Benthic and pelagic food resources for zooplankton in shallow high‐latitude lakes and ponds
Bibliographic record
Abstract
Summary 1. Shallow lakes and ponds are a major component of the northern landscape and often contain a high zooplankton biomass despite clear waters that are poor in phytoplankton. 2. In this study we quantified zooplankton food sources and feeding rates in the shallow waters of two contrasting high‐latitude biomes: subarctic forest tundra (Kuujjuarapik, Quebec) and high arctic polar desert (Resolute, Nunavut). Five substrate types were tested (beads, bacteria, picophytoplankton, filamentous plankton and microbial mats). Special attention was given to the role of benthos, a component that is usually poorly integrated into models of aquatic foodwebs. 3. Consistent with observations elsewhere in the circumpolar region, high concentrations of adult macrozooplankton occurred in all sites (up to 17 100 crustaceans m −3 ) while phytoplankton concentrations and primary productivity were low. The communities were composed of multiple species, including Daphnia middendorfiana , Hesperodiaptomus arcticus , Leptodiaptomus minutus , Artemiopsis stefanssoni and Branchinecta paludosa . 4. Detritus made 89–98% of the planktonic resource pool and bacteria contributed the highest biomass (up to 29 mg C m −3 ) of the planktonic food particles available to zooplankton. Benthic resources were dominated by microbial mats that grew in nutrient‐rich conditions at the base of the ponds and which dominated overall ecosystem biomass and productivity. 5. All species were flexible in their feeding but there were large, order of magnitude differences in clearance rates among taxa. These differences likely resulted from different grazing strategies among cladocerans, copepods and fairy shrimps, and possibly also from adaptation to specific food types and size ranges that occur locally in these waters. 6. The subarctic cladocerans Ceriodaphnia quadrangula and D. middendorfiana , and the arctic fairy shrimp B. paludosa were observed to graze directly on the microbial mats and the feeding experiments confirmed their assimilation of benthic substrates. The other zooplankton species showed a more pelagic feeding mode but were capable of using microbial mat filaments, thus may be indirectly linked to benthic processes via resuspension. 7. Our study indicates that the classical aquatic food web in which phytoplankton provide the sole production base for grazers does not apply to northern shallow lakes and ponds. Instead, microbial mats increase the physical complexity of these high latitude ecosystems and likely play a role in sustaining their high zooplankton biomass.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".