expected in an East African tropical forest. For. Ecol. Manage. 261, 855–864. Brouwer, R., Falcão, M.P., 2004. Wood fuel consumption in Maputo, Mozambique. Biomass Bioenerg. 27, 233–245. Burke, D.M., Elliot, K.A., Holmes, S.B., Bradley, D., 2008. The effects of partial harvest on the understory vegetation of southern Ontario woodlands. For. Ecol. Manage. 255, 2204–2212. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. Springer, New York. Chapman, C.A., Chapman, L.J., 1997. Forest regeneration in logged and unlogged forests of Kibale National Park, Uganda. Biotropica 29, 396–412. Christensen, M., Heilmann-Clausen, J., 2009. Forest biodiversity gradients and the human impact in Annapurna Conservation Area. Nepal Biodivers. Conserv. 18, 2205–2221. Cleveland, W.S., 1979. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836. Craig, A., Macdonald, S.E., 2009. Threshold effects of variable retention harvesting on understory plant communities in the boreal mixedwood forest. For. Ecol. Manage. 258, 2619–2627. Digiovinazzo, P., Ficetola, G.F., Bottoni, L., Andreis, C., Padoa-Schioppa, E., 2010. Ecological thresholds in herb communities for the management of suburban fragmented forests. For. Ecol. Manage. 259, 343–349. Dufrêne, M., Legendre, P., 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., 2005. Global consequences of land use. Science 309, 570–574.
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Threshold change in forest understory vegetation as a result of selective fuelwood extraction in Nairobi, Kenya Takuya Furukawa a,⇑, Kazue Fujiwara b, Samuel K. Kiboi c, Patrick B. Chalo Mutiso c a
Graduate School of Environment and Information Sciences, Yokohama National University, 79-7 Tokiwadai, Hodogayaku, Yokohama 240-8501, Japan Graduate School in Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama 236-0027, Japan c School of Biological Sciences, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya b
a r t i c l e
i n f o
Article history: Received 5 January 2011 Received in revised form 16 May 2011 Accepted 18 May 2011 Available online 12 June 2011 Keywords: Ecological threshold Fuelwood use Species preference Species diversity pattern Invasive alien plant Understory vegetation
a b s t r a c t We examined whether heavy fuelwood collection can cause threshold change in understory forest community and evaluated how selective wood extraction might lead to delayed forest recovery in an urban forest of Nairobi, Kenya. Piecewise regression which represents strongest support for threshold change provided the best fit for the relationships between understory floristic composition (i.e. DCA axis 1) and human disturbance gradients (i.e. canopy cover, and distance from the slum), where threshold changes were detected at c.a. 350 m from the slum and c.a. 30% canopy cover. Only one tree species significantly indicated communities beyond the threshold while an aggressive invasive alien plant (IAP) Lantana camara was strongly represented. Total species diversity along the two human disturbance gradients peaked before the threshold was reached, suggesting that decline in species diversity along the prevailing disturbance gradient might be able to forecast threshold change. Tree species richness in the understory rapidly declined as the threshold was surpassed while other growth forms (i.e. shrubs, herbs and climbers) were relatively unaffected. The effect of selective tree cutting was indirectly impacting the forest understory as species richness pattern of preferred and non-preferred species paralleled that of trees and shrubs, respectively. Thickets of L. camara can negatively affect indigenous flora and its establishment was favored under selective fuelwood extraction removing certain tree species while leaving the IAP untouched. Shading can readily eliminate the IAP, but weak tree regeneration beyond the threshold suggested forest recovery might be delayed for longer than expected because of the interaction between selective fuelwood use and the IAP. Ó 2011 Elsevier B.V. All rights reserved.
