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Article

Habitat Filtering Covers the Strength of Density Dependence and Functional Density Dependence on Seedling Survival in Cangshan Mountain, Southwest China

by
Zhe Feng
,
Junjie Wu
*,
Jiwen Sun
,
Xiaoli Yu
,
Liping Wang
,
Fengxian Chen
and
Xiaokang Hu
College of Agriculture and Biological Science, Dali University, Dali 671003, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(1), 76; https://doi.org/10.3390/f15010076
Submission received: 20 November 2023 / Revised: 23 December 2023 / Accepted: 28 December 2023 / Published: 30 December 2023
(This article belongs to the Section Forest Biodiversity)
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Abstract

:
Conspecific negative density dependence (CNDD) is an important mechanism for species coexistence and community dynamics. Phylogenetic negative density dependence (PNDD) and functional negative density dependence (FNDD) are extensions of CNDD, and many studies have shown that they have become powerful and reliable methods for exploring the mechanisms of species coexistence. However, most studies have focused on only one or two of these mechanisms and have not considered whether and how habitat variables affect the detection of these density dependences. To investigate the relative importance of these mechanisms, we set up three 0.09 ha dynamic plots at Cangshan Mountain in southwest China, and used generalized linear mixed models to analyze how the survival of 546 woody plant seedlings was affected by neighborhood density and habitat variables. Our results showed that heterospecific seedling density dependence and functional trait density dependence played key roles in seedling survival. Habitat factors, phylogenetic densities, and adult neighbors had no significant effect on seedling survival in the three plots. However, habitat filtering covered the detection of density dependence and functional trait density dependence. Our study demonstrates that failure to control for habitat variables may obscure the importance of density dependence and functional trait density dependence on seedling survival.

1. Introduction

Understanding the mechanisms underlying species coexistence and their intrinsic drivers remains a major challenge for ecologists [1,2,3,4]. Ecologists have proposed a multi-species coexistence theory to explain species coexistence and community dynamics at local scales, but the universality of each mechanism and its intrinsic driving mechanisms remain controversial [1,2,3,4]. Conspecific negative density dependence (CNDD) is an important mechanism [5,6,7], and many previous studies have attempted to document CNDD by examining the relationships of plant performance with the densities of conspecific neighbors [5,6,7]. High densities of conspecific adults reduce seedling survival because of host-specific adversaries [8,9]. Wu et al. [6] found that seedling survival was significantly affected by conspecific adult neighbor density. Although CNDD is an important process for maintaining species coexistence [10,11], the grouping of neighbors into conspecific and heterospecific is overly simplistic [6]. Recent studies have found that closely related species have stronger competition for resources and shared more natural enemies, a phenomenon known as phylogenetic negative density dependence (PNDD), which is an extension of CNDD across evolutionary distance [6,12]. Pu et al. [13] found that CNDD played a significant role in the survival of Abies nephrolepis and Picea koraiensis seedlings, whereas PNDD was critical for the survival of Pinus koraiensis and Betula platyphylla seedlings. However, the universality of these hypotheses requires further investigation.
Studies of community ecology based on phylogenetic methods are usually based on the hypothesis that phylogenetic relationships between species can replace the similarity of functional traits between species; that is, the hypothesis that functional traits are phylogenetically conserved [14,15,16]. However, the degree of phylogenetic conservation of different functional traits is not consistent [17]. Natural enemies do not directly determine whether plants have the same history of progress, but instead respond directly to relevant functional traits [18]. Therefore, the interactions between neighbors may be more dependent on functional trait density. However, methods based solely on functional traits have limitations. We cannot measure all functional traits and may miss those that are particularly important in the neighborhood effect. Phylogenetic methods can provide more comprehensive species similarity information than some monitored functional traits [19]. In addition, plant functional traits not only represent an increase or decrease in plant survival rate due to differences in resource demand and access among tree species, but also directly reflect the response of plants to environmental changes [20,21,22]. Species with resource-conservative strategies, such as a smaller specific leaf area (SLA), larger wood density (WD), larger seed mass (SM), and larger leaf dry mass content (LDMC), generally exhibit stronger resistance to restricted conditions and, therefore, have a higher probability of survival because more resources are invested in plant defense [23,24,25,26]. This approach has become a powerful and reliable method for exploring community dynamics using plant functional traits [23,27,28,29].
Beyond biotic interactions, the abiotic variables are also important drivers of local community composition [26], such as light availability [6], soil nutrients [30], altitude [28], and soil moisture content [31]. In addition, conspecific individuals, species with similar functional traits, and species with close phylogenetic relationships may congregate in similar habitats. Therefore, the interaction between habitat variables and density dependence contributes to plant performance [6,32,33,34]. To elucidate such scenarios, models that consider the effects of neighbors’ density, functional trait density, or phylogenetic density effects with and without abiotic variables are required [6].
In this study, we set up three 0.09 ha dynamic plots on the western slope of Cangshan Mountain in Yunnan Province in Southwest China, and the data of 546 seedlings in three censuses were used. Using generalized linear mixed models, we explored the relative importance of conspecific density dependence, phylogenetic density dependence, functional trait density dependence, and habitat factors on seedling survival. Models were built with and without habitat variables to explore whether and how habitat filtering affected the strength of density dependence. We specifically asked the following questions: (1) What is the relative importance of density dependence, phylogenetic density dependence, functional trait density dependence, and habitat factors? (2) Does habitat filtering affect the detectability of density dependence, and how?

