Positive Niche Construction: Incorporating Facilitative Microhabitat into Mechanistic Niche Modeling
Bibliographic record
Abstract
Researchers in the fields of conservation biology and invasion ecology aim to predict the dispersal of species in a reproducible manner, based on quantifiable relationships between target organisms and their environment, through a process known as mechanistic niche modelling. By identifying physiological constraints unique to an organism and calculating its budget of key resources in a given location, sophisticated estimates of potential activity and fecundity can be developed. These spatially-dependent dynamic energy budgets (DEBs) currently ignore the general phenomenon of biological facilitation and, in particular, positive niche construction, wherein a single species improves the suitability of their local habitat for future individuals by modifying their own microclimate. From bed-forming mussels to canopy-forming trees, diverse species can modify their own microclimates by increasing habitat complexity and, in doing so, ameliorate the same physical stressors explicitly considered in mechanistic niche models. To demonstrate the applicability of facilitative habitat in these models, this study selects two habitat-forming organisms and employs DEB-based hindcasting tools to simulate (1) the growth and allocation impacts of temperature regulation in Mytilus edulis (blue mussel) beds and (2) the near-ground micrometeorological impacts of Fagus (beech) tree canopy coverage. A significant reduction in growth and reproductive capacity in blue mussels beyond their optimal temperature and an overall amelioration of temperature and water stress below the beech canopy were observed. Although further research is required to refine the microclimate and micrometeorological impacts assumed for these model organisms, these results suggest that maturing around conspecifics can facilitate persistence in otherwise poor quality habitat. Thus, both species are predicted to have a significant Allee niche, demonstrating the need to incorporate facilitative habitat into mechanistic niche models, especially those used to predict climate change-induced range shifts.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".