phyr

phyr implements integration of phylogenetic relationships into species-distribution, community composition, trait correlation, bipartite network, and phylogenetic diversity analyses as an R package with core routines in C++.

Key Features:

• Phylogenetic Species-Distribution Models: Enables fitting of species-distribution models that incorporate phylogenetic relationships to assess evolutionary influences on species distributions and community structure.
• Spatiotemporal-Phylogenetic Autocorrelation Models: Supports models that account for phylogenetic autocorrelation across spatial and temporal dimensions to analyze dynamics within ecological communities.
• Phylogenetic Trait-Based Bipartite Network Models: Facilitates modelling of bipartite interaction networks based on species traits while incorporating phylogenetic information.
• Trait Correlation Estimation: Estimates phylogenetically independent trait correlations while accounting for measurement error to investigate adaptive syndromes and evolutionary patterns.
• Phylogenetic Diversity Metrics: Computes alpha and beta phylogenetic diversity metrics for assessing biodiversity and community composition from a phylogenetic perspective.
• C++ Implementation: Implements core functions in C++ for efficient computation of phylogenetic model components.

Scientific Applications:

• Community composition analysis: Investigates how evolutionary relationships shape community assembly and species co-occurrence patterns.
• Species distribution modelling: Explores the role of phylogeny in explaining and predicting species geographic distributions.
• Trait evolution and adaptive syndromes: Tests trait correlations and evolutionary hypotheses while accounting for phylogenetic covariance and measurement error.
• Temporal and spatial dynamics: Analyzes spatiotemporal patterns in communities with phylogenetic autocorrelation.
• Species interaction networks: Models bipartite networks (e.g., mutualistic or antagonistic interactions) incorporating trait and phylogenetic information.

Methodology:

Uses Brownian motion and Ornstein–Uhlenbeck models of evolution to represent phylogenetic terms as phylogenetic covariance matrices, with core functions implemented in C++.