The Subhabitat Dependence of Biogeographic Pattern
Bibliographic record
Abstract
We introduce and test the subhabitat dependence hypothesis (SDH) in biogeography. This hypothesis posits that biogeographic pattern within a region differs when determined with species abundance data from different subhabitat types. It stems from the notion that the main abiotic factors that drive species distribution in different subhabitat types across a biogeographic region often vary differently across space. To test the SDH, we measured the abundance of algae and sessile invertebrates in two different subhabitats (high intertidal zone and mid-intertidal zone) at eight locations along the Atlantic Canadian coast. We conducted multivariate analyses of the species abundance data to compare alongshore biogeographic pattern between both zones. For both subhabitat types, location groupings based on community similarity not always responded to geographic proximity, leading to biogeographic patchiness to some extent. Nonetheless, both biogeographic patterns were statistically unrelated, thus supporting the SDH. This lack of concordance was most evident for southern locations, which clustered together based on high-intertidal data but showed considerable alongshore patchiness based on mid-intertidal data. We also found that the ordination pattern of these eight locations based on sea surface temperature data was significantly related to biogeographic pattern for the mid-intertidal zone but not for the high intertidal zone. This finding supports the rationale behind the SDH due to the longer periods of submergence experienced by the mid-intertidal zone. Overall, we conclude that biogeographic pattern within a region can depend on the surveyed subhabitat type. Thus, biological surveys restricted to specific subhabitats may not properly reveal biogeographic pattern for a biota as a whole or even just for other subhabitats. As many studies generate biogeographic information with data only for specific subhabitats, we recommend testing the SDH in other systems to determine its domain of application.
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.
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) | 0.000 | 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".