Integrating hydrological connectivity and zooplankton composition in Arctic ponds and lakes
Bibliographic record
Abstract
Abstract Arctic landscapes are characterised by their numerous ponds and lakes. With increased climate warming and consequent changes in hydrological connectivity, the ecology of many of these waterbodies is expected to change. We sampled 13 ponds and 22 lakes for zooplankton and various limnological and physical environmental variables in the vicinity of Cambridge Bay, Nunavut (69.1169° N, 105.0597° W), with the aim of testing how well the degree of hydrological connectivity explained patterns in species composition and abundance and contributed to crustacean production. Ponds were hydrologically isolated while the lakes were arranged in four lake chains and ranged from the headwaters to highly inter‐connected lakes receiving water from one to 768 upstream lakes. In all sites combined, 77 zooplankton species were found, including 56 rotifers, six copepods, 11 cladocerans, two fairy shrimp species, a mysid, and a tadpole shrimp. We show that the zooplankton communities differed between hydrologically isolated (ponds) and connected (lakes) systems, with 17 species unique to ponds and 20 to lakes. Furthermore, the communities were more similar within lake chains and hence in lakes closer to each other. In ponds, distances between waterbodies had no impact on community similarity. Zooplankton abundance was higher in lakes (255 ind/L) than in ponds (65 ind/L) due to the higher number of rotifers that accounted for nearly 80% of the zooplankton abundance in lakes. In ponds, rotifers, cladocerans, and copepods were equally abundant. In terms of biomass, cladocerans represented 65% of total biomass in all waterbodies except for a chain of deep lakes that had abundant fish communities. In these lakes, cladoceran abundance and biomass were low and probably limited by fish predation. Zooplankton production was higher in ponds (4.6 mgC m −3 day −1 ) than in lakes (1.7 mgC m −3 day −1 ), and within lakes was lowest in the chain composed of large lakes (1.3 mgC m −3 day −1 ). The high production in ponds was closely linked to high zooplankton biomass and further explained by high gross primary production supported by relatively high nutrient concentrations in these shallow systems. Our study emphasises that hydrological connectivity is key to shaping zooplankton communities, although fish presence also appears to affect them. It shows that isolated ponds (that currently account for c 83% of all waterbodies on southern Victoria Island) are hotspots of aquatic biological productivity that probably play an essential role in Arctic freshwater landscapes. Finally, our study provides critical baseline information on the current composition of Arctic zooplankton communities important for estimating climate change impacts.
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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) | 0.000 | 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".