Evidence map›Paper›PMID 35048202›Full record

ArticleEnvironmental monitoring and assessment2022

Modeling potential habitats and predicting habitat connectivity for Leucanthemum vulgare Lam. in northwestern rangelands of Iran.

Sahar Samadi Khanghah, Mehdi Moameri, Ardavan Ghorbani, Raoof Mostafazadeh, Asim Biswas

Abstract read
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Article in Environmental monitoring and assessment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Analysis of a Potentially Suitable Habitat forPlants (Basel, Switzerland) · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
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

5 authors.

Sahar Samadi KhanghahFaculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
Mehdi MoameriFaculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. moameri@uma.ac.ir.ORCID http://orcid.org/0000-0003-2917-4736
Ardavan GhorbaniFaculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
Raoof MostafazadehFaculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.ORCID http://orcid.org/0000-0002-0401-0260
Asim BiswasSchool of Environmental Sciences, University of Guelph, Guelph, Canada.ORCID http://orcid.org/0000-0003-0801-3546

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Invasive plants can alter the function and structure of ecosystems resulting in social, economic, and ecological damage. Effective methods to reduce the dominance of invasive plants are needed. The present study was aimed at modeling the invasive species Leucanthemum vulgare Lam. in the rangelands of the Namin region in northwest Iran, as well as predicting the habitat connectivity of this species to detect areas with high habitat connectivity. Modeling of potential habitats was performed using logistic regression (LR) and maximum entropy (MaxEnt); the ensemble map which resulted from these was used to predict habitat connectivity using the electrical circuit method. Topography (elevation, slope, and aspect), climate (precipitation and temperature), and soil (acidity, electrical conductivity, soil texture, calcium, magnesium, sodium, phosphorus, potassium, organic carbon, organic matter, saturation percentage, and total neutralizing value) were used in this study. The presence and absence points of the L. vulgare were recorded using a stratified-random sampling method by means of a global positioning system. Soil samples were collected at a depth of 0 to 30 cm where L. vulgare was present and also where it was absent. According to the results, in LR, the variables of temperature, phosphorus, organic matter, and sand and in the MaxEnt, the variables of sand, total neutralizing value (TNV), and silt were the most influential factors on the distribution of L. vulgare. The appraisal of the MaxEnt performance and the precision of the model prediction were 0.97. The Kappa indices for the predicted map obtained from the LR and MaxEnt models were 0.80 and 0.73, respectively. The models' evaluation indicated that both models were able to predict the distribution of L. vulgare habitats with a high level of accuracy; however, LR was more reliable. According to the LR prediction, 9.91% (10,556.25 ha) of the Namin region was attacked by L. vulgare. Connectivity assessment showed that the current density spread of L. vulgare continued from the northeast of the Namin region toward the southeast. On the other hand, the higher current density spread was demonstrated in the eastern region (rangelands adjacent to Fandoghlu forests), and other rangelands which are more threatened by the invasion of L. vulgare. Identifying sites exposed to invasive species can help implement programs to prevent invasive species from invading areas where management and prevention should be implanted to prevent and/or reduce the spread.

Indexed as

EcosystemLeucanthemumEnvironmental MonitoringIntroduced SpeciesIranCurrent densityDistribution modelingInvasive speciesLogistic regressionMaxEntNamin region

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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.