The study of species and phylogenetic diversity on karst forest along Lijiang River

doi:10.21203/rs.3.rs-2621308/v1

Download PDF

Research Article

The study of species and phylogenetic diversity on karst forest along Lijiang River

https://doi.org/10.21203/rs.3.rs-2621308/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Forest community is the key point of global biodiversity conservation for its large number of species. Over the centuries, many scholars have tried to reveal the community diversity of forests such as tropical rainforests. But we know very little about karst forests because it is distributed in very few areas of the world. Lijiang river basin, a subtropical karst forest area, is a vital biodiversity hotspot in South China. In this paper, we aimed to reveal the characteristics of forestry species diversity in the karst area of Lijiang River by Hill diversity and two beta diversity partitioning method. We also studied the phylogenetic diversity and established a pedigree tree for the karst forest in the whole basin. Our findings showed that alpha diversity and beta diversity was quite different between the watershed. The alpha diversity indices in the upper reaches were significantly higher than that of the rest reaches, while the beta diversity was extreme variation among planted forest than natural forest. The decomposition of beta diversity in these karst forests showed that the diffusion limitation was the main driving force causing the beta diversity difference. The phylogenetic structure showed that the midstream karst forest had more distantly related species than other basins. We found that upstream forest was under species invasion while midstream karst forest was under factitious threatened. We suggest that the karst forests in the Lijiang River basin should be protected immediately. This study also filled the gap in the study of karst forests in subtropical regions.

karst forest

Lijiang River

alpha diversity

beta diversity

phylogenetic diversity

Biodiversity is of great significance to human survival and development. It is a hotspot of ecological research (Rezende et al. 2018), and an important part of the world biodiversity monitoring network. Biodiversity is not only related to the service function of ecosystem (Zhou et al. 2022), but also a bridge to communicate the productivity and the stability of ecosystem via forest structural complexity (Ehbrecht et al. 2021; Zhou et al. 2022). As the most important ecosystem on Earth, all forests type on earth should be given proper and due protections (Alroy 2017; Luby et al. 2022), whether they’re tropical rainforests, broadleaf forests, coniferous forests or bush forests, even man-made shelterbelts and timber forests. However, forests in karst areas of southwest China are at risk of being destroyed due to the rapidly economic development and lack of simultaneous awareness of biodiversity conservation (Gu et al. 2022; Zhang et al. 2022). The special eco-geographical environment of karst area makes the vegetation in this area different from other regions, and the karst geology makes this area prone to geological disasters of landslides, which will damage the vegetation growth in this area to a certain extent. Hence, measuring and understanding the biodiversity on karst forest is very urgent.

The measurement of diversity helps us to understand the composition of species diversity. In order to describe the diversity of forest in a given area, various diversity indices were selected to reflect the species information obtained from the sample plot data (Mirazadi et al. 2017). Diversity, in different aspects, was focused on species, phylogenetic, and functional diversity and other aspects of diversity, which often used different diversity indices to reveal them (Pavoine and Bonsall 2011). Among them, species diversity was used to measure the number of species and species individuals in a given area (Whittaker 1972). It was the first proposed and the most basic, and the most extensively studied while still under further research by so far (Roswell et al. 2021). To measure species diversity, modern ecologists use two important tools: coverage and Hill diversity, which aims to estimate diversity in an unbiased way instead of using traditional species diversity index such as Shannon index and the Simpson index that were under decades of valid critiques leveled against them (Roswell et al. 2021). Chao were committed to using parametric and non-parametric methods of rarefaction and extrapolation in combination with Hill numbers, estimating and comparing species diversity through equal sample size or sample coverage (Chao and Chun-Huo 2016; Chao and Ricotta 2019; Chao et al. 2020). Then a series of famous R packages to measure and compare species diversity were developed, such as “SpadeR”, “iNEXT” and “iNEXT.3D” (Chao et al. 2016; Hsieh et al. 2016; Chao et al. 2022). The use of these R packages will help us to evaluate regional biodiversity more deeply.

In different with the mentioned species diversity above called alpha diversity, which focus on measurement beneath the habitat, there are also another species diversity measurement among habitat where refers to the replacement rate of species along the environmental gradient, which was named beta diversity (Socolar et al. 2016). There were many beta diversity indices, such as Sorensen dissimilarity, Jaccard dissimilarity and so on. overall, βsim index perform best (Barwell et al. 2015). Beta diversity is usually determined by two antithetic processes: species turnover (or species replacement), and nestedness (or difference in richness). Two beta diversity partitioning methods come to dominate: the BAS method (Baselga and Leprieur 2015) and the POD method (Podani and Schmera 2016). The BAS method partitions the whole beta diversity into turnover and nestedness components, while the POD method partitions the whole beta diversity into species replacement and richness difference components (Tatsumi et al. 2021). The drawbacks of these two methods are that the nestedness components of BAS method cannot accurately calculate the nesting pattern in a strict sense, and the index representing species replacement in POD method cannot reasonably describe its ecological significance in some cases (Podani and Schmera 2011; Baselga 2012). Therefore, the beta diversity of a community in a special area should be discussed by combining these two methods according to the actual situation (Legendre 2014).

Besides species diversity, another important tool to characterize botany difference between two regions is phylogenetic diversity. Phylogenetic diversity often reveals the degree of genetic relationship of species in the region and thus implies the information on the historical process of species in the region, which was also concerned by many scholars (Paz et al. 2021; Rito et al. 2021). The composition and construction of plant community species in a region are the result of the interaction of various ecological processes and long evolution, and based on this, Faith put forward the famous Pedigree diversity index (PD)(Faith 1992). Meanwhile, Net relatedness index (NRI) and Nearest relatedness index (NTI) were usually used to characterize community structure (Swenson 2011). Therefore, species in a community in different regions will present different phylogenetic relationships, that is, community phylogenetic structure. Through systematic analysis of the phylogenetic structure and correlation degree among different community species, it is helpful to explore the status of community species composition and the main causes and driving factors of community construction.

