Patterns of Species Co-occurrence, Null Models, and the Influence of Biotic and Abiotic Factors: A Species Pairwise Approach
Bibliographic record
Abstract
How natural communities are structured and what factors influence such structure has been a pervasive subject in ecology for more than one century. Among several approaches used to determine the structure of communities, species co-occurrence analysis has been a subject of controversy since the classical debate between Diamond and Connor-Simberloff. Their main contrasts were whether ecological communities differed from random groupings of species, the role biotic factors had in generating the observed assemblage patterns, and the methods to discern structures of communities different from random (null models). Here, my main goals were to improve approaches to species co-occurrence analysis, to improve our ability to discern whether communities are structured non-randomly, and to extend them to testing hypotheses related to ecological mechanisms. First, I analyzed co-occurrence patterns using a species pairwise approach, different from the typical whole-community approach, to discern the relationship between multiple species of lake fishes across thousands of lakes in Ontario. Specifically, I tested whether the co-occurrence patterns observed across multiple species pairs from several different watersheds exhibit consistent patterns. I used subsets of species to test specific hypotheses related to the roles of predation, competition, and environmental filtering in structuring fish communities. Following, I examined the influence of habitat size on the observed co-occurrence patterns. I compared species co-occurrence patterns across different categories of lake area and depth to determine whether and how these variables affected the associations observed among multiple species pairs. Finally, I determined whether species-pair associations can be predicted from simple ecological traits. I analyzed the relationship between three biologically relevant traits (body size, temperature preference and trophic level) and the co-occurrence values observed between each species pair. I detected a significant effect of temperature preference and trophic level on the observed co-occurrence patterns. Unexpectedly, body size did not exhibit a significant effect on the co-occurrence patterns. My research showed the strong effect of interspecific predation on community composition, that signals of non-random community assembly are strongly dependent on the abiotic conditions, that analyses at the whole-community level may dilute underlying ecological signals, and that species co-occurrence patterns reflect differences in species traits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".