Advanced
Altitudinal patterns and determinants of plant species richness on the Baekdudaegan Mountains, South Korea: common versus rare species
Altitudinal patterns and determinants of plant species richness on the Baekdudaegan Mountains, South Korea: common versus rare species
Journal of Ecology and Environment. 2013. Sep, 36(3): 193-204
Copyright ©2013, The Ecological Society of Korea
This is an Open Access article distributed under the terms of the CreativeCommons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use,distribution, and reproduction in any medium, provided the original workis properly cited.
  • Received : May 20, 2013
  • Accepted : September 05, 2013
  • Published : September 27, 2013
Download
PDF
e-PUB
PubReader
PPT
Export by style
Article
Author
Metrics
Cited by
TagCloud
About the Authors
Chang-Bae Lee
Korea Green Promotion Agency, Korea Forest Service, 121 Dunsanbukro, Seogu, Daejeon 302-831, Korea
flora1@kgpa.or.kr
Jung-Hwa Chun
Division of Forest Ecology, Korea Forest Research Institute, 57 Hoegiro, Dongdaemungu, Seoul 130-712, Korea
Tae-Won Um
Department of Forest Science, Sangji University, Wonju 220-702, Korea
Hyun-Je Cho
Korea Forest Ecosystems Institute, 7-19 Hobukro 43gil, Bukgu, Daegu 702-110, Korea

Abstract
Altitudinal patterns of plant species richness and the effects of area, the mid-domain effect, climatic variables, net primary productivity and latitude on observed richness patterns along the ridge of the Baekdudaegan Mountains, South Korea were studied. Data were collected from 1,100 plots along a 200 to 1,900 m altitudinal gradient on the ridge. A total of 802 plant species from 97 families and 342 genera were recorded. Common and rare species accounted for 91% and 9%, respectively, of the total plant species. The altitudinal patterns of species richness for total, common and rare plants showed distinctly hump-shaped patterns, although the absolute altitudes of the richness peaks varied somewhat among plant groups. The mid-domain effect was the most powerful explanatory variable for total and common species richness, whereas climatic variables were better predictors for rare plant richness. No effect of latitude on species richness was observed. Our study suggests that the mid-domain effect is a better predictor for wide-ranging species such as common species, whereas climatic variables are more important factors for range-restricted species such as rare species. The mechanisms underlying these richness patterns may reflect fundamental differences in the biology and ecology of different plant groups.
Keywords
INTRODUCTION
Mountains are important habitats for a diversity of organisms in continental ecosystems. The altitudinal gradients formed in mountain ecosystems are an important physical factor that influences biodiversity and species distribution patterns because altitude affects temperature and precipitation, thus influencing the ecological and physiological adaptation of plants, mammals, birds and invertebrates (Lomolino 2001). Therefore, mountains represent a remarkable, distinctive system valuable for evaluation of ecological and biogeographical patterns and theories of species diversity (Körner 2000, Grau et al. 2007).
Many studies have documented the altitudinal richness patterns of plants (Lee et al. 2012), mammals (Rowe 2009), birds (McCain 2009) and invertebrates (Liew et al. 2010). Observed patterns have differed among taxa and regions. Three main types of richness pattern in relation to increasing altitude are reported: (1) a monotonic decrease, (2) a plateau at low altitudes, and (3) a humpshaped distribution with high richness at intermediate altitudes (Rahbek 2005). Of these patterns, the humpshaped pattern is reported to be the most common. Although the mechanisms underlying altitudinal richness patterns are still subject to debate, typical explanations include the influence of variables such as climate, area, geometric constraints or the mid-domain effect (MDE), productivity and evolutionary history (McCain 2009). Climatic variables are considered to be the most widely supported predictors of worldwide species richness (Rowe 2009), because they directly limit a species’ distribution when the physiological tolerance of the species is exceeded and indirectly affect photosynthetic activity and other biological processes. Previous studies indicate that the area of altitudinal bands explains a large proportion of the variation in species richness (Karger et al. 2011), in a similar manner to the well-known species–area relationships. Species–area predictions posit that a larger area provides increased habitat diversity, which may harbor a larger number of species, and an increase in area is accompanied by both a decrease in the extinction rate and an increase in speciation or colonization (Rosenzweig 1995). Recent studies suggest that the MDE, or geometric constraints, is also highly effective at explaining altitudinal patterns of species richness (Kluge et al. 2006, McCain 2009). The MDE postulates that geometric constraints on species ranges within a bounded domain yield a middomain peak in richness regardless of ecological factors (Colwell and Lees 2000). The MDE is abiotic and stochastic, and is founded on the premise that the spatial distribution of species richness is constrained by the shape of landmasses and by species range size. Under these conditions, random replacement of species ranges within a bounded domain creates an overlap of species ranges and thus a peak of species richness toward the center of the geographical domain (Colwell and Hurtt 1994, Colwell and Lees 2000). Productivity is an additional variable that may influence species richness patterns. Although the relationship between species richness and productivity is controversial, with disagreement over whether productivity controls or is controlled by species richness (Loreau et al. 2001), productivity is frequently cited as a fundamental determinant of species richness (Chalcraft et al. 2004). Waide et al. (1999) reviewed productivity–species richness relationships and identified four types of relationship: negative, positive, unimodal, and no relationship.
