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Record W4381191465 · doi:10.3390/ecologies4020026

Population Fluctuations of the Deer Mouse (Peromyscus maniculatus) in Old-Field and Bunchgrass–Sagebrush Habitats: The Role of Agricultural Setting and Optimum Habitat

2023· article· en· W4381191465 on OpenAlexafffundabout
Thomas P. Sullivan, Druscilla S. Sullivan

Bibliographic record

VenueEcologies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsUniversity of British Columbia
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPeromyscusTussockDeer mouseBiologyPopulationHabitatPerennial plantEcologyCompetition (biology)Interspecific competitionSeasonal breederGrasslandAbundance (ecology)Population densityOld fieldOverwinteringDemography

Abstract

fetched live from OpenAlex

In semiarid regions, the deer mouse (Peromyscus maniculatus) is a major small mammal species occupying perennial grassland habitats that include old-fields, native bunchgrass–sagebrush, and some agricultural settings. We investigated population changes in deer mouse populations in perennial grasslands, both natural and old-field, from 1982 to 2003 in southern British Columbia, Canada. Hypotheses (H) predicted that P. maniculatus populations will have (H1) multiannual fluctuations in abundance driven by population increases from extended breeding in summer and winter; (H2) relaxed spring reorganization events in some years leading to higher overall recruitment and survival; and (H3) interspecific competition with montane voles that causes deer mice to be lower in density when voles are higher. P. maniculatus populations in old-field and grass–sagebrush sites had clearly defined periods of high “peak” mean numbers (32–52/ha) and other times of low mean numbers (20–22/ha). Based on mean annual peak density in autumn, deer mouse populations exhibited fluctuations of 3–4 years in both habitats, but this pattern was not always present. The greater numbers of P. maniculatus in high than low years was directly related to population increases from extended breeding seasons and an increased number of lactating females, thereby supporting H1. Spring breeding season declines occurred but were similar or less in high than low years of mean abundance and were relaxed in comparison to forest populations of deer mice in other studies. Thus, H2 was supported for recruitment with high numbers of young-of-the-year breeding and total number of juvenile recruits but for survival was equivocal. Total summer survival was consistently higher in high than low population years but juvenile productivity in all years was poor. Mean abundance of P. maniculatus and M. montanus in old-field sites were highly correlated, and hence H3 was not supported. This latter result is the first, to our knowledge, of P. maniculatus coexisting in a similar pattern of population fluctuations with a Microtus species in a mainland grassland habitat. Higher than average precipitation in the year preceding a peak population of deer mice may have enhanced herbaceous vegetation and contributed to population increases in both habitats. We conclude that the old-field habitat associated with this agricultural setting provides optimum habitat for P. maniculatus and facilitates multiannual population fluctuations in this species.

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)0.000
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 score0.229
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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