Luz Valerie Pascal

This paper uses value of information analysis to guide simplifying adaptive management models for ecosystems under climate change, finding that model complexity should depend on non-stationarity and uncertainty levels.

The paper develops an adaptive management framework using AI to determine optimal time limits for technology development, balancing costs, benefits, and uncertainties in dynamic systems like ecosystems.

EEMtoolbox is an R package that enables flexible ensemble ecosystem modeling using either accept-reject or sequential Monte Carlo methods, supporting multiple model structures for conservation applications.

This paper introduces a simple population dynamics model that can handle both beneficial and harmful interactions, demonstrating its applicability in data-poor ecological scenarios.