Evidence map›Paper›PMID 32417982›Full record

ArticleEnvironmental monitoring and assessment2020

Dry and wet miombo woodlands of south-central Africa respond differently to climate change.

Percy Jinga, Jason Palagi

Abstract read
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In one paragraph

Article in Environmental monitoring and assessment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

2 authors.

Percy JingaBiological Sciences Department, Bindura University of Science Education, Private Bag, 1020, Bindura, Zimbabwe. pjinga@buse.ac.zw.ORCID https://orcid.org/0000-0002-2231-5316
Jason PalagiBiological Sciences Department, Sewanee: The University of the South, 735 University Avenue, Sewanee, TN, 37383, USA.

Funding

Bindura University of Science Education Not applicable
6 · The paper itself

Abstract

It is important to understand how species distributions will shift under climate change. While much focus has been on species tracking temperature changes in the northern hemisphere, changing precipitation patterns in tropical regions have received less attention. The aim of the study was to estimate the current distribution of wet and dry miombo woodlands of sub-Saharan Africa and to predict their distributions under different climate change scenarios. A maximum entropy method (Maxent) was used to estimate the distributions and for projections. Occurrence records of dominant tree species in each woodland were used for modeling, together with altitude, soil characteristics, and climate variables as the environmental variables. Modeling was done under all four representative concentration pathways (RCPs) and three general circulation models. Three dominant tree species were used in models of dry miombo while seven were used for wet miombo. Models estimated dry miombo to cover almost the entire known distribution of miombo woodlands while wet miombo were estimated to predominate in parts of Angola, southern Democratic Republic of Congo, Malawi, Tanzania, Zambia, and Zimbabwe. Future climate scenarios predict a drier climate in sub-Saharan Africa, and as a result, the range of dry miombo will expand. Dry miombo were predicted to expand by up to 17.3% in 2050 and 22.7% in 2070. In contrast, wet miombo were predicted to contract by up to - 28.6% in 2050 and - 41.6% in 2070. A warming climate is conducive for the proliferation of dry miombo tree species but unfavorable for wet miombo tree species.

Indexed as

Climate ChangeEnvironmental MonitoringForestsAfrica, CentralAngolaMalawiSouth AfricaTanzaniaZambiaZimbabweClimate changeEcological niche modelMaxentMiombo woodlandsRepresentative concentration pathwaySpecies distribution

Identifiers

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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.