Karst area has attracted much attention from scholars because of its unique geological characteristics and hydrological conditions. Lijiang River area is a typical karst landform feature area in the world. The karst forest in this region is still under threatened, so it is much urgent to carry out comprehensive research and make protection of forest biodiversity along Lijiang River. This study took different types of karst forests in the upper, middle and lower reaches of the riparian zone of the Lijiang River as the research object, then studied and discussed the following issues: ①What was the basic situation of Lijiang Karst forest, including species diversity and phylogenetic diversity? ②Was there any difference in the species composition structure of karst forests in the upper, middle and lower reaches of the Lijiang River? ③Were there any spatial differences in alpha and beta diversity and phylogenetic diversity between forest in the upper, middle and lower reaches of the Lijiang River? ④What were the main ecological processing factors affecting forest diversity difference in the karst zone of the Lijiang River? By answering these scientific questions about species diversity and phylogenetic diversity of karst forest along Lijiang River would help us to reveal the current situation of biodiversity of forest communities in the Lijiang River, and provided a scientific basis for the targeted protection of forest community in this region.

Study area, sites, and plots

Lijiang River is located in the northeast of Guangxi Zhuang Autonomous Region, China, with a total drainage basin of about 6000 km² and a total length of about 214km. The geographic coordinates are 24°18′-25°41′N, 109°45′-110°40′E. This area belongs to subtropical humid monsoon climate, with mild climate, abundant rainfall, high vegetation coverage, rich species, and typical karst landform characteristics. The basin is dominated by subtropical evergreen broad-leaved forests, including natural forests such as fir and economic fruit trees. Lijiang River Basin is one of the first batch of national key scenic spots, and it was also the earliest area in China to develop tourism. It is one of the largest and most beautiful karst landscape sightseeing areas in the world.

Based on the field survey of woody plant communities, a total of 30 typical karst forest communities plot with a size of 20 m × 20 m were established in the upstream, midstream and downstream along Lijiang River area from October 2021 to July 2022 (Fig. 1), which were performed according to the standard ForestGeo protocol (https://forestgeo.si.edu/node/145665/). During the survey, every 20 m × 20 m sample plot was divided into four 10 m × 10 m small quadrats. We recorded the altitude of the sample plot, surveyed each woody plant in each quadrat, and identified them to species, relative coordinates, diameter at breast height (DBH,woody plants with DBH ≥ 1cm), basal diameter (woody plants with DBH < 1cm and higher than 1m), and tree height. Common woody plant in the plots along the Lijiang River Basin included Loropetalum chinense, Cornus macrophylla, Machilus nanmu, Cinnamomum wilsonii, Pinus massoniana, Larix gmelinii, Phyllostachys edulis and so on (please see Table S1 in Appendix S).

Estimation of species diversity and phylogenetic structure

We used Hill numbers to characterize the alpha diversity of karst forest along Lijiang area. The Hill numbers use order q to determines sensitivity to species relative abundances. When q = 0, the Hill number ⁰D represents species richness index; When q = 1, the Hill number ¹D represents the exponential of Shannon-Wiener index; When q = 2, the Hill number ²D represents the inverse of Simpson index. The Hill number ⁰D, Species Richness index(S), Shannon-Wiener index(H) and Simpson index(D) are determined as follows in Table 1.

Due to spatial clustering often prevails in woody plants, individual plants cannot be modeled as independent sampling units, which violating the basic sampling assumptions for the model based on abundance data: analyses based on abundance data are more sensitive to clustering or aggregation of individuals, compared to those based on multiple incidence data (Colwell et al. 2012). We thus turned abundance data into incidence data to avoid this conflict. Plots within different watershed location and each type were aggregated into the incidence-based datasets and were referred to as the upstream forest, midstream forest and downstream forest, along with as the natural forest and planted forest in the following analysis. The species frequency data were summarized in the frequency counts and are tabulated in Table S2.

Table 1

Alpha diversity indices used on Karst forest along Lijiang River
Indices	Formula	Formula letter meaning	Reference
Hill numbers	\(\begin{array}{c}{ }^{q}D={\left({\sum }_{i=1}^{S}{{P}_{i}}^{q}\right)}^{1/\left(1-q\right)},q\ge 0,q\ne 1\#\end{array}\)	Where N is the sum of the number of all species in the community, P is the relative abundance of species i, and S is the number of species in the study sample plot. When q = 0, the Hill number ⁰D represents species richness index; When q = 1, the Hill number ¹D represents the exponential of Shannon-Wiener index; When q = 2, the Hill number ²D represents the inverse of Simpson index.	(Chao and Ricotta 2019; Chao et al. 2020; Roswell et al. 2021)
Species Richness	\(\begin{array}{c}S=N\#\end{array}\)		(Whittaker 1972)
Shannon-Wiener index	\(\begin{array}{c}H=-\sum _{i=1}^{S}\left({P}_{i}\text{l}\text{n}{P}_{i}\right)\#\end{array}\)		(Shannon, 1948)
Simpson index	\(\begin{array}{c}D=1-{\sum }_{i = 1}^{S}{{P}_{i}}^{2}\#\end{array}\)		(Simpson 1949)
Pielou index	\(J=（-\sum _{i=1}^{S}\left({P}_{i}\text{l}\text{n}{P}_{i}\right)）/\text{log}S\)		(Pielou 1966)

We used both BAS and POD method to partition beta diversity, then to explain two antithetic processes. The calculation formula are as follows in Table 2.