Despite increased interest in altitudinal patterns of species richness in recent years, few studies have comprehensively analyzed the underlying mechanisms of richness patterns along altitudinal gradients. Moreover, recent rigorous comparative studies suggest that, although area, MDE, climatic variables, and productivity are frequently cited in studies of species richness on mountains, a single variable cannot fully explain the species richness patterns among different groups in a single taxon along altitudinal gradients, and that different variables may drive such patterns among different groups in mountain ecosystems (Wang et al. 2007, Watkins et al. 2006, Lee et al. 2013).
In this context, we examined the distribution of terrestrial plants along an altitudinal gradient on the ridge of the Baekdudaegan Mountains (hereafter ‘the Baekdudaegan’), South Korea. Using data from field surveys, we aimed to (1) explore the altitudinal patterns of species richness for total, common and rare plant species along the ridge of the Baekdudaegan; (2) evaluate the effects of area, MDE, two climatic variables (mean annual precipitation and temperature), and productivity on the altitudinal patterns of plant species richness; and (3) examine whether species richness patterns are related to latitude.
MATERIALS AND METHODS
- Study area
The study transect covered the main ridge of the Baekdudaegan (35°15′N to 38°22′N, 127°28′E to 129°3′E) in South Korea ( Fig. 1 ). The Baekdudaegan consists of about 487 mountains, hills and peaks along the Korean Peninsula and is a major resource for forest biodiversity (Korea Forest Research Institute 2003). The protected area of the Baekdudaegan was designated in September 2005 by the Korea Forest Service; the total protected area, including the main ridge, covers 2,634 km 2 (1,712 km 2 core area and 922 km 2 buffer zone). The main ridge extends about 650 km from Hyangnobong Peak of 1,287 m above sea level (a.s.l.) to Mt Jiri of 1,917 m a.s.l. in South Korea. One can travel along the ridgelines without crossing any rivers or streams. The altitudinal gradient of the main ridge extends from 200 to 1,909 m a.s.l. as indicated by a digital elevation model generated using a mosaic of 1:25,000 topographical maps produced by the National Geographic Information Institute that cover the study area ( Fig. 1 a).
The Baekdudaegan in South Korea belongs to a mountain ecoregion, and temperate deciduous and mixed forest biome (Korea Forest Research Institute 2003). The soil consists of granite, granite gneiss, and highly deformed and recrystallized sedimentary rocks (Shin 2002). Although the natural environment of the Baekdudaegan
Lager Image
Location, topography and explanatory variables recorded in the study area along the ridge of the Baekdudaegan Mountains in South Korea. Therelationships are shown between altitude and followings; (a) latitude, (b) area, (c) mean annual temperature (MAT), and (d) annual precipitation (MAP) and (e)enhanced vegetation index (EVI).
is poorly known because of insufficient survey data, the Baekdudaegan contains many biodiversity hotspots and offers natural habitats for abundant and varied fauna and flora. A total of 1,477 plant species are distributed on the Baekdudaegan (Korea Forest Research Institute 2003), which accounts for 35.2% of the vascular plant diversity on the Korean Peninsula.
The vegetation on the Baekdudaegan can be categorized into 49 communities, including seven planted communities (e.g., the Larix kaempferi community) and 42 natural vegetation communities (e.g., the Quercus mongolica community). The Korea Forest Research Institute (2003) divided the Baekdudaegan in South Korea into three regions on the basis of characteristic plant community groups: (1) the northern region, characterized by Acer komarovii and Betula ermanii , (2) the central region, characterized by Acer pseudosieboldianum and Fraxinus rhynchophylla , and (3) the southern region, characterized by Abies koreana and Fraxinus mandshurica . The vegetation on the Baekdudaegan can also be divided into four major zones along an altitudinal gradient. These altitudinal vegetation zones include (1) temperate (montane) deciduous broad-leaved and pine forest (<550 m a.s.l.) dominated by Pinus densiflora and Rhus tricocarpa , (2) temperate deciduous broad-leaved and coniferous mixed forest (550 to 1,100 m a.s.l.) dominated by Q. mongolica , Q. serrata , P. koraiensis , and Abies holophylla , (3) subalpine coniferous forest (1,100 to 1,600 m a.s.l.) dominated by Taxus cuspidata , A. koreana , and Abies nephrolepis , and (4) alpine forest (>1,600 m a.s.l.) dominated by B. ermanii and P. pumila (Kong 2007).
- Plant survey data
For field sampling, an imaginary 100-meter-wide transect was established in a north–south direction along the ridge of the Baekdudaegan, and the ridge was divided into 16 altitudinal bands each of 100 m altitude from 200 m a.s.l. to >1,700 m a.s.l.. Although sampling extended to 1,900 m a.s.l., the 1,700 m and higher range was treated as a single band because only a small number of plots were sampled and few plant species were observed above 1,700 m a.s.l. Data on the plant species present within each altitudinal band of the transect were recorded from May 2005 to August 2009. Vegetation sampling was performed to cover the most common and specific physiognomic vegetation types in each 100 m altitudinal band. Data were obtained for a total of 1,100 plots of 400 m 2 . Within each plot, plants were surveyed in accordance with the method of Braun-Blanquet (1965).
