Life cycle bioenergetics of the gray seal (Halichoerus grypus) in the Baltic Sea: Population response to environmental stress
Bibliographic record
Abstract
Wildlife population dynamics are shaped by multiple natural and anthropogenic factors, including predation, competition, stressful life history events, and external environmental stressors such as diseases and pollution. Marine mammals such as gray seals rely on extensive blubber layers for insulation and energy storage, making this tissue critical for survival and reproduction. This lipid rich blubber layer also accumulates hazardous fat soluble pollutants, such as polychlorinated biphenyls (PCBs), that can directly impact adipose function or be mobilized during periods of negative energy balance or transferred to offspring to exert further impacts on target tissues or vulnerable life stages. To predict how marine mammals will respond to ecological and anthropogenic stressors, it is necessary to use process-based modelling approaches that integrate environmental inputs, full species life history, and stressor impacts with individual dynamics of energy intake, storage, and utilization. The purpose of this study was to develop a full lifecycle dynamic energy budget and individual based model (DEB-IBM) that captured Baltic gray seal physiology and life history, and showcase potential applications of the model to predict population responses to select stressors known to threaten gray seals and other marine mammals around the world. We explore variations of three ecologically important stressors using phenomenological simulations: food limitation, endocrine disrupting chemicals that reduce fertility, and infectious disease. Using our calibrated DEB-IBM for Baltic gray seals, we found that continuous incremental food limitation can be more detrimental to population size than short random events of starvation, and further, that the effect of endocrine disruptors on population growth and structure is delayed due to bioaccumulation, and that communicable diseases significantly decrease population growth even when spillover events are relatively less frequent. One important finding is the delayed effect on population growth rate from some stressors, several years after the exposure period, resulting from a decline in somatic growth, increased age at maturation and decreased fecundity. Such delayed responses are ignored in current models of population viability and can be important in the correct assessment of population extinction risks. The model presented here provides a test bed on which effects of new hazardous substances and different scenarios of future environmental change affecting food availability and/or seal energetic demands can be investigated. Thus, the framework provides a tool for better understanding how diverse environmental stressors affect marine mammal populations and can be used to guide scientifically based management.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".