University of Würzburg

About: University of Würzburg is a education organization based out in Wurzburg, Bayern, Germany. It is known for research contribution in the topics: Population & CAS Registry Number. The organization has 31437 authors who have published 62203 publications receiving 2337033 citations. The organization is also known as: Julius-Maximilians-Universität Würzburg & Würzburg University.

Environmental factors driving the effectiveness of European agri‐environmental measures in mitigating pollinator loss – a meta‐analysis

Abstract: In Europe, agri-environmental schemes (AES) have been introduced in response to concerns about farmland biodiversity declines. Yet, as AES have delivered variable results, a better understanding of what determines their success or failure is urgently needed. Focusing on pollinating insects, we quantitatively reviewed how environmental factors affect the effectiveness of AES. Our results suggest that the ecological contrast in floral resources created by schemes drives the response of pollinators to AES but that this response is moderated by landscape context and farmland type, with more positive responses in croplands (vs. grasslands) located in simple (vs. cleared or complex) landscapes. These findings inform us how to promote pollinators and associated pollination services in species-poor landscapes. They do not, however, present viable strategies to mitigate loss of threatened or endangered species. This indicates that the objectives and design of AES should distinguish more clearly between biodiversity conservation and delivery of ecosystem services.

Merkel Cell Polyomavirus-Infected Merkel Cell Carcinoma Cells Require Expression of Viral T Antigens

Abstract: Merkel cell carcinoma (MCC) is the most aggressive skin cancer. Recently, it was demonstrated that human Merkel cell polyomavirus (MCV) is clonally integrated in ∼80% of MCC tumors. However, direct evidence for whether oncogenic viral proteins are needed for the maintenance of MCC cells is still missing. To address this question, we knocked down MCV T-antigen (TA) expression in MCV-positive MCC cell lines using three different short hairpin RNA (shRNA)-expressing vectors targeting exon 1 of the TAs. The MCC cell lines used include three newly generated MCV-infected cell lines and one MCV-negative cell line from MCC tumors. Notably, all MCV-positive MCC cell lines underwent growth arrest and/or cell death upon TA knockdown, whereas the proliferation of MCV-negative cell lines remained unaffected. Despite an increase in the number of annexin V-positive, 7-amino-actinomycin D (7-AAD)-negative cells upon TA knockdown, activation of caspases or changes in the expression and phosphorylation of Bcl-2 family members were not consistently detected after TA suppression. Our study provides the first direct experimental evidence that TA expression is necessary for the maintenance of MCV-positive MCC and that MCV is the infectious cause of MCV-positive MCC.

What do interaction network metrics tell us about specialization and biological traits

Abstract: The structure of ecological interaction networks is often interpreted as a product of meaningful ecological and evolutionary mechanisms that shape the degree of specialization in community associations. However, here we show that both unweighted network metrics (connectance, nestedness, and degree distribution) and weighted network metrics (interaction evenness, interaction strength asymmetry) are strongly constrained and biased by the number of observations. Rarely observed species are inevitably regarded as "specialists," irrespective of their actual associations, leading to biased estimates of specialization. Consequently, a skewed distribution of species observation records (such as the lognormal), combined with a relatively low sampling density typical for ecological data, already generates a "nested" and poorly "connected" network with "asymmetric interaction strengths" when interactions are neutral. This is confirmed by null model simulations of bipartite networks, assuming that partners associate randomly in the absence of any specialization and any variation in the correspondence of biological traits between associated species (trait matching). Variation in the skewness of the frequency distribution fundamentally changes the outcome of network metrics. Therefore, interpretation of network metrics in terms of fundamental specialization and trait matching requires an appropriate control for such severe constraints imposed by information deficits. When using an alternative approach that controls for these effects, most natural networks of mutualistic or antagonistic systems show a significantly higher degree of reciprocal specialization (exclusiveness) than expected under neutral conditions. A higher exclusiveness is coherent with a tighter coevolution and suggests a lower ecological redundancy than implied by nested networks.