River Herring Conservation in Freshwater: Investigating Fish Reproductive Success and the Educational Value of Citizen Monitoring Programs
Bibliographic record
Abstract
Over the last century anadromous alewife (<em>Alosa psuedoharengus</em>) and blueback herring (<em>Alosa aestivalis</em>), collectively called river herring, suffered drastic declines throughout their range from Newfoundland (Canada) to North Carolina (USA). A 2011 petition to include river herring in the Endangered Species Act (ESA) was rejected, partly due to inadequate information towards identifying coast-wide population status. Additionally, knowledge gaps were identified with basic ecology of the river herring life cycle in freshwater, including species reproductive strategies. In Chapter 2, I investigated how body size, spawning arrival time, and sex influence river herring reproductive success. I collaborated with the Massachusetts Division of Marine Fisheries to introduce adult river herring (421, 266, and 410 individuals in 2013, 2014, and 2015, respectively) into Pentucket Pond (Massachusetts, USA), which historically had river herring but is currently inaccessible to upstream migrants. Using fin clips from stocked adult fish and juveniles collected in the lake each summer, I genotyped individuals and constructed pedigrees with 15 microsatellites. River herring had small (mean =1.1) families and spawned multiple times with multiple mates from May to June. Females were more successful than males. Earlier arrival and larger body size were independent indicators of reproductive success. These results provide critical river herring life history information for the freshwater component of population models that will inform management of this at-risk species. Presently, most river herring populations are monitored using data from citizen counts of spawning adults entering freshwater. Involving citizen watershed groups in data collection and may provide ancillary benefits beyond collection of population-level count data. In Chapter 3, I used pre-and post-surveys to assess how involvement in one citizen monitoring program influenced participants’ environmental knowledge, attitudes, and behaviors. Participants began the program with high scores for broader scientific and pro-environmental attitudes and pro-environmental behaviors. After the program, participants reported increased connection to nature, citizen science involvement, river herring knowledge, and engagement in outdoor recreation. For participants, engagement with nature was the most important program benefit. These results provide an additional case study to the citizen science literature and demonstrate that citizen science programs can help participants connect with the environment.
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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 | 0.001 | 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 | 0.001 |
| 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".