<p>Notes: F values of parameters included in best-fit models for each response variable are given. Empty cells indicate parameters that were considered initially in models but finally were not included in best-fit models according to log-likelihood tests. Tree-tree layer; Shrub-shrub layer; regen-regeneration layer; * <i>p</i><0.05; ** <i>p</i><0.01; *** <i>p</i><0.001.</p><p>Best-fit linear mixed-effect models testing the effects of forest type, slope, slope position, and first-order interactions on beta diversity components.</p
<p>Taxonomic group was included as a random factor to control for possible taxonomic dependence. Whe...
<p>Per species, only models within two AIC<sub>c</sub> (Akaike’s Information Criterion) units from t...
<p>Alternative models were fitted with single predictor variables. Listed are AIC and Chi<sup>2</sup...
<p>Notes: F values of parameters included in best-fit models for each response variable are given. E...
*<p>Parameters in bold have significant coefficients (<i>p</i><0.05). For the forest biome study, el...
<p>Showing the best-fit linear regression (dashed lines) and quadratic regression (solid lines). For...
<p>Coefficients vary by Sample Period where they differ significantly from population coefficients, ...
<p>Results of linear mixed effects models testing precipitation change (PC) and functional group (FG...
Regression analysis is one of the most popular statistical modeling tools, which can define linear o...
<p>Results of mixed effects models testing the effects of sampling period, landscape characteristics...
<p>A) Fixed effects including all the variables. B) Fixed effects excluding income per capita. Accor...
<p>The models assumed a normally distributed response with grass height log-transformed. Transect wa...
Types of forest datasets Forest datasets are usually hierarchical e.g. needles within branches branc...
Aims The aim of this guide is to provide practical help for ecologists who analyze data from biodi...
<p><i>Note:</i> See definitions for parameters in the text; Parameters that are not significant were...
<p>Taxonomic group was included as a random factor to control for possible taxonomic dependence. Whe...
<p>Per species, only models within two AIC<sub>c</sub> (Akaike’s Information Criterion) units from t...
<p>Alternative models were fitted with single predictor variables. Listed are AIC and Chi<sup>2</sup...
<p>Notes: F values of parameters included in best-fit models for each response variable are given. E...
*<p>Parameters in bold have significant coefficients (<i>p</i><0.05). For the forest biome study, el...
<p>Showing the best-fit linear regression (dashed lines) and quadratic regression (solid lines). For...
<p>Coefficients vary by Sample Period where they differ significantly from population coefficients, ...
<p>Results of linear mixed effects models testing precipitation change (PC) and functional group (FG...
Regression analysis is one of the most popular statistical modeling tools, which can define linear o...
<p>Results of mixed effects models testing the effects of sampling period, landscape characteristics...
<p>A) Fixed effects including all the variables. B) Fixed effects excluding income per capita. Accor...
<p>The models assumed a normally distributed response with grass height log-transformed. Transect wa...
Types of forest datasets Forest datasets are usually hierarchical e.g. needles within branches branc...
Aims The aim of this guide is to provide practical help for ecologists who analyze data from biodi...
<p><i>Note:</i> See definitions for parameters in the text; Parameters that are not significant were...
<p>Taxonomic group was included as a random factor to control for possible taxonomic dependence. Whe...
<p>Per species, only models within two AIC<sub>c</sub> (Akaike’s Information Criterion) units from t...
<p>Alternative models were fitted with single predictor variables. Listed are AIC and Chi<sup>2</sup...