Spatial distribution manipulation and analysis (Pieridae)

Knowledge graph centered on Spatial distribution manipulation and analysis (Pieridae) with 27 nodes and 101 connections. Top connected: Ligusticum porteri, Lepidoptera, Boechera, native species, space use.

Method synopsis

Systematic manipulation of invasive species spatial distribution patterns varying coverage, patch number, size, shape, and distances. Statistical analysis of resulting population responses using linear models.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure as described in the canonical source

Steps below were extracted from the paper that introduces this protocol — Fine-Grained Distribution of a Non-Native Resource Can Alter the Population Dynamics of a Native Consumer (2015), PloS one. Implementations in other papers (listed below) may differ.

1. Manipulate spatial distribution patterns
   Created 40 simulation scenarios by systematically varying six spatial attributes of T. arvense distribution: total coverage (1.5×, 4×, 8× observed), number of patches (1, 10 patches), mean patch area (minimum, large), patch shape (compact, elongated), distance to nearest native host (close, far), distance to nearest T. arvense patch (close, far). Maintained native host plant distributions unchanged.
   Quantities: 6 spatial attributes tested, 40 total simulation scenarios, coverage varied from observed 2.7% up to 8× observed, patch numbers from 1 to maximum possibleDuration: Systematic manipulation across all scenario combinationsConditions: All scenarios based on observed 2000 plant distribution as baseline
   Equipment: Spatial analysis software
2. Conduct statistical analysis
   Calculated population growth rate R (geometric mean of Nt/Nt-1) and allele frequency change G (geometric mean of pt/pt-1) for each simulation. Used t-tests via 'glm' function in R to determine spatial attributes explaining R and G. Constructed linear models with R = cover + dist + area + thdist + shape, tested correlations, and conducted ANOVA to assess relationships between population growth and allele frequency change.
   Quantities: Linear models analyzing 6 spatial predictor variables, correlation analysis between R and G across 40 scenariosDuration: Post-simulation analysis phaseConditions: Statistical analysis of simulation outputs
   Equipment: R statistical software version 3.0.1, glm function

Typical Equipment

• R statistical software version 3.0.1
• spatial analysis software

Output Measurements

• spatial pattern metrics
• population growth correlations
• effect sizes

Papers Using This Protocol (1)

Species Studied With This Protocol