Conservation planning under climate change: accounting for adaptive potential and migration capacity in species distribution models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Aim A number of assumptions underpinning the use of species distribution models to predict biological responses to climate change are violated for temperate and boreal tree species that are widespread, long‐lived and genetically adapted to local climate conditions. To address this situation, we propose a methodology to account for the potential effects of genetic structure, adaptive potential and limited migration capacity. Location British Columbia, Canada. Methods Similar to the widely used ‘no migration’ and ‘unlimited migration’ scenarios, we employ more refined biological response scenarios to evaluate the potential effects of genetic adaptation to local environments and the capacity of species to adapt and migrate. These scenarios are realized by two sets of geographic delineations that partition the species range into multiple populations and that subdivide the study area into smaller landscape units. Results In a case study for British Columbia, we demonstrate how the approach can be used to evaluate the adequacy of a reserve system of 906 protected areas to ensure long‐term maintenance of forest genetic resources for 48 tree species. We find that between 35% and 85% of locally adapted populations in protected areas are maintained under a median climate change scenario until the end of the century. A sensitivity analysis shows that assumptions about migration and adaptation capacity of species have a major effect on the projected conservation status. Main conclusions We propose that the results of species distribution models have practical value for conservation planning if the focus is on maintenance rather than loss of suitable habitat. Accounting for genetic structure, adaptive potential and migration capacity through best‐case and worst‐case scenarios provide important information to effectively allocate limited resources available for conservation action.
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.
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 | 0.001 | 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) | 0.000 | 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 it