AimsThe aim of this guide is to provide practical help for ecologists who analyze data from biodiversity–ecosystem functioning experiments. Our approach differs from others in the use of least squares-based linear models (LMs) together with restricted maximum likelihood-based mixed models (MMs) for the analysis of hierarchical data. An original data set containing diameter and height of young trees grown in monocultures, 2- or 4-species mixtures under ambient light or shade is used as an example.MethodsStarting with a simple LM, basic features of model fitting and the subsequent analysis of variance (ANOVA) for significance tests are summarized. From this, more complex models are developed. We use the statistical software R for model fitting and to demonstrate similarities and complementarities between LMs and MMs. The formation of contrasts and the use of error (LMs) or random-effects (MMs) terms to account for hierarchical data structure in ANOVAs are explained.Important FindingsData from biodiversity experiments can be analyzed at the level of entire plant communities (plots) and plant individuals. The basic explanatory term is species composition, which can be divided into contrasts in many ways depending on specific biological hypotheses. Typically, these contrasts code for aspects of species richness or the presence of particular species. For significance tests in ANOVAs, contrast terms generally are compared with remaining variation of the explanatory terms from which they have been ‘carved out’. Once a final model has been selected, parameters (e.g. means or slopes for fixed-effects terms and variance components for error or random-effects terms) can be estimated to indicate the direction and size of effects.

AimsTopography has long been recognized as an important factor in shaping species distributions. Many studies revealed that species may show species–habitat associations. However, few studies investigate how species assemblages are associated with local habitats, and it still remains unclear how the community–habitat associations vary with species abundance class and life stage. In this study, we analyzed the community–habitat associations in a subtropical montane forest.MethodsThe fully mapped 25-ha (500×500 m) forest plot is located in Badagongshan Nature Reserve in Hunan Province, Central China. It was divided into 625 (20×20 m) quadrats. Habitat types were classified by multivariate regression tree analyses that cluster areas with similar species composition according to the topographic characteristics. Indicator species analysis was used to identify the most important species for structuring species assemblages. We also compared the community–habitat associations for two levels of species abundances (i.e. abundant and rare) and three different life stages (i.e. saplings, juveniles and adults), while accounting for sample size effects.Important FindingsThe Badagongshan plot was divided into five distinct habitat types, which explained 34.7% of the variance in tree species composition. Even with sample size taken into account, community–habitat associations for rare species were much weaker than those for abundant species. Also when accounting for sample size, very small differences were found in the variance explained by topography for the three life stages. Indicator species of habitat types were mainly abundant species, and nearly all adult stage indicator species were also indicators in juvenile and sapling stages. Our study manifested that topographical habitat filtering was important in shaping overall local species compositions. However, habitat filtering was not important in shaping rare species’ distributions in this forest. The community–habitat association patterns in this forest were mainly shaped by abundant species. In addition, during the transitions from saplings to juveniles, and from juveniles to adults, the relative importance of habitat filtering was very weak.

Analyzing how climate change affects vegetation distribution is one of the central issues of global change ecology as this has important implications for the carbon budget of terrestrial vegetation. Mapping vegetation distribution under historical climate scenarios is essential for understanding the response of vegetation distribution to future climatic changes. The reconstructions of palaeovegetation based on pollen data provide a useful method to understand the relationship between climate and vegetation distribution. However, this method is limited in time and space. Here, using species distribution model (SDM) approaches, we explored the climatic determinants of contemporary vegetation distribution and reconstructed the distribution of Chinese vegetation during the Last Glacial Maximum (LGM, 18,000 14C yr BP) and Middle-Holocene (MH, 6000 14C yr BP). The dynamics of vegetation distribution since the LGM reconstructed by SDMs were largely consistent with those based on pollen data, suggesting that the SDM approach is a useful tool for studying historical vegetation dynamics and its response to climate change across time and space. Comparison between the modeled contemporary potential natural vegetation distribution and the observed contemporary distribution suggests that temperate deciduous forests, subtropical evergreen broadleaf forests, temperate deciduous shrublands and temperate steppe have low range fillings and are strongly influenced by human activities. In general, the Tibetan Plateau, North and Northeast China, and the areas near the 30°N in Central and Southeast China appeared to have experienced the highest turnover in vegetation due to climate change from the LGM to the present.

Evergreen broadleaved woody plants (EBWPs) are dominant components in forests and savanna of the global tropic and subtropic regions. Southern China possesses the largest continuous area of subtropical EBWPs distribution, harboring a high proportion of endemic species. Hotspot and gap analyses are effective methods for analyzing the spatial pattern of biodiversity and conservation and were used here for EBWPs in China. Based on a distribution data set of 6,265 EBWPs with a spatial resolution of 50 × 50 km, we measured diversity of EBWPs in China using four indices: species richness, corrected weighted endemism, relative phylogenetic diversity, and phylogenetic endemism. According to the results based on 10% threshold, 15.73% of China’s land area was identified as hotspots using at least one diversity index. Only 2.14% of China’s land area was identified as hotspots for EBWPs by all four metrics simultaneously. Most of the hotspots locate in southern mountains. Moreover, we found substantial conservation gaps for Chinese EBWPs. Only 25.43% of the hotspots are covered by existing nature reserves by more than 10% of their area. We suggest to promote the establishment and management of nature reserve system within the hotspot gaps.