Vegetation complexity and nesting resource availability predict bee diversity and functional traits in community gardens

Urban gardens can support diverse bee communities through resource provision in resource poor environments. Yet the effects of local habitat and landscape factors on wild bee communities in cities is still insufficiently understood, nor is how this information could be applied to urban wildlife conservation. Here we investigate how taxonomic and functional diversity of wild bees and their traits in urban community gardens are related to garden factors and surrounding landscape factors (e.g., plant diversity, amount of bare ground, amount of nesting resources, amount of landscape imperviousness). Using active and passive methods in 18 community gardens in Berlin, Germany, we documented 26 genera and 102 species of bees. We found that higher plant species richness and plant diversity as well as higher amounts of deadwood in gardens leads to higher numbers of wild bee species and bee (functional) diversity. Furthermore, higher landscape imperviousness surrounding gardens correlates with more cavity nesting bees, whereas a higher amount of bare ground correlates with more ground‐nesting bees. Pollen specialization was positively associated with plant diversity, but no factors strongly predicted the proportion of endangered bees. Our results suggest that, aside from foraging resources, nesting resources should be implemented in management for more pollinator‐friendly gardens. If designed and managed using such evidence‐based strategies, urban gardens can create valuable foraging and nesting habitats for taxonomically and functionally diverse bee communities in cities

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases

The influence of categorizing survival time on parameter estimates in a Cox model

With longer follow-up times, the proportional hazards assumption is questionable in the Cox model. Cox suggested to include an interaction between a covariate and a function of time. To estimate such a function in Stata, a substantial enlargement of the data is required. This may cause severe computational problems. We will consider categorizing survival time, which raises issues as to the number of cutpoints, their position, the increased number of ties, and the loss of information, to handle this problem. Sauerbrei et al. (2007) proposed a new selection procedure to model potential time-varying effects. They investigate a large dataset (N = 2982) with 20 years follow-up, for which the Stata command stsplit creates about 2.2 million records. Categorizing the data in 6-month intervals gives 35,747 records. We will systematically investigate the influence of the length of categorization intervals and the four methods of handling ties in Stata. The results of our categorization approach are promising, showing a sensible way to handle time-varying effects even in simulation studies. References: Sauerbrei, W., Royston, P. and Look, M. (2007). A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. (Biometrical Journal, in press)

Functional form for no. of positive nodes for two models.

<p>Predictor for univariable analysis of no. of positive lymph nodes derived by two Cox models assuming a linear effect (dashed line) or the significant improvement from the class of FP models (solid line).</p

Hazard ratios and discriminative ability of NPI.

<p>Hazard ratios and discriminative ability of NPI.</p

Estimated effects of NPI components.

<p>Estimated effects of NPI components.</p

REMARK type profile.

<p>REMARK type profile.</p

Kaplan-Meier curve for combinations of NPI(3) and hormone receptor.

<p>NPI groups are defined as 1: NPI<3.4; 2: 3.4≤NPI≤5.4; 3: NPI>5.4.</p

Kaplan-Meier estimates of survival probabilities for prognostic groups defined by the Nottingham Prognostic Index.

<p>Top NPI(3)– 3 groups, below NPI(6)– 6 groups.</p

MFP model.

<p>MFP model.</p