The effects of habitat filtering and non‐habitat processes on species spatial distribution vary across life stages
Bibliographic record
Abstract
PREMISE OF THE STUDY: Habitat filtering and non-habitat processes are two major processes affecting spatial distributions of species. Because trees at different life stages perform differently, the life stage of tree species could play an important role in shaping the spatial distribution of species and community assembly. Here, we examined the possible changes of spatial distributions of species and evaluated the shifts in the relative importance of habitat filtering and non-habitat processes across life stages in a 50-ha subtropical forest plot in China. METHODS: We modeled species distribution with and without life stages using three point process models. The performance of these models, with and without considering life stages, was evaluated by comparing the species-area curve and the degree of clustering. The relative effects of habitat filtering and non-habitat processes across life stages were quantified using a spatial variance decomposition method. KEY RESULTS: The incorporation of life stage considerably improved the goodness-of-fit of these point process models at both the community and species levels. Non-habitat processes explained about 90% of the total variation in spatial distribution, while habitat filtering explained about 10%. The relative importance of habitat filtering only increased slightly from sapling to adult stages. CONCLUSIONS: Point process models performed better when life stages are included, indicating the importance of considering life stage when modeling spatial distributions for understanding community assembly. The finding that habitat acts weakly and non-habitat processes act dominantly in determining spatial distributions of species suggests a strong dependence of spatial patterns on non-habitat processes.
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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 | 0.002 |
| 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".