Logistic growth curve fitting (Plantae)

Knowledge graph centered on Logistic growth curve fitting (Plantae) with 26 nodes and 75 connections. Top connected: Rocky Mountain Biological Laboratory, Climate Change Impacts, Penstemon strictus, Linum lewisii, groundwater-dependent vegetation drought stress.

Method synopsis

Fitting logistic growth models to disease prevalence time series to extract key epidemiological parameters for subsequent environmental correlation analysis.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — A multi-year case study highlighting the influence of hydrological conditions on epidemic dynamics in a natural plant pathosystem (2025), Oikos. Implementations in other papers (listed below) may differ.

1. Fit logistic growth models
   Logistic equations were fit using the Gauss-Newton algorithm in the nls() function in R version 4.2.0 to explore epidemics that displayed expected sigmoidal growth, excluding epidemics from 2023 and those with insufficient temporal coverage
   Quantities: Multiple epidemic curves per site per yearDuration: Post-collection analysis phaseConditions: Computational analysis
   Equipment: R statistical software version 4.2.0

Typical Equipment

• R statistical software version 4.2.0

Output Measurements

• maximum prevalence
• epidemic growth rate
• epidemic timing

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used