- Open Access
The effects of biodiversity gradient on plant mass and metabolism of individual submerged macrophytes
Ecological Processes volume 10, Article number: 38 (2021)
The effects of biodiversity on community function and services are frequently studied in the history of ecology, while the response of individual species to biodiversity remains great elusive. In this study, we determined the biodiversity effects on community productivity as well as species level plant mass and carbon (C) and nitrogen (N) metabolism of eight submerged plants. These macrophytes in Lake Erhai were selected and planted in a water depth of one meter along a diversity gradient of 1, 2, 4 or 8 species. Then, the community productivity or species level plant mass, soluble protein, free amino acid and soluble carbohydrate were correlated to species richness to determine the biodiversity effects on community and single species.
The results showed that the community level biomass was positively correlated to plant species richness although the species level plant mass of individual species responded differently to the overall plant species richness. Namely, only one plant mass positively correlated to species richness and the others decreased or showed no significant correlation with the increase of species richness. The soluble proteins of most macrophytes were positively correlated to species richness; however, both the free amino acid and soluble carbohydrate of the plants were negatively or not significantly correlated to species richness.
These results indicated that the selection effects might dominate in our aquatic communities and the negative impacts of biodiversity on C and N metabolism of the macrophytes increased with the increase of species richness, which might result from the strong competition among the studied species. The biodiversity effects on the plant mass, and C and N metabolism of individual submerged species were first reported in this study, while more such field and control experiments deserve further research.
The growth and reproduction of submerged macrophytes are determined by many biotic and abiotic factors (Bornette and Puijalon 2011; Geist 2011; O’Hare et al. 2018), and there is a wealth of research on this topic. For example, abiotic factors as light, temperature, nutrient, sediment, CO2 availability, water current, water level fluctuation, global change and biotic factors as interaction between plants, invasive species, herbivores, anthropogenic disturbance, biodiversity are always involved in these research programmes (Bornette and Puijalon 2011; Bakker et al. 2016; Hussner et al. 2017; Hilt et al. 2018; Sagerman et al. 2020). The ecosystem function and services affected by biodiversity are frequently assayed (Jiang et al. 2008; Emmerson et al. 2001; Giller et al. 2004; Hassler et al. 2014; Duffy et al. 2017; Ampoorter et al. 2020). For example, a positive diversity-productivity relationship is predominant in large scale studies or meta-analyses (Liang et al. 2016; Duffy et al. 2017; Ampoorter et al. 2020), which is usually explained by complementarity or sampling effects (Loreau and Hector 2001; Lefcheck et al. 2015). However, the pattern of species level plant mass over a species richness gradient remains largely unknown, although the average species level plant mass of individual plants in different species richness was measured in some previous studies (Hector et al. 2002; Hille Ris Lamber et al. 2004; Roscher et al. 2007; Marquard et al. 2009). In this type of experiments, the whole communities consist of the single plant species, which is indeed in the species pool. In addition, each species may respond differently to species richness because of the great differences among species (Firn et al. 2007; Jacob et al. 2010; Roscher et al. 2013). For this reason, some species may benefit from the increase of biodiversity to promote their plant mass, some species may suffer from the increase of biodiversity to reduce their plant mass, and some species may keep their plant mass stable with the increase of biodiversity (Wu et al. 2018). Accordingly, the responses of species level plant mass of individual plants to different species richness deserve further attention (Marquard et al. 2009; Wu et al. 2018).
For plant species, their growth is affected and reflected by their carbon (C) and nitrogen (N) metabolism (Cao et al. 2009; Yuan et al. 2016; Yu et al. 2017). Therefore, the growth conditions of plants can be indicated by their C and N metabolism. However, the effects of biodiversity on plant C and N metabolism are not well documented to our best knowledge, although other factors affecting plant C and N metabolism are frequently reported, such as NH4+ stress and low light availability (Cao et al. 2009), water depth (Yuan et al. 2016), high nitrogen loading (Yu et al. 2017), etc. To address this knowledge gap, it is necessary to determine the effects of biodiversity on plant C and N metabolism. Furthermore, whether C or N metabolism of the individual plant species responds differently to species richness, as species level plant mass of individual plants does because species are species-specific (Firn et al. 2007; Jacob et al. 2010; Wu et al. 2018, 2020). Moreover, whether the patterns of biodiversity effects on C or N metabolism of the individual plant species are in accordance with those of their species level plant mass.
