Resource niche partitioning and overlap among native and non-native lizards in an urban environment
Bibliographic record
Abstract
Non-native species must overcome a number of abiotic and biotic filters to successfully establish and spread into novel ecosystems. Their establishment and spread depend on the ability to acquire resources while avoiding or outcompeting native species. The Mediterranean House Gecko ( Hemidactylus turcicus Linnaeus, 1758) has been widely introduced into urban areas across tropical and temperate regions, yet studies documenting their resource use relative to native lizard species are still lacking. We examined patterns of resource use between Hemidactylus turcicus and a recipient lizard assemblage comprised of Green Anole ( Anolis carolinensis Voigt, 1832), Little Brown Skink ( Scincella lateralis Say, 1823), and Five-lined Skink ( Plestiodon fasciatus Linnaeus, 1758) in an urban ecosystem of eastern Texas. We used functional traits to investigate resource partitioning between Hemidactylus turcicus and native lizards regarding habitat, diet, and isotopic niche dimensions. Differences in resource use between Hemidactylus turcicus and native lizards were observed, as they utilized a greater array of arboreal perch types at lower temperatures, had generalist diet, and occupied a broader isotopic niche space compared to the native lizards. Overall, Hemidactylus turcicus appears a functionally unique species capable of exploiting novel resources along multiple niche axes possibly facilitating their establishment and spread into urban ecosystems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".