2014
DOI: 10.1371/journal.pone.0088761
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The Social Nestwork: Tree Structure Determines Nest Placement in Kenyan Weaverbird Colonies

Abstract: Group living is a life history strategy employed by many organisms. This strategy is often difficult to study because the exact boundaries of a group can be unclear. Weaverbirds present an ideal model for the study of group living, because their colonies occupy a space with discrete boundaries: a single tree. We examined one aspect of group living. nest placement, in three Kenyan weaverbird species: the Black-capped Weaver (Pseudonigrita cabanisi), Grey-capped Weaver (P. arnaudi) and White-browed Sparrow Weave… Show more

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Cited by 13 publications
(8 citation statements)
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“…Omitted variables cause change in the explained variance, and as there are several hundred models (in our case 500), we can determine the consequence of including or omitting the variables. The final outcome is a rank expressed as mean decrease accuracy (IncMSE%, a measure of sum of squares as a prediction error; the larger the value the larger the importance of a given variable) and mean decrease Gini (IncNodeGini, the impurity of the splits of the decision trees) [59]. RFR provides a measure of variable importance but a current limitation is that no systematic method exists to estimate the shared variances of the variables [60].…”
Section: Discussionmentioning
confidence: 99%
“…Omitted variables cause change in the explained variance, and as there are several hundred models (in our case 500), we can determine the consequence of including or omitting the variables. The final outcome is a rank expressed as mean decrease accuracy (IncMSE%, a measure of sum of squares as a prediction error; the larger the value the larger the importance of a given variable) and mean decrease Gini (IncNodeGini, the impurity of the splits of the decision trees) [59]. RFR provides a measure of variable importance but a current limitation is that no systematic method exists to estimate the shared variances of the variables [60].…”
Section: Discussionmentioning
confidence: 99%
“…We assessed variable importance using the percentage increase in Mean Squared Error (%IncMSE) after a factor was randomly permuted. %IncMSE estimates the contribution of each variable to the reduction in the mean squared error of the model (Breiman, 2001;Echeverry-Galvis et al, 2014). Factors with higher %IncMSE values are considered as more important in explaining the spatial distribution of soil CO2 and N2O emissions.…”
Section: Random Forest Algorithm For the Analysis Of Soil Co2 And N2o...mentioning
confidence: 99%
“…Random forest regression, exploiting lesser variables, omits variables during the calculations to change the explained variance and determines the intrinsic consequence of including or excluding the variables. The final outcome is a rank expressed as mean decrease accuracy (IncMSE%, a measure of sum of squares as a prediction error; the larger the value the larger the importance of a given variable) and mean increase Gini (IncNodeGini, the purity of the splits of the decision trees) (28).…”
Section: Machine Learningmentioning
confidence: 99%