Temporal and spatial patterns of zooplankton abundance and productivity in Lake Erie using an Optical Plankton Counter
Bibliographic record
Abstract
Distributions of crustacean zooplankton in Lake Erie were examined in 1998 and 1999 with an Optical Plankton Counter (OPC). Chapter 1 provides a new calibration of the OPC for use in freshwater and contrasts it with a previously developed calibration. Chapter 2 provides the first ever reported use of an OPC to examine seasonal dynamics of freshwater zooplankton. I found higher crustacean abundance and biomass (2–3x) in the central basin than the eastern basin during both years. Seasonal peak abundance and biomass occurred during June 1998 in both basins however during 1999 the east basin peak was not observed until July. Spring samples were dominated by cyclopoid copepods which gave way to large numbers of cladocerans in June which were in turn replaced by copepods, mostly calanoid copepods, in the summer and fall in both basins. Although the early season cladoceran peak in abundance and biomass is consistent with historical patterns in the lake it appears that there has been a shift to smaller Bosmina species from the large Daphnia species that were common during 1983–1987. Seasonal production estimates derived from the OPC samples are consistent with independent estimates using the egg ratio technique and indicate much greater production in the central basin than in the east basin during 1998 and 1999. Chapter 3 provides an overview of three commonly used techniques for spatial data analysis (Autocorrelation, Spectral Analysis, Wavelets) and one new technique (Principal Coordinates of Neighbour Matrices). I tested each technique on simulated data sets consisting of simple known spatial structure, complex known spatial structure, and real data with unknown spatial structure. All methods were able to identify the simple structures when presented in isolation, however the autocorrelation technique was unable to identify small-scale structure when present along with larger scale structure. Chapter 4 examines spatial structure in crustacean zooplankton of Lake Erie. Zooplankton showed patchy distributions at all spatial scales ranging from 7.5m to 10 000m. Principal Coordinates of Neighbour Matrices (PCNM) analyses revealed that most predictable repeating structure in both zooplankton biomass and chlorophyll biomass was restricted to spatial scales in excess of 504m regardless of the time of year. At these scales structuring mechanisms are likely physical in nature and relate to broadscale circulation patterns. The ability of the PCNM model to describe spatial structure varied seasonally with the lowest amount of variability being explained during mid-summer (July cruises) in both years. Total variance increased with depth as zooplankton distributions became patchier. (Abstract shortened by UMI.)
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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 | 0.001 | 0.001 |
| 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 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".