Unlocking ensemble ecosystem modelling for large and complex networks

The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Now, for the first time, larger and more realistic networks can be practically simulated

Analysis of sloppiness in model simulations: unveiling parameter uncertainty when mathematical models are fitted to data

This work introduces a Bayesian approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This novel approach identifies stiff parameter combinations that strongly affect the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily from the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of our technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting

Data-driven recommendations for enhancing real-time natural hazard warnings, communication, and response

The effectiveness and adequacy of natural hazard warnings hinges on the availability of data and its transformation into actionable knowledge for the public. Real-time warning communication and emergency response therefore need to be evaluated from a data science perspective. However, there are currently gaps between established data science best practices and their application in supporting natural hazard warnings. This Perspective reviews existing data-driven approaches that underpin real-time warning communication and emergency response, highlighting limitations in hazard and impact forecasts. Four main themes for enhancing warnings are emphasised: (i) applying best-practice principles in visualising hazard forecasts, (ii) data opportunities for more effective impact forecasts, (iii) utilising data for more localised forecasts, and (iv) improving data-driven decision-making using uncertainty. Motivating examples are provided from the extensive flooding experienced in Australia in 2022. This Perspective shows the capacity for improving the efficacy of natural hazard warnings using data science, and the collaborative potential between the data science and natural hazards communities

Time-series predictions for the semiarid Australia ecosystem network comparing the prior, standard-EEM, and SMC-EEM.

Time-series forecasts for the prior (grey), standard-EEM (red) and SMC-EEM (blue) ensembles simulated from a random initial condition. Depicted are the median (think lines) and 95% credible intervals (thin dotted lines) for each ensemble. Notice that the blue and red predictions are similar, demonstrating that the outputs of the standard-EEM and SMC-EEM methods are consistent. (TIF)</p

Equilibrium abundances for the Great Barrier Reef network.

Ensemble ecosystem modelling for an ecosystem network representing the Great Barrier Reef parameterised using standard-EEM and SMC-EEM. (a) The Great Barrier Reef ecosystem network [64] consists of 16 nodes, with connectance c = 0.4, and 118 parameters when represented as a Lotka-Volterra system. (b) Distributions of equilibrium abundances from the prior distribution (grey), and two independent SMC-EEM ensembles (light blue and dark blue) of ecosystem models. Note that the x-axes have been limited to visualise the distribution peaks, however the range of equilibrium populations for the prior distribution is very diffuse (and hence barely visible in these plots) compared to the ensemble-predicted distribution abundances. Here the independent SMC-EEM ensembles are consistent, demonstrating reproducibility.</p

Parameter distributions for the semiarid Australia ecosystem network comparing standard-EEM to SMC-EEM.

Marginal parameter distributions estimated using both the standard-EEM method (red) and the SMC-EEM method (blue). Species labels represent dingoes (D), mesopredators (M), large herbivores (H), small vertebrates (V), grasses (G), invertebrates (I), fires (F) and soil quality (S). Notice that the blue and red densities match almost exactly, demonstrating that the outputs of the standard-EEM and SMC-EEM methods are consistent. (TIF)</p

Five most tightly constrained parameter combinations for the Great Barrier Reef ecosystem network.

The eigenvector values for the first five eigenparameters, rescaled to be between -1 and 1. These values are shaded such that the darker colours indicates a greater contribution of the parameter to the important parameter combinations. The columns of this table can be interpreted using Eq (10). Notice, that the most important parameters are all growth rates for lower trophic species, and self-regulation for top predators. (TIF)</p

Eigenparameter distributions for the semiarid Australia ecosystem network comparing standard-EEM to SMC-EEM.

Marginal distributions of the nine stiffest eigenparameters estimated via the prior (grey), standard-EEM (red) and SMC-EEM (blue) ensembles. Notice that the blue and red densities match almost exactly, demonstrating that the outputs of the standard-EEM and SMC-EEM methods are consistent. (TIF)</p

Ensemble generation times for different network sizes.

The computation time required to parameterise an ensemble of 1000 feasible and stable ecosystem models using both the standard-EEM and SMC-EEM methods. This figure shows the medians (dots) and 7.5–92.5% quantiles (error bars) of computation times. Note, the computation time for any one ecosystem network was capped at 104 seconds due to the computational burden of the simulation study.</p

Eigenparameter distributions for the Great Barrier Reef ecosystem network comparing two independent SMC-EEM ensembles.

Distributions of the nine most constrained parameter combinations (stiffest eigenparameters) determined by an analysis of model sloppiness of a SMC-EEM ensemble. Here we compare the values of the eigenparameters for the prior distribution (grey), and two independent ensembles generated via the SMC-EEM algorithm (black and blue). (TIF)</p