Ecology

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In this study, the five main characteristics of green spaces that were measured were area, perimeter, perimeter-area ratio, leaf area index, and canopy density. The structure of parameter between them is shown in Table 3.Table 3 Parameter structure of the cooling and humidification effect based on the spatial characteristics of green spaces.Full size tableCorrelation between various spatial characteristics and cooling and humidifying intensity in green spacesSmall-size green spacesFigures 4 and 6 shows the results of linear regressions between spatial characteristics and the cooling effect in small-size green spaces. There were relatively weak correlations between area, perimeter, perimeter-area ratio, leaf area index and cooling intensity, and a strong correlation between canopy density and cooling intensity. Small-size green space has the weakest positive correlation between perimeter-area ratio and cooling intensity (R2 = 0.11), and its canopy density and cooling intensity have the strongest positive correlation (R2 = 0.64). Meanwhile, small-size green space has weakest negative correlation between perimeter and humidifying intensity (R2 = 0.17), and its leaf area index and humidifying intensity have significant positive correlation (R2 = 0.42). Figures 4a and 5a show that for every 1 ha increase in area of small-size green spaces, the cooling intensity increased by 1.026 °C, and the humidifying intensity decreased by 1.56%. Figures 4b and 5b show that for every 100 m increase in perimeter, the cooling intensity decreases by 1.06 °C, and the humidifying intensity decreased by 1.19%. Figures 4c and 5c show that for every 0.01 increase in the perimeter-area ratio, the cooling intensity increases by 1.12 °C, and the humidifying intensity increased by 1.46%. Figures 4d and 5d show that for every 0.1 increase in the leaf area index, the cooling intensity increases by 1.11 °C, and the humidifying intensity increased by 1.12%. Figures 4e and 5e show that each 0.01 increase in the canopy density, the cooling intensity increases by 1.60 °C, and each 0.1 increase in canopy density, the humidifying intensity increased by 1.15% (Fig. 6).
Figure 4Linear regressions between spatial characteristics and cooling intensity of small-size green spaces.Full size imageFigure 5Linear regressions of spatial characteristics and humidifying intensity of small-size green spaces.Full size imageFigure 6The correlation between the spatial characteristics of small-size green spaces and the intensity of cooling and humidifying (GA means green area; GP means green perimeter; GPAR means green perimeter-area ratio; LAI means leaf area index; CD means canopy density).Full size imageMedium-size green spacesFigures 7 and 9 shows the linear regressions between spatial characteristics and cooling intensity in medium-size green spaces. There was an extremely significant positive correlation between area and cooling intensity, an insignificant positive correlation between the leaf area index and cooling intensity, and a relatively weak negative correlation between the other three characteristics and cooling intensity. Medium-size green space has the weakest negative correlation between canopy density and cooling intensity (R2 = 0.12), and its green area and cooling intensity have the strongest positive correlation (R2 = 0.83). Meanwhile, medium-size green space has weakest negative correlation between perimeter-area ratio and humidifying intensity (R2 = 0.41), and its area and humidifying intensity have most significant positive correlation (R2 = 0.81). Figures 7a and 8a show that for every 1 ha increase in area of medium-size green spaces, the cooling intensity increased by 1.19 °C, and the humidifying intensity increased by 1.24%. Figures 7b and 8b show that for every 100 m increase in perimeter, the cooling intensity decreases by 1.02 °C, and the humidifying intensity increased by 1.17%. Figures 7c and 8c show that for every 0.01 increase in the perimeter-area ratio, the cooling intensity decreases by 1.29 °C, and the humidifying intensity decreased by 2.40%. Figures 7d and 8d show that for every 0.1 increase in the leaf area index, the cooling intensity increases by 1.37 °C, and the humidifying intensity decreased by 1.92%. Figures 7e and 8e show that each 0.01 increase in the canopy density, increases the cooling intensity decreases by 1.23 °C, and the humidifying intensity decreased by 6.48% (Fig. 9).Figure 7Linear regressions between spatial characteristics and cooling intensity of medium-size green spaces.Full size imageFigure 8Linear regressions of spatial characteristics and humidifying intensity of medium-size green spaces.Full size imageFigure 9The correlation between the spatial characteristics of medium-size green spaces and the intensity of cooling and humidifying (GA means green area; GP means green perimeter; GPAR means green perimeter-area ratio; LAI means leaf area index; CD means canopy density).Full size imageLarge-size green spacesFigures 10 and 12 shows the linear regressions between spatial characteristics and cooling intensity in large-size green spaces. There was an insignificant correlation between area and cooling intensity, a weak correlation between canopy density and cooling intensity, and a significant correlation between perimeter, perimeter-area ratio and the leaf area index and cooling