Quantifying habitat loss and modification from recent expansion of energy infrastructure in an isolated, peripheral greater sage-grouse population
Bibliographic record
Abstract
New technologies and increasing energy demand have contributed to rapid expansion of unconventional oil and gas development in the U.S. in the past two decades. Quantifying the effects of energy infrastructure on land cover and wildlife habitat is essential for informing land-use policy, developing wildlife conservation strategies, and projecting impacts of future development. The greater sage-grouse (Centrocercus urophasianus; GrSG) is a species of concern in sagebrush ecosystems of the western U.S. and Canada and the focus of widespread conservation and management efforts. Increasing energy development within GrSG range has prompted the need to quantify and predict impacts of energy infrastructure on their habitat and populations. We mapped the annual distribution, surface type, and activity level of energy and non-energy infrastructure in the Parachute-Piceance-Roan (PPR), a small, peripheral greater sage-grouse population in Colorado with expanding oil and gas development, from 2005 to 2015. During that time, the footprint of energy infrastructure more than doubled to 3,275 ha (+108.6%), including 195 new well pads, 930 ha of new pipelines, and 230 km of new roads. In contrast, non-energy infrastructure decreased to 532 ha (-8.3%). The majority of energy infrastructure present each year (77-84%) was supporting infrastructure (i.e. facilities, roads, pipelines) rather than well pads, with an average of 2.24 ± 0.52 SE ha of supporting infrastructure per ha of well pad. Pipelines comprised 74-80% of reclaimed surface and roads comprised 54-69% of disturbed surface across years. By 2015, anthropogenic infrastructure covered 2.70% of occupied range and 2.93% of GrSG habitat, and energy infrastructure covered 2.50% and 10.79% of two priority habitat management area zones in the PPR. Three land cover classes most affected by energy infrastructure were also those strongly selected by GrSG. Topographic constraints appear to concentrate energy infrastructure in areas with gentler topography that also have the highest GrSG use. Together, these patterns suggest that future energy development will cause substantial additional loss and modification of GrSG habitat in the PPR. Our findings are valuable for assessing surface disturbance caps for land-use management and projections of energy infrastructure effects on wildlife habitat in this and other expanding oil and gas fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".