Unifying Species Distributions, Community Science and the “Natural Removal Experiment” to Investigate Species Interactions at Broad Geographic Scales
Bibliographic record
Abstract
As species interactions influence species distributions, attempts to work backwards from observed species distributions to infer the effect of a potential interaction have been enticing. The “natural removal experiment,” an approach that tests for patterns consistent with competition by comparing a species’ habitat relationships in sympatry and allopatry with a potential competitor, has held promise, but has often been limited by the scales of data required. The recent expansion of community science-based datasets invites renewed opportunity for investigation using this approach. We revitalize the natural removal experiment by applying it to ask whether the distribution of the Chestnut-backed Chickadee (Poecile rufescens; CBCH) is consistent with competition with the Black-capped Chickadee (Poecile atricapillus; BCCH) in urban areas. Using data from the community science project eBird, we compared relationships of CBCH relative abundance to urban- and forest-related variables in individual urban-centres and across regions of allopatry and sympatry with the BCCH. As predicted under competition, we found that in allopatry, the CBCH adopted habitat relationships similar to the less forested, more urban habitats of the BCCH at both the urban-centre and regional scale. By applying predictive modeling to “imagine” either the absence or ubiquity of BCCH across the studied range of CBCH, we found that sympatry with the BCCH suppressed CBCH abundance in urban areas, and that in their absence CBCH abundance increased, consistent with competitive release. These lines of evidence suggest that the observed distribution of CBCH is consistent with that expected under competition with the BCCH in urban areas. Expanding beyond this case study, we discuss important considerations in the application of this approach, which presents a significant opportunity for researchers to harness community science to conduct more powerful investigations of species interactions extending to broad spatial scales.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| 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".