Table 2

Pairwise dissimilarities and their components of BAS method and POD method used in Karst forest along Lijiang River
Methods	Indices	Formula	Formula letter meaning	Reference
BAS method	Sorensen dissimilarity	\(\begin{array}{c}{\beta }_{sor}=\frac{b + c}{2a + b + c}\#\end{array}\)	In the formulas, a is the average number of common species in two quadrats, b is the average number of species obtained when entering adjacent quadrats, and c is the average loss of species when entering adjacent quadrats.	(Baselga, 2010)
	Turnover	\(\begin{array}{c}{\beta }_{sim}=\frac{min(b, c)}{{min}\left(b, c\right)+ a}\#\end{array}\)		(Baselga, 2010)
	Nestedness difference	\(\begin{array}{c}{\beta }_{sne}=\frac{\left\|b – c\right\|}{2a + b + c}\times \frac{a}{a+min\left(b,c\right)}\#\end{array}\)		(Baselga, 2010)
POD method	Jaccard dissimilarity	\(\begin{array}{c}{\text{β}}_{\text{cc}}\text{ }\text{=}\text{ }\frac{b + c}{a + b + c}\#\end{array}\)		(Dénes, 2011)
	Species replacement	\(\begin{array}{c}{\beta }_{-3}=\frac{2min\left(b,c\right)}{a + b + c}\#\end{array}\)		(Dénes, 2011)
	Richness difference	\(\begin{array}{c}{\beta }_{rich}=\frac{\left\|b - c\right\|}{a + b + c}\#\end{array}\)		(Dénes, 2011)

We then used phylogenetic diversity index (PD), the net relatedness index (NRI) and the nearest taxon index (NTI) to estimate phylogenetic structure of community. The calculation formula are as follows in Table 3.

Table 3

Phylogenetic diversity indices used on Karst forest along Lijiang River
Indices	Formula	Formula letter meaning	Reference
PD	\(\begin{array}{c}PD={\varSigma }_{c\in C}{L}_{c}\#\end{array}\)	In the formula, C represents the sum of all branches on the shortest path of all species in the connected phylogenetic tree, c represents a segment of branch at the connecting node, Lc represents the branch length of c, and MPD_s refers to the mean genetic distance of all species in the sample plot, that is, the observed value. MPD_r refers to the standardized average phylogenetic distance under the assumption of random model of species distribution, and SD(MPD_r) refers to the standard deviation of MPD_r. MNTD_s refers to the mean value of the distance between the nearest species lineage locus in the community, MNTD_r refers to the average value of the normalized distance of the nearest neighboring lineage under the assumption that the species distribution is random, and SD(MNT𝐷_r) refers to the standard deviation of MNTD_r.	(Faith 1992)
NRI	\(\begin{array}{c}NRI=-1\times \frac{{MPD}_{s}-{MPD}_{r}}{SD\left({MPD}_{r}\right)}\#\end{array}\)		(Swenson, 2011)
NTI	\(\begin{array}{c}NTI=\frac{{MNTD}_{s}-{MNTD}_{r}}{SD\left({MNTD}_{r}\right)}\#\end{array}\)		(Swenson, 2011)

Statistical analyses

The data analysis and drawing were completed in R4.1.2. Sample completeness, diversity estimation, rarefaction and extrapolation, and evenness were calculated and plotted by the “iNEXT.4steps” package (Chao et al. 2020). Using “batapart” package and “BAT” package to calculate beta diversity and its decomposition by BAS method and POD method, respectively (Baselga and Orme 2012; Cardoso et al. 2014). Using “ggplot2” package and its extension packages “corplot” and “ggtern” to draw thermal maps and related thermal maps (Ginestet 2011; Hamilton and Ferry 2018). We used “V. PhyloMaker” package and “picante” package to calculate for phylogenetic diversity (Kembel et al. 2010; Jin and Qian 2019). The phylogenetic tree that was constructed can be divided into three steps. Firstly, we assembled a species list that included all of species sampled (the data set of 242 total species) in this study. Secondly, all names in the present species list were proofread through the TPL website (http://www.theplantlist.org/, accessed on 11 Augest 2022). Our present species list was included in the TPL. Thirdly, a phylogenetic tree was generated based on the accepted 242 species list using the mega-tree function (scenario 3) in the R package “V. PhyloMaker” (Jin and Qian 2019).

Taxonomic alpha diversity of karst forest community along Lijiang River

A total of 30 experimental plots were along Lijiang River could be divided into natural forest (eighteen plots) and planted forest (twelve plots) in this area, yielding a total of 4,555 individuals belonging to 242 species recorded in all survey plots. In the upstream area, there were 1,781 individuals representing 134 species in the plots. In the midstream area, there were 1,467 individuals representing 84 species in the plots. In the downstream area, there were 1,307 individuals representing 99 species in the plots. In the natural forest, 3,203 individuals and 234 species were recorded, whereas in the planted forest, 1,352 individuals and 42 species were recorded.

We classified the woody plants survey data of the whole Lijiang River by basin and forest type. Although the three reference sample sizes differ greatly, the three estimated sample-completeness profiles in Fig. 2a are generally close to each other. For example, the sample coverage value for the upstream forest (1,781 individuals) is 88% and the corresponding value for the midstream forest (1,467 individuals) and downstream forest (1,307 individuals) is 87%, 86% respectively. The sampling curves for species richness (q = 0), extrapolated up to double the reference sample size, suggesting that the current data contains sufficient information to accurately estimate minimum species richness within each assemblage. Figure 2b reveals that, for each basin, the size-based rarefaction and extrapolation sampling curves for diversity of q = 2 stabilize, implying that our asymptotic diversity estimates for high abundance species satisfactorily to infer true diversities. Comparing the estimated asymptotic diversity profile (solid lines in Fig. 2c) and the corresponding observed/empirical diversity profile (dashed lines in Fig. 2c), we can assess the extent of undetected diversity within each basin and the diversity difference between the three datasets, separately for q = 0, 1 and 2 (Table 4). Figure 2e showed the evenness profile for diversity orders 0 < q ≤ 2. All measures consistently show that the evenness among species abundances in the upstream forest is higher than that in the downstream forest, and the midstream forest is the lowest among them, for a standardized 72.3% assemblage fraction (Table 4).

