Goodness-of-fit measures of evenness: a new tool for exploring changes in community structure

Growing concern about the fate of biodiversity, highlighted by the Convention on Biological Diversity's 2010 and 2020 targets for stemming biodiversity loss, has intensified interest in methods of assessing change in ecological communities through time. Biodiversity is a multivariate concept, which cannot be well‐represented by a single measure. However, diversity profiles summarize the multivariate nature of multi‐species datasets, and allow a more nuanced interpretation of biodiversity trends than unitary metrics. Here we introduce a new approach to diversity profiling. Our method is based on the knowledge that an ecological community is never completely even and uses this departure from perfect evenness as a novel and insightful way of measuring diversity. We plot our measure of departure as a function of a free parameter, to generate “evenness profiles”. These profiles allow us to separate changes due to dominant species from those due to rare species, and relate these patterns to shifts in overall diversity. This separation of the influence of dominance and rarity on overall diversity enables the user to uncover changes in diversity that would be masked in other methods. We discuss profiling techniques based on this parametric family, and explore its connections with existing diversity indices. Next, we evaluate our approach in terms of predicted community structure (following Tokeshi's niche models) and present an example assessing temporal trends in diversity of British farmland birds. We conclude that this method is an informative and tractable parametric approach for quantifying evenness. It provides novel insights into community structure, revealing the contributions of both rare and common species to biodiversity trends

Lianas and soil nutrients predict fine-scale distribution of above-ground biomass in a tropical moist forest

Acknowledgements. This study was supported by the FP7-PEOPLE-2013-IEF Marie-Curie Action – SPATFOREST. Tree data from BCI were provided by the Center for Tropical Forest Science of the Smithsonian Tropical Research Institute and the primary granting agencies that have supported the BCI plot tree census. Data for the liana censuses were supported by the US National Science Foundation grants: DEB-0613666, DEB-0845071, and DEB-1019436 (to SAS). Soil data was funded by the National Science Foundation grants DEB021104, DEB021115, DEB0212284 and DEB0212818 supporting soils mapping in the BCI plot. We thank Helene Muller-Landau for providing some data on tree height for some BCI trees. We also thank all the people that contributed to obtain the data.Peer reviewedPostprin

Some aspects of complex interactions involving soil mesofauna: analysis of the results from a Scottish woodland

Stepwise regression modelling and canonical correspondence analysis were used to analyse data on soil properties and the abundance of soil mesofauna collected from a woodland typical of the Borders of Scotland. The pattern of relationships revealed by stepwise regression models was different for each month, and the models compiled on the overall dataset were generally weaker than those compiled for separate months. Functional relationships among different microarthropods revealed by stepwise regression modelling are summarised in a structural model of their statistical associations. Interpretation of specific relationships revealed is given and implications for dynamic simulation models are discussed. Canonical correspondence analysis revealed that both microbial feeding nematodes (MF) and plant feeding nematodes (PF) appear to prefer a high level of bacteria and moisture, glomalin and organic matter in the soil. Close scrutiny, however, reveals that microbial feeding nematodes have a particularly high affinity to the sites with a high level of bacteria and organic matter, whilst plant feeding nematodes appear to be more associated with moisture and glomalin. Folsomia candida was abundant in sites with a higher pH level (pH ranged between 3.1 and 4.9), but was not abundant in sites with high ergosterol or a high bacteria, moisture, glomalin and organic matter level. However, other Collembola (mainly represented by Folsomia quadrioculata) appeared to be associated with high levels of ectomycorrhizal fungi. As F. candida is known to feed on fungal food sources, the results suggest that the relatively high local abundances of this collembolan might have caused local declines in ectomycorrhizal fungi, reflected, in turn, in the increase in pH. In addition, environmental plasticity of this species might have allowed them to expand into areas with low fungal density by utilising alternative food sources. The fact that F. candida was a dominant microarthropod in the majority of the samples collected in this research also supports this point. However, for those samples where F. candida were less abundant, overcompensatory fungal growth due to grazing by mites and other Collembola was implicated. Overall, our results suggest that both direct negative and indirect positive effects of the microarthropod community on specific fungal groups appear to take place. The differential effect of specific mesofaunal groups on other soil biota justifies their detailed representation in dynamic simulation models of soil ecosystems