TY - JOUR

T1 - A framework for coupling explanation and prediction in hydroecological modelling

AU - Surridge, Ben

AU - Bizzi, Simone

AU - Castelletti, Andrea

PY - 2014/11

Y1 - 2014/11

N2 - Causal explanation and empirical prediction are usually addressed separately when modelling ecological systems. This potentially leads to erroneous conflation of model explanatory and predictive power, to predictive models that lack ecological interpretability, or to limited feedback between predictive modelling and theory development. These are fundamental challenges to appropriate statistical and scientific use of ecological models. To help address such challenges, we propose a novel, integrated modelling framework which couples explanatory modelling for causal understanding and input variable selection with a machine learning approach for empirical prediction. Exemplar datasets from the field of freshwater ecology are used to develop and evaluate the framework, based on 267 stream and river monitoring stations across England, UK. These data describe spatial patterns in benthic macroinvertebrate community indices that are hypothesised to be driven by meso-scale physical and chemical habitat conditions. Whilst explanatory models developed using structural equation modelling performed strongly (r2 for two macroinvertebrate indices = 0.64-0.70), predictive models based on extremely randomised trees demonstrated moderate performance (r2 for the same indices = 0.50-0.61). However, through coupling explanatory and predictive components, our proposed framework yields ecologically-interpretable predictive models which also maintain the parsimony and accuracy of models based on solely predictive approaches. This significantly enhances the opportunity for feedback among causal theory, empirical data and prediction within environmental modelling.

AB - Causal explanation and empirical prediction are usually addressed separately when modelling ecological systems. This potentially leads to erroneous conflation of model explanatory and predictive power, to predictive models that lack ecological interpretability, or to limited feedback between predictive modelling and theory development. These are fundamental challenges to appropriate statistical and scientific use of ecological models. To help address such challenges, we propose a novel, integrated modelling framework which couples explanatory modelling for causal understanding and input variable selection with a machine learning approach for empirical prediction. Exemplar datasets from the field of freshwater ecology are used to develop and evaluate the framework, based on 267 stream and river monitoring stations across England, UK. These data describe spatial patterns in benthic macroinvertebrate community indices that are hypothesised to be driven by meso-scale physical and chemical habitat conditions. Whilst explanatory models developed using structural equation modelling performed strongly (r2 for two macroinvertebrate indices = 0.64-0.70), predictive models based on extremely randomised trees demonstrated moderate performance (r2 for the same indices = 0.50-0.61). However, through coupling explanatory and predictive components, our proposed framework yields ecologically-interpretable predictive models which also maintain the parsimony and accuracy of models based on solely predictive approaches. This significantly enhances the opportunity for feedback among causal theory, empirical data and prediction within environmental modelling.

KW - Causal explanation

KW - Ecological data

KW - Empirical prediction

KW - Extremely randomised trees

KW - Input variable selection

KW - Structural equation modelling

U2 - 10.1016/j.envsoft.2014.02.012

DO - 10.1016/j.envsoft.2014.02.012

M3 - Journal article

VL - 61

SP - 274

EP - 286

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1873-6726

ER -