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ISSN : 1598-5504(Print)
ISSN : 2383-8272(Online)
Journal of Agriculture & Life Science Vol.51 No.6 pp.1-14
DOI : https://doi.org/10.14397/jals.2017.51.6.1

The Effects of Spatial and Climatic Factors on Woody Plant Richness along an Extensive Altitudinal Gradient in South Korea

Chang-Bae Lee1, Hyungho Kim 2*
1Global Resources Team, Korea Forestry Promotion Institute, Seoul, 07570, Korea
2Division of Environmental Forest Sciences(Institute of Agriculture & Life Science), Gyeongsang National University, Jinju, 52828, Korea
Corresponding author: Hyungho Kim +82-55-772-1857+82-55-772-1859khh@gnu.ac.kr
20170208 20170413 20170421

Abstract

This study was conducted to clarify the richness patterns of woody plants along a temperate altitudinal gradient on the Baekdudaegan ridge, South Korea. The effects of the spatial and climatic factors on the observed altitudinal richness patterns were evaluated. We also tested Rapoport’s altitudinal effect, which relates the distribution of a species’ altitudinal ranges to the patterns in species richness. Woody plant data were collected from 1100 plots on the Baekdudaegan ridge. A total of 248 woody plant species from 47 families and 99 genera were found. The altitudinal pattern of the woody plant species richness on the Baekdudaegan ridge exhibited a clear hump-shaped pattern with a peak around 800 m. Spatial factors(mid-domain effect and area) were the primary drivers in the simple linear models, whereas the climatic factors(mean annual precipitation and temperature) had lower explanatory power. Multiple linear regression analysis revealed that the combined interaction between spatial and climatic factors affected the altitudinal richness patterns of the overall woody plants. Furthermore, the spatial and climatic factors were more important for large- and small-ranged woody plants, respectively. The results of Stevens’ method and the midpoint method do not support Rapoport’s altitudinal effect. The results suggest that a combined interaction between spatial and climatic factors influences the richness pattern of the total woody plant species. Furthermore, the relative importance of these factors depends on the range size of the woody plants species along an altitudinal gradient on the ridge of the Baekdudaegan ridge.


초록


    Introduction

    Mountain ecosystems are very crucial habitats for various organisms in the continental ecosystem. The altitudinal gradients in mountain ecosystems is a very important factor for species diversity and distribution pattern because altitude affects temperature and rainfall, thus controlling the eco-physiological adaptation of various organisms such as plants, animals, and insects (Brown, 2001; Lomolino, 2001). Therefore, mountain ecosystems are a remarkable distinct system for evaluating ecological patterns and theories of species richness(Körner, 2000; Grau et al., 2007).

    Many studies explore the patterns of plant species richness along altitudinal gradients and different patterns have been documented in different life forms and regions. Many macroecological studies report decreasing and hump-shaped patterns of plant species richness to be most common(Colwell & Lees, 2000; McCain, 2005; Rahbek, 1995; 2005). Hypotheses involving many factors including climate, area, geometric constraints, Rapoport’s altitudinal effect, productivity, and evolutionary history are proposed to clarify the altitudinal patterns of plant diversity. Rapoport’s altitudinal effect suggests that there are positive relationships between altitude and the altitudinal range size of species, leading to strong monotonic decline in plant species richness across altitudinal gradients(Stevens, 1992). The effect of area also explains a high proportion of the altitudinal patterns in species diversity to the well-known species-area relationships(Bachman et al., 2004; Fu et al., 2004). In recent years, the mid-domain effect (MDE) was recognized as one of the most distinct factors for hump-shaped patterns(Colwell & Hurtt, 1994; Colwell & Lees, 2000; Colwell et al., 2004b). The MDE predicts that geometric constraints with boundaries result in increasing redundancy of species ranges toward the center of the domain, leading to humpshaped patterns of species richness along the altitudinal gradient(Colwell & Lees, 2000). However, Rapoport’s altitudinal effect, area, and MDE are all related to spatial ranges, distributions, and capacities for species in a restricted domain and therefore cannot directly provide ecological and biogeographical explanations for the causes of the distributions of individual taxa.

    Climate is recognized a primary driver of biodiversity patterns worldwide(Hawkins et al., 2003; Rowe, 2009). In contrast with spatial-related factors, climatic factors such as temperature and precipitation could have direct and indirect effects on species richness patterns along altitudinal gradients(Sanders et al., 2003). The physiological stress of climatic extremes can directly limit species distribution(Connell, 1961). Furthermore, climate may also affect species diversity and distribution through its effects on primary productivity, including photosynthesis and other biological processes (Hawkins et al., 2003; Storch et al., 2006).

