ArticleMethods in ecology and evolution2019
A scalable model of vegetation transitions using deep neural networks.
Article in Methods in ecology and evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
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Who cites it
8 citing papers in PubMed.
- Spatial modeling of forest-savanna bistability: impacts of fire dynamics and timescale separation.Journal of mathematical biology · 2026Article
- A harmonized database of European forest simulations under climate change.Data in brief · 2024Article
- Novel light regimes in European forests.Nature ecology & evolution · 2024Review
- Widespread regeneration failure in forests of Greater Yellowstone under scenarios of future climate and fire.Global change biology · 2021Article
- Tackling unresolved questions in forest ecology: The past and future role of simulation models.Ecology and evolution · 2021Review
- Confronting an individual-based simulation model with empirical community patterns of grasslands.PloS one · 2020Article
- A scalable model of vegetation transitions using deep neural networks.Methods in ecology and evolution · 2019Article
- Harnessing Deep Learning in Ecology: An Example Predicting Bark Beetle Outbreaks.Frontiers in plant science · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In times of rapid global change, anticipating vegetation changes and assessing their impacts is of key relevance to managers and policy makers. Yet, predicting vegetation dynamics often suffers from an inherent scale mismatch, with abundant data and process understanding being available at a fine spatial grain, but the relevance for decision-making is increasing with spatial extent.We present a novel approach for scaling vegetation dynamics (SVD), using deep learning to predict vegetation transitions. Vegetation is discretized into a large number (10
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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.