Our sample completeness analysis showed that the undetected species richness within the natural forest data and planted forest data are, respectively, at least 150.58 (≥ 39%) and 47.73 (≥ 53%), and nearly all highly abundant species were detected (Fig. 3a). The undetected species in the natural forest assemblage cover about 8% of the assemblage's individuals, and about 15% for the planted forest assemblage. For Shannon diversity (q = 1) and Simpson diversity (q = 2), our analysis demonstrates that the natural forest assemblage is significantly more diverse than the planted forest assemblage; this conclusion is valid for any standardized coverage up to 100% (Fig. 3b). The above asymptotic analysis implies that, for diversities of q = 0, 1 and 2, the true diversity of the entire assemblage of the natural forest is significantly higher than that of planted forest. The difference is statistically significant because the two 95% confidence bands in Fig. 3c do not overlap. Under the maximum coverage of 78.5%, the difference in species richness, Shannon diversity and Simpson diversity between the natural forest and planted forest assemblages are, respectively, 193.33, 148.67 and 115.26 species (Table 4). Thus, the major difference between the forest type lies in rare species. That is, according to this dataset, the increase in richness from natural forest to planted forest is mainly due to an increase in rare species. This difference is also statistically significant. Figure 3d reveals that this conclusion is valid not only for comparing the two entire assemblages, but also for any standardized sample coverage up to unity. For species richness, our data are sufficient to infer the true richness of the entire assemblage. Diversity and evenness measures can be computed up to a standardized coverage value of C_max = 78.5% (the lower coverage of the two extrapolated samples when each is extrapolated to double the reference sample size), as previously defined. All measures consistently show that the evenness among species abundances in the natural forest is almost as the same as that in the planted forest, for a standardized 78.5% assemblage fraction (Fig. 3e).

Table 4

The numerical values for the three special cases of q = 0, 1 and 2 for incidence-based woody plants collected from karst area along Lijiang River
Quantifying sample completeness and comparing diversities among karst forest along Lijiang River
Classified by basin (Fig. 1a-e)				Classified by forest type (Fig. 2a-e)
STEP1. Sample completeness profiles (panel a in each figure)
Completeness	q = 0	q = 1	q = 2	Completeness	q = 0	q = 1	q = 2
Upstream forest	64%	74%	88%	Natural forest	61%	75%	92%
Midstream forest	39%	57%	87%	Planted forest	47%	63%	85%
Downstream forest	51%	63%	86%
STEP2. Asymptotic analysis (panel b and c in each figure)
Diversity	q = 0	q = 1	q = 2	Diversity	q = 0	q = 1	q = 2
Upstream forest				Natural forest
Asymptotic	210.38	171.81	133.02	Asymptotic	384.58	283.61	199.62
Empirical	134.00	111.90	92.57	Empirical	234.00	185.60	146.37
Undetected	76.38	59.91	40.45	Undetected	150.58	98.01	53.25
Midstream forest				Planted forest
Asymptotic	214.54	150.43	90.85	Asymptotic	89.73	67.87	49.59
Empirical	84.00	70.38	57.01	Empirical	42.00	35.78	30.32
Undetected	130.54	80.05	33.84	Undetected	47.73	32.09	19.27
Downstream forest
Asymptotic	192.89	148.73	97.20
Empirical	99.00	81.85	65.28
Undetected	93.89	66.88	31.92
STEP3. Non-asymptotic coverage-based rarefaction and extrapolation analysis (panel d in each figure)
Maximum standardized coverage C_max = 72.3%				Maximum standardized coverage C_max = 78.5%
Diversity	q = 0	q = 1	q = 2	Diversity	q = 0	q = 1	q = 2
Upstream forest	120.85	103.22	86.85	Natural forest	255.60	198.08	152.90
Midstream forest	130.64	100.55	70.06	Planted forest	62.27	49.41	37.64
Downstream forest	123.10	97.60	86.85
STEP4. Evenness among species abundances (panel e in each figure)
Evenness	Pielou J’	q = 1	q = 2	Evenness	Pielou J’	q = 1	q = 2
Upstream forest	0.97	0.85	0.72	Natural forest	0.95	0.77	0.60
Midstream forest	0.95	0.77	0.53	Planted forest	0.94	0.79	0.60
Downstream forest	0.95	0.79	0.59
Note: See Fig. 1 and Fig. 2 for the corresponding profiles with 95% confidence intervals.

Taxonomic beta diversity of karst forest community along Lijiang River

Beta diversity among different plots along Lijiang River were compared and decomposed. The results were showed in Fig. 4 and Fig. 5. The mean values of taxonomic beta diversity (β_sim index) were 0.83 (between 0–1, sd = 0.26). Figure 3 showed that taxonomic beta diversity of karst forest along Lijiang River is significant among natural forest in all basin and planted forest in upstream and midstream (P < 0.05).

The relative proportions of species turnover and nestedness components on taxonomic beta diversity decomposed by BAS method were 91.74% and 4.13%. The relative proportions of species replacement and richness components on taxonomic beta diversity decomposed by POD method were 44.42% and 50.09%. In summary, species turnover was the dominant component of taxonomic beta diversity based on BAS method of karst forest along the Lijiang River, while richness component was the dominant component of taxonomic beta diversity based on POD method of karst forest along the Lijiang River.

Each point in the figure represents a plot pair whose position is determined by S (species similarity), βsne or rich (species richness difference component) and βsim or repl (species turnover component), and each triplet sums to 1. The green line in the picture represents the average species similarity, average species turnover and average species richness.

Phylogenetic structure of karst forest community along Lijiang River

We built a pedigree tree of the whole Lijiang River basin and its upper, middle and lower reaches (Fig. 6), and mapped PD, NRI and NTI in each plot of the whole Lijiang River basin (Fig. 7). In general, the pedigrees of most sample plots were clustered, while some sample plots were divergent, such as nm2, nd7. For the whole Lijiang River basin, the average PD was 1511.064, the standard deviation was 798.798, the average NRI was − 0.348, the standard deviation was 1.673, the average NTI was 0.091, and the standard deviation was 1.776. The phylogenetic diversity in upstream forest and downstream forest were clustered and phylogenetic diversity in midstream forest were divergent, just as the phylogenetic beta diversity between plots showed that karst forest in midstream is different from that in the other basins (Fig. 8).