2. Materials and Methods

2.1. Study Site

The research sites are located on Cangshan Mountain (25°70′11″ N, 100°04′56″ E) in southwestern China. The elevation of the plot ranged from 2503 m above sea level (asl) to 2895 m asl (Figure 1). Cangshan Mountain has a temperate climate with a mean annual temperature of 16.1 °C. The region undergoes a distinct seasonal shift, with a pronounced rainy season extending from May to October, followed by a dry season commencing in November and persisting until April of the following year. The mean annual precipitation is 1059 mm, and the rainy season accounts for 80% of this total [35]. In the year 2020, we established three forest dynamics plots of 0.09 hectares (30 m × 30 m) each. All woody species (diameter at breast height (DBH) ≥ 1 cm) were tagged, identified, and measured [36].

2.2. Seedling Quadrats

In November 2020, 64 seedling quadrats were established within each plot (Figure 1). All woody seedlings with a DBH < 1 cm and a height ≥ 10 cm were identified, tagged, and measured in each seedling quadrat. The first census was conducted in November 2020, with subsequent annual censuses during the same month. During each census, the state (alive or dead) of all woody seedlings was recorded. Three censuses had been conducted by December 2022.

2.3. Neighborhood Variables

The total seedling neighbor density of each seedling was defined as the number of seedlings within the quadrat. Conspecific and heterospecific seedling neighbor densities were defined in the same manner. Tree, shrub, and liana seedlings were monitored at the censuses, and all were included in the calculation of seedling neighbor densities.
We calculated two adult neighborhood density variables to quantify the local adult neighborhood effects: the conspecific adult neighbor density index (CI) and the heterospecific adult neighbor phylogenetic dissimilarity index (PI). We calculated the CI as the summed basal area (BA) of adults in the same plot as the focal seedling [37]. We calculated the PI as the sum of the product of the phylogenetic distance (PD) between the focal seedling and the neighbor tree and the BA of the neighbor tree [20]. A phylogenetic tree was constructed using the V.phylomaker II package [38] in R (v. 4.3.1; R Development Core Team 2023). The equations are as follows:
C I = ∑ i N B A i  
P I = ∑ i N B A i × P D i  
where N is the number of adult neighbors. We did not use distance weighting in these models because the farthest distance in our plots did not exceed 30 m, and some studies have suggested an interaction between plants within 30 m [39]. In addition, some studies still believe that directly dividing adults into conspecific and heterospecific is inaccurate; therefore, we used PI as the heterospecific adult neighbor density index [6,40].

