Population asynchrony within and between trophic levels have contrasting effects on consumer community stability in a subtropical lake
Data files
Aug 25, 2024 version files 43.22 KB
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README.md
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the_data_used_in_the_manuscript.xlsx
Abstract
1. Clarifying the effects of biodiversity on ecosystem stability in the context of global environmental change is crucial for maintaining ecosystem functions and services. Asynchronous changes between trophic levels over time (i.e., trophic community asynchrony) are expected to increase trophic mismatch and alter trophic interactions, which may consequently alter ecosystem stability. However, previous studies have often highlighted the stabilising mechanism of population asynchrony within a single trophic level, while rarely examining the mechanism of trophic community asynchrony between consumers and their food resources.
2. In this study, we analysed the effects of population asynchrony within and between trophic levels on community stability under the disturbances of climate warming, fishery decline, and de-eutrophication, based on an 18-year monthly monitoring dataset of 137 phytoplankton and 91 zooplankton in a subtropical lake.
3. Our results showed that species diversity promoted community stability mainly by increasing population asynchrony both for phytoplankton and zooplankton. Trophic community asynchrony had a significant negative effect on zooplankton community stability rather than that of phytoplankton, which supports the match-mismatch hypothesis that trophic mismatch has negative effects on consumers. Furthermore, the results of the structural equation models showed that warming and top-down effects may simultaneously alter community stability through population dynamics processes within and between trophic levels, whereas nutrients act on community stability mainly through the processes within trophic levels. Moreover, we found that rising water temperature decreased trophic community asynchrony, which may challenge the prevailing idea that climate warming increases the trophic mismatch between primary producers and consumers.
4. Overall, our study provides the first evidence that population and trophic community asynchrony have contrasting effects on consumer community stability, which offers a valuable insight for addressing global environmental change.
README: Population asynchrony within and between trophic levels have contrasting effects on consumer community stability in a subtropical lake
https://doi.org/10.5061/dryad.w3r228118
Description of the data and file structure
We have submitted our raw data (the_data_used_in_the_manuscript.xlsx).
the_data_used_in_the_manuscript
Station: Name of stations in the Lake Donghu during investigation, including Station I, Station II, and Station III.
Start_year&month: the start year&month of the one-year the moving window.
End_year&month: the end year&month of the one-year the moving window.
Water_temperature: the average water temperature (℃) in the moving window.
Fish_yield: the average fish yield (kg/ha) in the moving window.
Total_nitrogen: the average concentration of total nitrogen (TN, mg/L) in the moving window.
Total_phosphorus: the average concentration of total phosphorus (TP, mg/L) in the moving window.
Phytoplankton_population_stability: Phytoplankton population stability in the moving window.
Phytoplankton_population_asynchrony: Phytoplankton population asynchrony in the moving window.
Phytoplankton_community_stability: Phytoplankton community stability in the moving window.
Zooplankton_population_stability:Zooplankton population stability in the moving window.
Zooplankton_population_asynchrony: Zooplankton population asynchrony in the moving window.
Zooplankton_community_stability: Zooplankton community stability in the moving window.
Trophic_community_asynchrony: Trophic community asynchrony in the moving window.
Phytoplankton_Simpson: Phytoplankton Simpson diversity (monthly average phytoplankton Simpson diversity index at each station in the moving window, range from 0.54 to 0.84).
Phytoplankton_Evenness: Phytoplankton Pielou’s evenness (monthly average phytoplankton Pielou’s evenness index at each station in the moving window, range from 0.46 to 0.73).
Phytoplankton_Shannon: Phytoplankton Shannon diversity (monthly average phytoplankton Shannon diversity index at each station in the moving window, range from 1.29 to 2.29).
Phytoplankton_Richness: Phytoplankton richness (monthly average number of phytoplankton species observed at each station in the moving window, range from 12.91 to 34.17).
Zooplankton_Simpson:Zooplankton Simpson diversity (monthly average zooplankton Simpson diversity index at each station in the moving window, range from 0.37 to 0.77).
Zooplankton_Evenness: Zooplankton Pielou’s evenness (monthly average zooplankton Pielou’s evenness index at each station in the moving window, range from 0.42 to 0.77).