Forestry community composition in karst zone along Lijiang River

Diversity can reveal community composition. Because of its unique geographical environment, karst area has attracted much attention from scholars (Liao et al. 2022; Zhang et al. 2022). The forest in Lijiang River region has been very sound since ancient times, but its integrity has been damaged from upstream to downstream in recent years. Although some studies predicted that the forestry area along Lijiang River will continue to shrink and degrade due to urbanization, this survey found that the karst forest is in good living conditions overall, with high species richness and completing forest structure. However, what worries us is that we truly have observed the phenomenon of bamboo invasion in many upstream karst forest areas along the Lijiang River, that was the individuals of moso bamboo accounts for 36.5% of the forests we investigated, and this kind of biological invasion would decrease plant diversity despite increase microbial biomass and taxonomic diversity by affect associated soil microbial communities (Xu et al. 2014). Some study also showed that the invasive plant species was structured by soil and habitat type, especially in urban area (Szumanska et al. 2021). However, when it encroaches on other species, it can dramatically reduce the biodiversity of the area (Chen et al. 2022). Therefore, we must pay attention to the impact of biological invasion on karst forest along the Lijiang River.

Alpha diversity of karst forest along Lijiang River

Studying alpha diversity will help us to explore the regional species growth situation. Many studies have shown that forest species diversity tends to decrease gradually from the upstream to the downstream of the basin (Vazquez-Delfin et al. 2022), which is caused by geographical environmental factors (Paz et al. 2021). At the same time, the diversity of natural forests is inherently higher than that of artificial forests, which is due to the respective characteristics of these two forests (Cao et al. 2019). However, alpha diversity of karst forest in upstream was higher than that in the midstream and downstream, but not significant, while natural forest was significantly higher than that of planted forest. The forest species and individuals in the upstream and downstream were more abundant than those in the midstream and forest species and individuals in natural forest were more affluent than those in planted forest, generally. The evenness of karst forest in different basin was at the order of upstream forest ༞ midstream forest ༞ downstream forest, while the evenness of karst forest between natural forest and planted forest was almost the same.

The reason why forest species and individuals in the upstream and downstream of Lijiang River were more abundant than those in the midstream might because the midstream is located in the urban area, hence larger human disturbance will frequently destroy the habitat of the riparian forest community, which was resulting in fewer species and individuals in the midstream riparian zone (Druckenbrod et al. 2019). We could also view this from the water quality variation in the middle and lower reaches of the Lijiang River that this area had a strenuous activities from human (Zhu et al. 2022). However, we saw the estimated diversity in midstream rises rapidly, compared to the observed diversity. Human disturbance may reduce species richness, but moderate disturbance will increase species richness also (Depecker et al. 2022; Wei et al. 2022). Edge effects may contribute to the variation of species richness as it would increase species diversity (Krishnadas et al. 2019). Meanwhile, altitude and soil factors may explain the spatial variation of tropical forest communities, and then affect the species diversity of the community (Morelli et al. 2020). Therefore, we suggest to set up more environmental buffer zones in the middle reaches of the basin and plant more different kinds of trees in the plantations (Shafique et al. 2021). In addition, we need to continue to increase the scope and area of the survey to improve the sample coverage.

Beta diversity and its decomposition of karst forest along Lijiang River

Exploring beta diversity helps us to understand the differences in species composition among regions, while decomposing beta diversity is to explore the internal mechanisms that cause such differences. It was generally believed that species turnover among communities was jointly driven by ecological processes such as diffusion limitation and environmental filtration, while species richness differences among communities were mainly driven by habitat filtration on regional environmental gradients (Jamoneau et al. 2018). Many studies believed that species turnover components are the main contributions of beta diversity differences in forest (Wang et al. 2018; Sonne et al. 2022). Thus, beta diversity differences in forest are mainly caused by diffusion limitation and environmental filtration.

Our study showed that the beta diversity of karst forest communities in Lijiang River was in the order of midstream forest > upstream forest > downstream forest, and the community composition in the middle reaches was significantly different from that in the upstream and downstream. The results also showed that the upstream forest and downstream forest beta diversity were quite different(P < 0.05), but the diversity was mainly composed of the richness difference༈91.74%༉, different from other studies (He et al. 2022). This may be caused by the small latitude span and insufficient altitude difference among our plots, since our survey area was not very large span in space (Morelli et al. 2020). Edge effects reduced alpha diversity but not beta-diversity (Krishnadas et al. 2019). That is why alpha diversity in midstream karst forest is lower than other forest in upstream and downstream but beta diversity in midstream karst forest is not significantly different from other forest in upstream and downstream.

Phylogenetic diversity of karst forest along Lijiang River

The study of phylogenetic diversity is crucial for us to distinguish the size of kinship caused by the historical evolution of species in this region. Since Faith put forward his famous Pedigree diversity index(Faith 1992), the study of phylogenetic diversity is just unfolding (Paz et al. 2021; Benítez-Malvido et al. 2022; Zhang et al. 2022). Some scholars used phylogenetic diversity to study the loss of regional diversity (Brown et al. 2020), while others use it to study the threatened status of species (Véron et al. 2019). The quantification of plant community phylogenetic structure is based on species phylogenetic distribution and interspecific phylogenetic distance. When habitat filter plays a leading role in community construction, species with similar adaptability and similar functions are selected by the environment, resulting in the coexistence of closely related species, and the phylogenetic structure of the community shows significant phylogenetic agglomeration. In this case, the NRI and NTI of the community are relatively high. When interspecific competition plays a dominant role in community construction, competitive exclusion occurs, which makes it difficult for species with similar ecological niches to coexist. The community is mainly composed of species with large differences in life history strategies, that is, species with relatively distant relatives. The community phylogenetic structure shows a significantly divergent pattern, and the NRI and NTI of the community are relatively low.

