Statistical ecology comes of age

Abstract

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Toward a community ecology of landscapes: predicting multiple predator–prey interactions across geographic space

“…Yet such approaches often lack the nuanced natural history understanding or insights about species spatial interactions needed to explain and predict how and why species occur and interact the way they do across space (Paine , Gimenez et al. ). Furthermore, the emphasis of statistical analysis of pattern has led to uneven emphasis on descriptive methodology at the expense of developing and testing conceptual theory (Scheiner ).…”

Expert-based ecosystem services capacity matrices: Dealing with scoring variability

“…Bayesian methods effectively require a degree of expertise in how to write, fit and interpret Bayesian models. However, they do enable to integrate different kinds of information (here, the expert scores, confidence scores and theirs uncertainties were highlighted) in an integrated statistical modelling framework (Gimenez et al, 2014) as well as to estimate all the parameters associated with these variables. In particular, Bayesian models hold great potential if the aim is to gain a more inferential point of view.…”

“…For provisioning services and regulating services, the ES list has been based on the European CICES classification (Haines-Young and Potschin, 2013). We considered provisioning services, regulating services and cultural services.…”

How does choice of statistical method to adjust counts for imperfect detection affect inferences about animal abundance?

“…The increasing sophistication of analysis methods to better handle the noise and sources of bias in ecological data sets has led to greater confidence in inferences and made it possible to investigate a wider range of questions in ecology (Gimenez et al . ). Greater sophistication, however, often comes with the cost of increased complexity, which can hamper implementation in various ways, including the need for additional statistical expertise, reduced accessibility of results to other ecologists and to land managers (LaDeau ), increased probability of making a mistake when implementing a complex model (Gimenez et al .…”

“…Greater sophistication, however, often comes with the cost of increased complexity, which can hamper implementation in various ways, including the need for additional statistical expertise, reduced accessibility of results to other ecologists and to land managers (LaDeau ), increased probability of making a mistake when implementing a complex model (Gimenez et al . ) and potential problems with parameter identifiability (Welsh, Lindenmayer & Donnelly ; but see Guillera‐Arroita et al . ).…”