1. Introduction Theoretical work and recent empirical evidence in a variety of ecosystems have demonstrated that ecosystems can exhibit abrupt and nonlinear responses to human disturbance (Scheffer et al., 2001; Folke et al., 2004). The level of disturbance at which ecological conditions change abruptly and nonlinearly is called ‘ecological threshold’ (Groffman et al., 2006). Substantial loss of ecosystem function and biodiversity can occur when thresholds are surpassed, while change in the opposite direction can lead to Abbreviations: IAP, invasive alien plant; DCA, detrended correspondence analysis; AIC, Akaike’s information criterion; INSPAN, indicator species analysis; RF, relative frequency; GAM, generalized additive model. ⇑ Corresponding author. Present address: United Nations University Institute of Advanced Studies, 6F International Organizations Center, Pacifico-Yokohama, 1-1-1 Minato-Mirai, Nishiku, Yokohama 220-8502, Japan. Tel.: +81 45 221 2320; fax: +81 45 221 2302. E-mail addresses: [email protected] (T. Furukawa), [email protected] (K. Fujiwara), [email protected] (S.K. Kiboi), [email protected] (Patrick B. Chalo Mutiso). 0378-1127/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2011.05.030
recovery if threshold changes are reversible (Bestelmeyer, 2006). Such notion is changing the conceptual underpinning of habitat management (Suding and Hobbs, 2009) and applications to forest management are increasing (Craig and Macdonald, 2009; Gooden et al., 2009; Digiovinazzo et al., 2010). The notion of ecological thresholds applied to vegetation management can be classified into two concepts: one that investigates the pattern of change (i.e. whether an abrupt change occurs and at what level of disturbance), and the other that investigates both the pattern and irreversibility of change (Bestelmeyer, 2006). The former is used as ‘preventive threshold’ that represents the point at which disturbance should be controlled to prevent drastic changes in ecological conditions. It is defined using pattern threshold based on community structure (e.g. community composition, species diversity) under a prevailing disturbance regime (Bestelmeyer, 2006). It is readily translated to management guidelines (Radford et al., 2005; Suding and Hobbs, 2009) as it does not require extensive examination of process rates (e.g. erosion rate, dispersal/colonization rate) and degree of degradation (e.g. nutrient availability,
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habitat occupancy) that determines the irreversibility of change (Bestelmeyer, 2006). However, efforts to evaluate likelihoods of irreversibility by inferring the mechanism of threshold change are necessary for successful forest management even when dealing with preventive threshold (Suding et al., 2004; Suding and Hobbs, 2009). Identification of pattern threshold has often been based on changes in floristic composition, plant growth form, and species diversity along the prevailing disturbance gradient (Sasaki et al., 2008; Craig and Macdonald, 2009; Digiovinazzo et al., 2010). Additionally, in some forest ecosystems, invasive alien plants (IAPs) have played a deterministic role in threshold response (Gooden et al., 2009). Severity of disturbance determines whether communities surpass the threshold, but whether they experience delayed or altered forest succession is determined by feedback mechanisms (e.g. novel community structure, trophic interactions, landscape connectivity and seed source) that maintain the alternative stable state (Suding et al., 2004; Suding and Hobbs, 2009). In fact, delayed forest recovery has been reported in some East African forests where creation of large gaps (i.e. clearcuts) led to arrested succession due to changes in feedback mechanisms, such as altered understory species composition, limited seed dispersal and trampling by elephants (Chapman and Chapman, 1997; Bonnell et al., 2011). In Africa, both rural and urban poor households rely heavily on local and regional forests as source of energy (i.e. fuelwood and charcoal) (Abbot and Homewood, 1999; Brouwer and Falcão, 2004). Even without changes in forest area (Foley et al., 2005), intensive extraction of fuelwood can have significant effects on forest structure and biodiversity (Christensen and Heilmann-Clausen, 2009). Selective species extraction has been widely reported in fuelwood collection activities (Tabuti et al., 2003; Ramos et al., 2008), resulting in heavier impacts imposed on certain woody species (Pote et al., 2006). Forest management in developing countries thus needs to find a way to combine local sustainable use of natural resources and nature conservation (Millennium Ecosystem Assessment, 2005) by grasping how disturbance peculiar to human use can affect forest communities in particular ways. In this study, we examined whether heavy fuelwood collection can cause threshold change in understory forest communities and investigated whether selective wood extraction can create feedback mechanisms that might lead to delayed forest recovery in an urban forest of Nairobi, Kenya. We focused our survey on the forest understory, which is not directly affected by fuelwood extraction but plays an important role in forest recovery that influences forest dynamics, development, and productivity in both the short and long term (Burke et al., 2008). Pattern threshold was examined for change in understory floristic composition along the tree cutting gradient, and was compared with species diversity patterns of all species and of different growth forms. We evaluated the effect of selective wood extraction by comparing species diversity patterns of preferred and non-preferred species to the identified threshold and other species diversity patterns. Information on species preference was based on another study investigating wood use preference of the fuelwood collectors in the same study site (Furukawa et al., in revision). Through the study, we discussed how selective wood use might underlie threshold change in forest understory and possibly cause delayed tree regeneration.
