This post recalls the journey on how we ended up developing cxr (acronym for CoeXistence relationships in R), an R package for quantifying interactions among species and their coexistence relationships. In other words, it provides tools for telling apart the situations in which different species can persist together in a community from the cases in which one species completely overcomes another.
This issue contains two Applications articles and two Open Access articles. These four papers are freely available to everyone, no subscription required.
–Paco: An R package that assesses the phylogenetic congruence, or evolutionary dependence, of two groups of interacting species using both ecological interaction networks and their phylogenetic history.
–Open MEE: Open Meta-analyst for Ecology and Evolution (Open MEE) addresses the need for advanced, easy-to-use software for meta-analysis and meta-regression.It offers a suite of advanced meta-analysis and meta-regression methods for synthesizing continuous and categorical data, including meta-regression with multiple covariates and their interactions, phylogenetic analyses, and simple missing data imputation.
Despite how far modelling has taken us in science, the use of models remains controversial. Modelling covers a huge range of common practices, from scaled models of ships to determine the shape that will have the least resistance to water to complex, comprehensive ‘models of everything’. A great example of the latter is the Earth System Model. This model aims to understand the changes in global climate by taking into account the interaction between physical climate, biosphere, the atmosphere and the oceans. Basically, a model of how the Earth works.
The controversy in the use of modelling resides in how accurately the model describes reality and the level of confidence we have in its outputs. The first argument can be a bit counter-intuitive: sometimes, a very simple model can be a great predictor. Actually, the conventional view in ecology is that simple models are more generalisable than complex models, although this view is being challenged. However, the level of confidence, or the level of uncertainty, that we have in the outputs of the model is a crucial point. We need to be able to accurately determine our levels of uncertainty if we want people to trust our models. Continue reading →