    Although spatial factors such as the MDE, area, and Rapoport’s altitudinal effect, as well as climatic factors including mean annual precipitation(MAP) and temperature(MAT), are the most frequently proposed and reported reasons explaining the correlation between species diversity and altitude, just one factor cannot contribute solely to the distribution patterns of species richness patterns along altitudinal gradients. Recent rigorous comparative studies suggest that the interactions between spatial and climatic factors determine the patterns of species richness along altitudinal gradients in mountains(Cardelús et al., 2006; Fu et al., 2006; Watkins et al., 2006; McCain, 2007; 2009).

    In the present study, we describe the richness patterns of woody plant species along an altitudinal gradient on the Baekdudaegan ridge, South Korea. The aims of this study were to (1) explore the altitudinal patterns of woody plant richness along the ridge of the Baekdudaegan ridge; (2) assess the effects of spatial factors such as area and MDE and climatic variables such as MAT and MAP on the altitudinal richness patterns of woody plants; (3) evaluate the relative importance of these spatial and climatic factors; and finally, (4) explore the correlation between altitudinal range size and altitude to test the validity of Rapoport’s altitudinal effect.

    Materials and methods

    1.Study area

    The study area is on the ridge of the Baekdudaegan (35°15′–38°22′N, 127°28′–129°3′E), whose main ridge extends approximately 649 km from Hyangnobong (1287 m) to Mt. Jiri(1917 m), South Korea(Fig. 1). Therefore, it is possible to travel along the ridge without crossing streams. The Baekdudaegan consists of approximately 487 mountains, hills, and peaks. It is the main mountain range of the Korean Peninsula and is a major source for forest biodiversity(Korea Forest Research Institute, 2003). In september 2005, the Korea Forest Service designated a protected area of the Baekdudaegan, including the main ridge covering 2634 km2(core area, 1712 km2; buffer zone, 922 km2). Although the natural environment of the Baekdudaegan is not thoroughly described because of insufficient survey data, it is certain that the Baekdudaegan has various biodiversity hot spots and offers natural habitats for abundant and various species fauna and flora. The Korea Forest Research Institute(2003) reports that a total of 1477 plant species are distributed along the ridge of the Baekdudaegan, which account for 35.2% of all the vascular plants in the Korean Peninsula. The Baekdudaegan, South Korea, lies in the temperate zone. It has 49 communities of vegetation, including 7 planted and 42 natural vegetation communities, such the Pinus densiflora and Quercus mongolica communities. The altitudinal gradient of the main ridge extends from 200 to 1909 m based on the digital elevation model generated by mosaicking together 1:25,000 topographical maps covering the study area, produced by the National Geographic Information Institute.

    2.Woody plant data

    The study transect covers the main ridge of the Baekdudaegan. To implement field survey, an 100-mwide transect was made in a south-north direction along the Baekdudaegan ridge, and the ridge was divided into 16 altitudinal bands from 200 to more than 1700 m. Although the sampling covered up to approximately 1900 m, more than 1700 m was considered to be a single band because only a small number of plots were surveyed and a small number of woody plant were recorded above this altitudinal band. The woody plant data were recorded in all altitudinal bands of this transect from May 2005 to August 2009. Vegetation survey was implemented to determine the most common and specific physiognomic vegetation types in each altitudinal band. The plant data were collected from a total of 1100 plots of 400 m2. Within each plot, woody plants were sampled according to the method of Braun-Blanquet(1965).

    We divided the ranges of altitude into 100-m bands to explore the relationship between woody plant species richness and altitude. The data from the sampled plots within one altitudinal band were pooled, and the number of species observed in each band was considered to be a measure of richness. As in the method used by Jetz & Rahbek(2002), we also divided the woody plants into two groups: large ranged species(50%) and small ranged species(50%).

    To evaluate the completeness of the sampling and the control for the sampling effort, two distinct methods were applied for the woody plant data of each altitudinal band(Cardelús et al., 2006). First, a sample-based rarefaction approach(Colwell et al., 2004a) was calculated to compare the richness between each altitudinal band. Second, we calculated nonparametric estimators to reduce the bias caused by undersampling, using incidence-based estimators such as ICE, Cho2, Jackknife1, and Jackknife2. Nonparametric estimators and sample-based rarefaction curves were calculated from incidence data using the EstimateS software version 8.2(Colwell, 2009).

