Bayesian inference in ecology

By Aaron M. Ellison. 2004

747 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/92a8aa1bc585829c8070d4d78a83d41694148ac4

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian (2009, background): “For example, Gotelli and Ellison (2004: 91, 92, 96) cogently state that " absence of evidence is not evidence of absence; failure to reject a null hypothesis is not equivalent to accepting a null hypothesis (although it is often treated that way).”
WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods (2009, methodology): “…coupled with recent advances in computational power and the availability of flexible and reliable software have led to a great increase in the application of Bayesian methods within the last three decades, population ecology being no exception (Clark 2005; Ellison 2004; McCarthy 2007).”
Weak Identifiability in Models for Mark-Recapture-Recovery Data (2009, methodology): “The use of computational Bayesian methods for model-fitting in biology has increased in recent years ( Ellison 2004; Clark 2005), including population ecology (Brooks et al. 2000a, 2002).”
Effect size, confidence interval and statistical significance: a practical guide for biologists (2007, methodology): “Another way of selecting models (and estimating parameters), which is recently gaining popularity in biology, is a Bayesian approach (for more details see Basáñez et al., 2004; Ellison, 2004; Clark, 2005; Clark & Gelfand, 2006; McCarthy, 2007).”
Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges (2010, methodology): “Several new modeling techniques are now available which explicitly take into account uncertainty as an input to themodel and not only as an output statistic (e.g. Bayesian techniques, Ellison, 2004; Clark, 2005).”
The incidence and prevalence of thyroid dysfunction in Europe: a meta-analysis. (2014, background): “A frequentist CI means that in repeated samples, 95% of the intervals will include the real value of the mean, whereas the CI indicates that, with a probability of 95% (11), the real value is between these intervals.”
Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, background): “The areas of research where this has been applied successfully include various biological processes in, for example, genetics [459, 460], biochemistry [461], cognitive sciences [462], ecology [463], etc.”
Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys (2008, background): “(2002); Brooks (2003); Ellison (2004). We augmented the observed species-detection data with 100 all-zero histories, which was sufficient as shown by the posterior for total community size, which had an upper bound well below O = 203 in our analysis (see Fig.”
A protocol for data exploration to avoid common statistical problems (2010, methodology): “…practices (Roberts, Stewart & Pullin 2006; Pullin & Knight 2009), and applied ecologists have become increasingly sophisticated in the statistical methods that they use (e.g. Ellison 2004; Stephens et al. 2005; Robinson & Hamann 2008; Koper & Manseau 2009; Law et al. 2009; Sonderegger et al. 2009).”