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Ground Water 23 , 17—25 Use of an ATP assay to determine viable microbial biomass in Fennoscandian Shield groundwater from depths of 3— m. Methods 70 , — Neidhardt, F. Escherichia coli and Salmonella typhimurium American Society for Microbiology, Lomstein, B. Recently, increasing studies prove that species identity Roscher et al. It is demonstrated that interspecific interaction and species compositional difference have important effects on diversity patterns and productivity Chase and Leibold, ; Chase and Ryberg, However, many studies confound the effects of species identity and interaction on the BPR pattern and treat them as a whole to explain the biological mechanism of the BPR pattern Chase and Leibold, ; Chase and Ryberg, Previous BPR experiments used an analysis of variance method to separate the effects of species composition and diversity on the community structure.
However, the method did not provide information on contributions of different species to the ecosystem structure. It is necessary to distinguish the effects of specific species identity and interspecific interaction on the BPR pattern for a better understanding of biological mechanisms of the BPR pattern.
Therefore, we applied the diversity-interaction DI model proposed by Kirwan et al. Furthermore, many abiotic factors, such as temperature, nutrition level, and water availability, affect the BPR pattern Ma et al. In terrestrial ecosystems, Palpurina et al.
In aquatic ecosystems, connectivity of the watershed and heterogeneity in the environmental factors are demonstrated to be prominent factors that affect the BPR pattern of the macrophyte community at a large scale Chase and Leibold, ; Chase and Ryberg, However, the underlying abiotic mechanism of the macrophyte BPR pattern on a local scale has not been explored yet.
Although many environmental factors, such as light, temperature, nutrient content of water bodies, and substrate characteristics, affect the growth and distribution of aquatic vegetation Barko and Smart, ; Jeppesen et al. Spence and Hudon et al. To be specific, there is a sequence from emergent to floating-leaved and submerged vegetation with increasing water depth. Nevertheless, how environmental factors, especially water depth and its effect on macrophyte distribution, affect the BPR pattern of the macrophyte community is rarely studied.
In addition, most studies are based on species richness in the study of the BPR pattern Whittaker, However, other biodiversity indices, such as diversity e.
This is because these indices incorporate the proportional abundance of each species within the community Lembrechts et al. Nonetheless, the ecological meanings these indices deliver are different. Species richness, the Shannon-Wiener index, and Shannon diversity are sensitive to the presence of rare categories in an ecological community Bandeira et al.
Simpson diversity is primarily a measure of dominance concentration because it is a good indicator of the dominance of one or several species over other species Whittaker, Evenness indices, the degree to which abundances are equitably divided among species, represents whether the abundance of a species in the community is regular. High evenness represents the uniform distribution of species in the community with similar abundance, and low evenness represents an uneven abundance of species in the community with the existence of dominant species Schleuter et al.
Chalcraft et al. However, the aquatic ecosystem may perform dissimilar patterns from the terrestrial ecosystem due to the specific habitat surrounded by a water column. Aquatic macrophytes provide numerous ecological and economic services, such as offering a habitat and food to aquatic animals and birds, supplying raw materials for social production Costanza et al.
Investigation of the BPR pattern of the aquatic macrophyte community is critical to understand the aquatic ecosystem function. Therefore, we conducted an investigation of Liangzi lake, which is located in the middle and lower reaches of the Yangtze River in China, suffers medium human disturbance, and conserves many rare and endangered species Fang et al.
We hypothesized that 1 there are unimodal relationships between multiple measures of biodiversity and biomass; 2 both species identity and interspecific interaction shape the biodiversity—biomass pattern biomechanically; and 3 environmental factors, especially water depth, affects the community structure and biodiversity—biomass pattern.
It is under the mesotrophic level with average total nitrogen of 0. It is a typical macrophyte-dominated shallow lake i. The dominant submerged species are Myriophyllum spicatum , Vallisneria natans , Ceratophyllum demersum , Potamogeton malaianus , Hydrilla verticillata , etc.