Our study found that, along the whole Lijiang River, the phylogenetic diversity of karst forest in different areas was quite different. In general, the phylogenetic diversity was in an aggregated state, which may be explained by that they were composed of closely related species. While the midstream forest was different from that in upstream and downstream since it was divergent. It represented by that the forest communities in the midstream were mostly composed of species with remote relatives. This may be due to the special geographical conditions of karst forests, which made it difficult for species to circulate, and each habitat was very different, and in this case, species with similar adaptability to the environment were often screened through habitat filtering under natural conditions (Pavoine and Ricotta 2019). The phylogenetic diversity in the midstream forest was divergent may be due to the flat terrain in the midstream, which was conducive to the circulation of species. At the same time, as a long-term population gathering area, the habitats were much more geographically similar caused by human activities, compared to upstream forest and downstream forest. While, it may also because these trees in midstream were planted by human as landscape tree on a high probability.

Suggestions on the protection of karst forests along Lijiang River

The maintenance of forest community diversity was conducive to its resistance to natural disturbance (Jactel et al. 2017), while human disturbance will lead to the reduction of forest community diversity (Pimm et al, 2014). The alpha diversity of riparian forest in the upper and lower basin of the Lijiang River was high, while the alpha diversity of riparian forest in the middle reaches was low. Appropriate measures should be taken to protect the riparian forest in the middle reaches of the Lijiang River. One reason for the decrease in species composition and diversity in riparian forests was that the greater human disturbance in midstream urban areas than other areas. Therefore, by building a natural park as buffer area around the riparian zone of Lijiang River, and controlling, reducing the intensity of human activities in the riparian zone will help to restore and enhance the diversity of the karst forest ecosystem and its service functions. The species turnover component of karst forests in the Lijiang River was the main driving force for the difference in beta diversity, suggesting that the protection of karst forests in the Lijiang River should cover the whole river basin, rather than focusing only on areas with less species richness (Gianuca et al. 2017).

The study of species diversity of karst forest community could reveal its species composition, distribution status and community differences, meanwhile the study of phylogenetic diversity would represent the historical succession process of the forest vegetation in the survey area. The number and species distribution of forest species along the Lijiang River basin were different in the upper, middle and lower reaches, resulting in significant spatial differences in alpha diversity and beta diversity among forest communities. The alpha diversity in the upstream was significantly higher than that in the middle and downstream. Because both altitude and soil conditions affected the alpha diversity level of forest communities in the riparian zone of Lijiang River. Although habitat filtration and dispersal limitation acted together on the distribution of species diversity in karst forest communities of the Lijiang River, the decomposition of beta diversity showed that the turnover component was the main contribution, indicating that dispersal limitation was the main driver of beta diversity differences in karst forest communities of the Lijiang River. This study provided theoretical support for the targeted protection of karst forest in the Lijiang River. It is suggested that the protection of karst forest should cover the whole Lijiang River basin, however, more attention should focus on the areas with large human disturbance in the middle reaches, also.

Acknowledgements We thank Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, China for data sharing.

Author contributions Zhao Yi conceived and designed the study. Li Lin and Wei Shi-guang revised the paper. Zhou Jing-gang, Wen zhi-feng, Zhong jian-jun, Shen kai-hui conducted sampling design, field and lab work. All co-authors reviewed and gave final approval for publication.

Funding This work was supported by the Guangxi Natural Science Foundation (2022GXNSFAA035583, 2020GXNSFAA159108) and National Natural Science Foundation of China (32060305).

Data Availability The data underlying this article will be shared upon reasonable request to the corresponding author.

Code availability Not applicable.