2.4. Functional Traits

We amassed a data collection of six functional traits, leaf chlorophyll content (LCH), maximum height (Hmax), specific leaf area (SLA), leaf area (LA), leaf thickness (LT), and leaf dry mass content (LDMC), from a total of 30 seedling species. These traits are important indicators of plant ecological strategies [41]. At least five healthy seedlings of each species and 149 individual seedlings were collected. The Hmax values for all species were procured from the publication Flora of China [42]. To determine the leaf traits of the seedlings, we collected five mature and healthy leaves from each individual. The detailed methodologies employed for these procedures can be found in the Handbook on Functional Trait Collection [43].
We performed principal component analysis (PCA) on these six traits for all species to minimize trait redundancy. The PCA results revealed that the first three PCs accounted for 80% of the trait variation across all species. The results corresponding to each PC axis are presented in Supplementary Table S1.

2.5. Habitat Variables

The habitat variables for each of the 192 seedling quadrats were characterized by four factors: canopy openness, soil properties, slope, and climate.
Light availability within the seedling plots was estimated in November 2022 using hemispherical photographs captured using a Nikon Coolpix 4500 digital camera equipped with a 180° fish-eye lens (Tokyo, Japan). Photographs were taken under uniformly overcast conditions, either during early dawn or late dusk. We captured three to five photos of each plot (1 m above the ground) [44]. The canopy openness index, indicative of light availability, was obtained using Gap Light Analyser software (GLA, version 2.0) [45].
The measurement methods for climate variables and soil physicochemical properties used in this study were as follows: The 0.09 ha plot was divided into regular grid squares of 15 m × 15 m, and a soil sample was collected at a depth of 10 cm (excluding litter and humus) from a randomly selected location within each square. One sample was randomly drawn from each square, and four samples were taken from each plot for a total of 12 samples. We measure total nitrogen and carbon in soil using a dry combustion method. The soil is finely ground and air-dried, and we use the Vario EL cube CHNOS analyzer (Element Analysensysteme GmbH, Hanau, Germany) [13]. The total phosphorus in the soil was measured using HNO3-HClO4-HF digestion inductively coupled plasma optical emission spectrometry (ICP-OES) using an iCAP 6300 ICP-OES Spectrometer (Thermo Fisher, Waltham, MA, USA) [46]. The volumetric soil water content (%) and soil temperature (°C) were measured in the late dry season of 2022 using the mean values of three replicates taken randomly around the center of each seedling quadrat using a TDR probe (MPM-160B, ICT International Pty Ltd., Armidale, New South Wales, Australia) at a depth of 5 cm [47]. The climate variables included annual mean atmospheric temperature (MAT), average temperature of the warmest month (MTWM), and average temperature of the coldest month (MTCM). Data were obtained using HOBO temperature and humidity recorders (HOBO U23 Pro v2, Onset Computer Corporation, Bourne, MA, USA) at the study sites. In addition, the elevation was measured using a handheld GPS (Monterra, Garmin, Olathe, KS, USA) during the establishment of the forest dynamics plots.
To mitigate the collinearity of habitat variables in our models, we employed PCA on the four habitat variables using the Vegan package [48] in R. TN, TP, TC, elevation, MAT, MTWM, and MTCM were not included in the PCA, but were utilized for subsequent analysis. In different plots, the three principal components represented between 73.73% and 88.94% of the four habitat variables. The results corresponding to each PC axis are presented in Supplementary Tables S2 and S3.

