Bioclimatic equilibrium for lichen distributions on disjunct continental landmasses
Bibliographic record
Abstract
Bioclimatic models assume that species distributions reflect their sensitivity to macroclimate, the so-called bioclimatic equilibrium. This has proven to be a controversial assumption. Here we perform a new test in the hypothesis of climatic equilibrium by comparing species’ bioclimatic space between two independently derived spatial distributions in Britain and North America. A presence-only statistical model (MAXENT) was used to construct bioclimatic response surfaces for 25 lichens in North America. These models were then projected onto British climate space. We tested the following: (1) the statistical congruence between likelihood values for North American bioclimatic space projected onto Britain and species’ observed British distributions, and (2) the extent to which the projection for a species matched its observed British distribution pattern better than the distributions for an alternative suite of species. In general, there is good evidence for bioclimatic equilibrium when comparing species distributions in North America and Britain. However, bioclimatic test 1 (statistical congruence) and bioclimatic test 2 (spatial matching) were failed by six (24% of cases) and four (16% of cases) species, respectively. Although there is general support for bioclimatic modelling in lichens, the species that failed a test of equilibrium would have been difficult to predict based on prior knowledge; however it may be explained by taxonomic uncertainty and (or) the existence of multiple correlated environmental drivers.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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; both teacher heads agree on what is shown here.
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".