We determined the C and N metabolism of the studied submerged macophytes through measuring the contents of the soluble protein, free amino acid and soluble carbohydrate of these plants as former researchers (Cao et al. 2009; Yuan et al. 2016; Yu et al. 2017). In this study, we hypothesize that (1) species level plant mass of individual species increases with overall species richness as community biomass due to complementarity effects (Loreau and Hector 2001; Lefcheck et al. 2015); and (2) all of the soluble protein, free amino acid and soluble carbohydrate of the submerged plants increase with overall species richness because of complementarity effects (Loreau and Hector 2001; Lefcheck et al. 2015). To test these hypotheses, we conducted a field pot experiment in 2018 to study the effects of biodiversity on species level plant mass as well as C and N metabolism of submerged plants in a biodiversity experiment in Lake Erhai. Furthermore, we discussed the feasibility of explaining the relationship between diversity and productivity (hereafter DPR) through the change of species level plant mass responding to species richness.
Materials and Methods
The field experiment was conducted from August to October in 2018 (16 weeks), at Hongshan Bay (25°86′N, 100°14′E) in Lake Erhai, Dali City, Yunnan Province, China. This bay is characterized by light winds and waves. The study area is located in a warm plateau climate with an average annual temperature of 15.7 °C, a maximum temperature of 34 °C and a minimum temperature of –2.3 °C. The annual precipitation, sunshine duration and frost free period are 1024 mm, 2345 hours and 228 days, respectively. Eight common native submerged species in Lake Erhai were selected in this experiment (Ye et al. 2018). They are Ceratophyllum demersum, Myriophyllum spicatum, Potamogeton maackianus, Stuckenia pectinata, Hydrilla verticillata, Vallisneria natans, Potamogeton nodosus and Potamogeton lucens. All collected submerged macrophytes were washed and brushed softly with enough tap water and then the apical unbranched shoots (15 cm in length and similar in morphology) (clonal ramet with three leaves for V. natans) were then cut for planting. The number of plant species was controlled in every pot with a circular area of 706.5 cm2 (soil surface), 25 cm depth and pre-weighed 8 kg of sediment in the west littoral zone of Lake Erhai (Organic matter: 15.3–19.4 g kg-1; total nitrogen: 1.52–1.78 g kg-1, total phosphorus: 0.16–0.23 g kg-1 and pH: 6.12–6.93, about 15 cm thick soil layer). Each plant was planted in 5 cm depth. Plants were randomly cultivated with 1, 2, 4 or 8 selected species in communities (pots). Each diversity level was replicated eight times. In this experiment, we used four levels of diversity (1, 2, 4 and 8 species) and eight replicates, resulting in a total of 32 pots. Every pot was received 12 g plant cuttings, where the plant biomass was equally divided for each kind of species. All pots were suspended at one meter water level using a vinyl coated chain. The depth was measured from the top of the pot to the water surface. The pots were protected from fishes and other big aquatic animals by nets. Moreover, other appropriate care was carried out to minimize any disturbance. For example, other weeds (all plants not planted initially) were carefully removed from the pots when they were still small enough. For each pot, plants (roots and shoots) were harvested sixteen weeks later when roots were separated from soil by soaking the pot in water for 40 minutes and gently washing the soil away. Individual plant in each pot was dried at 70 °C for 72 h and measured as its species level plant mass. The total weight of all species level plant mass in each pot was considered its community level biomass. During experiment phase, the water level fluctuated between –10 cm and +20 cm.
For the single plant in every pot, the dry plant was ground into powder with a pestle and mortar. About 30 mg of the powder was extracted with 10 ml 80% ethanol at 80 °C. After centrifugation, the supernatant was used for free amino acid (FAA) and soluble carbohydrate (SC) determination. The FAA was determined by ninhydrin colorimetry method using alanine as a stand (Yemm and Willis 1954). The SC was determined by phenol method using glucose as a standard (Yemm and Cocking 1955). The soluble protein (SP) was determined using Coomassie brilliant blue G-250 (Bradford 1976) with bovine serum albumin as a stand.
