Preprints

Filtering by Subject: Applied Statistics

Multimodel approaches are not the best way to understand multifactorial systems

Benjamin Bolker

Published: 2023-07-23
Subjects: Applied Statistics, Ecology and Evolutionary Biology

Information-theoretic (IT) and multi-model averaging (MMA) statistical approaches are widely used but suboptimal tools for pursuing a multifactorial approach (also known as the method of multiple working hypotheses) in ecology. (1) Conceptually, IT encourages ecologists to perform tests on sets of artificial models. (2) MMA improves on IT model selection by implementing a simple form of [...]

The missing link: discerning true from false negatives when sampling species interaction networks

Michael D Catchen, Timothée Poisot, Laura J. Pollock, et al.

Published: 2023-01-18
Subjects: Applied Statistics, Biodiversity, Ecology and Evolutionary Biology, Environmental Monitoring

Ecosystems are composed of networks of interacting species. These interactions allow communities of species to persist through time through both neutral and adaptive processes. Despite their importance, a robust understanding of (and ability to predict and forecast) interactions among species remains elusive. This knowledge-gap is largely driven by a shortfall of data—although species occurrence [...]

Interpolation of temporal biodiversity change, loss, and gain across scales: a machine learning approach

Petr Keil, Jonathan Chase

Published: 2022-03-15
Subjects: Applied Statistics, Biodiversity, Ecology and Evolutionary Biology, Life Sciences, Physical Sciences and Mathematics, Statistics and Probability

1. Estimates of temporal change of biodiversity, and its components loss and gain, are needed at local and geographical scales. However, we lack them because of data in-completeness, heterogeneity, and lack of temporal replication. Hence, we need a tool to integrate heterogeneous data and to account for their incompleteness. 2. We introduce spatiotemporal machine learning interpolation that can [...]

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