Freshwater ecosystems provide ecosystem services essential to human well-being, such as provisioning of water and fishery resources, but they are the most vulnerable to human disturbances and concerns have been raised about their loss of biodiversity. The ancient Lake Biwa is one of valuable Asian freshwater ecosystem because of its high biodiversity and endemism but it has been exposed to various severe anthropogenic environmental changes during the past half century. Although we have great concern about the biodiversity loss in this lake, it remains highly unknown what kind of human disturbances cause deterioration of the ecosystem. Elucidation of environmental pressures for the biodiversity loss is important to illuminate subjects for conservation of biodiversity. We particularly focus on a littoral benthic macroinvertebrate fauna because its habitats, located in the interface between aquatic and terrestrial ecosystems, are those most vulnerable to human activities. We found that littoral benthic macroinvertebrate diversity was largely affected by pH, temperature, phytoplankton biomass, benthic microalgae biomass, and coverage of submerged plants. These environmental variables are largely associated with environmental problems that occurred in the Lake Biwa: eutrophication, warming, and massive submerged plant expansion. This finding suggests that past environmental problems caused serious impacts on the biodiversity of Lake Biwa.
File List Data files Data files are in ASCII format (tab-delimited text files). The file convention is: variable.name.ext File extensions: ASCII = .txt ; compressed files = .zip ; PDF = .pdf. Data and metadata files have been compressed using Microsoft Windows XP file manager (right click / send to / compressed (zipped) folder). Eleven tab-delimited text files have been grouped and compressed as BIODEPTH.PROCESSES.zip BIODEPTH.PROCESSES.zip 100 kilobytes, (11 files) 1. Design.txt (lines=481, columns=9) plot, location, block, composition, species.richness, functional.richness, grasses, legumes, forbs 2. Observed.Species.Richness.txt (lines=1441, columns=9) year, plot, location, block, composition, species.richness, functional.richness, legumes, species.observed 3. Cover.txt (lines=1441; columns=9) year, plot, location, block, composition, species.richness, functional.richness, legumes, cover 4. Shoots.txt (lines=1441; columns=9) year, plot, location, block, composition, species.richness, functional.richness, legumes, biomass 5. Partitioning.txt (lines=1129; columns=11) year, plot, location, block, composition, species.richness, functional.richness, legumes, net.effect, complementarity.effect, selection.effect 6. Canopy.txt (lines=481; columns=10) plot, location, block, composition, species.richness, functional.richness, legumes, height3, light3, gravity3 7. Canopy.Layers.txt (lines=2003; columns=13) plot, location, block, composition, species.richness, functional.richness, legumes, layer.top, layer.bottom, layer.thickness, midpoint, biomass, density 8. N.vegetation.txt (lines=481; columns=10) plot, location, block, composition, species.richness, functional.richness, legumes, mass.g.m2, N.percent, N.g.m2 9. Roots.txt (lines=481; columns=8) plot, location, block, composition, species.richness, functional.richness, legumes, root3 10. N.soil.txt (lines=368; columns=8) plot, location, block, composition, species.richness, functional.richness, legumes, N.total 11. Decomposition.txt (lines=481; columns=9) plot, location, block, composition, species.richness, functional.richness, legumes, cotton3, wood3. Description Abstract We present a database of 15 response variables documenting the relationship between plant diversity and ecosystem functioning within the European BIODEPTH network of plant-diversity manipulation experiments. The data quantify key ecosystem processes and related variables: (1) Observed species richness; (2) Vegetation percent cover; (3, 4) Plant biomass above- and belowground; (5–8) Average height of leaf canopy, canopy biomass density and center of gravity, percentage of transmitted PAR at ground level; (9, 10) Decomposition of wooden sticks and cotton strips; (11, 12) Nitrogen pools in aboveground vegetation and available soil nitrogen; (13–15) The net, selection, and complementarity effects following the additive-partitioning method. Plant diversity was manipulated in terms of richness -- both species richness (numbers of species per plot) and functional-group richness (numbers of plant functional groups per plot) and species composition. Our plant functional-group categorization separated N-fixing legumes from other herbaceous species and grasses from the remaining herbaceous species. Results of the analysis of the 15 ecosystem-process response variables in relation to the explanatory variables given in the description of the experimental design above are reported in a companion paper for which this paper is a linked supplement. Differences between sites explained substantial and significant amounts of the variation of most of the ecosystem processes examined. However, against this background of geographic variation, all the aspects of plant diversity and composition we examined (i.e., both numbers and types of species and functional groups) produced significant, mostly positive impacts on ecosystem processes. Analyses using the additive-partitioning method revealed consistent complementarity effects, which were stronger than the more variable selection effect. In general, communities with a higher diversity of species