Reconstruction of historical pollination rates reveals linked declines of pollinators and plants

Widespread reports of low pollination rates suggest a recent anthropogenic decline in pollination that could threaten natural and agricultural ecosystems. Nevertheless, unequivocal evidence for a decline in pollination over time has remained elusive because it was not possible to determine historical pollination rates. Here we demonstrate a widely applicable method for reconstructing historical pollination rates, thus allowing comparison with contemporary rates from the same sites. We focused on the relationship between the oil-collecting bee Rediviva peringueyi (Melittidae) and the guild of oil-secreting orchid species (Coryciinae) that depends on it for pollination. The guild is distributed across the highly transformed and fragmented lowlands of the Cape Region of South Africa. We show that rehydrated herbarium specimens of Pterygodium catholicum, the most abundant member of the guild, contain a record of past pollinator activity in the form of pollinarium removal rates. Analysis of a pollination time series showed a recent decline in pollination on Signal Hill, a small urban conservation area. The same herbaria contain historical species occurrence data. We analyzed this data and found that there has been a contemporaneous shift in orchid guild composition in urban areas due to the local extirpation of the non-clonal species, consistent with their greater dependence on seeds and pollination for population persistence.

Re-thinking commercial real estate market segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.