We divided the altitude range into 100 m bands to examine the relationship between plant species diversity and altitude. Plant data for the same altitudinal band were pooled and the number of species observed in each band was considered to be a measure of richness. Plant species were classified into three groups including total, common and rare plant species based on the Rare Plants Data Book of Korea (Lee 2009). The plant species checklists for each altitudinal band are available in Lee et al. (2013).
- Explanatory variables
Two space-related variables, area and MDE, were investigated in relation to species richness. To test species– area relationships, we calculated the area of each altitudinal band along the 100-meter-wide transect. Calculations were performed using a digital elevation model with the 3D Analyst extension in ArcGIS. The MDE null model was used to test the influence of geometric constraints on the spatial patterns of species richness along the altitudinal gradient. We used a novel, discrete MDE model based on Colwell and Hurtt’s (1994) continuous Model 2, which does not require the use of interpolated ranges (Fu et al. 2006). RangeModel ver. 5 software (Colwell 2006) was used for simulation. The simulation process was repeated 5,000 times and expected mean richness and its 95% confidence intervals were used to assess the effects of geometric constraints on the altitudinal gradient. Unlike many recent studies, we did not use interpolated species richness modified from actual distribution records. The justification for interpolation is that undersampling creates gaps in altitudinal distribution (Kluge et al. 2006). However, three problems with interpolation are reported (Grytnes and Vetaas 2002, Diniz-Filho et al. 2003, Kluge et al. 2006). First, it disrupts the crucial control of sampling area and intensity as species are added that were not, in fact, present in the plots. Second, interpolation might artificially increase richness to a higher degree at intermediate altitudes, because gaps are filled only between the lower and upper range limits; this essentially assumes that no individuals of a species have been missed beyond the observed range limits, but that individuals have been missed at sampling points within the range limits. Third, species richness at nearby altitudes is more similar than at distant altitudes, and the resulting spatial autocorrelation inflates Type І errors. The spurious effects of autocorrelation increase when using interpolated distribution data. However, many studies on altitudinal richness patterns use interpolated data and comparisons of such studies with our non-interpolated results might be difficult. Therefore, we also calculated the interpolated richness for total, common and rare plant species. Observed and interpolated richness patterns showed the same pattern along the altitudinal gradient and were strongly correlated (total species, R 2 = 0.92; common species, R 2 = 0.92; rare species, R 2 = 0.93; P < 0.001 in each comparison). Thus, we only present results derived from the observed richness values without interpolation in this study.
The two climatic variables used in this study were mean annual temperature (MAT) and precipitation (MAP). We used digital climate maps produced by the Korea Meteorological Administration and National Center of Agrometeorology to extract the meteorological parameters for each altitudinal band (Yun 2010). The MAT data were dated from 1971 to 2008 and the MAP data were dated from 1981 to 2009. The spatial resolution of the raster data was 30 m for MAT and 270 m for MAP. The MAT and MAP were calculated for each altitudinal band in the transect.
As a proxy for aboveground net primary productivity we used the enhanced vegetation index (EVI), which is preferred over the normalized difference vegetation index because it is insensitive to soil or atmospheric effects and adjusts the red wavelength as a function of the blue wavelength to minimize brightness-related soil effects (Adhikari et al. 2012). MODIS-driven EVI images, composited at 16-day intervals, were downloaded in tiles for the period from January 2004 to December 2009 and mosaicked together using the MODIS reprojection tool. The averaged annual EVIs were used to assess the relationship between the diversity indices and productivity.
- Statistical analysis
The relationships between species richness and the explanatory variables were analyzed for each individual variable using a simple linear regression. Such a linear model tests only for a linear relationship between the potential explanatory variable and species richness, but several scenarios under which a unimodal model is more biologically reasonable are plausible (Kluge et al. 2006). Therefore, we also used a polynomial regression model to detect curvilinear relationships by including a quadratic
Lager Image
Observed and predicted species richness and 95% confidence intervals for the predicted mid-domain effect richness as a function of altitude fortotal, common, and rare plant species along the ridge of the Baekdudaegan Mountains, South Korea.
Lager Image
Relationship between altitude and species richness for (a) total, (b) common, and (c) rare plant species along the ridge of the BaekdudaeganMountains, South Korea. Solid and hollow circles indicate observed and rarefied species richness, respectively.
term in the regression function. In addition, we used forward stepwise multiple regression models to establish the relative importance of area, MDE, MAT, MAP and EVI as explanatory variables for species richness. Forward stepwise multiple regressions were used to find a set of independent variables that together provided the best fit for diversity indices by minimizing the sum of squared residuals. All linear and quadratic terms for the explanatory variables were used in forward stepwise multiple regressions. Simple and forward stepwise multiple regression models were analyzed with S-PLUS ver. 8.0 (Insightful Corp., Seattle, WA, USA). All possible analyses were conducted for each of the three plant groups.