intensity. Medium-size green space has the weakest negative correlation between green area and cooling intensity (R2 = 0.35), and its leaf area index and cooling intensity have the strongest positive correlation (R2 = 0.92). Meanwhile, medium-size green space has weakest negative correlation between perimeter-area ratio and humidifying intensity (R2 = 0.11), and its leaf area index and humidifying intensity have most significant positive correlation (R2 = 0.39). Figures 10a and 11a show that for every 1 ha increase in area of large-size green spaces, the cooling intensity decreased by 1.02 °C, and the humidifying intensity decreased by 1.22%. Figures 10b and 11b show that for every 100 m increase in perimeter, the cooling intensity decreases by 1.05 °C, and the humidifying intensity decreased by 1.34%. Figures 10c and 11c show that for every 0.005 increase in the perimeter-area ratio, the cooling intensity decreases by 1.43 °C, and each 0.01 increase in perimeter-area ratio, the humidifying intensity decreased by 1.27%. Figures 10d and 11d show that for every 0.1 increase in the leaf area index, the cooling intensity increases by 2.41 °C, and the humidifying intensity increased by 1.37%. Figures 10e and 11e show that each 0.1 increase in the canopy density, the cooling intensity increased by 3.69 °C, and the humidifying intensity decreased by 2.84% (Fig. 12).Figure 10Linear regressions of spatial characteristics and cooling intensity of large-size green spaces.Full size imageFigure 11Linear regressions of spatial characteristics and humidifying intensity of large-size green spaces.Full size imageFigure 12The correlation between the spatial characteristics of large-size green spaces and the intensity of cooling and humidifying (GA means green area; GP means green perimeter; GPAR means green perimeter-area ratio; LAI means leaf area index; CD means canopy density).Full size imageQuantitative analysis of the microclimatic effects of different types of green spacesQuantitative analysis of the effects of different types of green space on cooling intensityFigure 13 shows the linear regressions between the different types of green spaces and cooling intensity. There were negative correlations between green spaces a short, medium, and long distance from a water body and cooling intensity in small-size green spaces, medium-size green spaces and large-size green spaces. The negative correlation between the distance to a water body and cooling intensity in medium-size green spaces was most significant (R2 = 0.985). The greater the distance to a water body, the lower the cooling intensity. For medium-size green spaces, for every 1/4 increase in the distance ratio, the cooling intensity decreased by 0.81 °C. For small-size green spaces, for every 1/4 increase in the distance ratio, the cooling intensity decreased by 1.04 °C. For large-size green spaces, for every 1/4 increase in the distance ratio, the cooling intensity decreased by 1.36 °C. For small-, medium-, and large-size green spaces, there was a positive correlation between canopy density and cooling intensity. There was a most significant positive correlation between canopy density and cooling intensity in large-size green spaces (R2 = 0.941). The greater the canopy density, the greater the cooling intensity. For large green spaces, for every 0.5 increase in canopy density, the cooling intensity increased by 0.16 °C. For small-size green spaces, for every 0.5 increase in canopy density, the cooling effect increased by 0.15 °C. For medium-size green spaces, for every 0.5 increase in canopy density, the cooling intensity increased by 0.16 °C.Figure 13Linear regressions between the distance from different types of green spaces to water areas, canopy density and cooling intensity.Full size imageQuantitative analysis of the effects of different types of green space on humidifying intensityFigure 14 shows the linear regression between the distance of a green space from a water body, canopy density and humidifying intensity. There was a negative correlation between the distance to a water body and humidifying intensity in small, medium, and large green spaces. The negative correlation between the distance to a water body and humidifying intensity in small green spaces was most significant (R2 = 0.996). The longer the distance, the lower the humidifying intensity. For small green spaces, for every 1/4 in-crease in the distance ratio, the humidifying intensity decreased by 4.23%. For medium-size green spaces, for every 1/4 increase in the distance ratio, the humidifying intensity decreased by 3.02%. For large-size green spaces, for every 1/4 increase in the distance ratio, the humidifying intensity de-creased by 6.14%. For small, medium, and large green spaces, there was a positive correlation between canopy density and humidifying intensity. The positive correlation between canopy density and humidifying intensity in medium-size green spaces was extremely significant (R2 = 0.925). The greater the canopy density, the greater the humidifying intensity. For medium-size green spaces, for every 0.5 increase in canopy density, the humidifying intensity increased by 3.29%. For small-size green spaces, for every 0.5 increase in canopy density, the humidifying intensity increased by 3.17%. For large-size green spaces, for every 0.5 increase in canopy density, the humidifying intensity increased by 4.06% (Fig. 15).
