Eur J Forest Res (2012) 131:453–461 DOI 10.1007/s10342-011-0519-z

ORIGINAL PAPER

Seedling recruitment patterns in a 20 ha subtropical forest plot: hints for niche-based processes and negative density dependence Yue Bin • Guojun Lin • Buhang Li • Linfang Wu • Yong Shen • Wanhui Ye

Received: 17 August 2010 / Revised: 8 April 2011 / Accepted: 18 April 2011 / Published online: 11 May 2011 Ó Springer-Verlag 2011

Abstract Seedling recruitment can be influenced by seed dispersal, conspecific density dependence, and environmental factors. These forces are variant in space. In this study, seedling recruitment was investigated by the inverse modeling method. The inverse modeling framework here was made up of two components: a conspecific effect and a declining function. Power functions (P) and constant conspecific (C) effects were tried. Two types of declining functions were tried: isotropic (I) and anisotropic (A). Thus, the combination of conspecific effect and declining function generated four candidate models: PI, PA, CI, CA. These four models were used to study the seedling recruitment of 13 species in a 20 ha forest plot in subtropical China. It was found that PI, PA, CI, CA are the best models for two, three, five, and three species, respectively. Negative exponents in P were found in three species, which may indicate negative density-dependent mortality. Among those species that supported an anisotropic component, all moderately shade-tolerant and shadetolerant species except Calophyllum membranaceum had

Communicated by C. Ammer. Y. Bin  G. Lin  L. Wu  Y. Shen  W. Ye (&) Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, 510650 Guangzhou, China e-mail: [email protected] Y. Bin  G. Lin  B. Li  Y. Shen Graduate University of Chinese Academy of Sciences, 10049 Beijing, China B. Li Department of Ecology, School of Life Science/State Key Laboratory of Biocontrol, Sun Yat-sen University, 510275 Guangzhou, China

higher possibilities of successful recruitment if their altitudes were relatively low, consistent with their ecological niches. The shade intolerant species, Castanopsis fissa produces seeds weighing 6–250 times more than other species. Yet, its seedling recruitment was more successful at higher altitudes, which again was consistent with its ecological niche. Our research indicated that it is necessary to take anisotropic forces into account when investigating seed dispersal and seedling recruitment in regions with complex topography, and that the niche-based processes and density-dependent mortality at least play some part in constructing the seedling distribution pattern. Keywords Conspecific effect  Inverse modeling  Density dependence  Forest dynamic plot  Niche

Introduction Seedling recruitment is a bottleneck in tree establishment (Queenborough et al. 2007). The spatial pattern of seedling recruitment influences the long-term distribution patterns of species (Queenborough et al. 2007), and can have significant effects on the composition and abundance of plant communities (Leak and Graber 1976). Furthermore, recruitment limitation has been theoretically demonstrated to be a mechanism promoting species coexistence and community diversity maintenance (Abrams 1984; Pacala et al. 1996). Therefore, factors that influence seedling recruitment are of great importance to forest ecologists and researchers. The spatial pattern of seedling recruitment is primarily constrained by seed dispersal in the forest. There have been ongoing interests in long distance seed dispersal (e.g., Bullock and Clarke 2000; Bohrer et al. 2005), which is

