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Record W3015099946 · doi:10.1002/ecy.3062

Habitat fragmentation changes top‐down and bottom‐up controls of food webs

2020· article· en· W3015099946 on OpenAlexaff
Rong Wang, Xin Zhang, Yuanyuan Li, Jianguo Wu, Fangliang He, Xiaoyong Chen

Bibliographic record

VenueEcology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsEcologyHabitatPredationSeedlingAbiotic componentBiologyHabitat destructionTrophic cascadeHabitat fragmentationFragmentation (computing)EcosystemDefaunationPopulationFood webAgronomy

Abstract

fetched live from OpenAlex

Top-down and bottom-up controls regulate the structure and stability of ecosystems, but their relative roles in terrestrial systems have been debated. Here we studied a hydro-inundated land-bridge system in subtropical China and tested the relative importance of these two controls in determining the rodent-mediated regeneration of a locally dominant tree species. Our results showed that both controls operated in terrestrial habitats and that their relative importance switched as habitat size changed. Habitat loss initially removed predators of rodents that released rodent populations and triggered massive seed predation (top-down control), leading to reduced seedling establishment. A further reduction in habitat size led to decrease in rodent population that was supposed to increase seedling survival of the tree species, but the decline in habitat size deteriorated the abiotic environments (bottom-up control) that severely prevented seedling recruitment. As the ongoing global land use change is creating increasing number of small-sized forest fragments, our findings provide novel insights into the restoration of seriously fragmented forests.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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.

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

Citations25
Published2020
Admission routes1
Has abstractyes

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