However, Liangzi Lake was seriously flooded in Xu et al. Therefore, macrophytes were surveyed in the shore area of Liangzi Lake as shown in Figure 1. Figure 1. Map of Liangzi Lake. The red dots represent the locations of sampling sites. From July 19 to August 7, i. To be specific, one or two plots were placed in small communities, and three to four plots were placed in communities with high species richness and complicated community structure.
Whole plants containing shoots and roots were collected and washed to remove sediment and surface residues. The plant materials collected from each site were classified based on species and life-form i. Community biomass refers to the total biomass of all species in each plot in our study. First, water depth was measured in each plot. Then, incident photosynthetically available irradiance — nm was measured three times at the air—water surface and different underwater depths using a fiber optic sensor LIA, LI-COR, Inc.
Light attenuation for photosynthetically active radiation was valued as the light attenuation coefficient, Kd m —1. Finally, physical and chemical parameters were measured at each site at 1 m underwater.
Water samples were collected in ml clean bottles, stored in a portable refrigerator and transported to Liangzi Lake National Station immediately for analyses of total nitrogen TN and total phosphorus TP. Seven biodiversity indices, including four diversity indices and three evenness indices, were computed as follows:. The DI model, improving diversity effects models proposed by Loreau and Hector , separates the contribution of different species and interspecific interactions to ecosystem function Kirwan et al.
In our study, the DI model was employed to test how individual species and interaction between species pairs affect the biodiversity—biomass pattern by comparing hierarchical linear DI models Kuebbing et al. Four hierarchical linear DI models Table 1 were used to test alternative hypotheses about the effect of species identities and interspecific interactions on the biomass in aquatic macrophyte communities Kirwan et al. By comparing the fit and analyzing the variance of these models, we can evaluate how species and interactions between pairwise species explain the BPR pattern Kirwan et al.
The null model M0 assumes that ecosystem function does not change with diversity but with a function of total abundance M of the community. The above models constitute a hierarchy of complexity describing species identity and interaction effects. By comparing the fit of models in this hierarchy, we can test biological hypotheses about how species identity and interspecific interaction effects contribute to ecosystem function Kirwan et al.
The significant difference between M1 and M0 demonstrates that species differ in their individual monoculture performances, between M3 and M1 demonstrates that there is a diversity effect on average, and between M3 and M2 demonstrates that separate pairwise interactions differ Kirwan et al.
To examine the influence of the biomass of the macrophyte community on diversity indices i. We then performed least-squares quadratic regressions. We deem the relationship curvilinear if the quadratic term is significantly different from zero and the overall model is significant. We compared two statistical models to select the most appropriate fit to the data. The Akaike information criterion AIC was calculated to help select the best model for each of the biodiversity indices.
The least-squares quadratic regressions were chosen finally due to the lower AIC value. DI models were fitted by ordinary multiple regression, and comparisons between hierarchical models were made using the differences in AIC Connolly et al. Differences in communities with different dominant species in biomass and biodiversity were compared using one-way ANOVA by post hoc Bonferroni tests for multiple comparisons, respectively, with the R packages agricolae Mendiburu, Redundancy analysis RDA was used to rank the aquatic macrophytes of four life-forms and species on all environmental gradients by the R package vegan Oksanen et al.
Stepwise regression was performed to eliminate the collinearity of environmental factors and screen out the major factors that influence the BPR pattern. The least-squares linear regressions were performed to examine the effects of water depth on biomass i. Notably, free-floating species were not included in this analysis due to their extremely low frequency 2 of 78 plots. The above analyses were performed using R version 3.
The present survey recorded 33 aquatic plant species, including 12 submerged species, seven floating-leaved species, two free-floating species, and 12 emergent species Supplementary Table 1. The total biomass of all plots ranged from 48 to g m —2. The water depth was between 40 and cm. The basic physical and chemical properties and nutritional conditions of the water body are given in Supplementary Table 2.
