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Record W1970081402 · doi:10.1155/2015/732946

Relationships between Plant Biodiversity and Soil Fertility in a Mature Tropical Forest, Costa Rica

2015· article· en· W1970081402 on OpenAlexaff
Martin Nadeau, Thomas P. Sullivan

Bibliographic record

VenueInternational Journal of Forestry Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of British Columbia
FundersUniversidad de Costa Rica
KeywordsSpecies richnessSoil fertilityBiodiversityTemperate climateDiversity indexEcologyTemperate forestGeographySpecies diversityForestryBiologyEnvironmental scienceSoil water

Abstract

fetched live from OpenAlex

We aimed to study relationships between plant biodiversity and soil chemical fertility in a mature tropical forest of Costa Rica. Soil samples were collected in nine sampling plots (5 m by 25 m) in order to identify P, K, Ca, Mg, Fe, Zn, Mn, Cu, and Al and total N contents, soil fertility index, CEC, pH, and C/N ratio. Furthermore, species richness, Shannon-Wiener and Simpson’s species diversities, structural richness, and structural diversity were calculated for each plot. Simple linear regression analyses were conducted. Tree species richness was inversely related to concentration levels of K, Ca, and P, CEC, and soil fertility index. Therefore, higher tree species richness tended to be found on sites with lower soil fertility, which is the complete opposite of temperate forests. As a result, tropical and temperate forest ecology should be considered separately. Shannon-Wiener tree species diversity was positively correlated to C/N ratio. Herb structural richness was positively correlated with soil fertility index and P concentration. Therefore, herb structural richness may be a good indicator of soil fertility. This study gives important insights on ecological relationships between plant biodiversity and soil chemical fertility in a primary tropical forest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.154
GPT teacher head0.331
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2015
Admission routes1
Has abstractyes

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