Alroy J (2017) Effects of habitat disturbance on tropical forest biodiversity. Proc. Natl. Acad. Sci. U.S.A 23:6056-6061. https://doi.org/10.1073/pnas.1611855114
Barwell LJ, Isaac NJ, Kunin WE (2015) Measuring beta-diversity with species abundance data. J Anim Ecol 4:1112-1122. https://doi.org/10.1111/1365-2656.12362
Baselga A (2012) The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography 12:1223-1232. https://doi.org/10.1111/j.1466-8238.2011.00756.x
Baselga A, Orme CDL (2012) betapart: an R package for the study of beta diversity. Methods in Ecology and Evolution 5:808-812. https://doi.org/10.1111/j.2041-210X.2012.00224.x
Baselga A, Leprieur F (2015) Comparing methods to separate components of beta diversity. Methods in Ecology and Evolution 9:1069-1079. https://doi.org/10.1111/2041-210x.12388
Benítez-Malvido J, Álvarez-Añorve MY, Ávila-Cabadilla LD et al (2022) Phylogenetic and functional structure of tree communities at different life stages in tropical rain forest fragments. Global Ecology and Conservation 36:e02113. https://doi.org/10.1016/j.gecco.2022.e02113
Brown JL, Paz A, Reginato M et al (2020) Seeing the forest through many trees: Multi-taxon patterns of phylogenetic diversity in the Atlantic Forest hotspot. Diversity and Distributions 9:1160-1176. https://doi.org/10.1111/ddi.13116
Cao L, Liu H, Fu XY et al (2019) Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests. Forests 2:145. https://doi.org/10.3390/f10020145
Cardoso P, Rigal F, Carvalho JC et al (2014) Partitioning taxon, phylogenetic and functional beta diversity into replacement and richness difference components. Journal of Biogeography 4:749-761. https://doi.org/10.1111/jbi.12239
Chao A, Chun‐Huo C (2016) Species Richness: Estimation and Comparison. Wiley StatsRef: Statistics Reference Online https://doi.org/10.1002/9781118445112.STAT03432.PUB2
Chao A, Ricotta C (2019) Quantifying evenness and linking it to diversity, beta diversity, and similarity. Ecology 12:e02852-1-e02852-15. https://doi.org/10.1002/ecy.2852
Chao A, Ma KH, Hsieh TC et al (2016) SpadeR: Species-Richness Prediction and Diversity Estimation with R. https://github.com/AnneChao/SpadeR/ Accessed 13 June 2022
Chao A, Henderson PA, Chiu CH et al (2022) Erratum to: Measuring temporal change in alpha diversity: A framework integrating taxonomic, phylogenetic and functional diversity and the iNEXT.3D standardization. Methods in Ecology and Evolution 11:2663-2663. https://doi.org/10.1111/2041-210x.14012
Chao AN, Kubota Y, Zeleny D et al (2020) Quantifying sample completeness and comparing diversities among assemblages. Ecological Research 2:292-314. https://doi.org/10.1111/1440-1703.12102
Chen X, Chen X, Huang S et al (2022) Impacts of Moso bamboo (Phyllostachys pubescens) invasion on species diversity and aboveground biomass of secondary coniferous and broad-leaved mixed forest. Front Plant Sci 13:1001785. https://doi.org/10.3389/fpls.2022.1001785
Colwell RK, Chao A, Gotelli NJ et al (2012) Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. Journal of Plant Ecology 1:3-21. https://doi.org/10.1093/jpe/rtr044
Depecker J, Asimonyio JA, Miteho R et al (2022) The association between rainforest disturbance and recovery, tree community composition, and community traits in the Yangambi area in the Democratic Republic of the Congo. Journal of Tropical Ecology 6:426-436. https://doi.org/10.1017/S0266467422000347
Druckenbrod DL, Martin-Benito D, Orwig DA et al (2019) Redefining temperate forest responses to climate and disturbance in the eastern United States: New insights at the mesoscale. Global Ecology and Biogeography 5:557-575. https://doi.org/10.1111/geb.12876
Ehbrecht M, Seidel D, Annighofer P et al (2021) Global patterns and climatic controls of forest structural complexity. Nat Commun 1:519. https://doi.org/10.1038/s41467-020-20767-z
Faith DP (1992) Conservation Evaluation and Phylogenetic Diversity. Biological Conservation 1:1-10. https://doi.org/10.1016/0006-3207(92)91201-3
Gianuca AT, Declerck SA, Lemmens P et al (2017) Effects of dispersal and environmental heterogeneity on the replacement and nestedness components of beta-diversity. Ecology 2:525-533. https://doi.org/10.1002/ecy.1666
Ginestet C (2011) ggplot2: Elegant Graphics for Data Analysis. Journal of the Royal Statistical Society Series a-Statistics in Society 1:245-245. https://doi.org/0.1111/j.1467-985X.2010.00676_9.x
Gu YY, Lin NF, Ye X et al (2022) Assessing the impacts of human disturbance on ecosystem services under multiple scenarios in karst areas of China: Insight from ecological conservation red lines effectiveness. Ecological Indicators 142:109202. https://doi.org/10.1016/j.ecolind.2022.109202
Hamilton NE, Ferry M (2018) ggtern: Ternary Diagrams Using ggplot2. Journal of Statistical Software Cn3:1-17. https://doi.org/10.18637/jss.v087.c03
He Y, Liang SC, Liu RH et al (2022) Beta Diversity Patterns Unlock the Community Assembly of Woody Plant Communities in the Riparian Zone. Forests 5:https://doi.org/10.3390/f13050673
Hsieh TC, Ma KH, Chao A et al (2016) iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods in Ecology and Evolution 12:1451-1456. https://doi.org/10.1111/2041-210x.12613
Jactel H, Bauhus J, Boberg J et al (2017) Tree Diversity Drives Forest Stand Resistance to Natural Disturbances. Current Forestry Reports 3:223-243. https://doi.org/10.1007/s40725-017-0064-1
Jamoneau A, Passy SI, Soininen J et al (2018) Beta diversity of diatom species and ecological guilds: Response to environmental and spatial mechanisms along the stream watercourse. Freshwater Biology 1:62-73. https://doi.org/10.1111/fwb.12980
Jin Y, Qian H (2019) V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 8:1353-1359. https://doi.org/10.1111/ecog.04434
Kembel SW, Cowan PD, Helmus MR et al (2010) Picante: R tools for integrating phylogenies and ecology. Bioinformatics 11:1463-1464. https://doi.org/10.1093/bioinformatics/btq166
Krishnadas M, Kumar AN, Comita LS (2019) Edge effects reduce alpha-diversity but not beta-diversity during community assembly in a human-modified tropical forest. Ecol Appl 8:e01996. https://doi.org/10.1002/eap.1996
Legendre P (2014) Interpreting the replacement and richness difference components of beta diversity. Global Ecology and Biogeography 11:1324-1334. https://doi.org/10.1111/geb.12207
Liao HW, Jiang ZC, Zhou H et al (2022) Isotope-Based Study on Nitrate Sources in a Karst Wetland Water, Southwest China. Water 10:1533 https://doi.org/10.3390/w14101533
Luby IH, Miller SJ, Polasky S (2022) When and where to protect forests. Nature 7925:89-93. https://doi.org/10.1038/s41586-022-05096-z