2. Materials and methods 2.1. Study area Research was conducted in Ngong Road Forest Reserve (1°190 S, 36°450 E; 1750–1830 m a.s.l.), Nairobi, Kenya. The reserve is located
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about 7 km southwest of the city center and borders to the east onto Kibera, the largest slum in Africa (Fig. 1). It consists of approximately 600 ha of semi-deciduous indigenous forest, 500 ha of plantation (mostly Eucalyptus spp.), along with some small patches of grassland. The survey was conducted in the largest contiguous indigenous forest patch of approximately 500 ha within the reserve (Fig. 1). The soils are mainly red friable clays (latosolic soils) and dark-grayish brown mottled clays. The mean annual temperature is 17.6 °C and the mean annual precipitation is approximately 1000 mm, most of which occurring from April to May and November to December. Although the reserve had experienced selective logging in the past, it still harbors one of the few and largest remnants of the ‘plateau forest’ (or Brachylaena–Croton forest) that once covered a vast area of the lower dry slopes of the central highlands (Lind and Morrison, 1974). The indigenous forest is dominated by Brachylaena huillensis, and Croton megalocarpus in the canopy layer of 25 m or more in height, Teclea simplicifolia, Teclea trichocarpa and others in the subcanopy (Kigomo et al., 1990; Hayashi et al., 2006). Forest understory found under closed canopy is relatively sparse and graminoids and woody/semi-woody herbs are common (Hayashi et al., 2006). Study on species composition has indicated low similarity with other forests in the country (Kigomo et al., 1990), suggesting the importance of the reserve in conserving regional biodiversity. Many inhabitants of the slum both with and without permits visited the reserve daily to collect fuelwood. Forest rangers have advised fuelwood collectors to collect dead and fallen wood only, but the large number of fresh stumps indicated active harvesting of live trees. Comparison of stumps and remaining trees indicated strong species preference for fuelwood use, and tree cutting increased towards the slum though minor cutting was found throughout the reserve (Furukawa et al., in revision). Despite daily patrols, declining national woodfuel supply and rising fuel demand in the city (Kituyi et al., 2001) are probably making fuelwood collection an easy means of earning cash and meeting daily consumption needs of populations living adjacent to the reserve. Occasional cattle grazing has been permitted inside the forest during severe droughts, but the main anthropogenic disturbance for the past several decades is focused on tree clearing and fuelwood collection.
2.2. Data collection In order to effectively capture understory vegetation change along the human disturbance gradient, we limited our survey along paths extending from the slum towards the forest interior (from east to west). Four paths originated from the slum which diverged and converged complexly and eventually disappeared in the forest (Fig. 1). In August 2008 and April 2009, understory vegetation and canopy cover were sampled along these paths using transects placed along the human disturbance gradient. Since we anticipated that vegetation change is more rapid near the gradient source (i.e. the slum), we increased sampling resolution toward the slum. Two transects were placed on each path running through the shrubbery outside the reserve between the slum and the forest fence, while in the forest, transects were placed every 100 m up to 1 km from the fence and every 200 m thereafter (Fig. 1). Transect belts of 20 m by 2 m (40 m2) were placed parallel to the paths, 0.5–1 m from the path edge. Transects occurring in eucalyptus plantations and open grasslands with shallow soil were omitted from the analysis. In each transect, vegetation was sampled by recording the occurrence of plants lower than 3 m in height. We took three hemispherical digital photos (at the center and ends of each belt) at 2 m from the ground and calculated percent canopy cover, which was later averaged to represent each transect. In the
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Fig. 1. Map of study area, showing Ngong Road Forest Reserve and Kibera slum. The survey was conducted in the largest contiguous indigenous forest patch (dark gray) surrounded by the fence, and the location of the belt transects are indicated by the dots.