2.6. Statistical Analysis

This investigation focused on the survival dynamics within the plots during two intervals (2020–2021 and 2021–2022). There were 524 trees in 2020, which was reduced to 475 trees in 2021 and 437 trees in 2022. Over this three-year period, 109 trees died and there were 22 new recruits.
Generalized linear mixed models (GLMMs) were constructed using the lme4 package [49] in R to model the probability of seedling survival as a function of explanatory variables with binomial errors [50]. Given the indeterminate age of the seedlings in this study, we incorporated seedling height as a covariate in our models. This was performed to account for the observation that larger seedlings tended to exhibit higher survival rates, thereby excluding the effects of age on survival. All continuous explanatory variables were standardized by subtracting the mean value of the variable (across all individuals in the analysis) and dividing by one standard deviation prior to analysis. This normalization allowed us to directly compare the relative importance of the explanatory variables [51]. The means and ranges of all continuous explanatory variables used in the analysis are listed in Table S4.
To mitigate the effect of spatial autocorrelation on our findings, we integrated random effects for the seedling quadrats into our model [6]. Recognizing that seedlings of different species are likely to exhibit varied responses to local neighboring variables, we factored species identity into a random effect [31]. Furthermore, acknowledging that interannual variation could significantly affect seedling survival, we incorporated the interval as a random effect.
We analyzed the four models for the three plots at two intervals between 2020 and 2022 (Table 1). To explore the effects of habitat filtering on density dependence, we constructed Models I and II. Model I included seedling height, conspecific seedling density, heterospecific seedling density, CI, and PI. Model II added the first three principal components of habitat variables (PC1, PC2, and PC3) to Model I. To assess whether the effect of functional trait density dependence on seedling survival was affected by habitat variables, we constructed Models III and IV. Model III included the first three principal components of the functional trait variables (LPC1, LPC2, and LPC3) based on Model I. Model IV included PC1, PC2, and PC3 based on Model III. The estimated coefficients represent the relative strength of the effects of the variables, and coefficients > 0 indicate positive effects on seedling survival, whereas coefficients < 0 indicate negative effects. We also computed the variance of each fixed, random, and residual effect in the four models (Table S5).
Considering the differences between species, we incorporated species-specific random slopes for each significant seedling survival variable. We used likelihood ratio tests to assess the significance of supplementary species-specific random slopes. If the p values were less than 0.05, we deduced that there was indeed a disparity in the coefficients of the neighboring variables among species.

3. Results

3.1. The Effects of All Factors on Seedling Survival in Three Plots

We found that the variables affecting the survival rate of seedlings varied among the three plots (Figure 2). Heterospecific seedling density had a significant negative effect on seedling survival in plot A (Figure 2). The first principal component of functional traits (LPC1) was significantly and positively correlated with seedling survival in plot B (Figure 2). Heterospecific seedling density, seedling height, and the second principal component of functional traits (LPC2) positively affected seedling survival in plot C (Figure 2). No significant effects of adult density on seedling survival were observed in any of the three plots.

3.2. Detection of Habitat-Filtering Effects on Density Dependence and Functional Trait Density Dependence for Seedling Survival

The coefficients for heterospecific seedling density were smaller in Model I and III (without habitat variables) compared to Model II and IV (with habitat variables) in plots A and C (Table 2). A smaller conspecific adult density coefficient was observed in Model I (without habitat variables) than in Model II (with habitat variables) in plot B (Table 2).
In plots B and C, the significant coefficients of LPC1 and LPC2 were smaller in Model III (without habitat variables) than in Model IV (with habitat variables; Table 2). In addition to the LPC2 of plot C, we found that adding habitat variables also increased the variance explained by functional traits, whereas variances explained by random effects remained constant (Table S5).
Species-specific random slopes were added to the significant variables in the four models for the three plots. The results showed that the distribution of the species-specific coefficients of heterospecific seedling density in Model II changed significantly in plot C (p = 0.028) (Figure 3). Considering the habitat variables in Model II in plot C, we found that 93.33% of the focal species suffered stronger positive effects of heterospecific seedling density than those in the equivalent non-habitat-informed models. In addition, the inclusion of species-specific random slopes for LPC1 and LPC2 did not cause significant changes (p > 0.5). Thus, we did not conduct further analyses of how habitat filtering affected the variation in functional trait density dependence among species.

