distribution

Simulations connecting metacommunity characteristics and biodiversity patterns

Abstract: 

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...

Data set ID: 

8016

Additional Project roles: 

753
754

Core Areas: 

Short name: 

ESM

Methods: 

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

Data sources: 

ESM_SIM_DATA_INPUT
ESM_SIM_DATA_OUTPUT
ESM_SIM_DATA_ANN_UNPRUNNED

Additional information: 

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. 

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