This project is being developed using a foundational and predictive system for geospatial simulation - the
SpaDES family of R packages (Chubaty & McIntire, 2021; McIntire et al., 2022). Our approach facilitates findable, accessible, interoperable and reusable science, paramount for conducting landscape modeling and prediction in ecology (Keane et al., 2015; Dietze et al., 2018; Stall et al., 2019). Moreover, such a framework allows for diverse models generated from previously siloed areas of expertise to be integrated into a continuous, adaptable and reusable workflow, directly conducting a cumulative effects analysis over large spatial extents. In other words, due to the flexibility and transparency of being built in R,
SpaDES empowers domain experts to build models with or without involving programmers to translate ecological mechanisms into code. With the
SpaDES platform “modules” of any sort can be created (i.e., any coherent idea or concept, such as “harmonize all data layers”, “estimate parameters for fire module”, “forest succession”, “forest harvesting”, “carbon accounting”, “caribou resource selection function prediction”), sometimes very quickly, and due to being compatible with R, they can be readily shared and integrated with other conceptually compatible modules (i.e. compute forest carbon pools and caribou resource selection while simulating harvesting on a dynamic landscape).
All these are unique advantages of the
SpaDES platform over other graphical user interface, or black-box software. The modular and open approach that
SpaDES speeds up scientific collaboration, as “interoperable” modules can be developed in parallel in many research groups. Similarly, with a single interoperable framework,
SpaDES promotes the use of tools for generating user friendly web apps to visualize and understand cumulative effects. With over 22,000 downloads and 37 independently developed modules and counting,
SpaDES is a Canadian product rapidly increasing in use across multiple scientific communities.
All project code will be publicly available from https://github.com/PredictiveEcology/WBI_forecasts.
This repository contains all code necessary to run the simulations and analyze the results of the models.