Statistical analyses were conducted using the software package R 3.5.2 (R Core Team 2018). Although linear fitting is often used to indicate the relationship between biodiversity and productivity for most of DPR experiments, we used best fit curve to describe their correlation as some non-linear relationships were observed in some previous research (Waide et al. 1999; Duffy et al. 2017; Ampoorter et al. 2020; Wu et al. 2021). The best model was selected by using the Akaike's information criterion (AIC) procedure through comparing the AIC values of the six common models (Linear, Quadratic, Exponential growth, Power, Rational and Logarithm). Accordingly, Spearman's Rank Correlation Coefficient was used to analyze the relationships between community biomass or plant species level mass and plant species richness. Relationships between SP, FAA and SC of the individual plant species and the species richness in the experiment were also analyzed by Spearman's Rank Correlation Coefficient.
Responses of Community productivity and species level plant mass
Our results showed that the species richness was significantly positively correlated to community biomass (productivity) (Fig. 1; R2 = 0.57, p < 0.0001). With regard to the species level plant mass, only plant mass of C. demersum was positively correlated with the species richness (Fig. 2A). However, plant masses of P. maackianus and P. lucens were not significantly correlated to the species richness (Fig. 2C, H, R2 = 0.01, p = 0.51 and R2 = 0.01, p = 0.64, respectively) and plant masses of the other five macrophytes were negatively correlated with the species richness (Fig. 2B, D–G, p < 0.05).
C and N metabolism
The SP contents of most submersed plants, especially of S. pectinata, were markedly positively correlated to species richness (Fig. 3B–G, p < 0.05) except those of C. demersum and P. maackianus, which demonstrated no significant correlation with the species richness (Fig. 3A, 3C, R2 = 0.14, p = 0.37 and R2 = 0.13, p = 0.17, respectively). However, the FAA contents of most submersed macrophytes, especially of S. pectinata, were prominently negatively correlated to the species richness (Fig. 4, p < 0.05) except that of P. maackianus, which exhibited no significant correlation with the species richness (Fig. 4C, R2 = 0.19, p = 0.26), and that of P. nodosus, which increased firstly and then dropped with the species richness (Fig. 4G, R2 = 0.51, p = 0.01). Similarly, the SC contents were distinctly negatively correlated to the species richness for M. spicatum and P. nodosus (Fig. 5B, 5G, p < 0.05) or displayed no significant correlation with the species richness for C. demersum, P. maackianus and H. verticillata (Fig. 5A, 5C, 5E, p > 0.05), while the SC contents of V. natans increased firstly and then dropped with the species richness (Fig. 5F, R2 = 0.65, p = 0.01).
Positive linear pattern of DPR
In this experiment, the results imply that community level biomass is significantly positively correlated with the species richness in the aquatic environment. These positive relationships are in accordance with most of the results of previous studies and recent meta-analyses (Tilman et al. 1996; Loreau and Hector 2001; Adler et al. 2011; Lefcheck et al. 2015; Grace et al. 2016; Mandal et al. 2018; Luo et al. 2019; Rita and Borghetti 2019). The positive linear pattern of DPR results from the over-yielding usually caused by complementarity effects or selection effects in high biodiversity communities (Loreau and Hector 2001). However, contrary to Wu et al. (2018), our results clearly suggest that the selection effect is dominated in this aquatic experiment as the plant mass of only one species increased with plant biodiversity, while those of other species decreased or kept static with plant biodiversity (Fig. 2). The results obviously indicate that strong competition increases among the studied species with species richness. But we do not know when complementarity effects or selection effects occur. So, which factor(s) determine either the complementarity effects or the selection effects, which play an important role in the over-yielding in DRP experiments, might be an ecological hotspot in the near future.
