Evidence map›Paper›PMID 34650134›Full record

ArticleScientific reports2021

Deforestation is the turning point for the spreading of a weedy epiphyte: an IBM approach.

Cleber Juliano Neves Chaves, Bárbara Simões Santos Leal, Davi Rodrigo Rossatto, Uta Berger, Clarisse Palma-Silva

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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.

Cleber Juliano Neves ChavesPrograma de Pós-Graduação em Ecologia e Biodiversidade, Instituto de Biociências, Universidade Estadual Paulista, Rio Claro, 13506-900, Brazil. cleberchaves@gmail.com.
Bárbara Simões Santos LealPrograma de Pós-Graduação em Ecologia e Biodiversidade, Instituto de Biociências, Universidade Estadual Paulista, Rio Claro, 13506-900, Brazil.
Davi Rodrigo RossattoDepartamento de Biologia, Universidade Estadual Paulista, Jaboticabal, 14884-900, Brazil.
Uta BergerInstitute of Forest Growth and Computer Sciences, Technische Universität Dresden, 01737, Tharandt, Germany.
Clarisse Palma-SilvaDepartamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, 13083-862, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid spread of many weeds into intensely disturbed landscapes is boosted by clonal growth and self-fertilization strategies, which conversely increases the genetic structure of populations. Here, we use empirical and modeling approaches to evaluate the spreading dynamics of Tillandsia recurvata (L.) L. populations, a common epiphytic weed with self-reproduction and clonal growth widespread in dry forests and deforested landscapes in the American continent. We introduce the TRec model, an individual-based approach to simulate the spreading of T. recurvata over time and across landscapes subjected to abrupt changes in tree density with the parameters adjusted according to the empirical genetic data based on microsatellites genotypes. Simulations with this model showed that the strong spatial genetic structure observed from empirical data in T. recurvata can be explained by a rapid increase in abundance and gene flow followed by stabilization after ca. 25 years. TRec model's results also indicate that deforestation is a turning point for the rapid increase in both individual abundance and gene flow among T. recurvata subpopulations occurring in formerly dense forests. Active reforestation can, in turn, reverse such a scenario, although with a milder intensity. The genetic-based study suggests that anthropogenic changes in landscapes may strongly affect the population dynamics of species with 'weedy' traits.

Indexed as

Conservation of Natural ResourcesIntroduced SpeciesTillandsiaBrazilGene FlowMicrosatellite RepeatsPopulation Dynamics

Identifiers

PMID34650134
PMCPMC8516858

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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.