Figure 14Linear regressions between the distance from different types of green space to water area, canopy density and humidifying intensity.Full size imageFigure 15Correlation of different green space types with water distance, canopy density and cooling and humidifying intensity.Full size imageEffect of shape and area of water bodies on microclimatic effects based on numerical simulationBanded waterWe constructed a numerical simulation model to explore the effects of a simulated increase in water body area on cooling and humidification. Figure 16 shows the simulated distribution characteristics of temperature and relative humidity after a 5% and 10% increase in water area at 14:00 when temperatures were high. The results suggest that between 7:00 and 10:00, with a 5% and 10% increase in water area, the air temperature was basically the same and the cooling effect was insignificant. However, between 12:00 and 19:00 and particularly in the hours between 13:00 and 16:00 when temperatures were highest, a 5% increase in water area produced a significant cooling effect, with a daily average value of 0.05 °C and a maximum value of 0.09 °C. A 10% increase in water area produced an extremely significant cooling effect, with a daily average value of 0.07 °C and a maximum value of 0.14 °C. From 11:00 to 19:00, a 5% increase in water area produced a significant humidifying effect, with a daily average value of 0.08% and a maximum value of 0.17%. A 10% increase produced an extremely significant humidifying effect, with a daily average value of 0.13% and a maximum value of 0.26% (See supplementary file).Figure 16Distribution characteristics of cooling and humidifying effects of simulated increase of banded water area at 14:00. (a) original cooling effect of banded water in the sample area; (b) cooling effect of 5% increase in water area; (c) cooling effect of 10% increase in water area; (d) original humidifying effect of banded water in the sample area; (e) humidifying effect of 5% increase in water area; (f) humidifying effect of 10% increase of water area.Full size imageMassive waterFigure 17 shows the simulated distribution characteristics of the cooling and humidifying effects after a 5% and 10% increase in the water area at 14:00 when temperatures were high. Between 8:00 and 19:00, a 5% and 10% increase in water area produced a significant cooling effect. At 19:00, the numerical simulation result was abnormal when the water area increased by 5% and 10%; at 13:00, the numerical simulation result was also ab-normal when the water area increased by 10%. After excluding the abnormal simulated data, a 5% increase in water area produced a cooling effect, with a daily average value of 0.06 °C and a maximum value of 0.10 °C. A 10% increase in water area produced an extremely significant cooling effect, with a daily average value of 0.10 °C and a maximum value of 0.18 °C. Between 11:00 and 19:00, a 5% increase in water area produced a significant humidifying effect, with a daily average value of 0.05% and a maximum value of 0.13%. A 10% increase in water area produced an extremely significant humidifying effect, with a daily average value of 0.13% and a maximum value of 0.27% (See supplementary file).Figure 17Distribution characteristics of cooling and humidifying effects of simulated increase of massive water area at 14:00. (a) original cooling effect of massive water in the sample area; (b) cooling effect of 5% increase in water area; (c) cooling effect of 10% increase in water area; (d) original humidifying effect of massive water in the sample area; (e) humidifying effect of 5% increase in water area; (f) humidifying effect of 10% increase of water area.Full size imageAnnular waterFigure 18 shows the simulated distribution characteristics of the cooling and humidifying effects after a 5% and 10% increase in the area of the annular water body at 14:00 when temperatures were high. Between 7:00 and 19:00, a 5% and 10% increase in water area produced a significant cooling effect. Between 11:00 and 16:00 when temperatures were high, a 5% increase in water area produced a cooling effect, with a daily average value of 0.06 °C and a maximum value of 0.14 °C°C and a 10% increase in water area produced an extremely significant cooling effect, with a daily average value of 0.13 °C and a maximum value of 0.28 °C. Between 7:00 and 19:00, a 5% and 10% increase in water area produced significant humidifying effects. Between 11:00 and 16:00 when temperatures were high, a 5% increase in water area produced an extremely significant humidifying effect, with a daily average value of 0.17% and a maximum value of 0.39% and a 10% increase in water area produced an extremely significant humidifying effect with a daily average value of 0.38% and a maximum value of 0.81% (See supplementary file).Figure 18Distribution characteristics of cooling and humidifying effects of simulated increase of annular water area at 14:00. (a) original cooling effect of annular water in the sample area; (b) cooling effect of 5% increase in water area; (c) cooling effect of 10% increase in water area; (d) original humidifying effect of annular water in the sample area; (e) humidifying effect of 5% increase in water area; (f) humidifying effect of 10% increase of water area.Full size image More

