ArticleEcology and evolution2019
Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation.
Article 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 9 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Bat community assembly in the ecotone of two neotropical biodiversity hotspots is determined by environmental filtering rather than historical factors.The Journal of animal ecology · 2026Article
- Detecting the ecological footprint of selection.PloS one · 2024Article
- Phylogenetic diversity and community assembly in a naturally fragmented system.Ecology and evolution · 2021Article
- A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities.Molecular ecology resources · 2021Article
- Ecological opportunity and adaptive radiations reveal eco-evolutionary perspectives on community structure in competitive communities.Scientific reports · 2021Article
- Productivity, niche availability, species richness, and extinction risk: Untangling relationships using individual-based simulations.Ecology and evolution · 2021Article
- The importance of genomic variation for biodiversity, ecosystems and people.Nature reviews. Genetics · 2021Review
- High-throughput sequencing for community analysis: the promise of DNA barcoding to uncover diversity, relatedness, abundances and interactions in spider communities.Development genes and evolution · 2020Review
- Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation.Ecology and evolution · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
Ecologists often use dispersion metrics and statistical hypothesis testing to infer processes of community formation such as environmental filtering, competitive exclusion, and neutral species assembly. These metrics have limited power in inferring assembly models because they rely on often-violated assumptions. Here, we adapt a model of phenotypic similarity and repulsion to simulate the process of community assembly via environmental filtering and competitive exclusion, all while parameterizing the strength of the respective ecological processes. We then use random forests and approximate Bayesian computation to distinguish between these models given the simulated data. We find that our approach is more accurate than using dispersion metrics and accounts for uncertainty in model selection. We also demonstrate that the parameter determining the strength of the assembly processes can be accurately estimated. This approach is available in the R package CAMI; Community Assembly Model Inference. We demonstrate the effectiveness of CAMI using an example of plant communities living on lava flow islands.
Indexed as
Identifiers
What OpenQuestion holds
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.