The paper demonstrates the potential of integrating economic and ecological models to generate ecological forecasts in the presence of alternative market conditions and land-use policy constraints.
A suite of models have been developed independently by various academic and government institutions worldwide to understand the dynamics of mangrove ecosystems and to provide ecological forecasting capabilities under different management scenarios and natural disturbance regimes.
When chained with other services providing data on climate change, eHabitat can be used for ecological forecasting and becomes a useful tool for decision-makers assessing different strategies when selecting new areas to protect.
He also attended the American Quaternary Association Biennial Meetings in Bozeman, Montana, where he presented his work "Informing Ecological Forecasting through Use of the Quaternary Mammal Fossil Record". He and Mark Carrasco initiated research on their new project to determine the long-term ecological baseline of mammals.
Ecological forecasting and data assimilation in a data-rich era.
Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified.
Let us put matters straight: ecological forecasts are imperative (Clark et al. 2001).
Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways.
These authors defined ecological forecasting as 'the process of predicting the state of ecosystems, ecosystem services, and natural capital' (Clark et al. 2001).
DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions.
A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible.
