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Record W3020484742 · doi:10.1101/2020.04.22.055111

Extent of similarity between agricultural and natural land covers shapes how biodiversity responds to agricultural expansion at landscape scales

2020· preprint· en· W3020484742 on OpenAlexaffabout
Scott Wilson, Niloofar Alavi-Shoushtari, Darren Pouliot, Gregory W. Mitchell

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British ColumbiaCarleton UniversityEnvironment and Climate Change Canada
Fundersnot available
KeywordsBiodiversityGeographyAgricultureEcosystemTransectEcologyEcosystem servicesAgricultural landContext (archaeology)AgroforestryLand useAbundance (ecology)Environmental scienceBiology

Abstract

fetched live from OpenAlex

Abstract The impact of agriculture on biodiversity depends on the extent and types of agriculture and the degree to which agricultural land contrasts with the natural ecosystem. Most research on the latter comes from studies on the influence of different agricultural types within a single ecosystem with far less study on how the natural ecosystem context shapes the response of biodiversity to agricultural production. We used citizen science data from agricultural areas in Canada’s Eastern Hardwood-Boreal (forest ecosystem, n=108 landscapes) and Prairie Pothole (prairie ecosystem, n=99) regions to examine how ecosystem context shapes the response of avian species diversity, functional diversity and abundance to the amount of arable crop and pastoral agriculture at landscape scales. Avian surveys were conducted along 8km transects of Breeding Bird Survey routes with land cover assembled within a 20km 2 landscape around each transect. The amount of agriculture at which species diversity peaked differed between the forest (15%) and prairie (51%) ecosystems, indicating that fewer species tolerated the expansion of agriculture in the former. In both ecosystems, functional diversity initially increased with agriculture and peaked at higher amounts (forest: 42%, prairie: 77%) than species diversity suggesting that functional redundancy was lost first as agriculture increased. Species turnover with increasing agriculture was primarily among functional groups in forest where a shift from a low to a high agriculture landscape led to a decline in the percent of the community represented by Neotropical migrants, insectivores, upper foliage gleaners and bark foragers, and an increase in the percent of the community represented by short-distance migrants, granivores, omnivores and ground gleaners. There were few distinct shifts in the percent of the community represented by different functional groups in the prairie ecosystem. Total abundance was the least sensitive measure examined in both ecosystems and indicated that species losses with agriculture are likely followed by numerical compensation from agriculture tolerant species. Our results highlight the importance of ecosystem context for understanding how biodiversity is affected by agricultural production with declines in diversity occurring at lower agricultural extents in ecosystems with lower similarity between natural and agricultural land covers. These findings allow for more specific conservation recommendations including managing for species intolerant to agriculture in prairie ecosystems and limiting the expansion of high contrast agriculture and the loss of semi-natural habitat, such as hedge rows, in historically forested ecosystems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes2
Has abstractyes

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