Figure 2. According to the comparison between hierarchical linear DI models, we found that species identity played an important role in the biodiversity—biomass pattern of the aquatic macrophyte community, which is proved by the significant difference between M1 and M0 Table 2.
However, we found no indication that pairwise interactions existed, on average or separately, by comparing M3 and M1, M3 and M2 Table 2 , full model results can be found in Supplementary Method. Moreover, the results of the one-way ANOVA show that the communities with different dominant species tended to have a disparate community structure Figure 3. Emergent species-dominated communities had high diversity and biomass, and communities dominated by submerged species C. Table 2.
Comparisons between hierarchical DI models testing biological hypotheses about the contribution of species identity and interspecific interactions to productivity. Figure 3. Error bars indicate standard error. Total nitrogen, water depth, and SS cumulatively accounted for After stepwise regression analysis, we found that water depth explained the most variation in many log-response ratio of biomass and diversity indices i.
Figure 4. WD, water depth. Red and blue bars represent positive and negative effects, respectively. Meanwhile, water depth significantly reduced most of diversity indices i. Figure 5. The least-squares linear regressions between water depth and biomass of A total, B emergent, C floating-leaved, and D submerged plants. What are some examples of an ecological niche? What is an example of an animal and their niche for the grassland savannah biome?
What are the niches of algae, duckweed, salvinia, and elodea? What is a quick explanation of the difference between a fundamental and realized niche? How is natural selection related to the concept of niche? What is an example of an ecological niche?
The relationship between species richness, climate moisture index, soil nutrient regime, stand age and aboveground biomass showed different response. We applied the GLMs to examine the combined effect among aboveground biomass, species richness, climate moisture index, soil nutrient regime and stand age Table 2.
The stand age was the most important driver in the aboveground biomass and Simultaneously, climate moisture index had a positive effect on the aboveground biomass and soil nutrient regime had a negative effect on the aboveground biomass. Species richness had no influence on the model predictions. Climate moisture index represented for Species richness could increase with better stand age and climate moisture index.
The stand age and climate moisture index were better links between species richness and aboveground biomass as mediation. The four models were used to analyze the relationship between species richness and aboveground biomass and infer the direct and indirect effects of stand age, climate moisture index and soil nutrient regime. The species richness had no direct effect on aboveground biomass.
Meantime, aboveground biomass increased with stand age and climate moisture index showing a positive direct effect on aboveground biomass Table 3.
The soil nutrient regime had a direct effect on aboveground biomass with a negative influence. The better hydrothermal condition could increase the aboveground biomass size. The links between species richness and aboveground biomass could be mediated with climate moisture index.
The coefficients are standardized prediction coefficients indicate each path. Climate moisture index had a positive direct effect on aboveground biomass. The direct path between aboveground biomass and species richness as well as soil nutrient regime became insignificant, but positive effects appeared on between aboveground biomass and stand age as well as climate moisture index Table 3. Additional total effects of the species richness were realized via changes of climate moisture index. Like the model in Fig 3C , stand age and climate moisture index had significant indirect effect on aboveground biomass.
Climate moisture index had an indirect effect on the aboveground biomass through the stand age. Species richness had no significant effect on aboveground biomass, and soil nutrient regime had a negative significant effect on aboveground biomass.
Our results effectively exhibited a complex and highly variable relationship between species richness and aboveground biomass by employing plots within a primary P.
If we only considered species richness and the aboveground biomass, we found that the positive linear regression appeared in the species richness-aboveground biomass relationship.
The species richness was important for driving power lending to clear differences in aboveground biomass change, and these results are confirmative of a multitude of previous studies showing that biodiversity had an effect on biomass production [ 11 , 16 ]. In contrast, other pine forest studies generally support the finding from experimental grasslands in a large scale [ 41 ]. We found remarkable reasons in the potential mechanisms driving the effect responses, which might be the result of the species diversity per se, or the addition of different functional groups with increased resource partitioning [ 2 , 16 ], such as some productive tree species depending on sampling effect.
