The ice age ecologist: testing methods for reserve prioritization during the last global warming
Bibliographic record
Abstract
ABSTRACT Aim We play the role of an ice age ecologist (IAE) charged with conserving biodiversity during the climate changes accompanying the last deglaciation. We develop reserve‐selection strategies for the IAE and check them against rankings based on modern data. Location Northern and eastern North America. Methods Three reserve‐selection strategies are developed. (1) Abiotic: the IAE uses no information about species–climate relationships, instead maximizing the climatic and geographic dispersion of reserves. (2) Species distribution models (SDMs): the IAE uses boosted‐regression trees calibrated against pollen data and CCSM3 palaeoclimatic simulations from 21 to 15 ka bp to predict modern taxon distributions, then uses these as input to the Z onation reserve‐ranking program. (3) Rank‐and‐regress: regression models are used to identify climatic predictors of zonation rankings. All strategies are assessed against a Z onation ranking based on modern pollen distributions. Analysis units are ecoregions and grid cells. Results The abiotic strategy has a negative or no correlation between predicted and actual rankings. The SDM‐based strategy fares better, with a significantly positive area‐corrected correlation ( r = 0.474, P < 0.001) between predicted and actual rankings. Predictive ability drops when grid cells are the analysis unit ( r = 0.217, P = 0.058). Predictive ability for the rank‐and‐regress strategy is similar to the SDM results. Main conclusions For the IAE, SDMs improve the predictive ability of reserve‐selection strategies. However, predictive ability is limited overall, probably due to shifted realized niches during past no‐analogue climates, new species interactions as species responded individually to climate change, and other environmental changes not included in the model. Twenty‐first‐century conservation planning also faces these challenges, and is further complicated by other anthropogenic impacts. The IAE's limited success does not preclude the use of climate scenarios and niche‐based SDMs when developing adaptation strategies, but suggests that such tools offer at best only a rough guide to identifying possible areas of future conservation value.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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 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".