    Unlike many recent studies, we did not use interpolated richness adjusted from actual distribution records. The interpolation method assumes a continuous species distribution along the gradient. However, the interpolation tends to inflate richness estimators in the center of the domain more than at the domain limits. Consequently, it increases or creates midaltitudinal peaks in richness(Grytnes & Vetaas, 2002). The use of interpolated distribution data also increases spatial autocorrelation and increases type І errors (Diniz-Filho et al., 2003; Kluge et al., 2006).

    However, because many researches on altitudinal patterns of species richness use interpolated data, comparing such studies with our study, which used non-interpolated data, might be difficult. Thus, we also computed the interpolated richness. As a result, the richness patterns between observed and interpolated methods were the same along the altitudinal gradient and were highly correlated(R2= 0.95, p<0.001). Thus, we used the non-interpolated observed richness for all the analyses, and the results were based on the richness values in this study.

    3.Explanatory factors

    Two spatial factors were investigated with respect to species richness: the MDE and the area. To test species-area relationships, we calculated the area at each altitudinal band in the 100-m-wide transects on the Baekdudaegan ridge. The calculations were performed using a digital elevation model and the ArcGIS 3D Analyst. The discrete MDE null model was used to determine the influence of geometric constraints on the spatial patterns of species richness along an altitudinal gradient. The RangeModel software version 5(Colwell, 2006) was used for simulation. The simulation was performed 5000 times, and we used the expected mean richness and its 95% confidence interval to assess the effects of MDE on the altitudinal gradients of species richness for each woody plant group(i.e., overall, large-, and small-ranged species).

    The two climatic factors used in this study are the MAP and the MAT. We used the digital climate maps produced by the Korea Meteorological Administration and the National Center of Agrometeorology to determine the meteorological parameters for each altitudinal band. The MAT was based on observation data from 1971 to 2008 and the MAP from 1981 to 2009. The spatial resolution of the raster data is 30 m for temperature and 270 m for precipitation. The MAP and the MAT were calculated at each altitudinal band in the 100-m-wide transects on the ridge of the Baekdudaegan.

    To test the potential of the individual factors such as MDE, area, MAP, and MAT on the altitudinal patterns of species richness, we performed simple ordinary least squares(OLS) regressions of species richness for each woody plant group. Multiple OLS models were also calculated to examine multiple interpretations for altitudinal patterns of species richness. SAM version 4.0(Rangel et al., 2010) was used for regression analyses.

    4.Rapoport’s altitudinal effect

    To examine the relationship between the extent of the altitudinal range of woody plant species and the altitudinal bands, we computed the altitudinal range size of species as the difference between the maximum and minimum altitudes in its distribution range. The altitudinal patterns in mean range size for the woody plant species were quantified by calculating (1) the mean range size of all the species present in an altitudinal band(Stevens, 1989) and (2) the range size only of species whose range midpoints are in a particular band(Rohde et al., 1993). The midpoint method was developed to overcome the statistical non-independence of spatial data in Stevens’ method. The relationship between altitudinal range size and band was assessed with first and second-order polynomial regression models. When the correlation between both variables is positive, Rapoport’s altitudinal effect is predicted to exist.

    Results

    1.Altitudinal richness pattern of woody plants

    A total of 248 woody plant species(47 families and 99 genera) were recorded in 1100 plots along the altitudinal gradient. Interpolated richness and nonparametric estimators(i.e., ICE, Chao 2, Jackknife 1, and Jackknife 2) were similar to the patterns of observed richness though somewhat higher than the observed number of species at all altitudinal bands (Table 1). The observed species richness of the woody plants exhibited clear hump-shaped patterns with a peak around 800 m. The species were classified according to range size, highlighting disparate contributions to overall species richness patterns (Fig. 2). There were greater correlation coefficients between the overall and large-ranged species richness (R2=0.99, p<0.001) than between the overall and small-ranged species richness(R2=0.86, p<0.001). Even though the sample-based species accumulation curves for all the altitudinal bands failed to reach an asymptote, these curves indicate a clearer midaltitudinal peak between 800 and 1200 m(Fig. 3).

    2.Regression of spatial and climatic factors

    Based on the results of the simple linear regression, the woody plant species richness strongly correlated with the MDE and the area across the datasets of the overall and large-ranged species. Meanwhile, the explanatory powers of the MDE and the area were lower for small-ranged species than for overall and large-ranged species along the Baekdudaegan ridge(Table 2).

    The multiple linear regression models with all the variables(model A) included the MDE, the MAP, and the MAT and explained more than 90% of the variation in the overall species richness of the woody plants. Area was the weakest predictor of variation in overall richness(Table 3). The second model(model B) with the area, the MAP, and the MAT, excluding the MDE, accounted for more than 80% of the variation in the overall richness of the woody plants. When the species data were classified into large and small-ranged woody plants, the effect of the MDE in the multiple regression models exhibited different patterns between the two groups. The explanatory power of the spatial factors for large-ranged species was high(the MDE in model A and the area in model B). Meanwhile, the spatial factors were weak predictors, whereas the two climatic factors, MAP and MAT, were good predictors for small-ranged species.