4. Discussion

Density dependence, functional trait density dependence, and habitat filtering have been identified as key mechanisms for maintaining species coexistence [23,29,52]. Previous studies have focused on only one or two of these mechanisms, which may ignore the potential effects of certain factors and lead to unreliable results. Therefore, a comprehensive examination of these three mechanisms can provide more insightful results. Our study demonstrates that density dependence and functional trait density dependence play important roles in seedling survival. Although habitat filtering had insignificant effects on seedling survival in our study, habitat variables affected the detectability of density dependence. These results are discussed in detail in the following sections.

4.1. The Relative Importance of Density Dependence, Functional Trait Density Dependence, and Habitat Variables in Three Plots

In our study, seedling height was positively associated with individual survival in plot C, which was consistent with previous findings [6,28,29]. Larger individuals are less sensitive to biological stress, as their greater height can allow them to gain more light resources and protect against damage by pathogens and herbivorous insects [12,53]. However, seedling height had insignificant positive effects in plots A and B. Oshima et al. [54] reported different responses to seedling height among species of Dipterocarpus and Shorea. Deutzia and Quercus were the dominant genera in plots A and B, whereas Viburnum was the dominant genus in plot C. This difference may have led to the different effects of seedling height on seedling survival across the three plots.
Seedling–seedling interactions are an important factor in maintaining forest community dynamics [6,30,55]. This interaction was an important driver of seedling survival in our study. Our results showed that heterospecific seedling density had a significant negative effect on seedling survival in plot A but a significant positive effect on seedling survival in plot C. This interesting phenomenon was attributed to the large difference in seedling density between the two plots. The average seedling density over three years was 0.735 plants/m2 in plot A, whereas it was 0.418 plants/m2 in plot C. A higher seedling density led to stronger competition [56], thereby reducing the survival rate of seedlings in plot A. More heterospecific seedlings reduced the attack of specific enemies, thereby improving the survival rate of the seedlings in plot C [12,56]. This pattern was consistent with the “species herd protection hypothesis” [57]. In addition, conspecific seedling density did not significantly affect seedling survival in the three plots, which is similar to the findings of Pu et al. [58] and Svenning et al. [59]; seedlings under a low density were not strongly affected by the conspecific seedling neighbors [59].
Seedling–adult interactions are also important factors in maintaining forest community dynamics [6,30,55]. However, the effects of conspecific adult neighbors on seedling survival were not significant in any of the three plots. This may be related to low conspecific adult densities in the three plots. The proportion of species without any conspecific adult neighbors was 61.54% in plot A, 75% in plot B, and 60% in plot C. The percentage of species with fewer than five conspecific adult neighbors was 92.31% in plot A, 87.5% in plot B, and 93.33% in plot C.
The impact of natural enemies and stronger competition for similar resources among closely related neighboring plants should lead to a negative effect of phylogenetic similarity on seedling survival. However, we did not find that seedling survival was affected by phylogenetic density dependence. The phylogenetic negative density dependence (PNDD) reported by Zhu et al. [60] was detected only at later stages, and Pu et al. [12] found that the strength of PNDD generally increased as life stages progressed. The stronger effect of PNDD in later life stages reflects the importance of phylogenetic relatedness in structuring tree communities and regulating species composition, as large trees maintain populations by producing seeds [12].
Several studies have reported the effects of functional trait density dependence on seedling survival [22,61]. Toledo-Aceves et al. [61] found that a higher leaf dry mass significantly increased seedling survival. Jiang et al. [28] found that a larger leaf area significantly increased seedling survival. Wright [22] suggests that there is a trade-off between tree growth and mortality rates, and one of the main driving factors of this trade-off is functional traits. In our study, seedlings with a higher leaf dry mass content had higher survival rates because a higher leaf dry mass content represented higher leaf damage resistance and host-specific enemy resistance [61]. A higher chlorophyll content significantly improved seedling survival in plot C. Seedlings with a higher chlorophyll content might have higher photosynthetic efficiency and better resistance to environmental stresses, such as drought, pests, and diseases [62].
However, we did not detect a significant effect of habitat variables on seedling survival in any of the three plots. This may be because some key habitat variables (such as soil nutrients) were not included in our analysis.