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In the present work, we extend the base model for the spatial mosquito population dynamics24 to include wild male mosquitoes and genetically modified male mosquitoes. Thus, five populations will be considered: the aquatic mosquito population, including larvae and pupae, the egg mosquito population, the reproductive female mosquito population, the wild male mosquito population, and the genetically modified male population. Similar approaches can be found in the literature25,26.In the following system, we represent mosquito population densities (mosquitoes per m(^2)) by: E – in the egg phase, A – in the aquatic phase, F – female in the reproductive phase, M – wild males, and G – genetically modified male mosquitoes. Due to the very high resistance of the egg phase (up to 450 days27) and as we are interested in an urban spatial macro-scale modeling, we do not consider the mortality in the egg phase. The model is described by the following system of partial differential equations:$$begin{aligned} {left{ begin{array}{ll} partial _t E &{} = alpha beta F M -e E, \ partial _t A &{} = e left( 1 – dfrac{A}{k} right) E -(eta _a+{mu _a})A, \ partial _t F &{} = nabla cdot (D_m nabla F) -mu _f F + reta _{a} A, \ partial _t M &{} = nabla cdot (D_m nabla M) -mu _m M + (1-r)eta _{a} A, \ partial _t G &{} = nabla cdot (D_g nabla G) -mu _{g}G + l, end{array}right. } end{aligned}$$
(1)
where ( alpha ) represents the proportion of wild male mosquitoes to the total number of male mosquitoes (wild males + genetically modified males); (beta ) represents the expected quantity of eggs from the successful encounter between wild females and males; e is the egg hatching rate; k is the carrying capacity of the aquatic phase; ( eta _a ) is the emergence rate for mosquitoes from the aquatic phase to the female or male phases; ( mu _a), (mu _f), (mu _m), and (mu _{g}) are the mortality rates of mosquitoes in the aquatic phase, females, males, and genetically modified males, respectively; r is the proportion of females to males (typically (r=0.5)); (l=l(x,y,t)) is the function representing the number of genetically modified mosquitoes released in a unit of time at any point of the domain; (D_m) is the diffusion coefficient of wild mobiles females and males; (D_g) is the diffusion coefficient of genetically modified males. The proposed model (1) can naturally deal with heterogeneous parameters, such as mortality, diffusion, and carrying capacity coefficients. Thus it is possible to model the influence of rain, wind, and human action. In the context of this work, we are considering that the city neighborhood is divided into two environments: houses and streets. Due to lack of data, we restrict the investigated heterogeneity only to the carrying capacity coefficient.The proposed model can be regarded as an extension of other “economic” models20,24 in the effort to qualitatively reproduce the complex phenomena by using as few parameters as possible. Following this idea, the carrying capacity was neglected in the egg phase because of the skip oviposition phenomenon28 i.e., the female lays the number of eggs that the place holds, without more space, she migrates to other environments to finish laying the eggs. We also do not consider this coefficient in the winged phase as limitations in the winged phase were not reported in any study. On the other hand, we consider it in the aquatic phases (larvae and pupae), where it is effective29.The term ( alpha ), which multiplies the probability of encounters between male and female, represents the impact of the insertion of genetically modified males in the mosquito population to the immobile phase and is defined as$$begin{aligned} alpha = left{ begin{array}{cc} 1, &{} text{ if } M=G= 0, \ dfrac{M}{M + G}, &{} text{ otherwise }. end{array} right. end{aligned}$$
(2)
Similar modeling approach can be found in the literature16. As the release rate of genetically modified males increases, the alpha value decreases, and, consequently, the probability of encounter between females and wild males also decreases. Thus, there is a greater probability of encounter between genetically modified males and females. This approach presents an advantage, when compared to the models found in the literature25, as System  (1) does not present singularities at the equilibrium states, allowing mathematical analysis and numerical simulations. From the biological point of view, the increment of male wild mosquitoes over some critical value does not affect the egg deposition. At first glance, the term FM can lead to a misunderstanding that such property is not satisfied in the presented model. However, in Section “Equilibrium points considering the application of genetically modified male mosquitoes,” we argue that both male and female populations possess mathematical attractor equilibria, blocking the wild male population from growing beyond this value.Finally, any acceptable population model should be invariant in the definition domain, meaning its solution does not present senseless values. Setting the variable domain as$$begin{aligned} 0 le E(x,y,t)< infty ,;; 0 le A(x,y,t) le k, ;; 0 le F(x,y,t)< infty ,;; 0 le M(x,y,t)< infty ,;; 0 le G(x,y,t) < infty , end{aligned}$$ (3) we can verify that it is invariant under the time evolution by the System (1). To prove this statement, it is sufficient to verify that the vector field defined by the right side of (1) points into the domain when (E, A, F, M, G) approaches the domain boundary. When E approaches zero, the right side of the first equation in (1) is not negative. When A approaches zero, the right side of the second equation in (1) is not negative. When A approaches k (bottom), the first term on the right side of the second equation in (1) tends to zero, while the second term remains negative. Since the term ( nabla cdot (D_m nabla F) ) cannot