Long term environmental monitoring using locally-relevant indicators: muskrat (ondatra zibethicus) population dynamics in Old Crow and recreational ecosystem services in Ottawa
Bibliographic record
Abstract
Multi-decade environmental monitoring is necessary to understand many of the effects of anthropogenic activities, yet the success of many long term monitoring programs has been limited and sporadic. In this thesis, I demonstrated the strengths of participatory approaches and locally-relevant environmental indicators as a solution for long term monitoring. In Chapter 1, I described the limited success of many long term monitoring programs, and outlined the current understanding of best practices in monitoring. In Chapter 2, I analyzed the impact of participatory approaches, together with innovative portable digital technologies, in a sample of publications and case studies describing environmental monitoring programs. I found the use of digital data entry can increase a program's management relevance while participatory adaptive monitoring, i.e. the collaborative definition of program questions, objectives, conceptual models, and approaches, improved program sustainability. I applied these principles in Chapter 3 by monitoring the environmental determinants, and cyclicity, of muskrat (Ondatra zibethicus) population dynamics in the Old Crow Flats (OCF), Yukon. I interpreted local ecological knowledge (LEK) in the development of questions, conceptual models, and interpretation of results. I found that LEK identified advancing ice phenology as a concerning source of environmental change, Landsat imagery confirmed 0.26 days/year of more open water over the past 31 years, and aerial and field surveys found a negative association between the open water season and muskrat densities. In Chapter 4 I compiled 219 time series of up to 8 years of muskrat abundance in the OCF to describe the first traveling wave of abundance in muskrats. Using spatial patterns of landscape resistance to muskrat movement, genetic relatedness, and population synchrony, I found this wave was likely caused by a combination of landscape obstacles and directional dispersal. In Chapter 5, I identified a parallel indicator that is locally-relevant for Ottawa, recreational ecosystem services on the Rideau Canal Skateway, and projected the availability and use of those services under climate warming. I found Ottawa's ice phenology to be shifting twice as rapidly as Old Crow's (0.5 days/year), and found this to be linked to an accelerated decline in the use of this cultural ecosystem service. Whether ice or animal, for most people the most recognizable and memorable forms of environmental change will occur in locally-relevant indicators. These indicators are the 'low hanging fruit' of environmental monitoring; with little resources they can form the basis of monitoring programs that stand the test of time.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".