Effects of a mining‐altered environment on individual fitness of amphibians and reptiles
Bibliographic record
Abstract
Abstract Pollution can affect wildlife directly through toxicity and indirectly through changes in biotic and abiotic factors, however, how these mechanisms interact in affecting free‐ranging animals remains poorly understood. By examining effects on individual fitness proxies, we aimed to determine the mechanisms behind documented amphibian and reptile declines in barren landscapes where vegetation was nearly decimated due to atmospheric metal and sulfur deposition from smelting operations in Sudbury, Ontario, Canada. We examined individual characteristics of snakes (Thamnophis sirtalis, Storeria occipitomaculata ) and frogs (Lithobates clamitans , L . pipiens , L . septentrionalis ) in replicated barren and reference sites, including standard metabolic rates (in snakes), body condition (all species), female reproductive status (snakes), limb deformity (frogs), and fluctuating asymmetry (all species). Frogs in barren sites (except L . septentrionalis ) were in poor body condition, and all but one case of limb deformities in frogs were found in barren sites. However, neither frogs nor snakes exhibited elevated frequency of fluctuating asymmetry in barren sites. Standard metabolic rates ( SMR ) were not elevated in either snake species, suggesting that metal toxicity does not present an energetic burden. Instead, female T . sirtalis from barren sites exhibited lower SMR and were in poor body condition, which is a predicted response to reduced food intake. By contrast, SMR and body condition of S . occipitomaculata were not different between barren and reference sites. While physiological responses differed between T . sirtalis and S . occipitomaculata , both species exhibited lower reproductive rates (proportion of pregnant females) in barren sites compared to reference sites. Thus, energy deficiency may explain reduced reproduction in T . sirtalis while reduced availability of suitable gestation microhabitat may be a limiting factor for S . occipitomaculata . Populations in smelting‐impacted habitats include individuals with potentially compromised fitness. The low values of our measured fitness proxies appear to have resulted from pollution‐induced changes in biotic and abiotic factors, in addition to (for frogs) or rather than (for snakes) from the direct toxicity. These results suggest that impact assessment based solely on toxicological data and species distributions can underestimate the full impacts of pollution, highlighting the need for integrated examination at multiple levels of biological organization.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".