Ecological Factors Driving Uptake of Anticoagulant Rodenticides in Predators (Abstract)
Bibliographic record
Abstract
In recent years, anticoagulant rodenticides have emerged as an important factor reducing the survival of many birds of prey and some predatory mammals (Berny and Gaillet 2008, Jacquot et al. 2013, Poessel et al. 2015, Serieys et al. 2015, Murray 2017). Understanding the ecological factors driving the exposure of predators is a key component in assessing the risk posed by anticoagulant rodenticides. We have reviewed the literature to better understand and synthesize the ecological factors driving AR exposure in predators, focusing on landscape and environmental management, traits of the exposed predators and the most common exposure pathways. On a global scale, the large output of ARs, in particular the more toxic and persistent second generation ARs into urban and agricultural settings and the relatively large footprint of these landscapes, has led to widespread AR exposure of many species, ranging from insects to large carnivores (Dowding et al. 2010, Sánchez-Barbudo et al. 2012, Serieys et al. 2015, Alomar et al. 2018). The methods of applying ARs vary widely, and can range from fastening bait stations to the outside or inside perimeter of buildings, to placing AR bait in underground burrows in fields, to mass application of ARs in agricultural fields and orchards (Corrigan 2001, Rattner et al. 2014). The scale of AR field application varies from a couple of hectares to mass application of ARs on a regional scale (3,000 - 10,000 km²) to control rodent outbreaks (Jacquot et al. 2013, Baldwin et al. 2014). General inferences can be made with regards to the traits of the most affected predators. We determined that at-risk predators tend to be nocturnal opportunistic predators for which rodents are a key dietary component, seasonally or year-round (Birks 1998, Jacquot et al. 2013, Hindmarch and Elliott 2015, Serieys et al. 2015). They also tend to be non-migratory and occupy habitats within, or in close proximity to landscapes that are heavily influenced by human activities such as intensive agriculture or urban areas (Birks 1998, Way et al. 2006, Elmeros et al. 2011, Christensen et al. 2012, Cypher et al. 2014, Nogeire et al. 2015, Poessel et al. 2015, Serieys et al. 2015, Hindmarch et al. 2017). Predators that consume rats in urban environments are disproportionately affected by ARs (Lambert et al. 1981, Hindmarch and Elliott 2014, 2015). As our understanding of how ARs are transferred up the food-chain is still limited, there is a need to further comprehend the extent to which non-target prey are being exposed to ARs in different landscapes, as we are frequently documenting AR residues in predators that do not typically prey on rodents (Dowding et al. 2010, Ruiz-Suárez et al. 2014, López-Perea et al. 2015). We recommend a focus on urban landscapes, where to date no exposure data has been collected on non-target prey. We also have a very limited understanding of non-target prey exposure in the urban-wildland/agricultural interface where opportunistic predators are known to hunt both habitat types interchangeably. Finally, we need to decipher whether the mounting evidence of exposure in predators translates into any sub-lethal and population levels effects. For a more in-depth review of this topic, we refer to the chapter “Ecological Factors Driving Uptake of Anticoagulant Rodenticides in Wildlife" (Hindmarch and Elliott 2018) in the book Anticoagulant Rodenticides and Wildlife (van den Brink et al. 2018).
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".