Landscape management for a landscape species: Understanding the impacts of anthropogenic factors on sage-grouse populations in Wyoming
Bibliographic record
Abstract
Greater Sage-grouse (Centrocercus urophasianus) are both an umbrella and flagship species for conservation concern throughout the intermountain west.For the past fifty years, sage-grouse populations have been experiencing a range wide decline.Long-term declines in sage-grouse abundance have prompted eight listing attempts for the species under provisions of the Endangered Species Act (ESA).Proximate causes of population decline(s) differ across sagegrouse distribution, but ultimately, the underlying cause is loss or degradation of suitable sagebrush land cover.Wyoming is arguably the most important region for sage-grouse management because Wyoming is not only at the center of sage-grouse distribution, but also hosts the largest sagegrouse population of any state in the species' 11 US State, 3 Canadian Province range.In 2008, then Governor, Dave Freudenthal signed an executive order that enacted the "Greater Sagegrouse Core Area Protection" that set aside 31 distinct regions called "Core Areas" which collectively encompas~24% of the total area of Wyoming.(State of Wyoming 2008).Upon establishment these Core Areas contained ~82% of the male sage-grouse in Wyoming at that time (State of Wyoming 2008, State of Wyoming 2010).However, since establishment, the Core Areas established by the SGEO have been redrawn or modified 4 times.For my research I investigated the overall effectiveness of the Core Areas (as established by the SGEO) in Wyoming in light of anthropogenic impacts.Using the Wyoming Game and Fish Department's (WGFD) sage-grouse lek database to characterize sage-grouse populations across Wyoming, along with other publically available data sets, we found that the Core Areas iv contained ~64% of all active leks and accounted for ~77% of male sage-grouse attending leks.Furthermore, we found that Core Areas had a higher average lek attendance (average males/core lek = 22±7.76;average males/non-core lek =9±2.25;t-test df=1,821 P <0.001) and had a lower probability of lek collapse (20.4% in non-core areas compared to 10.9% in Core Areas).From this I concluded that Core Areas are doing an adequate job of conserving Greater Sage-grouse in Wyoming.v ACKNOWLEDGMENTS To my family: when I first considered applying to graduate school, my father gave me one simple piece of advice.Get on the boat.Do not wait for other opportunities to come-seize what is present so that a great opportunity doesn't pass by.I am always reminded of his guidance.I would like to thank my mother for being there for me, taking my calls regardless of the hour.Without my parents, I never would have stood a chance of making it through.To that end, I would like to thank my entire family for being a strong presence in my life.To those who are not my family, but close enough to be: Alyssa Adams, Makayla Fitzpatrick and Lizzy Hile-Thank you for always being ready to sit and laugh with me, for always being willing to hear my gripes.My lab mates, thank you for being a steady presence in my life and making coming to work fun.And to Zachary Nickels, my voice of reason and my biggest fan, thank you for talking me off the ledge, challenging me to think differently and pushing me to do my best, always.To my mentors and role models: Andy, thank you for taking a chance me.You have helped me navigate this field in a way that I will never able to thank you enough for.To my committee (Jeffrey Beck, Andrew Kear and Yu Zhou
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".