Competition among species
As stated above, only plant mass of C. demersum increased with plant biodiversity, while those of other species decreased or kept static with plant biodiversity (Fig. 2). In this sense, plant mass of individual species may benefit from or suffer from or keep relative stable with the increase of biodiversity. We can speculate that the over-yielding in high biodiversity is the trade-off of all species. This result agrees well with the obversations of Ye et al. (2018) in Lake Erhai, where they suggested that the dominant species at the depth of one meter in the west littoral zone of Lake Erhai was C. demersum, which has the most competitive ability and can outperform other submerged species. Therefore, we can speculate that only the biomass of C. demersum increased with species richness and those of the other species declined with plant biodiversity is due to either the strong competition stress of C. demersum or the strong competition stress among the study species. Generally, plants firstly develop physiological adaptations responding to adverse environments (Hessini et al. 2009; Yuan et al. 2016; Sasidharan et al. 2018), such as the changes of many kinds of antioxidant enzymes, SC and FAA. Under stress conditions, the defense systems of plants can be overwhelmed and many types of antioxidant enzymes are induced to remove reactive oxygen species in plant cells (Alscher et al. 2002; Wu et al. 2009). Accordingly, the contents of SP in plant species increase with the increase of stress because all antioxidant enzymes are SPs. The SP contents of our study species were positively correlated with species richness (Fig. 3), which indicates that both the competition stress (suffered by these submerged macrophytes) and antioxidant enzymes (produced by these submerged macrophytes) increase with plant biodiversity. On the contrary, the FAA and (or) SC concentrations of the study species declined with the species richness (Figs. 4 and 5). This suggests that the production of FAA and (or) SC is inhibited in the study macrophytes when the competition stress, resulting from high plant biodiversity, is strong (Cao et al. 2009; Yuan et al. 2016; Yu et al. 2017). Thus, the biomass production and growth of the plants can be greatly affected, as observed in our study, because FAA and SC are important intermediates and buffering pool of C and N metabolism (Myers and Kitajima 2007; Yuan et al. 2016). These results are partially opposite to our two hypotheses, indicating most studied macrophytes suffered from the increase of biodiversity in our experiment. This might because that the light competition among these submerged species in this mesotrophic lake (Geist 2011; Ji et al. 2017; Ye et al. 2018) increased with biodiversity due to over-yielding (Clark and Tilman 2008; Borer et al. 2014; Cerabolini et al. 2016), as observed in our study.
Different responses of individual species
Our results showed that different species responded differently to species richness and even the different measured parameters of the same species responded diversely to the species richness in our aquatic communities, which agrees well with much previous research (Hector et al. 2002; Hille Ris Lamber et al. 2004; Roscher et al. 2007; Marquard et al. 2009; Wu et al. 2018, 2020). This result therefore implies that both plant species and plant variables cannot equally benefit from high plant species richness because of the great differences among species and the index-specificity within a species (Firn et al. 2007; Jacob et al. 2010; Roscher et al. 2013; Wu et al. 2018, 2020). This might be a good explanation of the formation of distinct function species (Whittaker 1965; Odum 1983), such as dominant, sub-dominant, companion, keystone, rare and redundant species, within a community in real nature because of their different benefits from biodiversity. However, this phenomenon cannot be observed within such a short experimental period and more extensive and intensive studies are needed to prove this. Accordingly, the four DRP patterns are very reasonably and frequently observed because the pattern is determined by tradeoffs between the plant masses of all species, which are actual species in the species pool consisting of the whole communities (Wu et al. 2018). Furthermore, the interplay of multiple particular abiotic and biotic experimental conditions makes the pattern of the DPR more complicated (Byrnes et al. 2014; Lefcheck et al. 2015). This might be the reason that there are no explicit explanations of the other three DPR patterns except the positive linear DPR pattern. However, the pattern of species level plant mass over the biodiversity gradients can be readily obtained whatever the abiotic or biotic factors change. This might make species level plant mass a great advantage over others in terms of mechanic explanations of DPR patterns though there are some little design flaws (e.g. less species) in our experiment. Therefore, we argue that a type of DPR pattern could be interpreted by analyzing the trends of species level plant mass over the biodiversity gradient. This might be a good alternative mechanism explaining the complicated DPR patterns and more extensive experiments are needed to further prove our hypothesis.
Community productivity was significantly positively correlated with plant biodiversity due to the selection effect. However, the relationships between species level plant mass, plant SP, FAA and SC and species richness are more complicated. The negative effects of biodiversity on the most of the studied species are attributed to the strong competition, especially light competition, among these submerged macrophytes. The analyses of the plant species level response to species richness might be a good alternative mechanism explaining the complicated DPR patterns, which are affected by many intricate factors.