4.2. The Effects of Habitat Variables on Density Dependence

While the effects of habitat variables on seedling survival did not reach significance in any of the three plots, the role of density dependence may be misinterpreted if the effects of habitat heterogeneity are not considered [11]. Previous studies have shown that adding habitat variables to density dependence can improve our understanding of the mechanisms of density dependence [5,63]. Wu et al. [6] demonstrated that focal species that accounted for habitat variables experienced more pronounced negative density dependence effects compared to those that did not consider habitat variables. Piao et al. [11] suggested that not thinking about how varying habitats affected things might make us misunderstand how density-dependent processes shape plant communities. Cao et al. [5] determined that CNDD had the greatest impact on seedling survival in habitats with lower soil moisture, while having a weaker effect in habitats with higher soil moisture. Therefore, density dependence tests must consider habitat heterogeneity [5,63].
Taking habitat variables into consideration made the effects of heterospecific seedlings appear more positive in plots A and C because of the different suitabilities of the habitats for heterospecific seedlings. The effects of conspecific adults appeared more negative in plot B. Because of the same suitable habitat for conspecific individuals, conspecific seedlings prefer to live in similar environments [6,58]. In summary, we conclude that the interaction between habitat filtering and density dependence plays an important role in seedling dynamics.

4.3. The Effects of Habitat Variables on Functional Trait Density Dependence

The relationship between seedling dynamics and the environment is well-understood, as has been reported in previous studies [1,55]. However, the role of functional traits in environment–demographic relationships is typically ignored [28]. Interactions between neighbors may be more dependent on functional trait density dependence. Differences in functional traits reflect differences in plant resource requirements, reproductive strategies, and tolerance to herbivores and pathogens [64,65]. Functional traits can be used to detect habitat-filtering mechanisms [66]. It is generally believed that the more similar the functional traits between tree species, the greater the negative interaction between neighbors, owing to their competition for similar resources [20,67]. Therefore, the simultaneous consideration of habitat filtering and functional trait density dependence may significantly increase the strength of functional trait density dependence. Therefore, we expected that habitat filtering would affect the detection of the effects of functional trait density dependence on seedling survival. We found that taking habitat variables into consideration made the effects of functional trait density dependence appear more positive in plots B and C because species with different functional traits were better-suited to different habitats. Taking habitat variables into account increased the variance explained by functional trait density dependence in the models (Table 2 and Table S5). These results suggest that habitat filtering covered the strength of functional trait density dependence on seedling survival. Due to the small spatial and temporal scale, our study may not accurately reflect plant dynamics. In addition, our study only considered some plant leaf functional traits, and did not consider other plant traits, such as leaf nitrogen content, leaf carbon content, and seedlings with different mycorrhizal types.

5. Conclusions

Density dependence, functional trait density dependence, and habitat filtering are important mechanisms driving seedling survival. However, their combined effects remain unclear. Our study increases awareness of the importance of the joint effects of habitat filtering and density dependence. Our results showed that seedling survival was most strongly affected by the density of heterospecific seedlings and functional trait density dependence, whereas conspecific negative density dependence, phylogenetic negative density dependence, and habitat filtering had no significant effects on seedling survival. Adding habitat variables into survival models increased the strength of the negative effects of conspecific adult density, positive effects of heterospecific seedling density, and functional trait density dependence on seedling survival. We conclude that future studies on neighborhood density dependence must consider habitat filtering and increase temporal and spatial scales in order to more accurately explore the roles of density dependence and functional trait density dependence in species coexistence.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15010076/s1, Table S1: The loadings of six functional trait variables on the principal components; Table S2: The loadings of four habitat variables on the principal components; Table S3: Plot parameters of environmental factors; Table S4: The ranges and means of all fixed effects in generalized linear mixed models with random effects; Table S5: Variance of all explanatory variables in the density-dependent model (Model I), the density + habitat model with the same neighborhood variables as that in the density-dependent model (Model II), the functional traits density-dependent model (Model III), and the functional traits + habitat model with the same neighborhood variables as that in the functional traits density-dependent model (Model IV), for the three-year census intervals of the three plots.

