MétaCan
Menu
Back to cohort
Record W4303628945 · doi:10.3390/ecologies3040032

Long-Term Changes in Abundance and Composition of Forest-Floor Small Mammal Communities in a Landscape with Cumulative Clearcutting

2022· article· en· W4303628945 on OpenAlexafffundabout
Thomas P. Sullivan, Druscilla S. Sullivan

Bibliographic record

VenueEcologies · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaHabitat Conservation Trust Foundation
KeywordsClearcuttingSpecies richnessMicrotusGeneralist and specialist speciesSeral communityEcologyDeer mouseAbundance (ecology)PeromyscusBiologySpecies diversityHabitat

Abstract

fetched live from OpenAlex

Responses of forest-floor small mammals to clearcutting are species-specific with generalists occupying a range of habitats, and specialists persisting on clearcuts for variable periods. We investigated the responses in abundance and species composition of small mammal communities to cumulative clearcutting of coniferous forests on a landscape that had four independent clearcutting events (Periods 1 to 4) over a 42-year interval from 1979 to 2020 in south-central British Columbia, Canada. We ask if the small mammal communities have changed significantly over these decades owing to removal of old-growth forest by clearcut harvesting. Hypotheses (H) predicted that the small mammal community would (H1) increase in abundance, species richness, and diversity on new clearcuts owing to the availability of early seral post-harvest habitats from cumulative clearcutting; and (H2) have higher mean abundance, species richness, and species diversity in clearcut than uncut forest sites, owing to availability of vegetative food and cover. A third hypothesis (H3) predicted that abundance of (i) early seral vegetation (herbs and shrubs) and (ii) small mammal populations, will be greater in ungrazed clearcut sites than in those grazed by cattle (Bos taurus). Mean total numbers of small mammals on new clearcuts declined in Periods 3 and 4, and hence did not support the abundance part of H1. Much of this decline was owing to low numbers of the long-tailed vole (Microtus longicaudus) and meadow vole (M. pennsylvanicus). Two generalist species: the deer mouse (Peromyscus maniculatus) and northwestern chipmunk (Neotamias amoenus), contributed to high mean species richness and diversity in Periods 2 and 3 before these metrics declined in Period 4, and hence partly supported H1. The similarity in mean total numbers of small mammals in Periods 2 to 4 did not support the abundance prediction of H2 that total numbers would be higher in clearcut than uncut forest sites. Higher mean species richness (Periods 2 and 3) and diversity (Period 3) measurements on clearcut than forest sites, particularly in the early post-harvest years, did support these parts of H2. The vegetation part (i) of H3 was not supported for herbaceous plants but it was for shrubs. The small mammal part (ii) of H3 that populations would be higher in ungrazed than grazed clearcut sites was supported for abundance but not for species richness or diversity. The decline and near disappearance of both species of Microtus was possibly related to the reduction in plant community abundance and structure from grazing (at least for shrubs) and potentially from drought effects associated with climate change. Loss of microtines from these early seral ecosystems may have profound negative effects on various ecological functions and predator communities.

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 score0.294
Threshold uncertainty score0.584

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.254
Teacher spread0.225 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueEcologiesSame topicAnimal Ecology and Behavior StudiesFrench-language works237,207