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A theoretical analysis regarding active vibration control of rotating machines with current-controlled electrodynamic actuators between machine feet and steel frame foundation and with velocity feedback of the machine feet vibrations is presented. First, a generalized mathematical formulation is derived based on a state-space description which can be used for different kinds of models (1D, 2D, and 3D models). It is shown that under special boundary conditions, the control parameters can be directly implemented into the stiffness and damping matrices of the system. Based on the generalized mathematical formulation, an example of a rotating machine—described by a 2D model—with journal bearings, flexible rotor, current-controlled electrodynamic actuators, steel frame foundation, and velocity feedback of the machine feet vibrations is presented where the effectiveness of the described active vibration control system is demonstrated.

Climate change impacts biodiversity and is driving range shifts of species and populations across the globe. To understand the effects of climate warming on biota, long-term observations of the occurrence of species and detailed knowledge on their ecology and life-history is crucial. Mountain species particularly suffer under climate warming and often respond to environmental changes by altitudinal range shifts. We assessed long-term distribution trends of mountain butterflies across the eastern Alps and calculated species’ specific annual range shifts based on field observations and species distribution models, counterbalancing the potential drawbacks of both approaches. We also compiled details on the ecology, behaviour and life-history, and the climate niche of each species assessed. We found that the highest altitudinal maxima were observed recently in the majority of cases, while the lowest altitudes of observations were recorded before 1980. Mobile and generalist species with a broad ecological amplitude tended to move uphill more than specialist and sedentary species. As main drivers we identified climatic conditions and topographic variables, such as insolation and solar irradiation. This study provides important evidence for responses of high mountain taxa to rapid climate change. Our study underlines the advantage of combining historical surveys and museum collection data with cutting-edge analyses.

In addition to promoting glucose homeostasis, glucagon-like peptide 1 (GLP-1) has a number of extra-pancreatic effects that regulate appetite and body weight. GLP-1 delays gastric emptying, which is vital for postprandial glucose (PPG) control. As GLP-1 is rapidly degraded by protease dipeptidyl peptidase-4, a number of degradation-resistant GLP-1 receptor agonists (GLP-1RAs) have been developed for the treatment of Type 2 diabetes mellitus. These agents can be broadly categorized as being short- or long-acting, based on their pharmacokinetic profile. Short-acting agonists predominantly affect PPG and delay gastric emptying in a sustained manner, whereas long-acting agents largely affect fasting plasma glucose and their delay in gastric emptying appears to be subjected to tachyphylaxis. Lixisenatide is a “short-acting” once-daily prandial GLP-1RA. This review provides an overview of the preclinical studies that are currently available and that evaluate the efficacy of lixisenatide on gastric emptying and PPG levels. The preclinical evidence outlined in this review supports the efficacy of lixisenatide in reducing PPG excursions and delaying gastric emptying. Furthermore, in contrast to long-acting agents, the actions of lixisenatide do not appear to be subjected to tachyphylaxis.

Null model analysis has been a popular tool for detecting pattern in binary presence-absence matrices, and previous tests have identified algorithms and metrics that have good statistical properties. However, the behavior of different metrics is often correlated, making it difficult to distinguish different patterns. We compared the performance of a suite of null models and metrics that have been proposed to measure patterns of segregation, aggregation, nestedness, coherence, and species turnover. We found that any matrix with segregated species pairs can be re-ordered to highlight aggregated pairs. As a consequence, the same null model can identify a single matrix as being simultaneously aggregated, segregated or nested. These results cast doubt on previous conclusions of matrix-wide species segregation based on the C-score and the fixed-fixed algorithm. Similarly, we found that recently proposed classification schemes based on patterns of coherence, nestedness, and segregation and aggregation cannot be uniquely distinguished using proposed metrics and null model algorithms. It may be necessary to use a combination of different metrics and to decompose matrix-wide patterns into those of individual pairs of species or pairs of sites to pinpoint the sources of non-randomness.

