Environmentally constrained null models: site suitability as occupancy criterion
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
Null models have proven to be an important quantitative tool in the search for ecological processes driving local diversity and species distribution. However, there remains an important concern that different processes, such as environmental conditions and biotic interactions may produce similar patterns in species distributions. In this paper we present an analytical protocol for incorporating habitat suitability as an occupancy criterion in null models. Our approach involves modeling species presence or absence as a function of environmental conditions, and using the estimated site‐specific probabilities of occurrence as the likelihood of species occupancy of a site during the generation of “null communities”. We validated this approach by showing that type I error is not affected by the use of probabilities as a site occupancy criterion and is robust against a variety of predictive performances of the species‐environmental models. We describe the expected differences when contrasting classical and the environmentally constrained null models, and illustrate our approach with a data set of Dutch dune hunting spider assemblages. Together, an environmentally constrained approach to null models will provide a more robust evaluation of species associations by facilitating the distinction between mutually exclusive processes that may shape species distributions and community assembly.
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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) | 0.315 | 0.010 |
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