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possibly related to metapopulation survival and the maintenance of genetic variability (Bohrer et al. 2005). However, given that only a small fraction of seeds are dispersed relatively far away from the mother plant (Nathan and Muller–Landau 2000), the major interest of plant ecologists remains in short-distance dispersal. Limited seed dispersal has been cited as a mechanism for species coexistence in species-rich communities (Hubbell 2001; Tuomisto et al. 2003; Harms et al. 2001; Valencia et al. 2004). Scientists have found some indirect support for limited seed dispersal: for example, Turnbull et al. (2000) reported that seedling recruitment increased in response to seed addition for about half of the species tested. Several field studies showed that the composition of seedlings in tropical forest canopy gaps closely reflected that of the adult communities around them (Dalling et al. 1998; Hubbell et al. 1999). Since Ribbens et al. (1994) proposed the inverse modeling method, seed and seedling patterns have been studied as the inverse modeling approach has developed (LePage et al. 2000; Uriarte et al. 2005; Muller–Landau et al. 2008; Schurr et al. 2008). For example, LePage et al. (2000) incorporated habitat information into the inverse modeling framework and studied the seedling survivorship in space. Uriarte et al. (2005) considered the effects of densitydependent mortality on the seedling survival pattern. Various dispersal functions have been used, such as twoparameter Weibull function (e.g., Ribbens et al. 1994; LePage et al. 2000), lognormal (e.g., Uriarte et al. 2005) and 2Dt (e.g. Schurr et al. 2008). Some ecologists have also compared the performance of various dispersal functions (Greene et al. 2004; Uriarte et al. 2005; Schurr et al. 2008), and it turns out that no dispersal function is consistently superior to the others. Though the dispersal kernels involved vary in these researches, they all agree with a general pattern that the probability that a seed arrives at specific position declines with increasing distance from the maternal tree. Most research concerning inverse modeling assumed that dispersal possibility decreases as distance increases, in the same manner in all directions. This isotropic assumption is often violated in many forests (Wagner et al. 2004; Wa¨lder et al. 2009). In regions with slopes and valleys, seed dispersal can be extensively influenced by gravity and mountain–valley breezes. These forces, however, are not isotropic in space. For example, gravity takes seeds and fruits to the earth. If the velocity of seeds and fruits is not zero after hitting the ground, gravity is more likely to take them downwards to the valley than up to the ridge. This is a source of anisotropic dispersal. Wind-dispersed species can exhibit anisotropic dispersal patterns due to prevailing wind directions; in mountain regions, mountain–valley breezes, the up-slope and down-slope winds, or a combination of both may occur because of solar radiation (Stull

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1989). Different strength of mountain–valley breezes may also give rise to anisotropic distribution of seeds and fruits, especially for light seeds or seeds with wings. Considering such anisotropic behavior can lead to more realistic models (Na¨ther and Wa¨lder 2003; Wagner et al. 2004). Seedling recruitment is then influenced by the survivorship in space. Conspecifics frequently are considered to negatively influence seedling survival. Such density dependence effects are generally supposed to result from the enhanced propagation of pests and species-specific herbivores and pathogens as conspecific biomass nearby becomes large (Janzen 1970; Connell 1971). Seedling recruitment is meanwhile influenced by a variety of microclimatic and edaphic factors (Augspurger 1984; Scholl and Taylor 2006). Such factors are often not isotropic in space. To understand how the above factors influence seedling recruitment, we use data from seedling stations scattered in a 20 ha plot to analyze anisotropic forces and the effects conspecifics have on seedling recruitment. Specifically, this study was conducted with the inverse modeling approach to answer three main questions: (1) How is seedling recruitment influenced by anisotropic forces? (2) How does the species’ ecological status influence seedling recruitment? (3) What is the role conspecifics play in seedling recruitment?

Materials and methods Study site Dinghushan (112°300 3900 –112°330 4100 E, 23°090 2100 –23°110 3000 N) is located in Guangdong province, China. The Reserve covers an area of 1,155 ha, with low mountains and hills. This region is characterized by a south subtropical monsoon climate, with a mean annual temperature of 20.9°C and a mean annual precipitation of 1,929 mm. A permanent 20 ha plot was established in Dinghushan Nature Reserve (the DHS plot) in November, 2004. All the trees with diameter at breast height (DBH) larger than 1 cm were tagged, mapped, measured for DBH and identified to species. The DHS plot is characterized by rough terrain with deep valleys, and the elevation ranges from 240 to 470 m (Wang et al. 2009). The plot was divided into 500 20 9 20 m subplots for mapping the trees. The altitudes at the four corners of every contiguous 20 9 20 m subplot were measured with a theodolite and cement piles with marks were erected on the corners. In the plot, there are 210 species and 71,617 individuals mapped, falling into 56 families and 119 genera (Ye et al. 2008). The altitudes of all the recorded individuals were obtained by the interpolation function in R 2.9.0 (R development core team 2008).

