SER

SER estimates short-period environmental regimes from time-series environmental and biotic data to characterize temporal patterns such as the frequency of mean and extreme events and rates of change.

Key Features:

• Versatility Across Variables: Supports environmental and biotic variables including nutrient concentration, light levels, and dissolved oxygen as inputs for regime estimation.
• Customizable Time Frames: Computes environmental-regime metrics over user-specified intervals (via the days_bf argument), allowing windows that range from days to years.
• Enhanced Ecological Insights: Incorporation of environmental-regime metrics can increase explained variation in temporal β-diversity and its components.

Scientific Applications:

• Ecological Studies: Analyzes temporal environmental dynamics to elucidate relationships with community metrics such as species diversity, distribution, and temporal β-diversity.
• Interdisciplinary Research: Applies temporal-regime analysis to other domains such as social science, socioeconomics, and epidemiology to study the effects of historical dynamics on target variables.

Methodology:

Applies a statistical framework to estimate environmental regimes by analyzing historical time-series environmental data alongside contemporary measurements, computing metrics over user-specified windows via the days_bf argument and including example datasets.