Implications of Climate-Driven Fallowing for Ecological Connectivity of Species At Risk
Bibliographic record
Abstract
Abstract Context. Climate change and agricultural intensification are modifying the configuration of natural lands within agricultural landscapes, further impacting species’ ability to move freely between remaining natural areas. These working landscapes have inherently high opportunity costs, making the establishment of additional permanent reserves for species movement unlikely. Objectives. Here we explore the potential for opportunistic and dynamic conservation reserves, in the form of temporary fallowed croplands, to increase connectivity in competing land use regions. Methods. We evaluate the potential for fallowed lands to facilitate habitat connectivity for at-risk species in the San Joaquin Valley (SJV), an intensive agricultural landscape in California. We perform landscape connectivity analyses to examine how historic drought-induced fallowing from 2011 to 2017 in the SJV region impacted connectivity within Kern County for the endangered, endemic San Joaquin kit fox (Vulpes macrotis mutica). Results. We found that an increase in temporary fallowing from 2011 to 2015/2017 in Kern County likely increased habitat connectivity for the kit fox. This finding was represented by reductions in average Cost-Weighted Distances (CWD), Effective Resistances, and CWD-to-Least Cost Path Ratios between core habitat areas, indicating that cumulative costs incurred by kit foxes travelling between primary habitats decreased. Conclusions. Our findings highlight that strategic and cooperative, yet temporary, conservation actions have the potential to reduce the conflict between biodiversity preservation and agricultural production in working landscapes while increasing landscape connectivity. Fallowing-based, agri-environmental schemes could help working areas meet statewide groundwater management policy targets while improving species’ mobility in the face of climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".