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Data In November 2007, 149 seed-seedling stations, each with one 0.7 9 0.7 m seed trap and three 1 9 1 m seedling quadrates, were set up along the trails but at least 7 m away in the plot to obtain data for long-term seed production and seedling recruitment (Fig. 1). We believe that trail dynamics does not have much influence on the demographic processes of the monitored seedlings because the trails are so narrow that it just allows a single person to walk through, and the branches of the trees along the trails can touch one’s shoulders when walking along. The stations were censused in March, 2008 and recensused every 3 months. Seedling is defined as the woody plant individuals with less than 1 cm DBH, the same as in Chen et al.’s research (2010). Because these 149 stations were highly aggregated in some part and they can hardly represent the seedling recruitment of the whole 20 ha plot, another 99 additional stations were constructed and censused in March, 2009 (Fig. 1). To locate these stations, we chose the positions of the cement piles erected in 2004 for potential additional stations because their coordinates in the plot were precisely measured when the plot was setup. The 99 additional stations were randomly selected from the cement piles whose distances to both the nearest erected seed-seedling station and the borders of the plot were larger than or equal to 20 m. On the chosen points, three 1 9 1 m seedling quadrates were erected on three sides of the cement piles. The sites for seedling quadrates were set to be 2 m away on the

455

left hand side, on the right hand side, and in the front when standing behind the cement pile and facing the ridge. We predetermined the positions of the sites for seedling quadrates so as to avoid any arbitrary influences. Tree seedlings were measured and identified to the species level. This study was based on the live tree seedlings in the original 149 stations in March 2009 and census data for the 99 additional stations, which means we had a total of 248 stations (Fig. 1). The combined data were a snap shot of tree seedlings in the 248 stations in March, 2009. We only analyzed the species with more than 50 individuals and present in over 25 stations. Yet, they represent a wide range of life histories of plants (Table 1). The measurements of seed and fruit traits of all these except Calophyllum membranaceum, Castanopsis fissa, Lindera metcalfiana, Neolitsea umbrosa, and Syzygium rehderianumb were obtained from the fruits and seeds collected from the 149 seed traps since they were setup. The diameters of fruits and seeds in three orthogonal directions were measured. The average diameter of three directions was taken as size (Table 1). The CV (standard deviation divided by mean) of three diameters was taken as shape, thus spherical fruits and seeds have a CV that is very close to zero. Not a single seed or fruit was collected for Calophyllum membranaceum, Castanopsis fissa, Lindera metcalfiana, Neolitsea umbrosa, and Syzygium rehderianumb. For those species, more limited information about the fruits and seeds was obtained from Song and Yi (1985). Method We predicted that the number of seedlings (Si) at station i was a combined result of the innate conspecific fecundity (CF) in the plot and the change of conspecific effect due to distance (P). The first component was   Dj b CFj ¼ a ; ð1Þ Dmin where Dj was the diameter of the jth tree at breast height, Dmin was the minimal diameter of reproducing trees, and a and b were parameters to be calibrated. We also used another conspecific effect, assuming that the conspecific effect was a constant after the individuals had achieved a minimum DBH. It took the form CFj ¼ a:

Fig. 1 The distribution of stations in the plot

ð2Þ

We supposed that the conspecific effect of the jth tree to reach the ith station followed p IPij ¼  ð3Þ  ; r2 pþ1 pl 1 þ uij

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Table 1 The information for the species relevant to this study Dmin Fruit mass Fruit size Fruit Seed mass Seed size Seed (cm)a (10-2 g) (mm) shape (10-2 g) (mm) shape