change the F sign, when F approaches zero, the right side of the third equation in (1) is not negative Since the term ( nabla cdot (D_m nabla M) ) cannot change the M sign, when M approaches zero, the right side of the fourth equation in (1) is not negative. Since the term ( nabla cdot (D_g nabla G) ) cannot change the G sign, when G approaches zero, the right side of the fifth equation in (1) is not negative. In the rest of this section, let us explain how to estimate one-by-one all the parameters used in this model from experimental data available in the literature. It is a challenging task as, typically, the development of the Ae. aegypti mosquito depends on food variation30, temperature variations14,15 and rainfall31. This data is not available in the literature in the organized and systematic form. Because of that, we assume the environment is under optimal conditions of temperature, availability of food, and humidity.How to estimate emergence rate ((eta _a)) The emergence rate describes the rate at which the aquatic phase of the mosquito emerges into the adult phases. In the present model, for simplicity, it was considered that no mosquito from the crossing between genetically modified males and females reaches adulthood. Thus, the emergence rate is calculated on the crossing between females and wild males. Under optimal conditions and feeding distribution, based on the literature30, the emergence rate is 0.5596 (text{ day}^{-1}).How to estimate diffusion coefficients ((D_m,D_g)) The diffusion coefficient is one of the most important parameters describing the mosquitoes’ movement. We use the methodology proposed in the previous work24 to obtain the diffusion coefficient of adult mosquitoes (females and males) and genetically modified males.The estimate is done by assuming that all mosquitoes are released at (0, 0), and their movement is described by the corresponding equation in (1) neglecting other terms than diffusion. The population starts spreading in all directions. We define the spreading distance R(t) as the radius of the region centered in (0, 0) where (90%) of the initial mosquitoes population density is present. In Silva et al.24 it is shown that$$begin{aligned} R(t) = sqrt{4Dt} ;text {erf}^{-1}(0.9). end{aligned}$$ (4) Now corresponding diffusion coefficient is estimated by using the average flight distance of the mosquitoes and the characteristic time related to their life expectancy. Under favorable weather conditions, the average lifetime flight distance of females and males is approximately32,33 65 m, while the same for GM males is34 67.3 m. Based on the literature, we consider that the characteristic time for wild females and males32 is 7 days, and the same for genetically modified males is34 2.17 days. Using (4) we estimate the values for (D_m) and (D_g) summarized in Table 1. It would be natural to consider that the mosquitoes’ movement changes in different environments. Unfortunately, we were unable to find the corresponding experimental data, and because of that, we considered that (D_m) and (D_g) are the same in streets and house blocks.How to estimate mortality rates ((mu _a), (mu _f), (mu _m), (mu _{g}))The mortality coefficient represents an average quantity of mosquitoes in the corresponding phase dying each day. As mentioned before, we disregard the mortality rate in the egg phase, as it is negligible due to its great durability27, it does not affect the numerical results, and it complicates analytical estimates. Thus, the aquatic phase mortality rate coefficient is equal to the same for larvae’s coefficient, which is approximately29 (mu _a = 0.025) (1/day).There is no solid agreement on the mortality rate of male and female wild mosquitoes in the literature. Although some results29,30 suggest they are similar, we follow these authors and consider them equal. Considering both natural death and accidental ones, approximately (10%) of females and male mosquitoes in the adult phase die at each day35. Under optimal conditions, the mortality coefficient can be estimated from this data by using the proposed model (1) by neglecting diffusion and emergence terms in the corresponding equation; details can be found in the previous work24. The resulting parameter values are summarized in Table 1.It would be natural to consider that the mosquitoes mortality rate depends on the environment. Unfortunately, we were unable to find the corresponding experimental data, and because of that, we considered that (mu _a), (mu _f), (mu _m), and (mu _{g}) are the same in streets and house blocks.How to estimate the expected egg number ((beta ))This coefficient represents the average quantity of eggs a wild female lays per day, assuming a successful meeting with a wild male. Considering the number of times a female lays eggs in its lifetime36, the average quantity of eggs per lay and the mosquito’s life expectancy, under favorable conditions, this coefficient is estimated as (beta = 34).How to estimate the hatching rate (e)This coefficient determines the average number of eggs hatching in one day. Experimental data37 suggest that, under optimal humidity conditions, the mean value of the hatch rate coefficient is 0.24 given a temperature of 28 ((^{circ })C), which is considered ideal for mosquito development. This is the value used in the present work.How to estimate carrying capacity coefficient (k)The carrying capacity k represents the space limitation of one phase due to situations present in the environment37,38, such as competition for food among the larvae39. In general, it depends on external factors such as food