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Record W2995875002 · doi:10.5070/v42811049

Ecological Factors Driving Uptake of Anticoagulant Rodenticides in Predators (Abstract)

2018· article· en· W2995875002 on OpenAlexaff
Sofi Hindmarch, E. Elliott John

Bibliographic record

VenueProceedings - Vertebrate Pest Conference · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsCanadian Sport Centre PacificUniversity of the Fraser ValleyEnvironment and Climate Change Canada
Fundersnot available
KeywordsPredationEcologyPredatorGeographyBiology

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.034
GPT teacher head0.265
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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