Species

Adaptation to light

Growth form

Aidia canthioides

Shade-tolerant

Subarbor 2

6.10

5.46

0.04

0.78

2.63

0.35

Aporosa yunnanensis

Shade-tolerant

Subarbor 3

5.94

6.62

0.13

3.16

4.83

0.22

Ardisia quinquegona

Intermediate shade-tolerant Shrub

1.2

2.91

4.44

0.27

2.90

4.44

0.27

Blastus cochinchinensis

Shade-tolerant

1

0.81

2.41

0.08

0.53

2.53

0.06

Shrub

Calophyllum membranaceum Shade-tolerant

Shrub

1

–

88.33

0.46

–

–

–

Castanopsis fissa

Shade intolerant

Arbor

6

–

66.67

1.41

133.40

–

–

Cryptocarya concinna Lindera metcalfiana

Intermediate shade-tolerant Arbor 8 Intermediate shade-tolerant Subarbor 5

6.73 50.33

0.40 0.01

12.24 –

6.73 –

0.40 –

Machilus chinensis

Intermediate shade-tolerant Arbor

Memecylon ligustrifolium

Shade-tolerant

Neolitsea umbrosa

Intermediate shade-tolerant Arbor

6

Ormosia glaberrima

Intermediate shade-tolerant Arbor

6

Syzygium rehderianum

Intermediate shade-tolerant Arbor

6

a

12.24 –

8

Subarbor 1.5

31.00

9.48

0.14

15.00

7.31

0.24

20.26

8.06

0.03

16.22

7.34

0.01

51.67

0.22

–

–

16.68

0.77

– 58.09 –

–

–

– 18.69 –

6.86

0.22

–

–

The minimum diameter for reproduction

where p, p and l were parameters, rij was the distance between the ith station and the jth tree (only horizontal and vertical axes were used). The Eq. 3 is actually a 2Dt dispersal kernel. Conspecific effect here mainly refers to seed dispersal and conspecific density dependence. 2Dt was a widely used seed dispersal kernel (Clack et al. 1999; Schurr et al. 2008), for simplification we supposed that other conspecific effects besides seed dispersal also took that form. When the anisotropic forces were taken into account, the conspecific effect of the jth tree to reach the ith station was modified slightly based on Eq. 3: APij ¼ IPij þ c

Atj  Asi ; rij

n X

CFj Pij :

i eSi SO i ; Oi !

ð6Þ

and the likelihood (L) for a set of N station was (Ribbens et al. 1994) L¼

N Si Oi Y e S i

i¼1

Oi !

ð7Þ

The set of values of parameters that maximized L were the values we are looking for. The best model was selected using Akaike’s information criterion (AIC). Both data analysis and figure drawing were done with R 2.9.0 (R development core team 2008).

ð5Þ

j¼1

By combining these functions, four models were obtained: Power conspecific effect and isotropic declining function (PI): Eqs. 1 and 3 Power conspecific effect and anisotropic declining function (PA): Eqs. 1 and 4 Constant conspecific effect and isotropic declining function (CI): Eqs. 2 and 3

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We predicted that the number of seeds and seedlings observed as following a Poisson distribution, where the mean of the Poisson distribution was predicted as Si (Ribbens et al. 1994). The likelihood of observing Oi seeds or seedlings when a mean of Si seeds or seedlings were expected under a Poisson distribution was

ð4Þ

where Atj denoted the altitude of the jth tree, Asi the altitude of the ith station, rij was the distance between the ith station and the jth tree (only horizontal and vertical axes are used), and c was a parameter to be calibrated. The number of seedlings predicted to be growing at the ith station was Si ¼

Constant conspecific effect and anisotropic declining function (CA): Eqs. 2 and 4

Results Generally, seedlings are distributed where there are conspecific adult trees (Fig. 2). Yet, it is not necessary that where there are adult trees, there are seedlings of the corresponding species (Fig. 2b, c, d, f, g, i, j, k, m). For some species, the abundance of seedling does not seem to be related to the abundance of adult trees (Fig. 2c, f, j, k, l, m). The best model for every species was selected according to AIC (Table 2). For 6 of the 13 species, the best model

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contained an anisotropic declining function (Table 3). These six species included two shrubs, three species reaching the canopy and one subcanopy member. The remaining seven species had models with an isotropic declining function, of which five were moderately shadetolerant. Whether the species favor an isotropic or anisotropic declining function was not related to fruit and seed traits (Fig. 3). Constant conspecific effect was supported by eight species, of which six were subcanopies or shrubs. All except one species that supported power conspecific effect were canopy members. The method produced poor fits (adjusted r square: R2a = 0.07–0.15) to four species, and good fits (R2a = 0.16–0.34) to six species. The method fitted Blastus cochinchinensis the best (R2a = 0.34; Fig. 4), but failed to produce a statistically valid model to explain the seedling distribution of Calophyllum membranaceum (R2a \ 0) and Memecylon ligustrifolium (R2a \ 0). The performance of the approach seemed to be related with the shade tolerance of the species: among the five shade-tolerant species in this study, four had relatively small (R2a B 0.15) or even negative R2a , and Blastus cochinchinensis was the only exception. Among the seven moderately shade-tolerant species, five had relatively large R2a (R2a [ 0.15), except for Ardisia quinquegona and Syzygium rehderianum. Growth form was another possible factor related to the R2a of the result. Two of the 3 shrubs and 3 of the 4 subcanopy species studied had relatively small or negative R2a (R2a B 0.15), and 4 of the 6 canopy species had relatively large R2a (R2a [ 0.15). Among different species, a, u, and p varied over several orders of magnitude (Table 3). Among those species that supported AI or AC, the parameter c was positive for all moderately shade-tolerant species and shade-tolerant species except Calophyllum membranaceum, and negative for Calophyllum membranaceum and the only light demanding species in this study. One of the species with negative c has 6 to over 250 times larger seed mass than the other species that support AI or AC but had positive c (Tables 1, 3).

