Editor Recommendation: Lianas and Soil Nutrients Predict Fine-Scale Distribution of Above-Ground Biomass in a Tropical Moist Forest

Post provided by Laura Graham

©Groume

©Groume

Datasets used by quantitative ecologists are getting more and more complex. So we need more complex models, such as hierarchical and complex spatial models. Typically, Bayesian approaches such as Markov chain Monte Carlo have been used. But these methods can be slow, making it infeasible to fit some models.

New developments in Integrated nested Laplace approximation (INLA) have made some of these complex models much faster to fit. Dedicated R packages (R-INLA and inlabru) make coding these Bayesian models much more straightforward. Also, INLA lets you fit of a class of models which allow for computationally efficient and flexible modelling of spatial data.

Quantitative Ecology 2018 keynote speaker Janine Illian is one of the driving forces behind making INLA an accessible statistical method for ecologists. In our Statistical Ecology Virtual Issue, we highlight a paper led by Alicia Ledo, with Dr Illian as a co-author, which applies INLA to a spatial modelling problem. In the Journal of Ecology article “Lianas and soil nutrients predict fine‐scale distribution of above‐ground biomass in a tropical moist forest” the authors are able to gain novel insights into the factors driving fine-scale carbon storage; insights that were made possible by the spatial modelling approach provided by INLA.

This type of modelling approach has recently been used in a broad range of subject areas across ecology including disease modelling; biotic interactions in species distribution modelling; and analysis of fisheries data. We highlight this paper because it shows how useful the complex spatial modelling approaches provided by INLA can be for researchers looking for insights relevant to global environmental change issues.

To find out more about, read the full Journal of Ecology article ‘Lianas and soil nutrients predict finescale distribution of aboveground biomass in a tropical moist forest

This article is part of the BES cross-journal ‘Statistical Ecology Virtual Issue’. All articles in this Virtual Issue will be available for a limited time.

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