Author Contributions

Conceptualization, J.W.; methodology, J.W. and Z.F.; software, Z.F.; validation, J.W. and Z.F.; formal analysis, Z.F.; investigation, Z.F.; resources, J.W., Z.F., J.S., X.Y., F.C., L.W. and X.H.; data curation, Z.F.; writing—original draft preparation, J.W. and Z.F.; writing—review and editing, J.W. and Z.F.; visualization, Z.F.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This study has been funded by the National Natural Science Foundation of China (Grant ID: 31901102).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

We thank Jia Lu, Shun Li, Yefu Zhou, Chunhong Wu, Chunchen Ni, Changxi Li, and Yi Luo for their assistance in data collection.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Locations of the three plots in Cangshan Mountain.
Figure 1. Locations of the three plots in Cangshan Mountain.
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Figure 2. Estimated effects (mean ± SE) of neighborhood variables and habitat variables on seedling survival for each of the three-year census intervals of the three plots in functional traits + habitat model (model IV in Table 1). (a) Plot A, (b) Plot B, (c) Plot C. The black circles indicate significant effects (p < 0.05), and white circles mean no significance (p ≥ 0.1). See Table 1 for variable abbreviations. Dash-dot lines mark 0 on the x-axis scale.
Figure 2. Estimated effects (mean ± SE) of neighborhood variables and habitat variables on seedling survival for each of the three-year census intervals of the three plots in functional traits + habitat model (model IV in Table 1). (a) Plot A, (b) Plot B, (c) Plot C. The black circles indicate significant effects (p < 0.05), and white circles mean no significance (p ≥ 0.1). See Table 1 for variable abbreviations. Dash-dot lines mark 0 on the x-axis scale.
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Figure 3. A comparison of the frequency distribution of species-specific coefficients of heterospecific seedling density between Model I and Model II in plot C. Bars to the right of the dashed zero line indicate species whose survival is increased by increasing heterospecific population density. (a) Model I (density model without habitat variables) and (b) Model II (density model with habitat variables).
Figure 3. A comparison of the frequency distribution of species-specific coefficients of heterospecific seedling density between Model I and Model II in plot C. Bars to the right of the dashed zero line indicate species whose survival is increased by increasing heterospecific population density. (a) Model I (density model without habitat variables) and (b) Model II (density model with habitat variables).
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Table 1. Description of the four candidate models.
Table 1. Description of the four candidate models.
Model FormPurpose
Model IDensity dependence modelSs = H + cons + hets + CI + PITo assess the role of neighbor densities on seedling survival
Model IIHabitat + density dependence modelSs = H + cons + hets + CI + PI + PC1 + PC2 + PC3To explore the influence of habitat filtering on the detection of density dependence
Model IIIFunctional trait density dependence modelSs = H + cons + hets + CI + PI + LPC1 + LPC2 + LPC3To assess the importance of functional traits in the survival model
Model IVHabitat + functional trait density dependence modelSs = H + cons + hets + CI + PI + PC1 + PC2 + PC3 + LPC1 + LPC2 + LPC3To explore the influence of habitat filtering on the detection of functional trait density dependence