Discussion The inverse modeling method has been used widely in the field of predicting seed and seedling distribution (Ribbens et al. 1994; LePage et al. 2000; Uriarte et al. 2005; MullerLandau et al. 2008). Yet, previous studies usually did not consider an anisotropic effect. Wagner et al. (2004) and Wa¨lder et al. (2009) were two of the very few exceptions. The simplest reason why directionality should be taken into consideration in seed dispersal models is that it can be observed in nature (e.g., Wagner et al. 2004). Anisotropy may be closer to reality for seed dispersal in some case due to wind direction (Stull 1989) and gravity, and for seedling

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distribution possibly due to anisotropic distribution of seeds and subsequent ecological processes influencing the survival of seedling. In our study, we modified the classic inverse modeling framework slightly so as to integrate an anisotropic effect due to altitude. The result showed that the anisotropic models can fit the data of six species better than the isotropic models, indicating that seedling recruitment for some species can be anisotropic around conspecific individuals. In contrast to our study, Wagner et al. (2004) found that seed dispersal consistently showed anisotropic dispersion. In their research, the studied objects are seeds and, as the authors implied in the context, the seeds are dispersed mainly by wind (Wagner et al. 2004). The two points are the main causes of anisotropy. On the contrary, the objects we studied are seedlings with a height of at least 20–30 cm with complex vascular tissue in stems and leaves, indicating that they have survived in the understorey for a long time and probably undergone severe competition and many environmentally filtering processes. Unsurprisingly, the resulting spatial pattern is more vague and complex, making it difficult to detect any underlying trends. Yet, some species still show anisotropic seedling recruitment around conspecifics. In contrast to wind-dispersed seeds for which wind direction is responsible for anisotropy (Wagner et al. 2004), the ecological preference of these species explain much of the observed pattern. Among those species that show anisotropic seedling recruitment, the parameter c was positive for all moderately shade-tolerant species and shade-tolerant species except Calophyllum membranaceum, suggesting that these species gain an extra benefit for successful recruitment when the altitude of their location decreases. This is consistent with their ecological trait because the sites with lower altitude are very easy to be shaded by the ridges in this topographically complex forest plot. Furthermore, it is evident that shade tolerance is significantly negatively correlated with drought tolerance, according to Niinemets and Valladares’s (2006) research involving 806 shrubs and trees. In mountain regions, the distribution of soil moisture is closely related to the relative altitude and soil moisture content on the lower slope is larger than on the upper slope (Xu et al. 2003). In an old-growth Douglas fir forest, researchers also found a strong moisture gradient related to elevation at soil depths of 30 and 50 cm; deeper soils were drier at higher elevations (He and Duncan 2000). The shade-tolerant and moderately shade-tolerant seedlings may have undergone some niche-based processes during which the seeds that germinate at higher altitude meet difficulty in survival because of a lack of soil moisture. The case of Castanopsis fissa provides evidence for this from the opposite aspect. Seeds of Castanopsis fissa weigh 6 to over 250 times more than the species with positive c. From

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Fig. 2 The distribution of seedlings and adult trees of the 13 species in the plot. Gray dashed lines are the contour lines depicting the landform of the plot. A gray triangle denotes an adult tree of the

corresponding species. Black circle denotes the existence of seedlings at specific station. The radius of a circle is proportional to the logarithm of the abundance of seedlings at the station

this biological trait, we can infer that these individuals may leave most of their seeds at low altitudes, resulting in large amount of seedlings at sites with lower altitudes. Yet, the current situation is that Castanopsis fissa has a negative c, indicating that the higher the altitude, the more seedlings can be found. This is in agreement with the light demanding feature of Castanopsis fissa rather than the biological feature of its seeds. Thus, niche-based processes

and species ecological preference for specific habitats may at least play some part in forming the seedling distribution pattern in this subtropical forest. In previous researches involving inverse modeling, the expected number of seeds or seedlings often increases with conspecific DBH (Uriarte et al. 2005; LePage et al. 2000). In those researches, the seedlings are in the newly emerged stage before competition or density dependence processes

