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Record W3152087912 · doi:10.1093/conphys/coab012

Horn today, gone tomorrow—dehorning as an anti-poaching practice for white rhinos

2021· article· en· W3152087912 on OpenAlexaff
Christine L. Madliger

Bibliographic record

VenueConservation Physiology · 2021
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsCarleton University
Fundersnot available
KeywordsFrench hornBiologyPoachingWhite (mutation)RhinocerosAgroforestryZoologyEcology

Abstract

fetched live from OpenAlex

Since the beginning of the 20th century, rhino populations have declined from 500 000 individuals to just 27 000. That means they are at just 5% of their population size since 1900! With increasing poaching pressure driven by a demand for rhino horn, the national parks and private reserves that are integral to the remaining rhinos’ survival often need on-the-ground security to protect them. But this type of protection can be expensive. As an alternative, some reserves in South Africa have turned to dehorning—a veterinary procedure that removes a portion of the rhino’s horn. But should managers worry that dehorning causes stress that could impact the welfare of the rhinos? This question is particularly pressing because when managers use dehorning as an anti-poaching tactic, they must trim the rhino’s horns every 1–3 years over its lifetime. Each time the procedure is performed, the rhino needs to be tranquilized. This repetition is necessary because the horns are made of keratin, the same material found in fingernails, claws and hooves. And just like those structures, rhino horns grow continuously. Researchers at the University of Brighton investigated whether the dehorning procedure causes long-term stress for wild white rhinos by analysing hormones in their faeces before and after the procedures. They measured stress hormones called glucocorticoids, which have been shown to rise when rhinos are faced with stressors such as translocation or captivity. They also measured reproductive hormones, which tend to be suppressed under conditions of stress. If the physiology of the rhinos is impacted, it could mean they will face issues when they try to mate and produce offspring. The rhinos in the study were darted from a helicopter and stabilized before having both of their horns trimmed, which is no easy task! The veterinary team must use a chainsaw to remove the horn material and then smooth the edges with a disc sander. They then reverse the effects of the anaesthetic to allow the rhinos to fully recover. This entire process takes about 15–20 minutes. Penny et al. (2020) found that dehorning did not cause a long-term elevation in stress hormone levels compared with rhinos who had not undergone the procedure. They also showed that repeatedly dehorning over multiple years did not lead to increased stress hormone levels or the suppression of reproductive hormones in either male or female rhinos. The researchers outline that further studies are needed to monitor the behaviour of rhinos after dehorning and to confirm that the procedure does not impact rhinos differently in other geographic areas or habitat types. Still, these results are encouraging. Dehorning is considered a relatively drastic measure, and so knowing that the rhinos’ physiology is not negatively affected by these procedures can help managers feel more confident about choosing it as an anti-poaching measure when the pressure to protect the rhinos’ safety is so high. Illustration by Erin Walsh; Email: ewalsh.sci@gmail.com Editor: Jodie L. Rummer

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.031
GPT teacher head0.314
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes1
Has abstractno

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