RESULTS
- General description
A total of 802 plant species belonging to 97 families and 342 genera were recorded from the 1,100 plots along the altitudinal gradient ( Table 1 ). More than half of these species were herbaceous (69%; 62 families, 249 genera, and 554 species) and woody plants accounted for 31% of the species (47 families, 99 genera, and 248 species). Common and rare species accounted for 91% and 9%, respectively, of the total plant species.
With increasing altitude, the area of the altitudinal bands increased sharply and then decreased above the 900 to 1,000 m band, thus showing a hump-shaped pattern ( Fig. 1 b). The MAT declined monotonically with increasing altitude ( Fig. 1 c), whereas the MAP increased along the altitudinal gradient ( Fig. 1 d). The EVI generally declined with increasing altitude ( Fig. 1 e). The MDE null model showed deviation of the observed species richness from simulated richness ( Fig. 2 ). The analysis revealed that for total, common and rare plant species, 56%, 63% and 63% of the data points, respectively, were outside the 95% confidence intervals of the values predicted by the MDE null model.
- Altitudinal richness patterns and range size
The richness of total and common plant species each showed a hump-shaped pattern with maximum richness recorded between 1,000 and 1,100 m ( Fig. 3 a and 3 b), whereas the richness of rare plant species peaked in the altitudinal band between 1,200 and 1,300 m and the maximum richness peak was higher than those of total and common species ( Fig. 3 c). The patterns of rarefied species richness were similar to the observed richness patterns for all three plant groups. Overall, species richness for all plant groups showed a distinctly hump-shaped pattern in relation to altitude, even though the absolute altitudes of the peaks differed somewhat among the plant groups.
Most plant species in the Baekdudaegan showed very narrow altitudinal ranges ( Fig. 4 ). The altitudinal range was ≤200 m for 29% of total plant species, 27% of common plant species, and 43% of rare plant species. No species was present in every altitudinal band. The relative proportion of rare species declined more rapidly than for total and common species with increasing range size (i.e., the altitudinal range of total and common species tended to be greater than that of rare species).
- Determinants of altitudinal richness patterns
Simple linear regressions showed that total species richness and common species richness were strongly correlated with area and the MDE, whereas rare species richness was correlated with the MDE and MAT (linear model in Table 2 ). The results for quadratic models differed
Observed richness among total, common and rare plant species for different altitudinal bands along the ridge of the Baekdudaegan Mountains, South Korea
Lager Image
Observed richness among total, common and rare plant species for different altitudinal bands along the ridge of the Baekdudaegan Mountains, South Korea
Lager Image
Range size distribution of plant species observed along the altitudinalgradient along the ridge of the Baekdudaegan Mountains, SouthKorea. X-axis values represent the upper boundary of 200 m altitudinalbands.
somewhat from those for simple linear models (quadratic model in Table 2 ) in that climatic variables were also significant predictors for species richness of total and common plants. Considering the multiple regression models, the results for models that included all linear and quadratic terms were similar to those that included only the linear terms ( Table 3 ). The forward stepwise multiple regression models showed that the MDE was the most important predictor of total and common species richness, whereas climatic variables such as MAT and MAP were the most powerful predictors of rare species richness.
- Latitudinal effect
Simple linear and quadratic models for explanatory variables and species richness along the ridge of the Baekdudaegan Mountains, South KoreaMDE, mid-domain effect, MAT, mean annual temperature, MAP, mean annual precipitation, EVI, enhanced vegetation index.
Lager Image
Simple linear and quadratic models for explanatory variables and species richness along the ridge of the Baekdudaegan Mountains, South Korea MDE, mid-domain effect, MAT, mean annual temperature, MAP, mean annual precipitation, EVI, enhanced vegetation index.
Forward stepwise multiple regression models for explanatory variables, including all linear and quadratic terms and diversity indices, along the ridge of the Baekdudaegan Mountains, South KoreaModel A included only linear terms for explanatory variables, whereas Model B included all linear and quadratic terms for explanatory variables. The magnitude of the t-value indicates the relative importance of each variable in the models.MDE, mid-domain effect, MAT, mean annual temperature, MAP, mean annual precipitation, EVI, enhanced vegetation index.
Lager Image
Forward stepwise multiple regression models for explanatory variables, including all linear and quadratic terms and diversity indices, along the ridge of the Baekdudaegan Mountains, South Korea Model A included only linear terms for explanatory variables, whereas Model B included all linear and quadratic terms for explanatory variables. The magnitude of the t-value indicates the relative importance of each variable in the models. MDE, mid-domain effect, MAT, mean annual temperature, MAP, mean annual precipitation, EVI, enhanced vegetation index.
A simple linear regression showed no significant correlation between species richness and latitude for all plant groups ( Fig. 5 ). These analyses do not support the presence of latitudinal effects on plant species richness patterns along the ridge of the Baekdudaegan.