Zooplankton_Shannon: Zooplankton Shannon diversity (monthly average phytoplankton Shannon diversity index at each station in the moving window, range from 0.79 to 1.77).
Zooplankton_Richness: Zooplankton richness (monthly average number of zooplankton species observed at each station in the moving window, range 5.83~12.92).
For more detailed information on the measurements and definitions of species diversity (i.e., Simpson, Evenness, Shannon, and Richness), stability (i.e., population stability and community stability), and asynchrony (i.e., population asynchrony and trophic community asynchrony) mentioned above, please refer to the Methods section (i.e., Estimation of diversity, stability, and asynchrony).
Files and variables
File: the_data_used_in_the_manuscript.xlsx
Methods
Study sites
Lake Donghu (30°31′–30°36′ N, 114°21′–114°28′ E) is a subtropical shallow lake in the Yangtze River basin in Wuhan, Hubei province, China. Lake Donghu contains more than 10 connected sub-lakes, with a total surface area of approximately 33 km2 and a catchment area of approximately 187 km2. The mean depth of Lake Donghu is 2.21 m.
Data obtained
An 18-year monthly dataset for Lake Donghu from 2003 to 2020 was gathered from the Donghu Experimental Station, which belongs to the framework of the Chinese Ecosystem Research Network (CERN). Lake Guozhenghu (11.37 km2) and Lake Tanglinhu (5.78 km2) are the first and second largest sub-lakes of Lake Donghu. Stations I and II are located in the coastal and pelagic zones of Lake Guozhenghu, respectively, and Station III is located in the pelagic zone of Lake Tanglinhu. Monthly surveys (in the order of Station III, Station II, and Station I) were conducted on sunny days on approximately the 15th to reduce the potential impacts of the weather and departed from the Donghu Experimental Station at 8:30 every morning.
In this study, the abiotic indicators monitored were water temperature (℃), the concentration of total nitrogen (TN, mg/L), and total phosphorus (TP, mg/L), while the biotic indicators were the biomass of phytoplankton (mg/L, covering Bacillariophyta, Chlorophyta, Chrysophyta, Cryptophyta, Cyanophyta, Euglenophyta, Pyrrophyta, and Xanthophyta), zooplankton (mg/L, covering Cladoceran, Copepod, and Rotifer), and fish yield (kg/ha). Mixed water samples were collected 0.5 m below the lake surface and 0.5 m above the lake bottom by using a 5 L Schindler sampler for subsequent hydrochemical parameter analysis and plankton identification. A 10 L mixed water sample was filtered through a 64 μm plankton net into a small square bottle, and 4% formalin reagent was immediately added to fix the crustacean zooplankton (i.e., Cladoceran and Copepod), while a 1 L mixed water samples were preserved with 1% Lugol’s iodine solution and concentrated to 50 mL after standing for 48 hours to analyse phytoplankton and rotifers. Although zooplankton are considered lower organisms and receive less attention from the animal ethics committees, we strive to minimize disturbance to their habitats during the sampling process, in addition to using standard methods.
The water temperature was recorded immediately. The TN and TP concentrations were determined according to standard methods [1]. The identification and biomass measurements of phytoplankton and zooplankton mainly referred to relevant professional books [1-6]. Both phytoplankton and zooplankton were identified as the smallest taxon (species or genera). For crustacean zooplankton samples, all individuals were counted at 40× magnification. The phytoplankton and rotifer samples were measured and counted under 400× magnifications with 0.1 mL and 100× magnifications with 1 mL after mixing, respectively. Given the changes in plankton nomenclature, we unified the species and genera of the plankton dataset based on information from relevant professional books [1-6] and consultations with other professionals. Between 2003 and 2020, 137 phytoplankton and 91 zooplankton species were identified at all three stations. The fish yield, mainly consisting of planktivorous fish (Hypophthalmichthys molitrix and Aristichthys nobilis), was obtained from the Fishery Management Committee of Lake Donghu.
