Dynamics of springtail and mite populations: the role of density dependence, predation, and weather
Bibliographic record
Abstract
Abstract 1. Ecological theory suggests that density‐dependent regulation of organism abundance will vary from exogenous to endogenous factors depending on trophic structure. Changes in abundance of soil arthropods were investigated at three trophic levels, springtails (Collembola), predaceous mites (Acari), and macro‐arthropods (spider, adult and larval beetles, centipedes). Predictions were that springtails are predator regulated and mites are food limited according to the Hairston et al . (1960) model, which predicts alternating regulation by competition and predation from fungi to springtails to mites to macro‐arthropods. The alternate hypothesis was based on the bottom‐up model of trophic dynamics, which predicts that each trophic level is regulated by competition for resources. 2. The relative contributions to springtail and mite population dynamics of endogenous (i.e. density‐dependent population growth related to food availability) and exogenous (i.e. predation and weather) factors were tested using time‐series analysis and experimental manipulation of water conditions. Box patterns were distributed within an aspen forest habitat located in the Canadian prairies and surveyed weekly from May to September 1997–1999. Each box depressed the leaf litter, creating a microhabitat island for soil arthropods that provided counts of invertebrates located immediately beneath the boxes. 3. Strong evidence was found for endogenous control of springtail and mite numbers, indicated by a reduction in population growth related to density in the previous week. Contrary to predictions, no evidence was found for regulation of springtail numbers by mites, or for regulation of mite numbers by macro‐arthropods. Springtail population growth rate was related positively to current springtail density (8 and 23% variation explained) and related negatively to 1‐week lagged density (85 and 58%), and related negatively to temperature (5 and 5%) for time‐series data and for experimental addition of water respectively. Mite population growth rate was related positively to current mite density (54%) and temperature (4%), and negatively to 1‐week lagged mite density (20%) and precipitation (6%) for time‐series analysis. For experimental addition of water, mite growth rate was related positively to current mite density (44%) and temperature (5%), and negatively to 1‐week lagged density (11%). Results differed from the Hairston et al . (1960) model predictions but were consistent with a bottom‐up view that springtail and mite populations were regulated intrinsically by competition for food and secondarily by temperature as a function of reproduction.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".