Fish assemblage composition within the floodplain habitat mosaic of a tropical lake (Tonle Sap, Cambodia)
Bibliographic record
Abstract
Abstract Understanding how habitat heterogeneity is linked to biodiversity patterns within flood–pulse catchments is needed for their effective conservation. To study those communities, researchers have begun to explore how local environmental factors influence species composition patterns at relatively fine scales within complex habitat mosaics. However, a robust description of the link between communities’ composition and their floodplain habitat characteristics remains poorly articulated. In the Tonle Sap Lake ( TSL ) of Cambodia, we evaluated whether mesohabitat‐scale (1–5 km) factors throughout the floodplain structured the fish assemblage temporally and spatially. First, we determined whether TSL fish assemblage changed seasonally and, if so, what assemblage components were driving that change. Second, we determined whether environmental factors structured fish composition across floodplain mesohabitat patches during the wet season. Third, we evaluated whether dominant species displayed affinities for specific areas within the floodplain during the wet season, potentially identifying critical mesohabitat patches for the fish assemblage. Fish were collected from nine sites along an elevational gradient traversing the floodplain every 3 months from March 2014 through January 2015. Correspondence analysis and analysis of variance were used to assess seasonal variation in the species composition. Canonical correspondence analysis and a mantel test were then used to identify the significance of environmental factors structuring the fish assemblage. Fish assemblages changed across seasons; in particular, migratory species occurrence decreased during the dry season. During the wet season, the fish assemblage was structured spatially by mesohabitat‐scale factors such as vegetation and water quality. Furthermore, the flooded forest mesohabitat directly adjacent to the permanent lake displayed particularly high species richness and abundance. However, a majority of TSL species appeared to be highly mobile, occurring across multiple floodplain mesohabitats. We concluded that fish assemblage structure and floodplain mesohabitat use within TSL may depend strongly upon the maintenance of the natural flow regime. Since the flow regime will probably be modified by upstream dam development, trade‐offs will emerge between the cycle of fishing on which local residents depend, and the hydropower and agricultural benefits of damming the Mekong River system.
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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) | 0.002 | 0.001 |
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".