The data and model described here with the purpose of understanding controls over biodiversity. A multi-scale approach to understand how local and regional factors affect the community assembly processes that drive emergent patterns. These data allows us to examin a wide range of parameter settings representing ecologically relevant scenarios. We used artificial neural networks (ANNs) to assess the sensitivity of diversity and variation partitioning metrics (calculated from simulation outcomes) to metacommunity parameter settings. Here you will find metadata details, dor detailed information about the simulations results and conclusions, please visit the associated publication at http://onlinelibrary.wiley.com/doi/10.1111/oik.03690/abstract;jsessionid...
ESM_sim_data parameter settings used in metacommunity simulation (MCSim), definitions and labels.
ESM_sim_data_2. Biodiversity outcomes for 100 different simulated metacommunity (MCSim) scenarios -- Definitions, ranges, labels.
ESM_sim_data_3. Estimates of contribution of MCSim parameters to variation in biodiversity metrics based on contribution statistics from artificial neural networks (ANNs) trained and validated on simulated metacommunity outcomes.
Methods, code description and heuristics are posted in the appendix of the associated publication at http://onlinelibrary.wiley.com/doi/10.1111/oik.03690/abstract;jsessionid...
The Metacommunities simulation code used is posted at http://doi.org/10.5281/zenodo.153999
This simulation is atemporal and non-localized. There is no physical geo-location or calendar time frame or specific season. However, simulations were performed in Aug 2016.