Weak effects of habitat type on susceptibility to invasive freshwater species: an Italian case study
Bibliographic record
Abstract
ABSTRACT Introduction of alien species is one of the major threats to aquatic biota and knowledge of the major correlates of their occurrence is pivotal in planning reliable conservation strategies. To understand whether specific freshwater habitats are more likely to be invaded than others, a dataset on the occurrence of 1604 species in 54 taxonomic groups from 181 sites across the Italian peninsula was gathered. The EUNIS habitat classification was used, selecting for the study's seven habitat types at the second EUNIS level, including lentic (EUNIS C1; 64 sites), lotic (EUNIS C2; 99 sites) and highly artificial (EUNIS J5; 18 sites) habitats. The aim of the study was to test whether the overall number of alien species and the proportion of alien species for each taxonomic group differed between habitat types and could be explained by environmental, human‐mediated, or climatic factors. Using generalized linear mixed effect models to account for potential confounding factors, only average air temperature of the site was a significant positive predictor of the occurrence of alien species, regardless of habitat type, species richness, and other climatic variables. A direct effect of temperature could be excluded given the origin of alien species, mostly from colder areas than Italy. Thus, an indirect effect could be hypothesized at the Italian latitudes, with warmer areas potentially more likely to be visited by tourists than colder areas. If this hypothesis is confirmed, the results of the analyses call for a compromise between the maintenance of recreational activities in the wild and the preservation of a natural environment to prevent the arrival and spread of alien species. On the other hand, no further recommendations can be implemented regarding habitat susceptibility to alien species. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".