ArticleEcology letters2014
Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series.
Article in Ecology letters, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 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
11 citing papers in PubMed.
- Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics.Research square · 2024Article
- Increasing environmental fluctuations can dampen variability of endogenously cycling populations.Royal Society open science · 2024Article
- Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics.bioRxiv : the preprint server for biology · 2024Article
- Artificial light at night (ALAN) causes shifts in soil communities and functions.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Forecasting in the face of ecological complexity: Number and strength of species interactions determine forecast skill in ecological communities.Ecology letters · 2022Article
- Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities.Ecological applications : a publication of the Ecological Society of America · 2021Article
- Reintroduced wolves and hunting limit the abundance of a subordinate apex predator in a multi-use landscape.Proceedings. Biological sciences · 2020Article
- A parametric interpretation of Bayesian Nonparametric Inference from Gene Genealogies: Linking ecological, population genetics and evolutionary processes.Theoretical population biology · 2018Article
- Ecological change points: The strength of density dependence and the loss of history.Theoretical population biology · 2018Article
- An updated perspective on the role of environmental autocorrelation in animal populations.Theoretical ecology · 2016Article
- Evidence and implications of higher-order scaling in the environmental variation of animal population growth.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series.
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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.