end, we obtained 116 transects (4640 m2) of understory vegetation and canopy cover data. 2.3. Data analysis In the following analyses, we used both distance from the slum and understory light environment to represent the human disturbance (i.e. tree cutting) gradient. Because distance from human settlements often reflects the intensity of fuelwood collection (i.e. the closer the stronger) and often strongly correlates to vegetation change and diversity patterns of forests under fuelwood use (Pote et al., 2006; Christensen and Heilmann-Clausen, 2009), we considered distance from the slum to represent fuelwood collection intensity. In contrast, canopy cover was considered as a direct indicator of the understory light environment reflecting both anthropogenic and any natural disturbances. In order to extract the change in understory vegetation along the human disturbance gradient, understory species composition data (occurrence of each species) for each transect were simplified using detrended correspondence analysis (DCA). The first axis of the ordination explained more than 60% of total variance far exceeding that of the subsequent axes, and showed significant correlations with the two disturbance gradients (Supplementary Appendix A) due to our survey design in which we attempted to eliminate extraneous influences other than tree cutting (i.e. eucalyptus plantation and grassland). Thus, we used the first axis of DCA ordination to represent understory vegetation change along the human disturbance gradient. As a preliminary search for threshold change in understory floristic composition, we fitted a locally weighted, nonparametric regression (LOESS) model (Cleveland, 1979) to each scatterplot of understory floristic composition (i.e. the score of DCA axis 1) plotted against the two disturbance gradients (i.e. distance and canopy cover) and found nonlinear relationships. Similarly, we examined the scatterplot between the two disturbance gradients in order to investigate the relationship between the fuelwood collection gradient and the understory light environment, where again the
LOESS fitting suggested a non-linear relationship. We did not use a multiple regression model to investigate the relationships among the three variables as the two human disturbance gradients (i.e. distance and canopy cover) had a strong correlation with each other (rp = 0.769, P < 0.0001) leading to multicollinearity (Silvey, 1969). Instead, we simply compared their relationships in detail one at a time. Evidence for threshold change was then examined by two linear regression models (i.e. explanatory variable untransformed and log10 transformed) and three non-linear regression models: inverse curve [yi = b0 + (b1/xi)], exponential curve [yi = b0 + b1 exp(b2 xi)], and piecewise regression [yi = b0 + b1 xi (for xi 6 a1), yi = b0 + b1 xi + b2(xi a1) (for a1 6 xi 6 a2), and yi = b0 + b1 xi + b2(xi a1) + b3(xi a2) (for xi > a2); sensu Toms and Lesperance (2003)]. Here, yi is the value for the ith observation (e.g. canopy openness or the score of DCA axis 1), and xi is the corresponding value for the explanatory variable (e.g. distance from slum or canopy cover). In piecewise regression, a1 and a2 are the estimated breakpoints, and the slopes of the lines are b1, b1 + b2, and b1 + b2 + b3, so b2 and b3 can be interpreted as the difference from the preceding slopes. When we assumed one breakpoint, the third equation was not used. When understory light environment was used as the response variable, canopy openness (i.e. 100 – canopy cover) was used so that the pattern shown in the diagram parallels the relationship between DCA axis 1 and distance. Model selection (i.e. determining the number and value of breakpoints) was based on Akaike’s information criterion (AIC) and AIC differences (i.e. DAIC). AIC differences of between 0 and 2 indicate substantial support for a model, whereas differences greater than 4 indicate low or no support for the model (Burnham and Anderson, 2002). Sound evidence for a threshold response (i.e. a discontinuity) requires that one of the piecewise regression models provides the best fit to the data, followed by the exponential, inverse, log10 -transformed, and untransformed linear models (Radford et al., 2005). Subsequently, in order to interpret species turnover along the disturbance gradients, we examined which species were representing different vegetation groups separated by the detected
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shrubs (48), trees (46), and climbers (30). The most frequent were Setaria plicalitis (grass), Clausena anisata (shrub), Meineckia phyllanthoides ssp. capillariformis (woody herb), Vernonia holstii (semiwoody herb), and Schoenoxiphium lehmannii (sedge) found in more than 75% of the transects (Supplementary Appendix C).
breakpoints. Since our dataset consisted of only binary data (presence/absence) and not abundance, we simplified indicator species analysis (INSPAN; Dufrêne and Legendre, 1997) to calculate and test the significance of relative frequency (RF) values only. The original INSPAN combines relative frequency and relative abundance to obtain indicator value of each species (Dufrêne and Legendre, 1997), but a simplified analysis using binary data can give similar results (Bakker, 2008). Thus, we calculated RF values (ranging from 0 to 1) for each species, and tested their significance using a Monte Carlo technique (McCune and Mefford, 1999) as in the original INSPAN. We used generalized additive models (GAMs), a nonparametric smoothing technique suited for more complex non-linear relationships, to examine the relationship between understory diversity (species richness) of different growth forms and of preference and disturbance (i.e. distance and canopy cover). Growth form classification followed that of Raunkiaer (1934) where we used four groups: trees (H P 8 m), shrubs (2 6 H < 8 m), herbs (H < 2 m), and climbers (both herbaceous and woody vines). We used the most and least preferred 25 woody species estimated in a different study in the same survey site (Furukawa et al., in revision) as preferred and non-preferred species, respectively (Supplementary Appendix B). Here, species preference reflected the probability of a stem of a given species being cut when the effect of stem size, accessibility, and patrolling were statistically excluded (Furukawa et al., in revision). In GAMs, response curves with increased complexity and nonlinearity have larger values of effective degrees of freedom (edf) (Wood, 2006), and we considered models with edf P 2 as evidence of nonlinear relationship. We assumed a Poisson error distribution for all GAMs since it is best suited for modeling species richness (Quinn and Keough, 2002). For nonlinear curves (edf P 2), we visually searched for peaks and declines and compared them with the breakpoints detected in the piecewise regression models. Statistical analyses were conducted using PC-ORD (MjM Software, Oregon) and R 2.10.1 (R Development Core Team, 2009).
