Evidence map›Paper›PMID 36979164›Full record

ArticleBiology2023

Occurrence Prediction of Riffle Beetles (Coleoptera: Elmidae) in a Tropical Andean Basin of Ecuador Using Species Distribution Models.

Gonzalo Sotomayor, Jorge Romero, Daniela Ballari, Raúl F Vázquez, Iván Ramírez-Morales, Henrietta Hampel, Xavier Galarza, Bolívar Montesinos, Marie Anne Eurie Forio, Peter L M Goethals

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Article in Biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Gonzalo SotomayorDepartment of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.ORCID 0000-0003-3743-4526
Jorge RomeroInstituto de Estudios del Régimen Seccional del Ecuador (IERSE), Facultad de Ciencia y Tecnología, Universidad del Azuay, Cuenca 010204, Ecuador.
Daniela BallariInstituto de Estudios del Régimen Seccional del Ecuador (IERSE), Facultad de Ciencia y Tecnología, Universidad del Azuay, Cuenca 010204, Ecuador.ORCID 0000-0002-6926-4827
Raúl F VázquezDepartamento de Ingeniería Civil, Facultad de Ingeniería, Universidad de Cuenca, Av. 12 de abril S/N, Cuenca, Azuay 010203, Ecuador.ORCID 0000-0003-2581-5372
Iván Ramírez-MoralesDINTA Research Group, Universidad Técnica de Machala, Machala 070213, Ecuador.ORCID 0000-0002-2245-0016
Henrietta HampelLaboratorio de Ecología Acuática (LEA), Facultad de Ciencias Químicas, Universidad de Cuenca, Av. 12 de abril S/N, Cuenca 010203, Ecuador.ORCID 0000-0003-2249-5369
Xavier GalarzaInstituto de Estudios del Régimen Seccional del Ecuador (IERSE), Facultad de Ciencia y Tecnología, Universidad del Azuay, Cuenca 010204, Ecuador.ORCID 0000-0002-5708-620X
Bolívar MontesinosMinisterio del Ambiente, Agua y Transición Ecológica, Dirección Zonal 6, Cuenca 010104, Ecuador.
Marie Anne Eurie ForioDepartment of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.ORCID 0000-0001-6675-4751
Peter L M GoethalsDepartment of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genera and species of Elmidae (riffle beetles) are sensitive to water pollution; however, in tropical freshwater ecosystems, their requirements regarding environmental factors need to be investigated. Species distribution models (SDMs) were established for five elmid genera in the Paute river basin (southern Ecuador) using the Random Forest (RF) algorithm considering environmental variables, i.e., meteorology, land use, hydrology, and topography. Each RF-based model was trained and optimised using cross-validation. Environmental variables that explained most of the Elmidae spatial variability were land use (i.e., riparian vegetation alteration and presence/absence of canopy), precipitation, and topography, mainly elevation and slope. The highest probability of occurrence for elmids genera was predicted in streams located within well-preserved zones. Moreover, specific ecological niches were spatially predicted for each genus.

Indexed as

Elmid generapresence–absence recordsrandom forestspecies distribution modelsstreams

Identifiers

PMID36979164
PMCPMC10045380

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Registered trials

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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.