Lager Image
Relationship between latitude and species richness for total, common, and rare plant species along the ridge of the Baekdudaegan Mountains,South Korea. N represents the number of sample plots in each latitudinal band.
DISCUSSION
In this study, we examined plant species richness patterns in relation to an altitudinal gradient and explored the underlying causal mechanisms of those patterns using primary data at a regional scale from the ridge of the Baekdudaegan, South Korea. Previous studies that demonstrate the existence of a relationship between altitude and species richness can be classified as representing either local or regional datasets (Romdal and Grytnes 2007). Many previous studies aimed to explain mechanisms at broad regional scales, such as countrywide or continental scales (Jetz and Rahbek 2002, Grau et al. 2007, McCain 2009, Alexander et al. 2011), using secondary distribution data derived from the literature. Studies focused at regional scales cover large areas and large fractions of the total biota as they combine data from numerous sources (Karger et al. 2011). In general, regional-scale studies, including the present study, presume that when examined at broad scales, mountain massifs are bounded with respect to macroecological processes. Therefore, such large-scale studies focus on altitudinal effects and consider that latitudinal effects on species diversity are either insignificant or extremely small (Marini et al. 2011). Indeed, in the current study, plant species richness did not show a significant relationship with latitude for any of the plant groups. This result supports the assumption that latitude is not a significant determinant of plant species richness patterns, at least on the basis of data from the Baekdudaegan ridge. Moreover, most previous analyses of the relationship between species richness and latitude used data from wide latitudinal bands (>10°) and at continental or hemispheric scales (Stevens 1989, Buckley et al. 2003, Cruz et al. 2005).
The cause of altitudinal richness patterns is a controversial topic in ecology and biogeography (Wang et al. 2007). Rahbek (2005) identified three main patterns of altitudinal species richness: (1) a monotonic decline with increasing altitude, (2) a plateau at low altitudes, and (3) a ‘hump-shaped’ distribution with peak richness at intermediate altitudes. The present study showed that species richness in all plant groups peaked at intermediate altitudes along the Baekdudaegan ridge, even though the absolute altitudes of the richness peaks differed somewhat among the three plant groups. At the most general level, the present results contribute to the growing body of evidence that, in mountainous regions, hump-shaped distributions of plant species richness predominate.
Altitudinal richness patterns are considered to reflect an optimal combination of space-related variables, such as area and the MDE (Wang et al. 2007), climatic variables including temperature and precipitation (Bhattarai and Vetaas 2003, Kluge et al. 2006), and productivity (Chalcraft et al. 2004). On the ridge of the Baekdudaegan, we observed that space-related variables (area and the MDE) strongly influence altitudinal patterns of total and common plant species richness as determined by simple linear models, and that climatic variables (MAT and MAP) also contribute significantly to the altitudinal patterns as determined by quadratic models. However, MDE and MAT were significant predictors of rare species richness pattern in simple linear models and only climatic variables contributed significantly to the altitudinal pattern of rare plant species in quadratic models. These results for the linear models were similar to those obtained with multiple regression models. In multiple regression models, the MDE was selected as the most powerful determinant of richness patterns in total and common plant species, whereas climatic variables were selected as the most powerful determinants of rare plant species richness pattern. The relative influence of each of these determinants on species richness may vary among regions and taxa. Below, we discuss how area, the MDE, climatic variables, and productivity may contribute to altitudinal patterns of plant species richness along the ridge of the Baekdudaegan.
The important influence of the MDE on species richness patterns along altitudinal gradients is well documented (Liew et al. 2010, Acharya et al. 2011, Lee et al. 2012). The MDE is a stochastic abiotic hypothesis that is based on the premise that spatial distributions of species richness are constrained by the shapes of geographical domains and by species range sizes. Under these conditions, the random replacement of species ranges within a bounded domain results in overlapping species ranges, with a greater number of overlapping ranges towards the center of the domain than at the margins; thus, higher species richness occurs in the central region of geographical domains than at the periphery (Colwell and Lees 2000). However, we also observed a deviation of diversity distributions from the MDE null hypothesis for all plant groups. Recent work in the eastern Himalayas indicates that tree species richness patterns strongly deviate from those predicted by the MDE null model (Acharya et al. 2011), as do species richness patterns for other plant groups (Kluge et al. 2006, Ah-Peng et al. 2012). The deviations may be caused by the presence of relatively large numbers of species with narrow altitudinal ranges (i.e., those present in only one or two bands); as in the present study, Ah-Peng et al. (2012) recorded a relatively large number of species with a small altitudinal range, which comprised species present in only one or two altitudinal bands. Therefore, we suggest that narrowly distributed species are likely to result in large deviations from the MDE null model for all plant groups. The degree of deviation from the MDE null model may also explain the influence of factors such as ecology, history, and evolution on observed distribution patterns (Acharya et al. 2011).
Area is also an important predictor of total and common plant species richness in both simple linear and quadratic regression models; however, area is a weak predictor of richness patterns in multiple regression models in which the MDE is one of the variables. This result may reflect the strong correlation between the MDE and area ( R 2 > 0.55, P < 0.001 for all plant groups), such that the MDE has a stronger influence on species richness than does area in multiple regression models. Therefore, we speculate that the effect of area is masked by the dominance of the MDE, at least in relation to plant species richness.