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Table 2 The AIC (Akaiker’s information criterion) for the four models fitted to the species seedling recruitment data Species

PIa

PAb

CIc

CAd

Aidia canthioides

1,977.45 1,964.63e 1,991.36 1,984.38

Aporosa yunnanensis

335.95

336.54

334.57

334.89

Ardisia quinquegona

466.15

464.38

460.62

462.36

Blastus cochinchinensis

593.83

536.85

596.85

531.66

Calophyllum membranaceum

527.69

526.31

526.78

525.97

377.84

367.84

376.01

365.51

Castanopsis fissa Cryptocarya concinna

1,681.05 1,678.06

1,730.46 1,832.70

Lindera metcalfiana

224.19

224.68

222.69

223.10

Machilus chinensis

361.50

363.20

372.38

377.02

Memecylon ligustrifolium

329.20

329.13

327.22

327.37

Neolitsea umbrosa

368.26

369.63

367.98

369.87

Ormosia glaberrima

680.29

677.57

715.50

734.62

Syzygium rehderianum

760.06

760.54

776.48

800.80

a b c

Power conspecific effect and isotropic declining function Power conspecific effect and anisotropic declining function Constant conspecific effect and isotropic declining function

d

Constant conspecific effect and anisotropic declining function

e

The lowest AIC for each species is in bold

take place, thus the DBH involved mainly stands for the fecundity of conspecifics. In our study, the seedlings are not newly emerged seedlings but have survived in the understory for a long time. This can be identified by their height (at least 20 cm) and the developed vascular tissue in stems and leaves. In this situation, we need to consider postdispersal processes (Nathan and Muller–Landau 2000). The initial positive relationship between adult DBH and the

abundance of newly emerged seedling can be masked if seedling success is negatively related to adult DBH. In other words, if there is strong, positively density-dependent mortality as a result of the conspecific adult nearby, the net conspecific effect can be negative. Conspecific density dependence has been proposed as a mechanism maintaining species diversity in forest communities (Janzen 1970; Connell 1971; Peters 2003; He and Duncan 2000). Densitydependent mortality possibly results from the enhanced propagation of pests and species-specific herbivores and pathogens (Janzen 1970; Connell 1971). Some researchers have pointed out that high conspecific density and conspecific basal area result in low survival for conspecific trees, especially for seedlings and saplings (Peters 2003; Webb and Peart 2000; He and Duncan 2000; Pigot and Leather 2008). Uriarte et al. (2005) also found that the majority of species studied supported a model with a density-dependent effect. Density-dependent effect is probably the reason why some species have negative exponents for the conspecific effect function (Eq. 1), indicating that the number of recruits expected at a given distance decline with increasing conspecific DBH, thus providing indirect evidence for the density dependence phenomenon. In our study, those species are Memecylon ligustrifolium, Syzygium rehderianum, and Cryptocarya concinna. In the Dinghu Mountain Nature Reserve, Cryptocarya concinna is the main food for Thelassodes quadraia, an insect (Huang et al. 1998). The speciesspecific pest possibly explains why Cryptocarya concinna seedling recruitment show a negative relationship with conspecific diameter. The other two species, Memecylon ligustrifolium and Syzygium rehderianum, are not as well studied as Cryptocarya concinna is.

Table 3 Adjusted R square and parameter estimates for the best model Species Aidia canthioides

