ANMPA ecological monitoring indicator guidance
Bibliographic record
Abstract
The Anguniaqvia niqiqyuam Marine Protected Area (ANMPA) is Canada’s first marine protected area (MPA) with conservation objectives based specifically on Indigenous knowledge and is the second MPA designated in the Canadian Arctic under the Oceans Act. A substantial amount of information about the ANMPA is held by Indigenous knowledge holders. This Indigenous knowledge was not accounted for at this meeting but community priorities derived by the ANMPA Working Group were provided to guide and inform discussion. Indigenous knowledge will continue to inform the finalized monitoring plan. In the last five years, increased scientific knowledge and data has been acquired in the MPA that has improved our understanding of ecosystem structure and function. This includes: biodiversity and ecology of fishes (coastal and offshore), winter ecology, including coastal ecosystem assessments, basic under-ice oceanographic parameters, as well as information on snow and ice thickness, completed as part of community-based projects, structural and functional ecosystem relationships for both the inshore and offshore systems of the area (e.g., enhanced understanding of trophic (food web) pathways) occurrence and timing of potentially colonizing species, and the first observation in the ANMPA of Pacific salmon in 2019, continued long-term harvest data regarding Arctic Char (Salvelinus alpinus) in Darnley Bay; and, presence/absence, timing, location, and group composition of Beluga whales (Delphinapterus leucas), Ringed Seals (Pusa hispida), and Bearded Seals (Erignathus barbatus). Two types of data collections are needed: 1. Foundational data: A number of key foundational data gaps exist, which hamper monitoring ecosystem structure and function and limit our understanding of baseline conditions against which change can be assessed and in regard to essential indicators (e.g., benthic habitat distribution). These data gaps include bathymetry, ocean currents, linkages and influences between inshore and offshore processes, marine productivity near Cape Parry, higher trophic level feeding, population dynamics of key species, and the roles these all play in supporting the conservation objectives. 2. Baseline data: Baseline data are essential to make educated assessments of the spatial (i.e., sample locations) and temporal (i.e., seasonal variations and timing) design, as well as frequency (i.e., replication, annual collection) of sampling to underpin effective monitoring. Longer-term data sets (i.e., obtained through monitoring) are also needed in order to identify change from underlying variability (baseline conditions). Indicators were organized into three categories: 1) indicators that provide background environmental context; 2) indicators on biological and food web integrity; and, 3) indicators for stressors and threats. In order to inform management and mitigation measures, monitoring needs to identify how an indicator has changed and why the change has occurred. This can be accomplished by identifying and monitoring a suite of linked indicators that relate to the structure and function of an ecosystem, and regularly testing and revising monitoring hypotheses. A validation and reporting process should be built into a monitoring plan to ensure that the selected indicators provide information relevant to the conservation objectives. Regular review and revision of these indicators is required as new knowledge is acquired in the system or in the event a new anthropogenic activity is introduced to the area (e.g., mining and associated infrastructure and shipping). Monitoring and ongoing assessment of the ecosystem in regards to the conservation objectives must therefore be adaptive to address emerging needs, and existing and new challenges. Important determinants of ecosystem integrity include the following list of indicator types based on the need to understand the structure of the ecosystem (i.e., set the stage), to differentiate natural variability in the system from change, and to develop effective parameters to detect change in the system and potential impacts from stressors (i.e., functional consequences): oceanographic parameters (e.g., temperature and salinity profiles, currents); primary producer abundance and composition; bathymetry and benthic habitat mapping; timing of sea-ice freeze-up and break-up and snow measurements; tropic linkages (i.e., diet studies) and quality and quantity of energy transfer among trophic levels (e.g., food as energy pathways) (i.e., diet studies); and, presence of species and linkages to habitat use (e.g., trophic roles and productivities of potential forage biota for valued species). Core oceanographic parameters and nutrient concentrations was recognized as being foundational to most other indicators. The most important oceanographic parameters to measure were temperature, salinity, fluorescence, and oxygen profiles, nutrients, carbonate chemistry, water clarity/turbidity/photosynthetically active radiation (PAR), oxygen isotope fractionation (to detect river input vs sea ice melt), currents and water movements (e.g., stratification, mixing), ancillary information (e.g., atmospheric drivers), background sounds (e.g., anthropogenic or natural such as during storms); and isoscapes (e.g., to assess movement of materials delivered by coastal erosion and freshwater inputs). Many of the parameters and indicators that were identified have strategies and protocols that can be undertaken from a community-based approach. A monitoring plan for the ANMPA that incorporates scientific and Indigenous knowledge needs to also consider prior and on-going monitoring in the area, be built on policies and guidance already in place, and focus on community priorities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.020 |
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".