Demographic pressure is leading to an ever-increasing demand for natural resources. In large parts of Sub-Saharan Africa, natural ecosystems have been transformed into agricultural land, pastures, plantations, or settlement areas. The last remnants of natural ecosystems are preserved for biological and cultural reasons. The Mijikenda Kaya forests in coastal Kenya are small forest remnants with high biological and cultural value. Given demographic pressure and a lack of awareness of the value of bio- and cultural diversity, the destruction of these forest habitats is alarming. Environmental education in schools may help to increase awareness for the need to preserve these ecosystems. In this study, we interviewed teachers from schools located around Kaya Kambe forest in coastal Kenya. We used a standardized questionnaire to analyze the degree of awareness through environmental education, teachers´ attitudes to environmental issues in general, and particularly to sacred Kaya forests. We found that environmental education is of low priority for teachers in schools assessed. One third of respondents do not incorporate environmental topics in their teaching at all. Teachers who have already had environmental education themselves and therefore have respective knowledge are prone to incorporate these topics in their lessons. However, there is hardly any reference to the conservation of Kaya forests for their spiritual heritage. Only teachers who have a relation with Kaya forests and the local culture considered the forest to be of high biological and cultural relevance and worth conserving. Our study shows that environmental education is still under-represented in schools in Kenya, and should be given a higher priority in the school curricula in order to increase awareness of the need to preserve biological and cultural diversity as well as natural resources for the future.

Warm-adapted species survived the cold stages of the past glacial-interglacial cycles in southern European peninsulas and recolonized major parts of Central and Northern Europe in the wake of postglacial warming. However, many of the genetic lineages which differentiated within these refugia predate even the Pleistocene. One of these refugia is the Italian Peninsula with its surrounding islands. In this meta-analysis, we compiled phylogeographic patterns of multiple species across this region. We transformed existing geographic information on 78 animal and plant species (with a total of 471 genetic lineages) within 17 predefined areas into presence/absence matrices. We elaborated three matrices: (i) only old lineages exclusively distinguished by deep splits, (ii) only young lineages distinguished by shallow (i.e. recent) splits, and (iii) presence/absence of the respective species. To infer biogeographic relationships between the predefined areas, we performed bootstrapped neighbour joining cluster analyses on these three matrices. In addition, we reviewed the geological history of Italy to identify causes of the observed biogeographic patterns. We found Sardinia and Corsica to be biogeographically closely linked with each other, and that they diverge strongly from all other regions. Sicily also diverges strongly from all other regions, while the intra-island differentiation was comparatively low. On the Italian mainland, Calabria exhibited the most pronounced biogeographic differentiation, often with several lineages present, resulting from old vicariance events within the region. Furthermore, southern Apulia and the Po Plain with adjoining areas of northern peninsular Italy displayed considerable distinctiveness. Admixture prevailed in the areas between these three regions. The ancient isolation of Sicily, as well as Sardinia plus Corsica, resulted in endemic lineages with only moderate recent exchange with adjacent mainland regions. Pronounced diversification occurs within the Italian Peninsula. The complex tectonic activities, such as shifting (micro)plates, submergence of major parts of peninsular Italy with the genesis of numerous Pliocene islands, in combination with the climatic cycles during the Pleistocene have most likely generated the current biogeographic pattern of species. Immigrations from the Balkan Peninsula into northern Italy partly accounted for the distinctiveness of this region.

This review identifies several important challenges in null model testing in ecology: 1) developing randomization algorithms that generate appropriate patterns for a specified null hypothesis; these randomization algorithms stake out a middle ground between formal Pearson-Neyman tests (which require a fully-specified null distribution) and specific process-based models (which require parameter values that cannot be easily and independently estimated); 2) developing metrics that specify a particular pattern in a matrix, but ideally exclude other, related patterns; 3) avoiding classification schemes based on idealized matrix patterns that may prove to be inconsistent or contradictory when tested with empirical matrices that do not have the idealized pattern; 4) testing the performance of proposed null models and metrics with artificial test matrices that contain specified levels of pattern and randomness; 5) moving beyond simple presence-absence matrices to incorporate species-level traits (such as abundance) and site-level traits (such as habitat suitability) into null model analysis; 6) creating null models that perform well with many sites, many species pairs, and varying degrees of spatial autocorrelation in species occurrence data. In spite of these challenges, the development and application of null models has continued to provide valuable insights in ecology, evolution, and biogeography for over 80 years.

