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Record W2296853843

Greater Sage Grouse on the Edge of Their Range: Leks and Surrounding Landscapes in the Dakotas

2003· article· en· W2296853843 on OpenAlexaboutno aff
Joe T. Smith

Bibliographic record

VenueOpen PRAIRIE (South Dakota State University) · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
Fundersnot available
KeywordsRange (aeronautics)GeographyHome rangeEcologyBiologyHabitat
DOInot available

Abstract

fetched live from OpenAlex

Sage grouse (Centrocercus spp.) have been declining throughout their range in the United States and Canada as well as in individual states and provinces. Sage grouse, including the combined range of both the greater sage grouse (C. urophasianus) and the Gunnison sage grouse (C. minimus), once occurred in 16 states and three Canadian provinces; now they only occur in 11 states and two Canadian provinces (Connelly and Braun 1997, Schroeder et al. 1999). Excluding states suggested as part of the Gunnison sage grouse range (Young et al. 2000), greater sage grouse range has declined from 13 states and three Canadian provinces to 11 states and two Canadian provinces (Schroeder et al. 1999). Excluding states suggested as part of the Gunnison sage grouse range (Young et al. 2000), greater sage grouse range has declined from 13 states and three Canadian provinces to 11 states and two Canadian provinces (Schroeder et al. 1999). Concerns are growing over the reasons for these declines. The habitat around active and historically active greater sage grouse leks in North Dakota and South Dakota was studied during 2001 and 2002 in an attempt to find reasons for desertion of once thriving leks and for apparent population declines. I collected information on current and historic (i.e., leks with 0-years) sage grouse lek counts and distributions in western North Dakota and South Dakota. A steady decline is evident when reviewing the entire recorded period of greater sage grouse surveys in North Dakota (1951-2002) and South Dakota (1972-2002). There is also apparent eastern edge abandonment of the active breeding range over the years. Land use patterns in these areas once occupied by greater sage grouse have likely changed and are now failing to meet their needs (Connelly and Braun 1997). I compared peripheral microhabitat and landscape characteristics to identify possible reasons for lek abandonment. For microhabitat data, I systematically selected 12 sample sites at equidistant points from each other within 1.5 km of the lek center. No differences (P > 0.10) were detected between sagebrush (Artemisia spp.) cover or sagebrush density around active leks versus that around historically active leks in North Dakota and South Dakota. Sagebrush density does appear to have a positive effect on greater sage grouse lek size in North Dakota and South Dakota. Landscape level data were recorded and assessed using satellite imagery. Comparisons were made of 1972-1976 and 1999-2000 percent tilled and non-tilled. These land use comparisons were made between current leks versus historically active leks, active leks versus random sites, and abandoned regions versus active regions. The 1999-2000 imagery illustrated that percent tilled ground, and thus fragmentation, was greater (P < 0.10) within abandoned areas than within active areas in North Dakota. However, 1972-1976 imagery revealed that this relationship has been static over the last 30 years. Thus, if the decline of sage grouse is the result of tilled ground infringements it had to occur prior to 1972. Sage grouse, a landscape species, may have been slow to show the effects of this land use change.

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 score0.207
Threshold uncertainty score0.411

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.021
GPT teacher head0.202
Teacher spread0.181 · 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

Citations8
Published2003
Admission routes1
Has abstractyes

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