    3.Rapoport’s altitudinal effect

    The simple linear regression models revealed no relationship between the altitudinal range size and band(Stevens’ method: R2=0.02, p=0.56; midpoint method: R2<0.01, p=0.90). The second-order polynomial regression models showed maximum altitudinal ranges at extreme altitudes with Stevens’ method(R2=0.77, p<0.001) and at intermediate altitudes with the midpoint method(R2=0.87, p<0.001). Therefore, these analyses do not support Rapport’s altitudinal effect(Fig. 4).

    Discussion

    On the ridge of the Baekdudaegan, woody plant species richness exhibited a hump-shaped pattern with altitude showing a peak approximately 800 m. Interpolated and nonparametric richness also followed a similar pattern, confirming the richness peak at intermediate altitudes. The hump-shaped distribution in species richness is the most usually recorded type in many organisms of various ecosystems(Rahbek, 2005). At the most common level, our study also reproduce the evidence that woody plant species richness exhibits a strong hump-shaped altitudinal pattern on mountains, although the peak altitudes of the species richness are somewhat different between studies (Oommen & Shanker, 2005; Kluge et al., 2006).

    In this study, we found that the explanatory powers of the two spatial factors, the MDE and the area, were stronger than those of the two climatic factors, MAP and MAT, in the simple linear regression model when all woody plant species were considered and the explanatory power of the two spatial factors was higher for large-ranged species than for smallranged species. The multiple OLS model also revealed that the two spatial factors had higher explanatory powers than the two climatic factors for overall and large-ranged species, whereas the climatic factors were stronger for small-ranged species.

    Several factors affect the observed species richness patterns along an altitudinal gradient. Area is one of these crucial factors determining altitudinal woody plant richness patterns and can indirectly and directly influence on species richness(Connor & McCoy, 1979; Rahbek, 1995). On the ridge of the Baekdudaegan, the area exhibited a hump-shaped pattern along the altitudinal gradient. This roughly corresponded to the altitudinal pattern in species richness, suggesting that area may affect the pattern of woody species richness. However, area weakly explained richness patterns in the presence of the MDE from multiple regression models. This may be because of the special relationship between area and the MDE because the area and the MDE were highly correlated in all the cases(R2>0.74, p<0.001), it is possible that area effect is replaced by the MDE in the multiple models. Therefore, we consider that the effect of the area is masked by the strength of the MDE, at least for woody plants in this study.

    Recent studies report that MDE is an important factor that explains plant species richness patterns along altitudinal gradients(Oommen & Shanker, 2005; Cardelús et al., 2006; Watkins et al., 2006; Wang et al., 2007). The MDE also accounts for a significant proportion of the altitudinal patterns of woody plant species richness on the ridge of the Baekdudaegan. In this study, we found that the MDE has stronger explanatory power for large-ranged species than for small-ranged species among all the woody plants. Our results support the finding that small-ranged species exhibit a smaller MDE peak than large-ranged species, although the absolute magnitudes of the MDE vary somewhat between the studies(Lee et al., 1999; Jetz & Rahbek, 2002; Colwell et al., 2004b; Mora & Robertson, 2005; Cardelús et al., 2006; Dunn et al., 2006; Kluge et al., 2006; Watkins et al., 2006).

    In simple linear regression, the climatic factors, MAP and MAT, were only weakly correlated with richness patterns across the datasets of the woody plants. However, climatic factors were important determinants, along with spatial factors, for overall woody species richness in the multiple linear regression models. Furthermore, the spatial factors had stronger explanatory power than climatic factors for overall and large-ranged species, whereas the climatic factors were the most important determinants for small-ranged species. Climate is an obvious factor affecting species distribution and richness in many areas, especially for vascular plants(Fang & Lechowicz, 2006). Fu et al.(2006) found that climatic factors are relatively more important than spatial factors for small-ranged species.