Mirazadi Z, Pilehvar B, Vajari KA (2017) Diversity indices or floristic quality index: Which one is more appropriate for comparison of forest integrity in different land uses? Biodiversity and Conservation 5:1087-1101. https://doi.org/10.1007/s10531-016-1287-3
Morelli MCM, de Souza CR, Morel JD et al (2020) Can small-scale altitudinal gradients predict spatial and temporal patterns in tropical forests? Journal of Forestry Research 5:1855-1865. https://doi.org/10.1007/s11676-020-01259-8
Pavoine S, Bonsall MB (2011) Measuring biodiversity to explain community assembly: a unified approach. Biol Rev Camb Philos Soc 4:792-812. https://doi.org/10.1111/j.1469-185X.2010.00171.x
Pavoine S, Ricotta C (2019) A simple translation from indices of species diversity to indices of phylogenetic diversity. Ecological Indicators 101:552-561. https://doi.org/10.1016/j.ecolind.2019.01.052
Paz A, Brown JL, Cordeiro CLO et al (2021) Environmental correlates of taxonomic and phylogenetic diversity in the Atlantic Forest. Journal of Biogeography 6:1377-1391. https://doi.org/10.1111/jbi.14083
Pielou EC (1966) The measurement of diversity in different types of biological collections. Journal of Theoretical Biology 13:131-144. https://doi.org/10.1016/0022-5193(66)90013-0
Podani J, Schmera D (2011) A new conceptual and methodological framework for exploring and explaining pattern in presence-absence data. Oikos 11:1625-1638. https://doi.org/10.1111/j.1600-0706.2011.19451.x
Podani J, Schmera D (2016) Once again on the components of pairwise beta diversity. Ecological Informatics 63-68. https://doi.org/10.1016/j.ecoinf.2016.01.002
Rezende CL, Scarano FR, Assad ED et al (2018) From hotspot to hopespot: An opportunity for the Brazilian Atlantic Forest. Perspectives in Ecology and Conservation 4:208-214. https://doi.org/10.1016/j.pecon.2018.10.002
Rito KF, Arroyo-Rodríguez V, Cavender-Bares J et al (2021) Unraveling the drivers of plant taxonomic and phylogenetic β-diversity in a human-modified tropical dry forest. Biodiversity and Conservation 4:1049-1065. https://doi.org/10.1007/s10531-021-02131-9
Roswell M, Dushoff J, Winfree R (2021) A conceptual guide to measuring species diversity. Oikos 3:321-338. https://doi.org/10.1111/oik.07202
Shafique MUU, Younis A, Asif M et al (2021) Exotic Tree Species Outnumbering Native Tree Species: an Alarming Situation for the Sustainable Urban Plantation Development. ResearchSquare https://doi.org/10.21203/rs.3.rs-1079672/v1
Simpson EH (1949) Measurement of Diversity. Nature 163:688.
Socolar JB, Gilroy JJ, Kunin WE et al (2016) How Should Beta-Diversity Inform Biodiversity Conservation? Trends Ecol Evol 1:67-80. https://doi.org/10.1016/j.tree.2015.11.005
Sonne J, Dalsgaard B, Borregaard MK et al (2022) Biodiversity cradles and museums segregating within hotspots of endemism. Proc Biol Sci 1981:20221102. https://doi.org/10.1098/rspb.2022.1102
Swenson NG (2011) Phylogenetic beta diversity metrics, trait evolution and inferring the functional beta diversity of communities. PLoS One 6:e21264. https://doi.org/10.1371/journal.pone.0021264
Szumanska I, Lubinska-Mielinska S, Kaminski D et al (2021) Invasive Plant Species Distribution Is Structured by Soil and Habitat Type in the City Landscape. Plants (Basel) 4:733. https://doi.org/10.3390/plants10040773
Tatsumi S, Iritani R, Cadotte MW (2021) Temporal changes in spatial variation: partitioning the extinction and colonisation components of beta diversity. Ecol Lett 5:1063-1072. https://doi.org/10.1111/ele.13720
Vazquez-Delfin P, Casas A, Vallejo M (2022) Adaptation and biocultural conservation of traditional agroforestry systems in the Tehuacan Valley: access to resources and livelihoods strategies. Heliyon 7:e09805. https://doi.org/10.1016/j.heliyon.2022.e09805
Véron S, Saito V, Padilla-García N et al (2019) The Use of Phylogenetic Diversity in Conservation Biology and Community Ecology: A Common Base but Different Approaches. The Quarterly Review of Biology 2:123-148. https://doi.org/10.1086/703580
Wang ZY, Wang YR, Zeng RC et al (2018) Random Forest based hourly building energy prediction. Energy and Buildings 171:11-25. https://doi.org/10.1016/j.enbuild.2018.04.008
Wei XH, Giles-Hansen K, Spencer SA et al (2022) Forest harvesting and hydrology in boreal Forests: Under an increased and cumulative disturbance context Forest Ecology and Management 522:120468. https://doi.org/10.1016/j.foreco.2022.120468
Whittaker RH (1972) Evolution and Measurement of Species Diversity. TAXON 2-3:213-251.
Xu Q-F, Jiang P-K, Wu J-S et al (2014) Bamboo invasion of native broadleaf forest modified soil microbial communities and diversity. Biological Invasions 1:433-444. https://doi.org/10.1007/s10530-014-0741-y
Zhang J, Zhang PQ, Song YY (2022) Comparative Water Environment Simulation Study of Two Typical Models with BMPs in a Karst Basin. Agriculture-Basel 1:69. https://doi.org/10.3390/agriculture12010069
Zhang XY, Ci XQ, Zhu RB et al (2022) Spatial phylogenetic patterns and conservation of threatened woody species in a transition zone of southwest China. Biodiversity and Conservation 8-9:2205-2225. https://doi.org/10.1007/s10531-022-02439-0
Zhou GY, Lucas-Borja ME, Eisenhauer N et al (2022) Understorey biodiversity supports multiple ecosystem services in mature Mediterranean forests. Soil Biology & Biochemistry 172:108774. https://doi.org/10.1016/j.soilbio.2022.108774
Zhou P, Zhang H, Huang B et al (2022) Are productivity and biodiversity adequate predictors for rapid assessment of forest ecosystem services values? Ecosystem Services 57:101466. https://doi.org/10.1016/j.ecoser.2022.101466
Zhu D, Cheng X, Li W et al (2022) Temporal and Spatial Variation Characteristics of Water Quality in the Middle and Lower Reaches of the Lijiang River, China and Their Responses to Environmental Factors. Int J Environ Res Public Health 19:80-89. https://doi.org/10.3390/ijerph19138089

No competing interests reported.

AppendixS.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

The study of species and phylogenetic diversity on karst forest along Lijiang River

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study area, sites, and plots

Estimation of species diversity and phylogenetic structure

Statistical analyses

Results

Taxonomic alpha diversity of karst forest community along Lijiang River

Taxonomic beta diversity of karst forest community along Lijiang River

Phylogenetic structure of karst forest community along Lijiang River

Discussion

Forestry community composition in karst zone along Lijiang River

Alpha diversity of karst forest along Lijiang River

Beta diversity and its decomposition of karst forest along Lijiang River

Phylogenetic diversity of karst forest along Lijiang River

Suggestions on the protection of karst forests along Lijiang River

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1