ArticleFrontiers in plant science2022
Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.
What it found
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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.
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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.
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Who cites it
35 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The association between self-reported vaping and the human oral microbiome: a systematic review.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026Pooled it
- Effect of probiotic supplementation on the gut microbiota diversity in healthy populations: a systematic review and meta-analysis of randomised controlled trials.BMC medicine · 2026Pooled it
- The Relationship Between the Gut Microbiome and the Aqua Cultured Fish, General Prospects Toward Fish Health: A Systematic Review.Applied biochemistry and biotechnology · 2025Pooled it
- Probiotic supplementation during antibiotic treatment is unjustified in maintaining the gut microbiome diversity: a systematic review and meta-analysis.BMC medicine · 2023Pooled it
- Trial
- Article
- α-Diversity analysis of hepatic transcriptome reveals distinct pathways in alcohol-associated hepatitis.JCI insight · 2026Article
- Ab-SELDON: Leveraging Diversity Data for an Efficient Automated Computational Pipeline for Antibody Design.Journal of chemical information and modeling · 2026Article
- The application ofMicrobiology spectrum · 2026Article
- Article
- Assessing Species Fractional Cover and α-Diversity in Boreal Peatlands Across Trophic Levels Using Hyperspectral Data.Ecology and evolution · 2025Article
- The emerging role of microbiota derived SCFAs in neurodegenerative disorders.Brain, behavior, & immunity - health · 2025Review
- The Role of Microbiota in the Pathogenesis of Bullous Pemphigoid and Pemphigus Vulgaris: Evidence, Controversies, and Perspectives.International journal of molecular sciences · 2025Review
- Exploring the role of microbiome in cystic fibrosis clinical outcomes through a mediation analysis.mSystems · 2025Article
- Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.Journal of translational medicine · 2025Article
- Higher Microbial Abundance and Diversity in Bronchus-Associated Lymphoid Tissue Lymphomas Than in Non-cancerous Lung Tissues.Cancer research and treatment · 2025Article
- Biodiversity and Evaluation of Genetic Resources of Some Coffee Trees Grown in Al-Baha, Saudi Arabia.Current issues in molecular biology · 2025Article
- Effect of different killing methods during curing on the phytochemical and bacterial composition ofFood chemistry: X · 2025Article
- Microbiome and Communication Disorders: A Tutorial for Clinicians.Journal of speech, language, and hearing research : JSLHR · 2025Review
- The Oral Microbiome in Diabetes, Arterial Hypertension, and Obesity: A Scoping Review.Advances in experimental medicine and biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments.
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Registered trials
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.