Species community structures respond strongly to habitat changes. These are either driven by nature or human activities. The biota of East African drylands responds highly sensitively to natural and anthropogenic impacts. Thus, seasonality strongly influences resource availability in a cyclic manner during the year, with cyclic appearance of the different developmental stages of invertebrates, while man-made landscape transformations profoundly and permanently modify habitat structures and, as a consequence, species communities. Butterflies are an excellent model group for the study of the effects of seasonality, and to test for biodiversity responses to anthropogenic activities such as habitat modification, degradation and destruction. We performed transect counts of adult butterflies in riparian forests and their adjoining areas, either dry savannahs with occasional pasturing (i.e. near-natural status) or farmland areas with fields, gardens and settlements (i.e. highly degraded status with lack of original vegetation). Transects were set along the river beds as well as at 250 m and 500 m distances parallel to these rivers, with eight transects per distance class and site (i.e. 48 transects in total). We recorded habitat structures for each transect. Counts were conducted during the dry and the rainy season, with 16 repetitions for each single transect, i.e. eight per season and transect. We compiled trait data on morphology, geographic distribution, ecology, behaviour, and life-history for all butterfly species encountered. Our results show higher species richness and numbers of individuals in farmland transects compared with the savannah region. Seasonal fluctuations of the detectable species abundances between the rainy and dry season were severe. These fluctuations were much more pronounced for the savannah than the farmland area, i.e. was buffered by human activities. Farmland and savannah support two distinct butterfly communities, with generalist species being more common in the farmland communities. Strict habitat associations were comparatively weak and typical dry savannah and riparian forest species were not clearly restricted to the near natural landscape.

Nestedness is a common biogeographic pattern in which small communities form proper subsets of large communities. However, the detection of nestedness in binary presence—absence matrices will be affected by both the metric used to quantify nestedness and the reference null distribution. In this study, we assessed the statistical performance of eight nestedness metrics and six null model algorithms. The metrics and algorithms were tested against a benchmark set of 200 random matrices and 200 nested matrices that were created by passive sampling. Many algorithms that have been used in nestedness studies are vulnerable to type I errors (falsely rejecting a true null hypothesis). The best-performing algorithm maintains fixed row and fixed column totals, but it is conservative and may not always detect nestedness when it is present. Among the eight indices, the popular matrix temperature metric did not have good statistical properties. Instead, the Brualdi and Sanderson discrepancy index and Cutler's index of unexpected presences performed best. When used with the fixed-fixed algorithm, these indices provide a conservative test for nestedness. Although previous studies have revealed a high frequency of nestedness, a reanalysis of 288 empirical matrices suggests that the true frequency of nested matrices is between 10% and 40%.

Soil bacteria and fungi play key roles in the functioning of terrestrial ecosystems, yet our understanding of their responses to climate change lags significantly behind that of other organisms. This gap in our understanding is particularly true for drylands, which occupy ∼41% of Earth´s surface, because no global, systematic assessments of the joint diversity of soil bacteria and fungi have been conducted in these environments to date. Here we present results from a study conducted across 80 dryland sites from all continents, except Antarctica, to assess how changes in aridity affect the composition, abundance, and diversity of soil bacteria and fungi. The diversity and abundance of soil bacteria and fungi was reduced as aridity increased. These results were largely driven by the negative impacts of aridity on soil organic carbon content, which positively affected the abundance and diversity of both bacteria and fungi. Aridity promoted shifts in the composition of soil bacteria, with increases in the relative abundance of Chloroflexi and α-Proteobacteria and decreases in Acidobacteria and Verrucomicrobia. Contrary to what has been reported by previous continental and global-scale studies, soil pH was not a major driver of bacterial diversity, and fungal communities were dominated by Ascomycota. Our results fill a critical gap in our understanding of soil microbial communities in terrestrial ecosystems. They suggest that changes in aridity, such as those predicted by climate-change models, may reduce microbial abundance and diversity, a response that will likely impact the provision of key ecosystem services by global drylands.