3.1. Threshold analysis Based on the analysis of AIC, the relationship between distance from the slum and canopy openness (i.e. 100 – canopy cover) was best represented by piecewise regression model with two breakpoints, first at 304.7 m and the second at 1711.0 m (Table 1a; Fig. 2a). The slope was quite steep up until the second breakpoint, and canopy openness did not show significant change thereafter (Fig. 2a). Similarly, the relationship between DCA axis 1 and distance from the slum was best predicted by piecewise regression with two breakpoints (355.5 m and 2166.0 m) over other linear and nonlinear models (Table 1b; Fig. 2b). Vegetation change was quite steep up to the first breakpoint, changed gently until the second breakpoint, and did not show significant change thereafter (Table 1b; Fig. 2b). Comparison of AIC values suggested one breakpoint for the relationship between the DCA axis 1 and canopy cover (Table 1c). Floristic composition changed rapidly up to 29.4% of canopy cover and the change became gradual as the canopy closed (Table 1c; Fig. 2c). Canopy cover was better in explaining change in understory vegetation (i.e. DCA axis 1) compared to distance from the slum as indicated by the generally lower AIC values (Table 1b and c). Our simplified INSPAN based on groups separated by the detected breakpoints showed distinct difference in floristic composition along the disturbance gradients (Fig. 3; Supplementary Appendix C). Transects closest to the slum (<355.5 m) were indicated by many shrubs and herbs (Fig. 3) and among the most frequent was an aggressive invasive alien shrub Lantana camara (Supplementary Appendix C). Herbs such as Cynodon dactylon and Commelina benghalensis were also frequent, and only one tree species Acokanthera schimperi had a significant RF value (Supplementary Appendix C). In the mid section (355.5 m to 2166 m), Uvaria scheffleri a shrub commonly found in forest gaps was most frequent, and many tree species such as B. huillensis, Scutia myrtina, and T. trichocarpa also had significant RF values (Fig. 3;
3. Results We recorded a total of 183 plant species in the 116 belt transects. Herbs were the top consisting of 59 species, followed by
Table 1 AIC values of linear regression models (i.e. explanatory variable untransformed and log10 transformed) and non-linear regression models (inverse curve, exponential curve, and piecewise regression) on the relationship between (a) distance from the slum and canopy openness, (b) distance and DCA axis 1, and (c) canopy cover (i.e. 100 – canopy openness%) and DCA axis 1. Up to two breakpoints were tested in piecewise regressions for relationships (a) and (b). The best fit models (DAIC = 0) are indicated by the lowest AIC value (bold) and are shown in Fig. 2. Model
No. of parameters
(a) Canopy openness vs. Distance Untransformed linear regression Log10 transformed linear regression Inverse curve Exponential curve Piecewise regression (single) Piecewise regression (double)
2 2 2 3 4 6
(b) DCA1 vs. Distance Untransformed linear regression Log10 transformed linear regression Inverse curve Exponential curve Piecewise regression (single) Piecewise regression (double) (c) DCA1 vs. Canopy cover Untransformed linear regression Log10 transformed linear regression Inverse curve Exponential curve Piecewise regression (single)
AIC
DAIC
Deviance
Breakpoints
948.5 887.1 969.7 879.6 879.6 870.8
77.7 16.3 99.0 8.8 8.8 0
22933.1 13511.9 27551.5 12448.5 12238.5 10956.3
NA NA NA NA 1527.0 m 304.7 and 1711.0 m
2 2 2 3 4 6
1236.3 1161.8 1188.6 1144.7 1141.1 1134.1
102.2 27.7 54.5 10.6 7.0 0
274249.8 144227.6 181701.7 122350.0 116656.9 106058.4
NA NA NA NA 389.2 m 355.5 and 2166.0 m
2 2 2 3 4
1141.8 1099.3 1157.5 1097.2 1080.4
61.4 19.0 77.2 16.8 0
121398.0 84213.9 139080.8 81249.1 69074.4
NA NA NA NA 29.4%
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Fig. 2. Piecewise regression models on the relationships between (a) distance from the slum and canopy openness, (b) distance and DCA axis 1, and (c) canopy cover (i.e. 100 – canopy openness%) and DCA axis 1. The best fit models were selected based on the analysis of AIC (Table 1).
