Evidence map›Paper›PMID 36261485›Full record

ArticleScientific reports2022

Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables.

Owusu Fordjour Aidoo, Fangyu Ding, Tian Ma, Dong Jiang, Di Wang, Mengmeng Hao, Elizabeth Tettey, Sebastian Andoh-Mensah, Kodwo Dadzie Ninsin, Christian Borgemeister

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Owusu Fordjour Aidoo *Department of Biological, Physical and Mathematical Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable Development, Somanya, Ghana.
Fangyu Ding *State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Tian MaState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Dong JiangState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Di WangState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. wangd.19b@igsnrr.ac.cn.
Mengmeng HaoState Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. haomm@igsnrr.ac.cn.
Elizabeth TetteyCouncil for Scientific and Industrial Research (CSIR), Oil Palm Research Institute, Coconut Research Programme, P. O. Box 245, Sekondi, Ghana.
Sebastian Andoh-MensahCouncil for Scientific and Industrial Research (CSIR), Oil Palm Research Institute, Coconut Research Programme, P. O. Box 245, Sekondi, Ghana.
Kodwo Dadzie NinsinDepartment of Biological, Physical and Mathematical Sciences, School of Natural and Environmental Sciences, University of Environment and Sustainable Development, Somanya, Ghana.
Christian BorgemeisterCentre for Development Research (ZEF), University of Bonn, Genscherallee 3, 53113, Bonn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The African coconut beetle Oryctes monoceros and Asiatic rhinoceros beetle O. rhinoceros have been associated with economic losses to plantations worldwide. Despite the amount of effort put in determining the potential geographic extent of these pests, their environmental suitability maps have not yet been well established. Using MaxEnt model, the potential distribution of the pests has been defined on a global scale. The results show that large areas of the globe, important for production of palms, are suitable for and potentially susceptible to these pests. The main determinants for O. monoceros distribution were; temperature annual range, followed by land cover, and precipitation seasonality. The major determinants for O. rhinoceros were; temperature annual range, followed by precipitation of wettest month, and elevation. The area under the curve values of 0.976 and 0.975, and True skill statistic values of 0.90 and 0.88, were obtained for O. monoceros and O. rhinoceros, respectively. The global simulated areas for O. rhinoceros (1279.00 × 10

Indexed as

ArecaceaeColeopteraSolanaceaeAnimalsMachine LearningPerissodactylaPest Control, Biological

Identifiers

PMID36261485
PMCPMC9581929

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.