Moving targets: safeguarding migratory pelagic species in a changing ocean
Bibliographic record
Abstract
Migratory and other highly mobile species, which rely on multiple, often spatially discrete and heterogeneous environments throughout their life cycles, play critical roles in the functioning and dynamics of communities and ecosystems. However, migratory species face multiple anthropogenically driven threats to their survival as they move between and use different areas. Understanding migratory species distributions and drivers of those distributions is essential to develop effective strategies that reduce or remove anthropogenic threats to their wellbeing and persistence. Yet, the spatial-temporal dynamism of migratory species movements and distributions, particularly under changing conditions, presents additional challenges for researchers and managers. Here, I use distribution modelling and quantitative analysis tools to examine the changing distributions of suitable habitat for marine pelagic species over horizontal (longitude and latitude) and vertical (depth) space, time and between ocean climates, and deconstruct how modelled distributions of prey can inform the design and management of area-based management tools for migratory seabird predators. First, I applied a species distribution model (Maxent) to explore the average monthly spatial-temporal dynamics of suitable habitat of the migratory pelagic forage fish capelin (Mallotus villosus) in Atlantic Canadian waters. I found that the distribution of habitat suitability varied across horizontal and vertical axes and among monthly models. Furthermore, I found that the importance of modelled covariates such as temperature varied between models. Next, I used a series of spatial and temporal analyses to examine how shifts in the North Atlantic Oscillation influenced the availability of suitable habitat over horizontal and vertical axes between 1998 and 2014. I found substantial stability in the location of predicted suitable capelin habitat between positive and negative phases. However, in six of the ten months modelled, predicted habitat suitability scores showed a declining trend over time. Finally, I present a framework for explicitly integrating changing prey availability into adaptive area-based management for seabirds throughout their migratory cycle. This framework focuses on using existing modelling, forecasting, and analysis tools to identify potential seabird foraging spaces, and allows for the input of new knowledge and data to provide managers with the best available information for iterative and adaptive decision-making.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".