availability, climate, terrain properties, making direct estimation almost impossible. In the Analytical results section, we show how to estimate this coefficient for each grid block. When considering spatial population dynamics in a heterogeneous environment, carrying capacity is one of the most influential parameters as it varies significantly. For example, house block offer more food and a shelter against natural predators resulting to a larger carrying capacity when compared with street environment. Following the literature32 we assume that the 80% of the mosquito’s breeding places are in houses resulting in the relation (k_h=5k_s), where (k_h) and (k_s) are the carrying capacities of the house blocks and in the streets.Genetically modified mosquitoes release rate (l)Function l(x, y, t) determines how many genetically modified mosquitoes are released in the location (x, y) at time t.In a normal situation, the sex ratio between males and females is 1 : 1. The increment of this proportion favoring GM males increases the probability of females to mate with these mosquitoes. As reported in the literature12,30 the initial launch size is 11 times larger than the adult female population, and it is done in some spots in the city. In this work, we analyze different release strategies maintaining the (11times 1) proportion in some scenarios.Table 1 All parameter values are directly taken or estimated from the literature as explained in section Modeling.Full size table More

DesignPlant richness. Sixteen locally frequent native plant species in the barren mountain areas (around Taizhou University, Zhejiang, China) invaded by the exotic plant Symphyotrichum subulatum60 were selected as the native species pool. These species were chosen because they spanned the dicotyledon plant taxonomy (including 7 Orders, 10 Families, and 14 Genus, in the Class Magnoliopsida), differed widely in their functional traits (related to height, life form, dominance in local communities, and leaf habit) (Supplementary Table 3), and were occasionally found to be associated with the invasive species Symphyotrichum subulatum60 in the local secondary-succession communities. With this species pool, we were able to imitate the locally natural, spatially stochastic, compositionally ruderal, and functionally varied plant community61, which is a typical attribute of the secondary-succession communities in the local barren mountains invaded by the exotic plant Symphyotrichum subulatum. Based on this native species pool, monocultures of each species (16 total), and random mixtures of 2, 4 or 8 species (with 10, 10, or 9 distinct assemblages, respectively) were designed, creating a complete set (Fig. 1d) of 45 different plant assemblages (pots) in total. Each plant assemblage was replicated 6 times, for a total of 270 pots. To eliminate the non-random effects during the 1-year development of the 270 pots, their distributions were randomized, such that not all replicates of an assemblage were next to each other (Fig. 1d–f).DroughtAfter 1-year development of the native plant assemblages, three drought treatments (non-, moderate-, and intensive-drought) were manipulated by adjusting irrigation using automatic drip irrigation systems, with 100%, 50%, and 25% of the equivalent to the amount received in the areas where native species were collected, respectively. Two random complete sets were selected for each drought treatment, each complete set being composed of 45 different plant assemblages (Fig. 1d–f).Exotic plant invasionNine months after drought treatment, the two complete sets (Fig. 1d) of each drought treatment were randomly exposed (invasion) or not exposed to (non-invasion) the invasive species Symphyotrichum subulatum (Michx.) G. L. Nesom (Fig. 1e, f). S. subulatum, an annual herbaceous plant native to North America, is a common invasive species in the subtropical and tropical regions of China18,60, and tends to interact with the native species via, for example, competing for space and resources62,63, enriching for pathogens or herbivores, and changing soil faunal, bacterial or fungal microbiomes18,64,65.ExperimentThe experiment based on the design mentioned above was conducted at Taizhou University, Zhejiang province, China (28.66°N, 121.39°E). The seeds of the 16 native plant species (Supplementary Table 3) and the soil were collected from nearby mountain areas (Wugui, 28.65°N, 121.38°E; Baiyun, 28.67°N, 121.42°E; Beigu, 28.86°N, 121.11°E). The seed-mixtures were obtained by mixing seeds of the 16 species pro rata, in proportion to germination rates. The soil (fine-loamy, mixed, semiative, mosic, Humic Hapludults) was sieved to pass a 2-mm mesh, and thoroughly mixed. 270 plastic pots (72 cm length × 64 cm width × 42 cm depth) were prepared, and each was filled with a 27-cm soil layer, followed by a 10-cm mixture of soil and vermiculite-compost to provide water-, air- and fertility-support for germination, seedling establishment, and plant growth (Supplementary Table 4).Native plant assemblagesAll the 270 pots were placed inside a plastic shelter, which allowed for both air ventilation and protection from rain. Each pot was sown with a seed-mixture of ca. 800 seeds. One month after germination, for each pot, the undesired seedlings were removed manually according to the plant richness design (Fig. 1d–f), and thus 32 vigorous seedlings (with the same number of seedlings per species, e.g., 4 seedlings for each species of the 8-species mixtures) were spatial-evenly retained. In this manner, the plant richness was manipulated for each plant assemblage. During the development