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Record W3092537588 · doi:10.69649/pachyderm.v61i.8

Evaluating uncertainty in estimates of large rhinoceros populations

2020· article· en· W3092537588 on OpenAlexfundno aff
Sam M. Ferreira, Danie Pienaar

Bibliographic record

VenuePachyderm · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersSouth African National ParksMcGill University
KeywordsRhinocerosGeographyEnvironmental scienceEcologyBiology

Abstract

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Estimates of the numbers of living rhinoceroses inform management interventions. Several techniques assist authorities in obtaining estimates. For large populations, authorities use sample-based methods. Estimates for the number of rhinos living in Kruger National Park (Kruger) make use of sample-based block surveys. Critics of this approach allege that the authorities place sample blocks mostly in areas with high rhino numbers and that this, together with correction for various biases, inflates estimates. The critics also claim that the percentage confidence intervals (PCIs) associated with estimates are too large and propose total area counts as an alternative. We assess these criticisms by comparing the results of a sample-based survey with those of a near concurrent total count. We found that sample surveys appeared to focus on areas with higher rhino densities, but rhino movements in and out of the survey area confounded results. Moreover, total counts do not produce reliable estimates when surveyors fail to account for biases inherent to all sampling procedures. Bias corrections used by sample surveys most likely underestimate the number of rhinos that surveyors miss, contrary to the allegations of critics that sample-based techniques inflate population estimates. Estimates that transparently report uncertainties detected a significant decline in white rhinos from 8,968 (95% CI: 8,394–9,564) in 2013 to 4,116 (95% CI: 2,994–5,726) in 2018. The trends in the black rhino population also indicate a decline from 627 (95% CI: 588–666) in 2009 to 291 (95% CI: 151–441) in 2018. Conducting block-based sample surveys for large populations that correct for biases provides useful information for decision makers. Given that South Africa, specifically Kruger makes substantial contributions to continental rhino numbers, reporting to international bodies such as CITES should transparently include the uncertainties associated with population estimates. Résumé Les estimations du nombre de rhinocéros vivants orientent les interventions de gestion. Plusieurs techniques permettent aux autorités d’obtenir des estimations. Pour les populations importantes, les autorités utilisent des méthodes basées sur des échantillons. Les estimations du nombre de rhinocéros vivant dans le parc national du Kruger (Kruger) s'appuient sur des enquêtes par sondage par blocs. Les détracteurs de cette approche allèguent que les autorités placent les blocs d'échantillonnage principalement dans des zones où le nombre de rhinocéros est élevé et que cela, combiné à une correction pour divers biais, gonfle les estimations. Les critiques affirment également que les intervalles de confiance en pourcentage (ICP) associés aux estimations sont trop importants et proposent comme alternative des comptages totaux des aires. Nous évaluons ces critiques en comparant les résultats d'une enquête par sondage aux résultats d'un comptage total qui a eu lieu presque simultanément. Nous avons constaté que les enquêtes par sondage semblaient se concentrer sur les zones à haute densité de rhinocéros, mais les résultats ont été faussés par le mouvement des rhinocéros dans et hors de la zone d'enquête. De plus, les comptages totaux produisent des estimations non fiables lorsque les enquêteurs ne tiennent pas compte des biais inhérents à toutes les procédures d’échantillonnage. Les corrections de biais utilisées par les enquêtes par sondage sous-estiment probablement le nombre de rhinocéros que les enquêteurs manquent, contrairement aux allégations des critiques selon lesquelles les techniques par sondage gonflent les estimations de population. Des estimations qui rapportent les incertitudes de manière transparente ont détecté une baisse significative des rhinocéros blancs de 8 968 (IC à 95%: 8 394–9 564) en 2013 à 4 116 (IC à 95%: 2 994–5 726) en 2018. Les tendances démographiques des rhinocéros noirs indiquent également un déclin de 627 (IC à 95%: 588–666) en 2009 à 291 (IC à 95%: 151–441) en 2018. Les enquêtes par sondage par blocs qui corrigent les biais et qui sont utilisées pour d’importantes populations, fournissent des informations utiles aux décideurs. Étant donné que l'Afrique du Sud et Kruger contribuent de manière substantielle au nombre de rhinocéros continentaux, les rapports aux organismes internationaux tels que la CITES devraient inclure les incertitudes associées aux estimations des populations de manière transparente.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.528

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.208
GPT teacher head0.377
Teacher spread0.169 · 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.

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

Citations4
Published2020
Admission routes1
Has abstractyes

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