The present regression analyses indicated that the climatic variables were the most powerful predictors for rare plant species. In general, rare species characteristically have a narrow geographical range and highly restricted habitat preferences compared with common species (Bevill and Louda 1999). Moreover, most rare species on the Baekdudaegan ridge are distributed at higher altitudes than common species (Cho et al. 2004). Indeed, climatic variables are indicated to be more important than space-related variables for range-restricted species such as rare species, whereas space-related variables are more important than climatic variables for wide-ranging species such as common species (Jetz and Rahbek 2002, Brehm et al. 2007, Lee et al. 2013). Although we excluded climatic variables from the simple linear and multiple regression models for total and common plant species richness, climatic variables still might play an important role as determinants of richness patterns for such plant species. Parabolic patterns of species richness in relation to climatic variables are best described using a quadratic function. If climatic variables contribute to observed patterns of total and common plant species richness, optimal ranges of temperature and precipitation are likely to occur at intermediate altitudes, and favorable climatic conditions at intermediate altitudes may lead to higher total and common plant species richness in these areas. Intermediate altitudes may provide optimal combinations of temperature and moisture levels for plant growth, and consequently higher resource availability to support the coexistence of a greater number of species (Kluge et al. 2006).
Remote sensing-based vegetation indices used as surrogates of primary productivity provide evidence for significant productivity–richness relationships, both linear and unimodal, which indicates that productivity estimates can be used to evaluate plant species richness patterns (Rowe 2009), albeit at different spatial scales depending on the taxonomic group under study (Hurlbert and Haskell 2003). However, we found little evidence that productivity (as measured by EVI) influences species richness patterns along the altitudinal gradient on the Baekdudaegan ridge. This finding indicates that the relationship between productivity and plant richness may be more complex than previously thought. Furthermore, poor support in the present study for a productivity–richness relationship questions the energy–diversity hypothesis for vascular plants, at least along the altitudinal gradient on the ridge of the Baekdudaegan. Energy input is indicated to be a strong predictor of richness only in far northern portions of the globe, and precipitation or the interaction between energy input and moisture shapes large-scale worldwide biodiversity patterns (Hawkins et al. 2003). However, the results presented here provide weak support for the productivity–diversity hypothesis and suggest that the relationship between productivity and richness at smaller spatial scales (e.g., that of a mountain range) may be incongruent with that found at larger spatial scales. Furthermore, Adler et al. (2011) intensively reviewed relationships between productivity and species diversity in mountainous areas across the globe and observed that a non-significant relationship is predominant between the two variables.
CONCLUSION
Although the absolute altitudes of the richness peaks vary among total, common and rare plant species, species richness patterns on the Baekdudaegan ridge show ‘hump-shaped’ patterns for each plant group. Regression analyses show that the MDE and climatic variables are the best predictors of altitudinal patterns of plant species richness on the Baekdudaegan ridge. The MDE is the most important explanatory variable for total and common species richness, whereas climatic variables are significant predictors for rare species richness. Our results are consistent with many previous studies, which indicate that the MDE is more important for wide-ranging species such as common species, whereas climatic variables are better predictors for range-restricted species such as rare species. Latitudinal effects are not supported as a determinant of species richness in the present study. Even though the MDE and climatic variables are the primary predictors in the best simple and multiple regression models, discrimination among these variables may not be possible by simple comparisons of regression coefficients because the MDE and climatic variables are strongly correlated with plant species richness. Altitudinal species richness patterns can be influenced by a number of climatic, spatial, and historical variables (Rahbek 2005, Acharya et al. 2011, Lee et al. 2013). In the present study, we examined the relationships between major explanatory variables and altitudinal patterns of plant species richness. However, we did not consider evolutionary or historical variables in our analysis. Further study into the influence of evolutionary history, including historical contingencies and niche conservatism, with spatial, climatic, and energy-related variables may provide insights into the factors that determine the altitudinal distribution of plant communities at macroecological scales.
Acknowledgements
We thank Keun-Wook Lee and Sang-Hyouk Seo for invaluableassistance with the fieldwork and data analysis.Jong-Hwan Lim and Sang-Gon Park are thanked for theirsupport and encouragement. This paper forms a part ofthe ‘Korea Big Tree Project’ funded by the Korea GreenPromotion Agency, Korea Forest Service.