of the 270 plant assemblages, the soil volumetric water content was controlled at ca. 20%, which was similar to that of the nearby mountainous soil, using the automatic drip irrigation systems. Weeds and undesired species were removed monthly (Fig. 1f).Drought treatmentAfter 1-year development of native plant assemblages, the drought treatments (non-, moderate-, and intensive-drought) were manipulated according to the experimental design mentioned above (Fig. 1d, e). Two complete sets (Fig. 1d) of different plant assemblages (2 × 45 pots) were selected for each drought treatment. Every other week, 40 pots each drought treatment were randomly selected for measuring soil water content and soil temperature at the depth of 0–20 cm, using the ProCheck analyzer (Decagon, Pullman, Washington, USA), and irrigation was adjusted accordingly using automatic drip irrigation systems. The irrigation for non-, moderate-, or intensive-drought was adjusted to accomplish an irrigation level amounts to 100%, 50%, or 25% that of the mountain areas where seeds were collected. Because of the distinct seasonal temperature and evaporation conditions, the irrigation frequencies were approximately daily in May-September, every other day in March–April and October–December, and weekly in January–February. With this manipulation, the volumetric soil water contents of non-, moderate-, and intensive-drought were controlled within ranges of 13.8–23.4%, 6.8–13.7%, and 1.4–7.4%, respectively, throughout the manipulation of drought treatment (Fig. 1e, f). Eight months after drought introduction, fresh litter was collected form the two replicate pots of each drought treatment, and then oven-dried at 40 °C, cut into ca. 2-cm pieces, and filled into litterbags (2-g litter in each litterbag).Invasion treatmentNine months after drought introduction, one complete set (45 pots) of the plant assemblages (Fig. 1d) from each drought treatment, was chosen and exposed to invasion disturbance by sowing 50 seeds of S. subulatum in each pot, and the other was specified as the non-invasion treatment (Fig. 1e, f). The prepared litterbags were embedded under the litter-layer of each pot (5 litterbags in each pot), correspondingly.SamplingSix months after invasion introduction, one litterbag was collected for litter-fauna extraction. Nine months after invasion, five soil cores (20-cm depth) were collected with augers (6.4 cm in diameter) and mixed for extraction of soil-fauna, and measurement of soil property and enzyme activity (Fig. 1f). The aboveground biomass of both native and invasive plants in each pot was harvested, sorted to species, oven-dried to a constant mass at 80 °C, and weighed. The belowground plant biomass was also sampled, sorted to native and invasive groups, oven-dried, and weighed (Fig. 1f).Plant, litter-, and soil-faunal communitiesPlant communitySince exotic plant invasion was treated as a disturbance factor, the biomass of the invasive species S. subulatum was not included for analyses concerning plant community and ecosystem (multi)functionality. The aboveground biomasses of native plant species in each of the 270 pots were collected for plant community analysis.Litter- and soil-faunal communitiesOne litterbag or fifty grams of mixed-soil samples were used for litter- or soil-fauna extraction using a Tullgren funnel apparatus (dry funnel method)66. The obtained microarthropods were stored in 70% alcohol, identified with double-tube anatomical lens, and classified to Family level. For both litter and soil samples, the numbers (abundances) of all faunal taxa were counted for litter/soil-faunal community analysis.Phylogenetic information of plant, litter-, and soil-faunal communitiesSimilar procedures were used to construct the plant and faunal phylogenetic trees. First, protein sequences of 12 faunal mitochondrial coding genes and 16 plant plastid coding genes (Supplementary Data 1) were obtained by searching plant or faunal taxonomies from NCBI protein database (https://www.ncbi.nlm.nih.gov/protein/) with Edirect software (https://www.ncbi.nlm.nih.gov/books/NBK179288/). All available sequences at plant species level or faunal Family level were fetched. If unavailable, the missing sequences were sampled from plant genus or faunal Order level. Sequoiadendron giganteum and Echinococcus were specified as out-group references for plant and faunal trees, respectively. Then, the sequences of each plant or faunal taxon were clustered at 97% or 90% identity independently, and the centroids were used as representative markers. The markers were aligned with MUSCLE67, followed by concatenation. Finally, using MEGA X68, the maximum likelihood trees were constructed based on BioNJ initial trees69 and 500 bootstrap checking nodal support. The parameters for plant tree construction were specified as follow: 70% partial deletion (with 4824 positions retained) and the best-fit substitution model JTT + G + I + F70,71; parameters for faunal tree: 90% partial deletion (2778 positions) and LG + G + I + F model71,72. The Linux codes for processing the protein sequences were submitted to GitHub (https://github.com/YuanGe-Lab/JZW_2022/tree/main/linux)The plant and faunal taxonomies, representative markers, and marker accessions are provided as Supplementary Data 1.Ecosystem function-related variablesA total of 14 individual function-related variables were collected. These variables belonged to three functional groups: (1) biomass production, including aboveground and belowground biomass of native plants, light interception efficiency, litter-fauna abundance, and soil-fauna