Fig. 3. Number of significant indicator species by growth forms in each group differentiated by the detected breakpoints identified in the corresponding piecewise regression in Table 1 and Fig. 2.
Supplementary Appendix C). In the farthest section (>2166 m), the most frequent indicators were herbs common in relatively undisturbed forest understory (e.g. S. lehmannii, Acalypha racemosa, and M. phyllanthoides ssp. capillariformis), along with some trees such as Elaeodendron buchananii and Drypetes gerrardii (Fig. 3; Supplementary Appendix C). The simplified INSPAN based on the canopy cover model (groups separated at 29.4% canopy cover) produced similar results where the open group (629.4%) resembled the group closest to the slum, and the other group (>29.4%) resembled the other two distance groups (the mid and farthest sections) combined (Fig. 3; Supplementary Appendix C). 3.2. Species diversity analysis The generalized additive models (GAMs) on the relationships between species richness and distance from the slum showed variable relationships among different growth forms (Fig. 4a and c). Total species richness changed nonlinearly along the spatial gradient (v2 = 44.62, edf = 3.3, P < 0.0001) peaking around 1400 m from the slum (Fig. 4a). Tree species richness also changed nonlinearly (v2 = 50.98, edf = 6.6, P < 0.0001) with a more complex curve where drastic decline was predicted around 700 m from the slum,
and relatively high values were predicted between the two breakpoints (Fig. 4c). The responses of other growth forms (shrubs, herbs and climbers) were rather linear (v2 = 17.39, edf = 1.8, P < 0.001; v2 = 7.47, edf = 1.0, P < 0.01; and v2 = 6.815, edf = 1.98, P = 0.052, respectively), and they generally increased as disturbance intensified (Fig. 4c). In the GAMs on the relationships between species richness and canopy cover, not only total and tree species richness (v2 = 31.54, edf = 2.9, P < 0.0001 and v2 = 62.85, edf = 3.4, P < 0.0001, respectively), but also shrubs and climbers (v2 = 19.03, edf = 2.6, P < 0.001 and v2 = 8.686, edf = 2.6, P < 0.05, respectively) responded nonlinearly. Species richness peaked around 50% for total and tree species richness and around 40% for shrubs and climbers, and all declined as disturbance intensified where trees decreased most abruptly (Fig. 4b and d). The response of herbs was linear (v2 = 8.165, edf = 1.1, P < 0.01) and species richness increased as the canopy opened (Fig. 4d). The GAMs on the relationship between species richness of preferred and non-preferred woody species and each disturbance gradient suggested that all relationships were non-linear. Preferred species richness peaked around 1700 m from the slum and decreased rapidly near the disturbance source (v2 = 30.52, edf = 4.8,
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Fig. 4. Generalized additive models (GAMs) showing the relationships between species richness and distance from the slum on the left hand side (a, c and e) and canopy cover on the right (b, d, and f). The top two (a and b) show the patterns of total species richness, where the solid lines represent the estimated models and the broken lines the upper and lower 95% confidence intervals. The middle (c and d) show the patterns of different growth forms: tree (T), shrubs (S), climbers (C) and herbs (H). The bottom two (e and f) show the patterns of preferred (bold solid) and non-preferred (bold broken) woody species with their upper and lower 95% confidence intervals (broken lines). The gray vertical lines represent the breakpoints detected in the corresponding piecewise regression models (Table 1; Fig. 2).