In our study, as a pioneer species in the tropical region of Yunnan Province, P. Alternatively, the amount of P. Simultaneously, understory species composition showed clear interregional scale differences. Sometimes, the understory conditions have the characteristic of more light and a dry environment, as some sun plants were able to occupy, contributing to more woody production.
Nevertheless, we obtained the interesting finding that species richness had an indirect affect on aboveground biomass as a potential maintenance mechanism in a primary P. The stand age and climate moisture index were important influence factors on the aboveground biomass, but climate moisture index was a better mediation in the links between species richness and aboveground biomass.
Aboveground biomass may be influenced indirectly by climate moisture index and soil nutrient regime through species richness according to the multivariate analysis. We can explain it preferably by using a complementary effect [ 7 ]. The reason is that signs of logging or resin tapping and other disturbances appear in the subtropical primary P.
Simultaneously, some evergreen broadleaf species have important functional trait such as sprouting for a better tree regeneration strategy in this forest community [ 25 ]. The shade-tolerant tree species became common dominant components with a better resource utilization rate by the niche differentiation and adaptation to environmental conditions.
Canopy tree species diversity increased strongly with regional productivity [ 22 ]. A positive biodiversity-biomass relationship appeared in the tropical fixed pine forest.
Previous studies showed that pine tree abundance had a negative impact on understory biomass production through light, water and soil nutrients, so pine trees had a strong inhibitory effect on the abundance of understory plants, which in turn led to lower understory species richness [ 28 ]. Because of this, strong higher upper storey and sub-storey enhanced community vertical structure in the mixed pine forest and P.
Environmental variations controlled the species richness-biomass relationships in the sampling natural systems as a causal pathway [ 11 ]. These results indicated the relationship between biodiversity and aboveground biomass are strongly dependent on variations of environment conditions, especially including of climate factors and soil disturbance [ 43 ]. In this research, our results contrast with four models including different biotic and abiotic factors.
Species richness just had indirect influence on the aboveground biomass after adding the climate moisture index into the models. In contrast to soil nutrient regime, climate can directly and indirectly affect and species richness and aboveground biomass through changing the species composition and community structure, and climate moisture index become a better mediation to link the relationship between species richness and aboveground biomass when we considered the effect of stand age.
Generally, relative lower soil nutrient conditions were responsible for the loss of diversity and the majority of biomass production [ 7 ], and the climate moisture index increased with the soil nutrient regime which was consistent with the complementary effects by resource apply [ 5 ].
We found the rich soil nutrient regime suited more broad-leaved species due to lesser disturbance, and produced higher tree species diversity and productivity via increased resource acquisition and utilization as well as facilitation among individuals [ 11 , 44 ].
Relatively more species-rich systems in our study had a strong effect, negatively influenced by resource supply, with a different finding from species-poor boreal forests [ 11 ]. Further explanation is that the species composition might affect energy fluxes based on particular attributes of species that exert especially important effects on resource uptake [ 2 ]. Our finding might illustrate that a lower nutrient regime leads to broad-leaved tree species biomass loss in the mixed pine forest, but more resource utilization was allocated for P.
Our study provides different insights into the mechanism, showing positive relationship between the species richness and the aboveground biomass in the primary P. The climate moisture index is crucial for the species richness-aboveground biomass relationship as a mediation variable in our study, which confirms previous studies.
However, more and more sampling plot in the primary P. Thus it is possible that more response variables and methods are in favour of the exploration of biodiversity-ecosystem functioning relationships in the complex forest ecosystem. We also thank Brad Seely and Cindy Q. Tang for revising the English draft. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract The relationship between biodiversity and biomass is an essential element of the natural ecosystem functioning.
Introduction Biodiversity and biomass are two critical variables in the plant community ecosystem [ 1 ]. Download: PPT. Fig 1.
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