References
Acharya BK , Chettri B , Bijayan L 2011 Distribution patternof trees along an elevation gradient of Eastern Himalaya,India. Acta Oecol 37 329 - 336    DOI : 10.1016/j.actao.2011.03.005
Adhikari D , Barik SK , Upadhaya K 2012 Habitat distributionmodelling for reintroduction of Ilex khasiana Purk., acritically endangered tree species of northeastern India. Ecol Eng 40 37 - 43    DOI : 10.1016/j.ecoleng.2011.12.004
Adler PB , Seabloom EW , Borer ET , Hillebrand H , Hautier Y , Hector A , Harpole WS , O’Halloran LR , Grace JB , Anderson M 2011 Productivity is a poor predictor ofplant species richness. Science 333 1750 - 1753    DOI : 10.1126/science.1204498
Ah-Peng C , Wilding N , Kluge J , Descamps-Julien B , Bardat J , Chuah-Petiot M , Strasberg D , Hedderson TAJ 2012 Bryophyte diversity and range size distribution alongtwo altitudinal gradients: continent vs. island. Acta Oecol 42 58 - 65    DOI : 10.1016/j.actao.2012.04.010
Alexander JM , Kueffer C , Daehler CC , Edwards PJ , Pauchard A , Seipel T MIREN Consortium 2011 Assembly of nonnativefloras along elevational gradients explained bydirectional ecological filtering. Proc Natl Acad Sci USA 108 656 - 661    DOI : 10.1073/pnas.1013136108
Bevill RL , Louda SM 1999 Comparisons of related rare andcommon species in the study of plant rarity. Conserv Biol 13 493 - 498    DOI : 10.1046/j.1523-1739.1999.97369.x
Bhattarai KR , Vetaas OR 2003 Variation in plant speciesrichness of different life forms along a subtropical elevationalgradient in the Himalayas, east Nepal. Global Ecol Biogeogr 12 327 - 340    DOI : 10.1046/j.1466-822X.2003.00044.x
Braun-Blanquet J 1965 Plant sociology: the study of plantcommunities. Hafner Publishing Company New York
Brehm G , Colwell RK , Kluge J 2007 The role of environmentand mid-domain effect on moth species richness alonga tropical elevational gradient. Global Ecol Biogeogr 16 205 - 219    DOI : 10.1111/j.1466-8238.2006.00281.x
Buckley HL , Miller TE , Ellison AM , Gotelli NJ 2003 Reverselatitudinal trends in species richness of pitcher-plantfood webs. Ecol Lett 6 825 - 829    DOI : 10.1046/j.1461-0248.2003.00504.x
Chalcraft DR , Williams JW , Smith MD , Willig MR 2004 Scale dependence in the species-richness-productivityrelationship: the role of species turnover. Ecology 85 2701 - 2708    DOI : 10.1890/03-0561
Cho HJ , Lee BC , Shin JH 2004 Forest vegetation structureand species composition of the Baekdudaegan MountainRange in South Korea. J Korean For Soc 93 331 - 338
Colwell RK 2006 RangeModel: A Monte Carlo simulation toolfor assessing geometric constraints on species richness.Version 5. User’s Guide and application published at: http://viceroy.eeb.uconn.edu/rangemodel
Colwell RK , Hurtt GC 1994 Nonbiological gradients in speciesrichness and a spurious Rapoport effect. Am Nat 144 570 - 595    DOI : 10.1086/285695
Colwell RK , Lees DC 2000 The mid-domain effect: geometricconstraints on the geography of species richness. Trends Ecol Evol 15 70 - 76    DOI : 10.1016/S0169-5347(99)01767-X
Cruz FB , Fitzgerald LA , Espinoza RE , Schulte JA 2nd 2005 The importance of phylogenetic scale in tests of Bergmann’sand Rapoport’s rules: lessons from a clade ofSouth American lizards. J Evol Biol 18 1559 - 1574    DOI : 10.1111/j.1420-9101.2005.00936.x
Diniz-Filho JAF , Bini LM , Hawkins BA 2003 Spatial autocorrelationand red herrings in geographical ecology. Global Ecol Biogeogr 12 53 - 64    DOI : 10.1046/j.1466-822X.2003.00322.x
Fu C , Hua X , Li J , Chang Z , Pu Z , Chen J 2006 Elevationalpatterns of frog species richness and endemic richnessin the Hengduan Mountains, China: geometric constraints,area and climate effects. Ecography 29 919 - 927    DOI : 10.1111/j.2006.0906-7590.04802.x
Grau O , Grytnes JA , Birks HJB 2007 A comparison of altitudinalspecies richness patterns of bryophytes with otherplant groups in Nepal, Central Himalaya. J Biogeogr 34 1907 - 1915    DOI : 10.1111/j.1365-2699.2007.01745.x
Grytnes JA , Vetaas OR 2002 Species richness and altitude:a comparison between null models and interpolatedplant species richness along the Himalayan altitudinalgradient, Nepal. Am Nat 159 294 - 304    DOI : 10.1086/338542
Hawkins BA , Field R , Cornell HV , Currie DJ , Guegan JF , Kaufman DM , Kerr JT , Mittelbach GG , Oberdorff T , O’Brien EM , Porter EE , Turner JRG 2003 Energy, water,and broad-scale geographic patterns of species richness. Ecology 84 3105 - 3117    DOI : 10.1890/03-8006
