Sammendrag
In the emerging field of restoration ecology, there is a very real demand for statistical models that allow for inference about the amount of time an ecosystem uses to meaningfully “recover” from a disturbed or impacted state to a purportedly more natural reference state.. However, the few current multispecies methods that attempt to address this challenge, are usually dependent on non-statistical dimension reduction methods such as NMDS in combination with linear modeling, making the robustness and meaningfulness of the resulting estimates potentially hard to assess. Here, we will instead propose and outline a novel approach to modeling multispecies time series based on Generalized Linear Latent Variable Models (GLLVMs). Over the last decade, the field of GLLVM modeling has proved itself as a fast and flexible model-based alternative to older ecological methods. By further incorporating models for non-stationary time series analysis into the GLLVM framework, we believe it possible to come up with a more statistically coherent way of modeling ecosystem change over time, with better ways to estimate statistical uncertainty, as well as providing ways to assess the relative fit of different time series models that might be realistic in terms of ecosystem change, such as directional random walks, Ornstein-Uhlenbeck processes and cyclic trends, to a time-structured species-site dataset.
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