Availability of data and materials
Please contact the author for data requests, and the data will be available online upon your request
The relationship between diversity and productivity
Free amino acid
Adler PB, Seabloom EW, Borer ET, Hillebrand H, Hautier Y, Hector A (2011) Productivity is a poor predictor of plant species richness. Science 333(6050):1750–1753. https://doi.org/10.1126/science.1204498
Alscher RG, Erturk N, Heath LS (2002) Role of superoxide dismutases (SODs) in controlling oxidative stress in plants. J Exp Bot 53(372):1331–1341. https://doi.org/10.1093/jexbot/53.372.1331
Ampoorter E, Barbaro L, Jactel H, Baeten L, Boberg J, Carnol M, Allan E (2020) Tree diversity is key for promoting the diversity and abundance of forest-associated taxa in Europe. Oikos 129(2):133–146. https://doi.org/10.1111/oik.06290
Bakker ES, Wood KA, Pagès JF, Veen GF, Christianen MJA, Santamaría L, Hilt S (2016) Herbivory on freshwater and marine macrophytes: a review and perspective. Aquat Bot 135:18–36. https://doi.org/10.1016/j.aquabot.2016.04.008
Borer ET, Seabloom EW, Gruner DS, Harpole WS, Hillebrand H, Lind EM, Bakker JD (2014) Herbivores and nutrients control grassland plant diversity via light limitation. Nature 508(7497):517–520. https://doi.org/10.1038/nature13144
Bornette G, Puijalon S (2011) Response of aquatic plants to abiotic factors: a review. Aquat Sci 73(1):1–14. https://doi.org/10.1007/s00027-010-0162-7
Bradford MM (1976) Rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of Protein-Dye binding. Anal Biochem 72:248–54. https://doi.org/10.1006/abio.1976.9999
Byrnes JEK, Isbell F, Lefcheck JS, Griffin JN, Hector A, Cardinale BJ (2014) Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol Evol 5(2):111–124. https://doi.org/10.1111/2041-210X.12143
Cao T, Xie P, Ni LY, Zhang M, Xu J (2009) Carbon and nitrogen metabolism of aneutrophication tolerative macrophyte, Potamogeton crispus, under NH4+ stress and low light availability. Environ Exp Bot 66(1):74–78. https://doi.org/10.1016/j.envexpbot.2008.10.004
Cerabolini BEL, Pierce S, Verginella A, Brusa G, Ceriani RM, Armiraglio S (2016) Why are many anthropogenic agroecosystems particularly species-rich? Plant Biosyst 150(3):550–557. https://doi.org/10.1080/11263504.2014.987848
Clark CM, Tilman D (2008) Loss of plant species after chronic low-level nitrogen deposition to prairie grasslands. Nature 451(7179):712–715. https://doi.org/10.1038/nature06503
Core Team R (2018) R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna
Duffy JE, Godwin CM, Cardinale BJ (2017) Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549(7671):261–264. https://doi.org/10.1038/nature23886
Emmerson MC, Solan M, Emes C, Paterson DM, Raffaelli D (2001) Consistent patterns and idiosyncratic effects of biodiversity in marine ecosystems. Nature 411(6833):73–77. https://doi.org/10.1038/35075055
Firn J, Erskine PD, Lamb D (2007) Woody species diversity influences productivity and soil nutrient availability in tropical plantations. Oecologia 154(3):521–533. https://doi.org/10.1007/s00442-007-0850-8
Geist J (2011) Integrative freshwater ecology and biodiversity conservation. Ecol Indic 11(6):1507–1516. https://doi.org/10.1016/j.ecolind.2011.04.002
Giller PS, Hillebrand H, Berninger UG (2004) Biodiversity effects on ecosystem functioning: emerging issues and their experimental test in aquatic environments. Oikos 104(3):423–436. https://doi.org/10.1111/j.0030-1299.2004.13253.x
Grace JB, Anderson TM, Seabloom EW, Borer ET, Adler PB, Harpole WS, Smith MD (2016) Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529(7586):390–393. https://doi.org/10.1038/nature16524
Hassler CS, Ridgway KR, Bowie AR, Butler ECV, Clementson LA, Doblin MA, Ellwood MJ (2014) Primary productivity induced by iron and nitrogen in the Tasman Sea: an overview of the PINTS expedition. Mar Freshwater Res 65(6):517–537. https://doi.org/10.1071/MF13137
Hector A, Bazeley-White E, Loreau M, Otway S, Schmid B (2002) Overyielding in grassland communities: testing the sampling effect hypothesis with replicated biodiversity experiments. Ecol Lett 5(4):502–511. https://doi.org/10.1046/j.1461-0248.2002.00337.x
Hessini K, Pablo MJ, Gandour M, Albouchi A, Soltani A, Abdelly C (2009) Effect of water stress on growth, osmotic adjustment, cell wall elasticity and water-use efficiency in Spartina alterniflora. Environ Exp Bot 67(2):312–319. https://doi.org/10.1016/j.envexpbot.2009.06.010
Hille Ris Lamber J, Harpole WS, Tilman D, Knops J, Reich PB (2004) Mechanisms responsible for the positive diversity-productivity relationship in Minnesota grasslands. Ecol Lett 7(8):661–668. https://doi.org/10.1111/j.1461-0248.2004.00623.x
Hilt S, Alirangues Nuñez MM, Bakker ES, Blindow I, Davidson TA, Gillefalk M, Sayer CD (2018) Response of submerged macrophyte communities to external and internal restoration measures in north temperate shallow lakes. Front Plant Sci 9:194. https://doi.org/10.3389/fpls.2018.00194
Hussner A, Stiers I, Verhofstad MJJM, Bakker ES, Grutters BMC, Haury J, Hofstra D (2017) Management and control methods of invasive alien freshwater aquatic plants: A review. Aquat Bot 136:112–137. https://doi.org/10.1016/j.aquabot.2016.08.002
Jacob M, Leuschner C, Thomas FM (2010) Productivity of temperate broad-leaved forest stands differing in tree species diversity. Ann Forest Sci 67(5):503–511. https://doi.org/10.1051/forest/2010005
Ji N, Wang S, Li Z (2017) Characteristics of dissolved organic phosphorus inputs to freshwater lakes: A case study of Lake Erhai, southwest China. Sci Total Environ 601:1544–1555
Jiang L, Pu Z, Nemergut DR (2008) On the importance of the negative selection effect for the relationship between biodiversity and ecosystem functioning. Oikos 117(4):488–493. https://doi.org/10.1111/j.0030-1299.2008.16401.x
Lefcheck JS, Byrnes JEK, Isbell F, Gamfeldt L, Griffin JN, Eisenhauer N (2015) Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat Commun 6(1):6936–6942. https://doi.org/10.1038/ncomms7936
Liang JJ, Crowther TW, Picard N, Wiser S, Zhou M, Alberti G, Schulze ED (2016) Positive biodiversity-productivity relationship predominant in global forests. Science 354:aaf8957
Loreau M, Hector A (2001) Partitioning selection and complementarity in biodiversity experiments. Nature 412(6842):72–76. https://doi.org/10.1038/35083573
Luo W, Liang J, Cazzolla GR, Zhao X, Zhang C (2019) Parameterization of biodiversity-productivity relationship and its scale dependency using georeferenced tree-level data. J Ecol 107(3):1106–1119. https://doi.org/10.1111/1365-2745.13129
Mandal S, Shurin JB, Efroymson RA, Mathews TJ (2018) Functional divergence in nitrogen uptake rates explains diversity-productivity relationship in microalgal communities. Ecosphere 9(5):e02228. https://doi.org/10.1002/ecs2.2228
Marquard E, Weigelt A, Temperton VM, Roscher C, Schumacher J, Buchmann N (2009) Plant species richness and functional composition drive overyielding in a 6-year grassland experiment. Ecology 90(12):3290–3302. https://doi.org/10.1890/09-0069.1
Myers JA, Kitajima K (2007) Carbohydrate storage enhances seedling shade and stress tolerance in a neotropical forest. J Ecol 95(2):383–395. https://doi.org/10.1111/j.1365-2745.2006.01207.x
O’Hare MT, Aguiar FC, Asaeda T, Bakker ES, Chambers PA, Clayton JS, Wood KA (2018) Plants in aquatic ecosystems: current trends and future directions. Hydrobiologia 812(1):1–11. https://doi.org/10.1007/s10750-017-3190-7
Odum HT (1983) Systems Ecology: an Introduction. John Wiley and Sons, New York, p 644
Rita A, Borghetti M (2019) Linkage of forest productivity to tree diversity under two different bioclimatic regimes in Italy. Sci Total Environ 687:1065–1072. https://doi.org/10.1016/j.scitotenv.2019.06.194
Roscher C, Schumacher J, Lipowsky A, Gubsch M, Weigelt A, Pompe S (2013) A functional trait-based approach to understand community assembly and diversity–productivity relationships over 7 years in experimental grasslands. Perspect Plant Ecol Evol Syst 15:139–149
