New insights into butterfly–environment relationships using partitioning methods

Abstract

Variation partitioning and hierarchical partitioning are novel statistical approaches that provide deeper understanding of the importance of different explanatory variables for biodiversity patterns than traditional regression methods. Using these methods, the variation in occupancy and abundance of the clouded apollo butterfly (Parnassius mnemosyne L.) was decomposed into independent and joint effects of larval and adult food resources, microclimate and habitat quantity. The independent effect of habitat quantity variables (habitat area and connectivity) captured the largest fraction of the variation in the clouded apollo patterns, but habitat connectivity had a major contribution only for occupancy data. The independent effects of resources and microclimate were higher on butterfly abundance than on occupancy. However, a considerable amount of variation in the butterfly patterns was accounted for by the joint effects of predictors and may thus be causally related to two or all three groups of variables. Abundance of the butterfly in the surroundings of the focal grid cell had a significant effect in all analyses, independently of the effects of other predictors. Our results encourage wider applications of partitioning methods in biodiversity studies.

Invariant allometric scaling of nitrogen and phosphorus in leaves, stems, and fine roots of woody plants along an altitudinal gradient

“…The variables that did not contribute significantly (P < 0.05) to the explained variation were excluded from the GLM (Tables 3, 4). Secondly, the partial General Liner Models (partial GLM) were used to separate the trait variations into different components: (1) a, b and c-the independent effects of PGF, climate and soil, respectively; (2) ab, ac, and bc-the interactive effects between PGF and climate, between PGF and soil, and between climate and soil, respectively; (3) abc-the interactive effect among PGF, climate and soil; (4) Unexplained variations ( Table 2, for details of the statistics, see Han et al 2011;Heikkinen et al 2005;Zhao et al 2014). …”

Temporal trend of green space coverage in China and its relationship with urbanization over the last two decades

“…The hierarchical partitioning provides, for each explanatory variable separately, an estimate of the independent and joint contribution with all other variables (Heikkinen et al, 2005;Chevan and Sutherland, 1991;Mac-Nally, 2000;Quinn and Keough, 2002). To test the statistical significance of the independent contributions of variables, we also performed a randomization routine which yielded Z-scores for the generated distribution of randomized independent contributions, and a measure of statistical significance based on an upper 0.95 confidence limit (Mac-Nally, 2002;Heikkinen et al, 2005).…”

Joint Control of Net Primary Productivity by Climate and Soil Nitrogen in the Forests of Eastern China

“…The variation partitioning with three explanatory matrices leads to the identification of seven fractions in this study, i.e., independent effects of MAT, MAP, and N re ; interactive effects of MAT and MAP, MAT and N re , MAP and N re , and the interactive effect of all variables. Further details about the method were given in Heikkinen et al (2005) [29]. Figure 1 was plotted with ArcGIS 10.1 software (Esri, Realands, CA, USA), and other graphs were performed by Sigma Plot 13.0 software (Systat Software Inc., San Jose, CA, USA).…”