Estimation of diversity, stability, and asynchrony
Based on the 18-year monthly monitoring biomass dataset, diversity, stability, and asynchrony indices were calculated with a one-year (12 continuous monitoring months) moving window, sliding every six months [7,8]. Here, we concurrently focused on four diversity indices: Simpson diversity (Simpson,1-∑pi2), pi is the proportional abundance of species i in the community), species richness (Richness, S, the number of species observed per month at each station), Shannon diversity (Shannon, -∑pi ln(pi)), and Pielou’s evenness index (Evenness,-∑pi ln(pi)/ln(S)). The average of each diversity index within each window at every station was used to determine species diversity. Temporal stability and asynchrony at the population and community levels were used to quantify and explore the seasonal dynamic characteristics of plankton biomass. Temporal stability, encompassing population stability and community stability, characterises the capacity of ecosystems to maintain functioning in a fluctuating environment. Temporal asynchrony includes population asynchrony and trophic community asynchrony, the former characterises the inconsistency of variations among populations within a trophic level, and the latter describes the strength of the temporal coupling relationship between adjacent trophic levels.
In this study, population stability was defined as the weighted average of species stability within a community, and was calculated as follows:
Population stability = ∑Ni = 1 mi/mc * μi/σi,
Where mi represents the total biomass of species i and mc represents the total community biomass, while μi and σi denote the mean and standard deviation of the biomass of species i in each plankton community, respectively, and N is the number of species in each plankton community throughout the year [9-10].
Community stability was weighed as follows:
Community stability = μT/σT ,
where μT and σT represent the mean and standard deviation of the biomass of each plankton community, respectively [9].
Population asynchrony was measured using:
Population asynchrony = 1 - σT2/(∑Ni = 1 σi)2 , ,
where σT2 is the temporal variance of the biomass of each plankton community and σi is the standard deviation of the biomass of species i in each plankton community with N species [11].
Combined the calculation method of population asynchrony [11], trophic community asynchrony was quantified as:
Trophic community asynchrony = 1 - σt2/(σp + σz)2,
where σt is the temporal variance of the total biomass of phytoplankton and zooplankton communities, and σp and σz are the standard deviation of phytoplankton community and zooplankton community biomass, respectively.
References
[1] Huang, X., Chen, W., & Cai, Q. (1999). Survey, observation, and analysis of lake ecology. Standard methods for observation and analysis in Chinese Ecosystem Research Network, Series V.
[2] Hu, H., & Wei, Y. (2006). The freshwater algae of China: systematics, taxonomy and ecology.
[3] Wang, Y. (2020). A Pictorial Guide to Common Aquatic Organisms in Chinese River Basins.
[4] Chiang, S.C., & Du, N.S. (1979). Fauna sinica, crustacea, freshwater cladocera.
[5] Shen, J., Tai, A., Zhang, C., Li, Z., Song, D., & Chen, G. (1979). Fauna sinica, crustacea, freshwater copepoda.
[6] Wang, J. (1961). Freshwater rotifer fauna in China.
[7] Pomati, F., Matthews, B., Jokela, J., Schildknecht, A., & Ibelings, B. W. (2012). Effects of re-oligotrophication and climate warming on plankton richness and community stability in a deep mesotrophic lake. Oikos, 121(8), 1317-1327. https://doi.org/10.1111/j.1600-0706.2011.20055.x
[8] Zhao, Q., Van den Brink, P. J., Xu, C., Wang, S., Clark, A. T., Karakoc, C., Sugihara, G., Widdicombe, C. E., Atkinson, A., Matsuzaki, S. S., Shinohara, R., He, S., Wang, Y. X. G., & De Laender, F. (2023). Relationships of temperature and biodiversity with stability of natural aquatic food webs. Nature Communications, 14(1), 3507. https://doi.org/10.1038/s41467-023-38977-6
[9] Lehman, C. L., & Tilman, D. (2000). Biodiversity, stability, and productivity in competitive communities. The American Naturalist, 156(5), 534-552. https://doi.org/10.1086/303402
[10] Thibaut L. M., & Connolly S. R. (2013). Understanding diversity-stability relationships: towards a unified model of portfolio effects. Ecology Letters, 16(2), 140-150. https://doi.org/10.1111/ele.12019
[11] Loreau, M., & de Mazancourt, C. (2008). Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. The American Naturalist, 172(2), E48-E66. https://doi.org/10.1086/589746