P < 0.001; Fig. 4e) whereas it did not show a distinct peak and decreased rapidly as the canopy opened (v2 = 34.67, edf = 3.0,
P < 0.001; Fig. 4f). Non-preferred species richness changed rather linearly along the distance gradient increasing towards the slum
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(v2 = 19.21, edf = 1.6, P < 0.001; Fig. 4e), but peaked around 50% canopy openness (v2 = 26.17, edf = 2.8, P < 0.001; Fig. 4f). Preferred species richness was generally higher than non-preferred species under low disturbance, but the relationship reversed under severe disturbance (Fig. 4e and f). 4. Discussion 4.1. Threshold change and species diversity patterns Threshold response in understory floristic composition (at c.a. 350 m from the slum and c.a. 30% canopy cover) was clearly demonstrated by the piecewise regression models selected over other linear and non-linear regression models (Table 1; Fig. 2). Although the pattern of understory vegetation change along the distance gradient somewhat paralleled the change in canopy openness, the locations of largest change in slope values were quite far from each other (c.a. 350 m and c.a. 1700 m, respectively) (Table 1a and b: Fig. 2a and b). Moreover, the response of understory vegetation to light availability suggested that severe disturbance (i.e. almost total clearance of the canopy) triggered the threshold response (Table 1c; Fig. 2c) rather than a simple reflection of the spatial distribution of disturbance as also indicated by the lower AIC value of the canopy cover model (Table 1). Once the threshold was surpassed, the vegetation changed from a gap community phase indicated by many indigenous tree seedlings/saplings to a Lantana thicket with almost no trees (Fig. 3; Supplementary Appendix C). Total species richness exhibited unimodal patterns along the two disturbance gradients and started to decline before the threshold in understory floristic composition was reached (Fig. 4a and b), which relationship has been demonstrated in many Mongolian rangelands under relatively benign environments (Sasaki et al., 2009). Forest managers might therefore use species diversity patterns along the prevailing disturbance gradient to forecast threshold change in understory species composition (Sasaki et al., 2009), but further work in forest ecosystems would be required to confirm its generality. As the threshold was surpassed, replacement of growth forms occurred in the understory as tree species diversity decreased dramatically while other growth forms were relatively unaffected (Fig. 4c and d), which was also reflected in the change in understory species composition (Fig. 3). Trees were most contributing to the pattern of total understory species richness, which suggested their importance in not only maintaining forest structure but also diversity. The abrupt decline in trees might be due to the occurrence of L. camara as negative effects to the indigenous flora has been reported throughout the world (Sharma et al., 2005) and in East Africa (Totland et al., 2005). Once the IAP is established, allelopathy and shading reinforce its dominance (Sharma and Raghubanshi, 2010), and establishment success and impacts to tree regeneration usually increases with larger gap size (Totland et al., 2005; Sharma and Raghubanshi, 2010). Therefore, L. camara might have played an important role in the observed threshold change by adding a shift in biological interactions underlying the threshold. 4.2. Selective wood extraction and likelihood of delayed forest recovery Human disturbance in the form of fuelwood extraction is known to impose heavier impacts on adult individuals of preferred woody species (Pote et al., 2006). In this study, we examined whether the effect of selective species extraction (information based on fuelwood species preference estimated in another study in the same study site (Furukawa et al., in revision)) extends to regenerating woody species, and investigated its relationship with
threshold change. Richness of preferred woody species in the understory rapidly declined before the threshold (Fig. 4e and f), while that of non-preferred species increased (Fig. 4e) or gently declined (Fig. 4f). The pattern of preferred and non-preferred species corresponded to the pattern of trees and shrubs, respectively (Fig. 4c–f), as preferred species mostly consisted of trees compared to non-preferred species with more shrubs and climbers (Supplementary Appendix B). Thus, selective fuelwood extraction was strongly related to understory diversity patterns and change in floristic composition (Fig. 3), and was negatively impacting the regeneration of trees, especially those preferred. Thus, communities beyond the threshold might experience long-term reduction in ecosystem services in terms of decreased overall resource value even if the tree stratum recovers. Because people preferred to cut tree species than shrubs or woody climbers and also avoided the prickly L. camara (Supplementary Appendix B; Furukawa et al., in revision), the establishment of the thicket was probably directly associated with selective species use. Studies have suggested that L. camara can be readily eliminated if canopy cover can be restored (Totland et al., 2005), but our data suggested weak regeneration of tree species under the IAP beyond the threshold (Figs. 3 and 4c and d). This in part can be due to active regeneration of the IAP under frequent disturbance providing no gaps for trees to regenerate. Selective fuelwood use might have facilitated weak forest recovery through indirect effects, such as limited seed dispersal. In fact, poor representation of trees in the soil seed bank have been reported in some African dry forests (Skoglund, 1992; Teketay and Granström, 1995). Even if trees establish under the Lantana thicket, it is most likely removed by the people before shading the IAP. Therefore, not only the strong pressure of fuelwood extraction, but also uneven impacts characteristic to human resource use play an important role in surpassing the threshold. 4.3. Management implications of threshold change Identification of pattern threshold is a key step for preventive forest management especially when management goals are set to prevent significant divergence in forest conditions (e.g. species diversity, community composition) from the reference state (Craig and Macdonald, 2009; Gooden et al., 2009; Digiovinazzo et al., 2010). However, in order to manage communities beyond the threshold (i.e. restoration), feedback mechanisms, if not a full examination of irreversibility through long-term monitoring, should be evaluated in order to assess likelihoods of delayed forest recovery and needs of active intervention (Bestelmeyer, 2006; Suding and Hobbs, 2009). Our results and literature review suggested selective wood extraction imposing differential impacts on woody species both directly and indirectly, and altered biological mechanisms by the IAP were underlying threshold change and leading to delayed recovery. Although controlling fuelwood collection pressure is a difficult socioeconomic task involving poverty alleviation in the slum and is beyond the scope of this study, our results suggested that reducing tree cutting is necessary for both preventive management and restoration. The use of Eucalyptus plantation for fuelwood might diverge some of the pressure imposed on the indigenous forest as long as timber production does not conflict with local wood use (Mead, 2005). Removal of L. camara might be promoted by introducing handicraft techniques to use the species to produce furniture (i.e. rattan) (Kannan et al., 2008) which can also provide alternative sources of income to the urban poor. Once tree cutting pressure is successfully reduced, tree planting might be conducted in heavily degraded areas as planted trees can increase seed dispersal by birds in East African forests and facilitate restoration (Omeja et al., 2011).
