Chapter 1 Introduction

Variance partitioning analysis in the HMSC (Hierarchical Modeling of Species Communities) package in R is a useful method for understanding how the variance in ecological data (such as species abundance or functional group presence) is explained by different sets of explanatory variables. Specifically, it allows you to partition the variance in community data (e.g. species abundance or presence/absence) across various components, such as spatial, temporal, and environmental factors, and to determine how each component contributes to the observed patterns.

Here are the main goals of variance partitioning with HMSC:

  1. Identify Sources of Variation: It identifies how much of the variation in species communities can be explained by different environmental variables (e.g. climate, habitat features, habitat quality, etc.), spatial factors (e.g. geographical distances, sampling site variation, etc.), and temporal factors (e.g. year-to-year variation, seasonality, etc.).

  2. Assess the Relative Importance of Variables: It quantifies the relative importance of each environmental variable, and spatial and temporal factor in explaining community composition (e.g. species abundance or presence/absence). This helps in understanding which factors most strongly drive biodiversity patterns.

  3. Modeling Complex Relationships: The HMSC package uses hierarchical models, which account for nested structures and correlations in data (e.g. within and between-site variation), making it useful in analyzing complex ecological data where variables operate at multiple scales.

  4. Handle Multiple Data Types: HMSC can integrate different types of data (e.g. species composition, environmental variables, spatial data, temporal data), which makes it suitable for comprehensive ecological modeling.

To perform variance partitioning, HMSC typically uses a multivariate approach to model community composition and then applies various methods to partition the total variance among the different explanatory variables (e.g. environmental, spatial, and temporal variables). The results give insights into the relative contribution of each variable or group of variables to the overall variation in species communities.

In summary, the goal of variance partitioning with HMSC is to better understand the ecological processes driving community structure by quantifying and separating the influences of multiple factors (e.g. environmental, spatial, and temporal variables). By using variance partitioning in HMSC, you can gain insights into the relative roles of environmental, spatial, and temporal factors in shaping ecological patterns and better understand the drivers of biodiversity and community structure.