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.

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 15^th 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-∑p_i²), p_i  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, -∑p_i ln(p_i)), and Pielou’s evenness index (Evenness,-∑p_i ln(p_i)/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 = ∑^N_{i = 1} m_i/m_c * μ_i/σ_i, 

Where m_i represents the total biomass of species i and m_c  represents the total community biomass, while  μ_i and σ_i  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 = μ_T/σ_T , 

where  μ_T and σ_T  represent the mean and standard deviation of the biomass of each plankton community, respectively [9].

Population asynchrony was measured using:

Population asynchrony = 1 - σ_T²/(∑^N_{i = 1} σ_i)² ,                                 , 

where σ_T²  is the temporal variance of the biomass of each plankton community and  σ_i 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 -  σ_t²/(σ_p + σ_z)²,

where σ_t is the temporal variance of the total biomass of phytoplankton and zooplankton communities, and σ_p  and σ_z  are the standard deviation of phytoplankton community and zooplankton community biomass, respectively.

References

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[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