abundance; (2) soil properties, including contents of soil organic carbon, soil nitrogen, soil phosphorus, and GRSP (relating to soil physical properties and stocks of carbon and nutrient73); and (3) processes, including rate of litter decomposition, and activities of β-glucosidase, protease, nitrate reductase and dehydrogenase.Light interception efficiency, the fraction of incident photosynthetically active radiation (PAR) intercepted by each plant community canopy, was determined between 12:00 and 14:00 on clear days using LI-191R line PAR sensors (LI-COR Inc., NE, USA), and the mean of 4 measurements (monthly from May to August the third year; Fig. 1f) was used. Total soil organic carbon and nitrogen were measured with an elemental analyzer (vario Max; Elementar, Germany). Total soil phosphorus was determined using the molybdenum blue method with a UV–visible spectrophotometer (Shimadzu, Kyoto, Japan). GRSP was determined using the method described by Shen et al.18. Litter decomposition rate was assessed by embedding litterbags and fitting litter mass loss against decomposition time (Fig. 1f). Enzyme activities were analyzed by the spectrophotometric method using the substrates, p-Nitrophenyl-β-d-glucopyranoside (pNPG; for β-glucosidase), caseinate (protease), nitrate (nitrate reductase) and triphenyltetrazolium chloride (TTC; dehydrogenase)18.Quantifying community stability and multifunctional stabilityCommunity data was comprised of native plant biomasses or faunal abundances, and the associated phylogenetic information. Multifunctionality data was comprised of 14 function-related variables, each variable (V) being transformed (V’) using the formula ({V}^{{prime} }=frac{V-{{{{{rm{min }}}}}}left(Vright)}{{{{{{rm{sd}}}}}}left(Vright)}) to guarantee even contribution to global variance. We calculated community similarity (1 minus Weighted-UniFrac distance) and multifunctional similarity (1 minus Bray–Curtis distance), based on the community data and the multifunctionality data, respectively. The specific subsets of each symmetric similarity matrix were used to assess three different aspects of stability: (1) Invariability (against stochastic fluctuations), reflected as the pairwise similarities (1476 pairs) within treatment groups, at same plant richness*drought*invasion condition; (2) Drought resistance, the similarities (2148 pairs) between drought (moderate- and intensive-drought) and non-drought treatments, at same plant richness*invasion condition; and (3) Invasion resistance, the similarities (n = 1611 pairs) between invasion and non-invasion treatments, at same plant richness*drought condition (Supplementary Fig. 1).We also assessed the three aspects of stability of each individual function in a similar way, but by calculating the similarity using the formula ({{{{{{{mathrm{SIM}}}}}}}}_{{ij}}=1-frac{|{V}_{i}-{V}_{j}|}{{V}_{i}+{V}_{j}}) (Vi and Vj are ith and jth elements in a function vector; SIMij is the similarity between Vi and Vj).Statistics and reproducibilityPERMANOVA (10,000 randomizations) was conducted to test the influences of the manipulated factors on ecosystem multifunctionality or communities of plant, litter- and soil-fauna, using “vegan::adonis” in R74. Mantel test (10,000 randomizations; Spearman’s R) was conducted to test the community-community or the community-multifunctionality relationships, using “vegan::mantel” in R74.As each similarity-pair of each aspect of community or multifunctional stability mentioned above was in strict correspondence to single level of each manipulated factor (plant richness, drought, and invasion) (Supplementary Fig.  1), the direct/indirect effects of treatments on the community or multifunctional stability can be assessed using SEM. To test direct and indirect effects (by modulating community stability) of the manipulated factors on multifunctional stability, we built three SEMs (Fig. 1a–c) based on three different aspects of stability (i.e., invariability, drought resistance, and invasion resistance) under the conditions of corresponding parings of manipulated factors (Supplementary Fig. 1), with the LAVAAN package75. The standardized paths (direct effects) in SEMs can be conceived as the partial correlations after teasing all side effects away. Bootstrapping with 10,000 randomizations was conducted to generate the unbiased mean effect. The significance of effect was tested using a Mantel-like permutation (10,000 randomizations) test76, where the null hypotheses (H0) were that the independent factors plant richness, drought, and invasion, had no direct/indirect effects (effect = 0) on multifunctional stability. Based on H0, permutation procedure was conducted by permuting the index of dependent factors (both columns and rows of a symmetric matrix; Supplementary Fig. 1) simultaneously to gain null models and null effects. p-values (probability of H0 acceptance) were calculated as the percentage of observed positive (or negative) effect that was greater (or less) than the null effects. We also assessed the direct and indirect effects of factors on the stability of each individual function based on the same SEMs, to consolidate our findings on multifunctional stability. The R codes and examples solving the permutation test for the significance of effects derived from SEMs that based on multidimensional similarity (or distance) were submitted to GitHub (https://github.com/YuanGe-Lab/JZW_2022/tree/main/R). All the analyses were conducted using R (https://www.r-project.org).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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