Changing fish invasion patterns associated with community fish species turnover in 15 Chinese plateau lakes
Bibliographic record
Abstract
Abstract Biological invasions threaten both biodiversity and ecosystem functioning, yet the role of functional and phylogenetic relationships between invaders and the recipient community in invasion success remains controversial. Two competing hypotheses—Darwin's naturalization hypothesis (DNH) and the preadaptation hypothesis (PAH)—offer contrasting predictions about whether invaders succeed by differing from or closely resembling the resident community. Empirical evidence indicates that support for these hypotheses is highly context‐dependent. We addressed four key hypotheses in fish communities in 15 lakes in south‐western China over a 75‐year period (1940–2015), divided into three intervals. First, we examined whether interpretations of Darwin's naturalization conundrum varied with species turnover, quantified by the loss of community integrity (LoI)—defined as the proportional decline of native species due to extirpations and establishments of non‐native species (NNS). Second, we employed a functional‐trait‐based network analysis to determine whether insights at the functional‐group scale offer clearer explanations of invasion patterns than those at a community scale. Additionally, we analysed the temporal patterns of species turnover to test whether they align with the ‘rich‐get‐richer’ or ‘biotic resistance’ hypotheses. Our results revealed that the PAH was supported in early stages of community turnover, though support changed to DNH as LoI increased. Network analyses at the functional‐group scale clarified these dynamic shifts, demonstrating that established NNS progressively occupied central roles within functional groups and increased overall differentiation among groups. Thus, trait‐based network analyses provided deeper insights compared with community‐scale analyses alone. The initial pattern of species turnover supported the ‘rich‐get‐richer’ hypothesis at both lake and functional‐group scales, with species‐rich communities experiencing higher numbers of invaders during the 1940s–1970s. Conversely, this pattern reversed over time, as the number of invaders between 1985 and 2015 was inversely related to resident species richness. By integrating traditional phylogenetic and functional distance analyses with novel functional‐trait‐based network approaches, our study highlights the context‐dependent nature of invasion success. This integrative framework advances our understanding of community assembly processes. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".