    The degree to which range size increases with altitude(i.e., Rapoport’s altitudinal effect) and the importance of the pattern for the biogeography of species richness has been vigorously debated during the last three decades(Stevens, 1989; Gaston et al., 1998; Sanders, 2002; Bhattarai & Vetaas, 2006; Ruggiero & Werenkraut, 2007). On the ridge of the Baekdudaegan, using Stevens’ method and the midpoint method, we found that the altitudinal distributions of the range size of the woody plant species did not follow Rapoport’s altitudinal effect. In any case, species distribution ranges result from combined interactions between many factors, such as functional traits, the evolution such as speciation and dispersal, and constraints resulting from habitat shape(Webb & Gaston, 2003). No general trends that apply to all biological organisms appear to exist for Rapoport’s altitudinal effect, supporting that the factors related to range size are complex and remain poorly understood(Grau et al., 2007).

    In conclusion, the woody plant species richness along the Baekdudaegan ridge depicts clear humpshaped pattern, even though the peak altitudes of the richness are different somewhat among the overall, large-ranged, and small-ranged species. The spatial factors(i.e., MDE and area) were the primary drivers of the simple linear models, whereas the climatic factors(i.e., MAP and MAT) had weak explanatory power. However, the multiple linear models indicate that the combined interaction between the spatial and climatic factors affects the hump- shaped pattern of the overall woody plant species. Furthermore, the spatial factors are more important for the large-ranged woody plants, whereas the climatic factors, MAP and MAT, have stronger explanatory power for the small-ranged woody plants. The results of the present study show that the relationship between the altitudinal range and band does not support Rapoport’s altitudinal effect.

    Many studies report that altitudinal richness patterns are influenced by various factors such as spatial, climatic, and historical factors and historical factors (Lomolino, 2001; Grytnes & Vetaas, 2002; Rahbek, 2005; Cardelús et al., 2006; Li et al., 2009; Acharya et al., 2011). In this study, we explored the correlations between spatial and climatic factors and woody plant richness patterns. However, evolutionary factors were not considered. Further study on evolutionary factors, including functional niche conservatism and historical contingency, might help ecologists obtain a better understanding for the altitudinal distribution patterns of plant communities with macroecological perspectives.

    Acknowledgment

    We thank Dr. Sang-Gon Park, Mr. Keun-Wook Lee and Mr. Sang-Hyouk Seo for their invaluable help during the fieldwork and the data analysis in this study. Thanks are also due to Dr. Hyun-Je Cho and Dr. Jung-Hwa Chun for their support and encouragement. This paper forms a part of the Korea Big Tree Project funded by the Korea Green Promotion Agency, Korea Forest Service.

    Figure

    JALS-51-1_F1.gif

    Location, topography, and climate diagrams of the study area: the ridge of the Baekdudaegan. The relationships between the altitudinal band and (a) the MAP and (b) the MAT, and (c) the area are shown. The MAP, the MAT, and the area were calculated at each altitudinal band in an imaginary 100-m-wide transect on the ridge of the Baekdudaegan Mountains, South Korea.

    JALS-51-1_F2.gif

    Observed and predicted richness and 95% confidence intervals for the MDE richness predictions (computed with 5000 replicates) as a function of altitude for (a) overall, (b) large-ranged, and (c) small-ranged species.

    JALS-51-1_F3.gif

    Sample-based rarefaction curves for the observed woody plant species at each altitudinal band along the ridge of the Baekdudaegan. The curves represent the rarefaction results for all the woody plant species observed at 16 altitudinal bands: (a) 200- to 500-m bands, (b) 600- to 900-m bands, (c) 1000 to 1300-m bands, and (d) 1400- to more than 1700-m bands.

    JALS-51-1_F4.gif

    Relationships between the altitudinal range size and bands using (a) Stevens’ method and (b) the midpoint method. The straight black lines show the simple linear regression models, and the dotted gray lines show the second-order polynomial models.

    Table

    The observed, interpolated, and nonparametric(i.e., ICE, Chao 2, Jackknife 1, and Jackknife 2) woody species richness estimates at different altitudinal bands along the ridge of the Baekdudaegan, South Korea

    Relationships between the species richness of the woody plants and the explanatory variables using simple OLS models for overall species and separated for large- and small-ranged species along the ridge of the Baekdudaegan, South Korea

    The magnitudes of t values indicate the variable importance in the models. Model fit was assessed using the Akaike information criteria(AIC) and smaller values indicate a better fit.
    MDE, mid-domain effect; MAP, mean annual precipitation; MAT, mean annual temperature.

    Multiple OLS models for the explanatory variables and the woody species richness, including overall species, and separated for large- and small-ranged species along the ridge of the Baekdudaegan, South Korea

    Model A includes all the explanatory variables, and model B excludes the MDE. Magnitudes of the t values indicate the variable importance in the models. Model fit was assessed using the AIC and smaller values indicate a better fit. See Table 2 for the abbreviations.
    Significance levels are *p<0.05, **p<0.01, and ***p<0.001.

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