and functional groups were more productive and utilized resources more completely by intercepting more light, taking up more nitrogen and occupying more of the available space. The ecosystem effects of plant diversity varied between sites and between years. However, in general, diversity effects were lowest in the first year and stronger later in the experiment. These analyses of our complete ecosystem process dataset largely reinforce our previous results, and those from comparable biodiversity experiments, and extend the generality of diversity–ecosystem functioning relationships to multiple sites, years, and processes. Key words: BIODEPTH, European plant-experiment network; biodiversity; complementarity; ecosystem functioning; ecosystem processes; functional groups; grassland field sites, European; plant diversity; selection effect; species richness. Metadata Class I: Data set descriptors Title or Theme of Data set: “BIODEPTH ecosystem processes” Name of Dataset Originator/Owner: “Prof. John Lawton” Citation for Data use: “Data provided by the BIODEPTH project” Data Abstract(purpose or context): “Ecosystem process responses to manipulation of plant diversity in European grasslands” E-mail Address of Data set Contact: “ahector@uwinst.unizh.ch” Key words: Ecosystem processes, biodiversity, grasslands” Research Period: “ 19950501 19991231” Location: “see Methods” Location: “see Methods ” Phone Number of Data set Contact: “00 41 (0)1 635 4804” Address1 of Data set Contact: “Institute of Environmental Sciences” Address2 of Data set Contact: “University of Zürich” Address3 of Data set Contact: “Winterthurerstrasse 190, Zürich 8057, Switzerland” Control Number: “[to be assigned by Ecological Archives?]” Class II: Research Origin Descriptors ...
Through complementary use of canopy space in mixtures, aboveground niche separation has the potential to promote species coexistence and increase productivity of mixtures as compared to monocultures. We set up an experiment with five perennial grass species which differed in height and their ability to compete for light to test whether plants partition light under conditions where it is a limiting resource, and if this resource partitioning leads to increased biomass production in mixtures (using relative yield‐based methods). Further, we present the first application of a new model of light competition in plant communities. We show that under conditions where biomass production was high and light a limiting resource, only a minority of mixtures outperformed monocultures and overyielding was slight. The observed overyielding could not be explained by species differences in canopy structure and height in monoculture and was also not related to changes in the canopy traits of species when grown in mixture rather than monoculture. However, where overyielding occurred, it was associated with higher biomass density and light interception. In the new model of competition for light, greater light use complementarity was related to increased total energy absorption. Future work should address whether greater canopy space‐filling is a cause or consequence of overyielding.
At eight European field sites, the impact of loss of plant diversity on primary productivity was simulated by synthesizing grassland communities with different numbers of plant species. Results differed in detail at each location, but there was an overall log-linear reduction of average aboveground biomass with loss of species. For a given number of species, communities with fewer functional groups were less productive. These diversity effects occurred along with differences associated with species composition and geographic location . Niche complementarity and positive species interactions appear to play a role in generating diversity-productivity relationships within sites in addition to sampling from the species pool.
Summary With ever‐increasing human pressure on ecosystems, it is critically important to predict how ecosystem functions will respond to such human‐induced perturbations. We define perturbations as either changes to abiotic environment (e.g. eutrophication, climate change) that indirectly affects biota, or direct changes to biota (e.g. species introductions). While two lines of research in ecology, biodiversity–ecosystem function ( BDEF ) and ecological resilience ( ER ) research, have addressed this issue, both fields of research have nontrivial shortcomings in their abilities to address a wide range of realistic scenarios. We outline how an integrated research framework may foster a deeper understanding of the functional consequences of perturbations via simultaneous application of (i) process‐based mechanistic predictions using trait‐based approaches and (ii) detection of empirical patterns of functional changes along real perturbation gradients. In this context, the complexities of ecological interactions and evolutionary perspectives should be integrated into future research. Synthesis and applications . Management of human‐impacted ecosystems can be guided most directly by understanding the response of ecosystem functions to controllable perturbations. In particular, we need to characterize the form of a wide range of perturbation–function relationships and to draw connections between those patterns and the underlying ecological processes. We anticipate that the integrated perspectives will also be helpful for managers to derive practical implications for management from academic literature.
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