R square 0.103

Best modela PA

u

p 4

6.10 9 10

2

a 1

1.07 9 10

-3

c 2

1.23 9 10

4

1.37 9 10

b -6

1.17

Aporosa yunnanensis

0.150

CI

2.43 9 10

1.16 9 10

1.30 9 10

–

Ardisia quinquegona

0.096

CI

8.63 9 100

2.84 9 10-1

1.13 9 101

–

–

Blastus cochinchinensis

0.343

CA

6.12 9 101

1.14 9 100

1.36 9 101

8.59 9 10-6

–

-0.005

CA

4.12 9 104

1.10 9 10-2

1.48 9 105

-3.41 9 10-8

–

2

-4

5

Calophyllum membranaceum

–

Castanopsis fissa

0.106

CA

2.03 9 10

9.84 9 10

2.51 9 10

-2.69 9 10-8

–

Cryptocarya concinna

0.223

PA

1.05 9 103

1.92 9 10-2

3.54 9 105

8.95 9 10-8

-15.24

1

3

0

Lindera metcalfiana

0.181

CI

1.95 9 10

1.27 9 10

1.45 9 10

–

–

Machilus chinensis

0.277

PI

8.60 9 100

2.74 9 10-4

6.57 9 103

–

2.74

-0.014

CI

1.79 9 100

1.85 9 10-4

1.87 9 104

–

-0.08

Neolitsea umbrosa Ormosia glaberrima

0.219 0.290

CI PA

5.24 9 102 7.00 9 100

6.63 9 10-4 6.42 9 10-2

4.99 9 104 6.52 9 102

– 1.18 9 10-6

– 2.66

Syzygium rehderianum

0.072

PI

8.67 9 102

4.53 9 10-2

4.13 9 104

–

-78.2

Memecylon ligustrifolium

a

Best model: PI: Power conspecific effect and isotropic declining function; PA: power conspecific effect and anisotropic declining function; CI: constant conspecific effect and isotropic declining function; CA: constant conspecific effect and anisotropic declining function

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Fig. 4 The predicted values by the best model versus the observed values of Blastus Cochinchinensis, the species with the highest adjusted R square in this study Fig. 3 Boxplots of seed/fruit traits of species grouped by the declining function they supported. In this figure, the studied species are classified by the declining function they support. Thus, we obtained two groups of species, the anisotropic group and the isotropic group (the horizontal axis). The distributions of seed/fruit traits (e.g., fruit mass in panel a, the vertical axis) were plotted as a box for the two groups of species. The notch in each ‘‘box’’ denotes the 95% confidence interval. If the notches of the two boxes in a panel (e.g., panel a) do not overlap, the traits (e.g., fruit mass in panel a) of the groups of species do not have significant difference. From this figure, we know that all the difference in traits were not significant

The relation between DBH and number of recruits at given distance is very diverse in form. Not all the species take the form of Memecylon ligustrifolium, Syzygium rehderianum, and Cryptocarya concinna. Aidia canthioides, Ormosia glaberrima, and Machilus chinensis have more recruits as conspecific DBH becomes larger. The other species have constant conspecific effects, including all of the shrubs, 2 of 3 subcanopy species, and one canopy species. Although assuming that seedling abundance increase with tree diameter, Uriarte et al. (2005) found that the relationship between tree diameter and the number of seedlings produced is fairly flat for the majority of species. Our research is not alone in appointing a constant to the relationship between the number of offspring and conspecific size. Wagner et al. (2004) also assumed that tree of different sizes gave birth to identical numbers of fruits. In our study, the species supporting constant conspecific effect are mainly shrubs and subarbors which do not have wide ranges of DBH. The resulting variation in conspecific effect due to DBH may be masked by the variation from

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other factors that are not considered in this research, e.g., the habitat conditions of adult trees and the seedlings (Schurr et al. 2008). Another possibility is that this effect was not calibrated well, given that Ribbens et al. (1994) found strong positive interactions between conspecific DBH and the number of recruits. Although widely used, the inverse modeling is not without methodological challenges. First, we cannot include all conspecifics that have an effect on the recruitment, although most of our seedling stations are over 20 m from the border of the plot. This is a problem that many other researchers also cannot avoid (LePage et al. 2000; Uriarte et al. 2005). Second, the best fit obtained is only the best among those simple models that were tried, and thus may not well describe the complex and individually variable seedling recruitment that are influenced by seed dispersal process, competition, and density dependence process. Third, animal’s effect on seed dispersal was not taken into account in this research. Despite their shortcomings, our fitted models explain an average of 15.7% of the total variations in seedling distribution among stations, slightly smaller than other similar studies (Muller–Landau et al. 2008). These models capture a substantial first outline of seedling recruitment of the studied species and provides an initial basis for predicting seedling recruitment in this forest plot. Acknowledgments The authors thank Yong Shen, Wenping Liu and many other individuals for their help with the field work. This study was funded by the Knowledge Innovation Project of the Chinese Academy of Sciences (KZCX-YW-430-03), the National Key Technology R&D Program (2008BAC39B02).

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