Novel community data in ecology -- properties and prospects
arXiv:2401.10860 · doi:10.1016/j.tree.2023.09.017
Abstract
New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales. The potential of these novel community data to standardize community observations at high spatial, temporal, and taxonomic resolution and at large spatial scale ('many rows and many columns') has been widely discussed, but so far, there has been little integration of these data with ecological models and theory. Here, we review these developments and highlight emerging solutions, focusing on statistical methods for analyzing novel community data, in particular joint species distribution models; the new ecological questions that can be answered with these data; and the potential implications of these developments for policy and conservation.
References in corpus (6)
- Seeing biodiversity: perspectives in machine learning for wildlife conservation
- The Multilayer Nature of Ecological Networks
- Machine Learning and Deep Learning -- A review for Ecologists
- A new method for faster and more accurate inference of species associations from big community data
- Inferring species interactions using Granger causality and convergent cross mapping
- eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring