Effects of biodiversity on the functioning of ecosystems: a summary of 164 experimental manipulations of species richness
Bibliographic record
Abstract
Over the past decade, accelerating rates of species extinction have prompted an increasing number of studies to reduce the number of species experimentally in a variety of ecosystems and examine how this aspect of diversity alters the efficiency by which communities capture biologically essential resources and convert them into new tissue. Here we summarize the results of 164 experiments (reported in 84 publications) that have manipulated the richness of primary producers, herbivores, detritivores, or predators in a variety of terrestrial and aquatic ecosystems and examined how this impacts (1) the standing stock abundance or biomass of the focal trophic group, (2) the abundance or biomass of that trophic group's primary resource(s), and/or (3) the extent to which that trophic group depletes its resource(s). Our summary includes studies that have focused on the top‐down effects of diversity, whereby researchers have examined how the richness of trophic group t impacts the consumption of a shared resource, and also studies that have focused on the bottom‐up effects of diversity, whereby researchers have examined how the richness of trophic group t impacts consumption of t by the next highest trophic level. The first portion of the data set provides information about the source of data and relevant aspects of the experimental design, including the spatial and temporal scales at which the work was performed. The second portion gives the magnitude of each response variable, the standard deviation, and the level of replication at each level of species richness manipulated. The third portion of the data set summarizes the magnitude of diversity effects in two ways. First, log ratios are used to compare the response variable in the most diverse polyculture to either the mean of all monocultures or the species having the highest/lowest value in monoculture. Second, data from each level of species richness are fit to three nonlinear functions (log, power, and hyperbolic) to assess which best characterizes the shape of diversity effects. The final portion of the data set summarizes any information that helps parse diversity effects into that attributable to species richness vs. that attributable to changes in species composition across levels of richness. The complete data sets corresponding to abstracts published in the Data Papers section of the journal are published electronically in Ecological Archives at 〈 http://esapubs.org/archive 〉. (The accession number for each Data Paper is given directly beneath the title.)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".