Roscher C, Schumacher J, Weisser WW, Schmid B, Schulze ED (2007) Detecting the role of individual species for overyielding in experimental grassland communities composed of potentially dominant species. Oecologia 154(3):535–549. https://doi.org/10.1007/s00442-007-0846-4
Sagerman J, Hansen JP, Wikstromet SA (2020) Effects of boat traffic and mooring infrastructure on aquatic vegetation: A systematic review and meta-analysis. Ambio 49(2):517–530. https://doi.org/10.1007/s13280-019-01215-9
Sasidharan R, Hartman S, Liu Z, Martopawiro S, Sajeev N, van Veen H, Yeung E, Voeseneka LACJ (2018) Signal dynamics and interactions during flooding stress. Plant Physiol 176(2):1106–1117. https://doi.org/10.1104/pp.17.01232
Tilman D, Wedin D, Knops J (1996) Productivity and sustainability influenced by diversity in grassland ecosystems. Nature 379(6567):718–720. https://doi.org/10.1038/379718a0
Waide RB, Willig MR, Steiner CF, Mittelbach G, Gough L, Dodson SI (1999) The relationship between productivity and species richness. Annu Rev Ecol Evol Syst 30(1):257–300. https://doi.org/10.1146/annurev.ecolsys.30.1.257
Whittaker RH (1965) Dominance and diversity in land plant communities: numerical relations of species express the importance of competition in community function and evolution. Science 147(3655):250–260. https://doi.org/10.1126/science.147.3655.250
Wu AP, Cao T, Wu SK, Ni LY, Xie P (2009) Trends of superoxide dismutase and soluble protein of aquatic plants in lakes of different trophic levels in the middle and lower reaches of the Yangtze River, China. J Integr Plant Biol 51(4):414–422. https://doi.org/10.1111/j.1744-7909.2009.00814.x
Wu AP, He Y, Ye SY, Qi LY, Liu L, Zhong W, Wang YH, Fu H (2020) Negative effects of a piscicide, rotenone, on the growth and metabolism of three submerged macrophytes. Chemosphere 250:126246. https://doi.org/10.1016/j.chemosphere.2020.126246
Wu AP, Liu J, He FF, Wang YH, Zhang XJ, Duan XD, Qian ZY (2018) Negative relationship between diversity and productivity under plant invasion. Ecol Res 33(5):949–957. https://doi.org/10.1007/s11284-018-1600-6
Wu AP, Ye SY, Yuan JR, Qi LY, Cai ZW, Ye BB, Wang YH (2021) The relationship between diversity and productivity from a three-dimensional space view in a natural mesotrophic lake. Ecol Indic 121:107069. https://doi.org/10.1016/j.ecolind.2020.107069
Ye BB, Chu ZS, Wu AP, Hou ZY, Wang SR (2018) Optimum water depth ranges of dominant submersed macrophytes in a natural freshwater lake. PLoS ONE 13(3):e0193176. https://doi.org/10.1371/journal.pone.0193176
Yemm EW, Cocking EC (1955) The determination of amino acids with ninhydrin. Analyst 80(948):209–213. https://doi.org/10.1039/an9558000209
Yemm EW, Willis AJ (1954) The estimation of carbohydrates in plant extracts by anthrone. Biochemistry 57:508–514
Yu Q, Wang HJ, Wang HZ, Li Y, Liang XM, Xu C, Jeppesen E (2017) Does the responses of Vallisneria natans (Lour.) Hara to high nitrogen loading differ between the summer high-growth season and the low-growth season? Sci Total Environ 601–602:1513–1521
Yuan G, Fu H, Zhong J, Lou Q, Ni L, Cao T (2016) Growth and C/N metabolism of three submersed macrophytes in response to water depths. Environ Exp Bot 122:94–99. https://doi.org/10.1016/j.envexpbot.2015.09.009
The authors would like to thank Professor Te Cao and Teng Hu for their sincerely help in this study. We also sincerely thank four anonymous reviewers for making constructive comments on the manuscript.
This study is financially supported by the National Natural Science Foundation of China (Nos. 31301725, 41401358 and 31200427), Hunan Provincial Natural Science Foundation (No. 2018JJ2162) and the Special Foundation for National Science and Technology Basic Research Program of China (2019FY102000).
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Qi, LY., Zeng, HY., Bai, ZX. et al. The effects of biodiversity gradient on plant mass and metabolism of individual submerged macrophytes. Ecol Process 10, 38 (2021). https://doi.org/10.1186/s13717-021-00316-2
- Species richness
- Submerged macrophyte