Hurlbert AH , Haskell JP 2003 The effect of energy and seasonalityon avian species richness and community composition. Am Nat 161 83 - 97    DOI : 10.1086/345459
Jetz W , Rahbek C 2002 Geographic range size and determinantsof avian species richness. Science 297 1548 - 1551    DOI : 10.1126/science.1072779
Karger DN , Kluge J , Krömer T , Hemp A , Lehnert M , Kessler M 2011 The effect of area on local and regional elevationalpatterns of species richness. J Biogeogr 38 1177 - 1185    DOI : 10.1111/j.1365-2699.2010.02468.x
Kluge J , Kessler M , Dunn RR 2006 What drives elevationalpatterns of diversity? A test of geometric constraints, climateand species pool effects for pteridophytes on anelevational gradient in Costa Rica. Global Ecol Biogeogr 15 358 - 371    DOI : 10.1111/j.1466-822X.2006.00223.x
Kong WS 2007 Biogeography of Korea Plants. GeoBook Publishing Company Seoul (In Korean)
Korea Forest Research Institute 2003 Ecological aspects of Baekdu Mountains in Korea and delineation of their management and conservation area. Korea Forest Research Institute Seoul [report no. 198] (In Korean)
Körner C 2000 Why are there global gradients in speciesrichness? Mountains might hold the answer. Trends Ecol Evol 15 513 - 514    DOI : 10.1016/S0169-5347(00)02004-8
Lee BC 2009 Rare plants: Data book of Korea. Korea National Arboretum Pocheon
Lee CB , Chun JH , Cho HJ , Song HK 2012 Altitudinal patternsof plant species richness on the ridge of the BaekdudaeganMountains, South Korea: area and mid-domain effect. For Sci Technol 8 154 - 160
Lee CB , Chun JH , Song HK , Cho HJ 2013 Altitudinal patternsof plant species richness on the BaekdudaeganMountains, South Korea: mid-domain effect, area, climate,and Rapoport’s rule. Ecol Res 28 67 - 79    DOI : 10.1007/s11284-012-1001-1
Liew TS , Schilthuizen M , bin Lakim M 2010 The determinantsof land snail diversity along a tropical elevationalgradient: insularity, geometry and niches. J Biogeogr 37 1071 - 1078    DOI : 10.1111/j.1365-2699.2009.02243.x
Lomolino MV 2001 Elevational gradients of species-density:historical and prospective views. Global Ecol Biogeogr 10 3 - 13    DOI : 10.1046/j.1466-822x.2001.00229.x
Loreau M , Naeem S , Inchausti P , Bengtsson J , Grime JP , Hector A , Hooper DU , Huston MA , Rafaelli D , Schmid B 2001 Biodiversity and ecosystem functioning:current knowledge and future challenges. Science 294 804 - 808    DOI : 10.1126/science.1064088
Marini L , Bona E , Kunin WE , Gaston KJ 2011 Exploring anthropogenicand natural processes shaping fern speciesrichness along elevational gradient. J Biogeogr 38 78 - 88    DOI : 10.1111/j.1365-2699.2010.02376.x
McCain CM 2009 Global analysis of bird elevational diversity. Global Ecol Biogeogr 18 346 - 360    DOI : 10.1111/j.1466-8238.2008.00443.x
Rahbek C 2005 The role of spatial scale and the perceptionof large-scale species-richness patterns. Ecol Lett 8 224 - 239    DOI : 10.1111/j.1461-0248.2004.00701.x
Romdal TS , Grytnes JA 2007 An indirect area effect on elevationalspecies richness patterns. Ecography 30 440 - 448
Rosenzweig ML 1995 Species diversity in space and time. Cambridge University Press Cambridge
Rowe RJ 2009 Environmental and geometric drivers ofsmall mammal diversity along elevational gradients in Utah. Ecography 32 411 - 422    DOI : 10.1111/j.1600-0587.2008.05538.x
Shin JH 2002 Ecosystem geography of Korea. In: Ecology ofKorea (Lee DW, Jin V, Choe JC, Son YW, Yoo SJ, Lee HY,Hong SK, Ihm BS, eds). Bumwoo Publishing Company Seoul 19 - 46
Stevens GC 1989 The latitudinal gradient in geographicalrange: how so many species coexist in the tropics. Am Nat 133 240 - 256    DOI : 10.1086/284913
Waide RB , Willing MR , Steiner CF , Mittelbach G , Gough L , Dodson SI , Juday GP , Parmenter R 1999 The relationship between productivity and species richness. Ann Rev Ecol Syst 30 257 - 300    DOI : 10.1146/annurev.ecolsys.30.1.257
Wang Z , Tang Z , Fang J 2007 Altitudinal patterns of seedplant richness in the Gaoligong Mountains, south-eastTibet, China. Divers Distrib 13 845 - 854    DOI : 10.1111/j.1472-4642.2007.00335.x
Watkins JE Jr , Cardelús C , Colwell RK , Moran RC 2006 Speciesrichness and distribution of ferns along an elevationalgradient in Costa Rica. Am J Bot 93 73 - 83    DOI : 10.3732/ajb.93.1.73
Yun JI 2010 Agroclimatic maps augmented by GIS technology. Korean J Agric For Meteorol 12 63 - 73    DOI : 10.5532/KJAFM.2010.12.1.063