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The lack of replications in other forest ecosystems limits the generality of our results as different species compositions and interactions might result in different vegetation responses considering the fact that an IAP played an important role in threshold response. Also, human disturbance may interact with forest species in different ways because species preference might be associated with different species characteristics depending on the way people use the forest (Ramos et al., 2008). Threshold distance can also vary depending on the intensity and longevity of disturbance as well as site conditions (Sasaki et al., 2008). We used canopy openness as an independent measure of disturbance directly representing the understory environment, in order to validate our results and reduce the uncertainty in predicted outcomes. The need of further research is high in developing countries, especially in Africa, where most of the fuel demand is met by supplies from local and regional forests. Acknowledgements This research was financially supported by Hioki E.E. Corporation, Global COE Program of ‘‘Global Eco-Risk Management from Asian Viewpoints’’ at Yokohama National University, and the Joint Research Program C of the Research Institute of Environment and Information Sciences, Yokohama National University. We also thank Akira S. Mori, Tomoyo Koyanagi and Akiko Sakai for their comments on earlier drafts of the manuscript, and Fabrice A.J. DeClerck for english editing. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.foreco.2011.05.030. References Abbot, J.I.O., Homewood, K., 1999. A history of change: causes of miombo woodland decline in a protected area in Malawi. J. Appl. Ecol. 36, 422–433. Bakker, J.D., 2008. Increasing the utility of indicator species analysis. J. Appl. Ecol. 45, 1829–1835. Bestelmeyer, B.T., 2006. Threshold concepts and their use in rangeland management and restoration: the good, the bad, and the insidious. Restor. Ecol. 14, 325–329. Bonnell, T.R., Reyna-Hurtado, R., Chapman, C.A., 2011. Post-logging recovery time is longer than expected in an East African tropical forest. For. Ecol. Manage. 261, 855–864. Brouwer, R., Falcão, M.P., 2004. Wood fuel consumption in Maputo, Mozambique. Biomass Bioenerg. 27, 233–245. Burke, D.M., Elliot, K.A., Holmes, S.B., Bradley, D., 2008. The effects of partial harvest on the understory vegetation of southern Ontario woodlands. For. Ecol. Manage. 255, 2204–2212. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. Springer, New York. Chapman, C.A., Chapman, L.J., 1997. Forest regeneration in logged and unlogged forests of Kibale National Park, Uganda. Biotropica 29, 396–412. Christensen, M., Heilmann-Clausen, J., 2009. Forest biodiversity gradients and the human impact in Annapurna Conservation Area. Nepal Biodivers. Conserv. 18, 2205–2221. Cleveland, W.S., 1979. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836. Craig, A., Macdonald, S.E., 2009. Threshold effects of variable retention harvesting on understory plant communities in the boreal mixedwood forest. For. Ecol. Manage. 258, 2619–2627. Digiovinazzo, P., Ficetola, G.F., Bottoni, L., Andreis, C., Padoa-Schioppa, E., 2010. Ecological thresholds in herb communities for the management of suburban fragmented forests. For. Ecol. Manage. 259, 343–349. Dufrêne, M., Legendre, P., 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., 2005. Global consequences of land use. Science 309, 570–574.
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