Soil macrofauna communities in Brazilian land-use systems
Bibliographic record
Abstract
Background: Soil animal communities include more than 40 higher-order taxa, representing over 23% of all described species. These animals have a wide range of feeding sources and contribute to several important soil functions and ecosystem services. Although many studies have assessed macroinvertebrate communities in Brazil, few of them have been published in journals and even fewer have made the data openly available for consultation and further use. As part of ongoing efforts to synthesise the global soil macrofauna communities and to increase the amount of openly-accessible data in GBIF and other repositories related to soil biodiversity, the present paper provides links to 29 soil macroinvertebrate datasets covering 42 soil fauna taxa, collected in various land-use systems in Brazil. A total of 83,085 georeferenced occurrences of these taxa are presented, based on quantitative estimates performed using a standardised sampling method commonly adopted worldwide to collect soil macrofauna populations, i.e. the TSBF (Tropical Soil Biology and Fertility Programme) protocol. This consists of digging soil monoliths of 25 x 25 cm area, with handsorting of the macroinvertebrates visible to the naked eye from the surface litter and from within the soil, typically in the upper 0-20 cm layer (but sometimes shallower, i.e. top 0-10 cm or deeper to 0-40 cm, depending on the site). The land-use systems included anthropogenic sites managed with agricultural systems (e.g. pastures, annual and perennial crops, agroforestry), as well as planted forests and native vegetation located mostly in the southern Brazilian State of Paraná (96 sites), with a few additional sites in the neighbouring states of São Paulo (21 sites) and Santa Catarina (five sites). Important metadata on soil properties, particularly soil chemical parameters (mainly pH, C, P, Ca, K, Mg, Al contents, exchangeable acidity, Cation Exchange Capacity, Base Saturation and, infrequently, total N), particle size distribution (mainly % sand, silt and clay) and, infrequently, soil moisture and bulk density, as well as on human management practices (land use and vegetation cover) are provided. These data will be particularly useful for those interested in estimating land-use change impacts on soil biodiversity and its implications for below-ground foodwebs, ecosystem functioning and ecosystem service delivery. New information: Quantitative estimates are provided for 42 soil animal taxa, for two biodiversity hotspots: the Brazilian Atlantic Forest and Cerrado biomes. Data are provided at the individual monolith level, representing sampling events ranging from February 2001 up to September 2016 in 122 sampling sites and over 1800 samples, for a total of 83,085 ocurrences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".