H is the height of focal seedlings. Neighborhood variables included the density of conspecific seedling neighbors (cons), the density of heterospecific seedling neighbors (hets), sum of conspecific adults’ basal areas (CI), sum of heterospecific neighbor phylogenetic dissimilarity index (PI), and the first three principal components of functional trait variables (LPC1, LPC2, and LPC3). Habitat variables included the first three principal components of canopy openness, soil properties, and slope (PC1, PC2, and PC3). Ss, seedling survival.
Table 2. Coefficient estimates for all explanatory variables in the density dependence model (Model I), the habitat variables + density dependence model (Model II), the functional trait density dependence model (Model III), and the habitat + functional trait density dependence model (Model IV), for the three-year census intervals of the three plots.
Table 2. Coefficient estimates for all explanatory variables in the density dependence model (Model I), the habitat variables + density dependence model (Model II), the functional trait density dependence model (Model III), and the habitat + functional trait density dependence model (Model IV), for the three-year census intervals of the three plots.
Explanatory
Variables		Plot APlot BPlot C
IIIIIIIVIIIIIIIVIIIIIIIV
Intercept2.741 ***2.735 ***2.773 ***2.807 ***4.201 ***4.336 **4.281 ***4.353 ***6.605 **7.169 **6.481 ***6.988 ***
Height0.531 NS0.533 NS0.602 NS0.676 NS0.258 NS0.260 NS0.136 NS0.110 NS2.563 *2.545 *3.109 **3.007 **
cons−0.426 NS−0.464 NS−0.395 NS−0.470 NS−0.237 NS−0.290 NS0.096 NS−0.006 NS−0.310 NS−0.252 NS−0.364 NS−0.267 NS
hets−0.513 **−0.467 *−0.517 **−0.406 *0.386 NS0.540 NS0.445 NS0.530 NS2.742 NS3.140 NS3.309 *3.799 *
CI0.310 NS0.321 NS0.324 NS0.304 NS−0.670 *−0.755 *−0.452 NS−0.461 NS−3.535 NS−3.905 NS−1.644 NS−2.462 NS
PI0.286 NS0.341 NS0.090 NS−0.032 NS−0.330 NS−0.408 NS0.009 NS0.003 NS−2.971 NS−3.267 NS−1.225 NS−2.177 NS
LPC1 −0.374 NS−0.598 NS 1.600 *1.749 * 3.073 NS3.093 NS
LPC2 0.134 NS0.238 NS 0.824 NS0.941 NS 2.050 **2.241 **
LPC3 0.216 NS0.238 NS −0.659 NS−0.690 NS 0.742 NS0.993 NS
PC1 0.142 NS 0.342 NS −0.390 NS −0.391 NS −0.311 NS −0.492 NS
PC2 −0.106 NS −0.226 NS −0.115 NS 0.072 NS −0.018 NS −0.050 NS
PC3 0.056 NS 0.115 NS −0.080 NS −0.165 NS 0.385 NS 0.406 NS
See Table 1 for variable abbreviations. *, **, and ***: significant at p < 0.05, p < 0.01, and p < 0.001; NS: not significant.
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Feng, Z.; Wu, J.; Sun, J.; Yu, X.; Wang, L.; Chen, F.; Hu, X. Habitat Filtering Covers the Strength of Density Dependence and Functional Density Dependence on Seedling Survival in Cangshan Mountain, Southwest China. Forests 2024, 15, 76. https://doi.org/10.3390/f15010076

AMA Style

Feng Z, Wu J, Sun J, Yu X, Wang L, Chen F, Hu X. Habitat Filtering Covers the Strength of Density Dependence and Functional Density Dependence on Seedling Survival in Cangshan Mountain, Southwest China. Forests. 2024; 15(1):76. https://doi.org/10.3390/f15010076

Chicago/Turabian Style

Feng, Zhe, Junjie Wu, Jiwen Sun, Xiaoli Yu, Liping Wang, Fengxian Chen, and Xiaokang Hu. 2024. "Habitat Filtering Covers the Strength of Density Dependence and Functional Density Dependence on Seedling Survival in Cangshan Mountain, Southwest China" Forests 15, no. 1: 76. https://doi.org/10.3390/f15010076

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