Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables

By R. Mac Nally. Biodiversity and Conservation, 2002

516 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/12083a1c4f2e2cde708a7061c5f547f4a2f13ddd

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Citations in the corpus

Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules (2010, background): “However multicollinearity among explanatory variables is known to generate spurious results with interpretable coefficients since effects cannot be unambiguously segregated (Mac Nally 2002).”
Tree effects on grass growth in savannas: competition, facilitation and the stress‐gradient hypothesis (2013, methodology): “Regression trees (De’ath & Fabricus 2000) and hierarchical variance partitioning (Mac Nally 1996, 2002) were also investigated as supplements to linear regression analyses to more fully explore the influence of the categorical variables (i.e. soil texture, bioclimatic zone and tree size) and test…”
Hydraulic microhabitats and the distribution of macroinvertebrate assemblages in riffles (2005, methodology): “…partitioning was undertaken to determine the ‘independent’ correlation of each of the simple physical measures (velocity, depth and substrate roughness) to macroinvertebrate abundance and taxonomic richness (Chevan & Sutherland, 1991; MacNally, 1996, 2002; Chapter 16 Quinn & Keough, 2002).”
Niche breadth and geographic range size as determinants of species survival on geological time scales (2015, methodology): “The approach provides a measure of the strength of effect of each factor that is largely independent from the effects of other factors (Chevan & Sutherland, 1991; Mac Nally, 2002). Hierarchical partitioning was conducted for all estimation combinations of the four variables using logistic regression”
Constrain to perform: Regularization of habitat models (2006, background): “Ano eason is that building predictive models and iden ng important predictor variables are two different ta MacNally, 2000, 2002): generally, consistent subs election and optimized prediction performance ca e achieved at the same time.”
Complex reef architecture supports more small‐bodied fishes and longer food chains on Caribbean reefs (2011, unknown): “We used hierarchical partitioning rather than traditional multiple 5 regressions or other multivariate analysis, because this method deals well with 6 multicollinearity of predictor variables (Mac Nally 2000, 2002).”
Promising the future? Global change projections of species distributions (2007, background): “Correlated environmental drivers cannot readily be dealt with in species distribution analysis (as is true for all fields of ecology: Mac Nally, 2002; Graham, 2003).”
Connecting social networks with ecosystem services for watershed governance: a social-ecological network perspective highlights the critical role of bridging organizations. (2012, methodology): “…is a statistical method that analyzes all possible models in a multiple regression to identify the contribution of each variable to the total variance, both independently and in conjunction with the other variables, to infer the impact of each variable (Chevan and Sutherland 1991, MacNally 2002).”
Effects of human‐modified landscapes on taxonomic, functional and phylogenetic dimensions of bat biodiversity (2015, methodology): “Statistical significance of the independent contribution of each explanatory variable was determined using a randomization approach with 1000 iterations and an a-level of 0.05 (Mac Nally, 2002).”
Modelling stream fish species distribution in a river network: the relative effects of temperature versus physical factors (2008, methodology): “The ratio |I ⁄ J| was also analyzed (Chevan & Sutherland 1991; Mac Nally 2000): if ratio |I ⁄ J| < 1, it indicates that the predictor variables are highly correlated.”
Selecting predictors to maximize the transferability of species distribution models: lessons from cross‐continental plant invasions (2017, methodology): “Using statistical algorithms to select the most relevant variables is common in ecology (Mac Nally, 2002, Cutler et al., 2007) and can be used in a hierarchical way (e.g. Roura-Pascual et al., 2009).”
Population trends of European common birds are predicted by characteristics of their climatic niche (2010, methodology): “…between predictors and draw inferences about the likely causality of variables, we further performed a hierarchical partitioning analysis (Mac Nally, 2002) with the same five predictors, using the hier.part package of the R statistical software (note that weighting is not feasible in…”