Herbivory and nutrients shape grassland soil seed banks

Anthropogenic nutrient enrichment and shifts in herbivory can lead to dramatic changes in the composition and diversity of aboveground plant communities. In turn, this can alter seed banks in the soil, which are cryptic reservoirs of plant diversity. Here, we use data from seven Nutrient Network grassland sites on four continents, encompassing a range of climatic and environmental conditions, to test the joint effects of fertilization and aboveground mammalian herbivory on seed banks and on the similarity between aboveground plant communities and seed banks. We find that fertilization decreases plant species richness and diversity in seed banks, and homogenizes composition between aboveground and seed bank communities. Fertilization increases seed bank abundance especially in the presence of herbivores, while this effect is smaller in the absence of herbivores. Our findings highlight that nutrient enrichment can weaken a diversity maintaining mechanism in grasslands, and that herbivory needs to be considered when assessing nutrient enrichment effects on seed bank abundance.EEA Santa CruzFil: Eskelinen, Anu. German Centre for Integrative Biodiversity Research; AlemaniaFil: Eskelinen, Anu. Helmholtz Centre for Environmental Research. Department of Physiological Diversity; AlemaniaFil: Eskelinen, Anu. University of Oulu. Ecology & Genetics; FinlandiaFil: Jessen, Maria Theresa. Helmholtz Centre for Environmental Research. Department of Physiological Diversity; AlemaniaFil: Jessen, Maria Theresa. German Centre for Integrative Biodiversity Research; AlemaniaFil: Jessen, Maria Theresa. Helmholtz Centre for Environmental Research – UFZ. Department of Community Ecology; AlemaniaFil: Bahamonde, Hector Alejandro. Universidad Nacional de La Plata. Ciencias Agrarias y Forestales; Argentina.Fil: Bakker, Jonathan D. University of Washington. School of Environmental and Forest Sciences; Estados UnidosFil: Borer, Elizabeth T. University of Minnesota. Department of Ecology, Evolution & Behavior; Estados UnidosFil: Caldeira, Maria C. University of Lisbon. Forest Research Centre. Associate Laboratory TERRA. School of Agriculture; Portugal.Fil: Harpole, William Stanley. German Centre for Integrative Biodiversity Research (iDiv); AlemaniaFil: Harpole, William Stanley. Helmholtz Centre for Environmental Research – UFZ. Department of Community Ecology; AlemaniaFil: Harpole, William Stanley. Martin Luther University. Institute of Biology; AlemaniaFil: Jia, Meiyu. University of Washington. School of Environmental and Forest Sciences; Estados UnidosFil: Jia, Meiyu. East China University of Technology. School of Water Resources & Environmental Engineering; China.Fil: Jia, Meiyu. Beijing Normal University. College of Life Sciences; China.Fil: Lannes, Luciola S. São Paulo State University-UNESP. Department of Biology and Animal Sciences; Brasil.Fil: Nogueira, Carla. University of Lisbon. Forest Research Centre. Associate Laboratory TERRA. School of Agriculture; Portugal.Fil: Venterink, Harry Olde. Vrije Universiteit Brussel (VUB). Department of Biology; BélgicaFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Porath-Krause, Anita J. University of Minnesota. Department of Ecology, Evolution & Behavior; Estados UnidosFil: Seabloom, Eric William. University of Minnesota. Department of Ecology, Evolution & Behavior; Estados UnidosFil: Schroeder, Katie. University of Minnesota. Department of Ecology, Evolution & Behavior; Estados UnidosFil: Schroeder, Katie. University of Georgia. Odum School of Ecology; Estados UnidosFil: Tognetti, Pedro M. Universidad de Buenos Aires. Facultad de Agronomía; Argentina.Fil: Tognetti, Pedro M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina.Fil: Tognetti, Pedro M. Swiss Federal Institute for Forest, Snow and Landscape Research WSL; SuizaFil: Yasui, Simone-Louise E. Queensland University of Technology. School of Biological and Environmental Sciences; Australia.Fil: Virtanen, Risto. University of Oulu. Ecology & Genetics; FinlandiaFil: Sullivan, Lauren L. University of Missouri. Division of Biological Sciences; Estados UnidosFil: Sullivan, Lauren L. Michigan State University. Department of Plant Biology; Estados UnidosFil: Sullivan, Lauren L. Michigan State University. W. K. Kellogg Biological Station; Estados UnidosFil: Sullivan, Lauren L. Michigan State University. Ecology, Evolution and Behavior Program; Estados Unido

Memory State Feedback Control for Time-Varying Delay Switched Fuzzy Systems

Consider the problem of memoryless state feedback controller for time-delay system, which cannot consider both the memoryless and the memory items in the system. Therefore, the memoryless state feedback controller has certain limitations and is more conservative. This paper addresses the memory state feedback control for the time-varying delay switched fuzzy systems based on T-S fuzzy model to overcome the problem discussed above. The state vector and input of the time-varying delay systems contain unknown time-varying delay with known bounds. The designed controller whose parameters are solvable can introduce past state information and reduce the system conservativeness. The more general Lyapunov-Krasovskii functional is selected and the switching law is designed in order to analyze the open-loop system stability, and the memory state feedback controller is designed for the closed-loop system and the criterion for its asymptotic stability. Discuss the solvability of the above two criteria. Finally, a numerical example is given. The simulation results show that the proposed method is more feasible and effective

Spatial Pattern and Ecological Process Difference Analyses of the Boundary Habitats of a Treeline Patch: A Case Study from the Li Mountain, North China

Treeline patches are among Earth’s most sensitive and are important model ecosystems for assessing climate change trends. To explore ecological factors that limit the species’ survival in treelines, the treeline patch of Li Mountain National Nature Reserve was selected as the research site. Pinus armandii (P. armandii), Betula albo-sinensis (B. albo-sinensis), and Betula utilis (B. utilis) were selected as research species based on their dominance. Two 50 m × 50 m plots were established separately from the upper and lower limits of the highest treeline patch for point pattern analysis. Five 10 m × 10 m quadrats per plot were sampled to investigate the flora and environmental factors. The results showed that: (1) Slope and community composition at tree layer in quadrates had significant differences between upper and lower limits. Pinus armandii had a greater population size at the upper limit. Seedling recruitment restricted population development for B. albo-sinensis at the lower limit and B. utilis at the upper limit and less regeneration of B. albo-sinensis at the upper limit. (2) More aggregation scales occurred at the upper limit, and P. armandii had more aggregation scales than the other two species at 0–25 m. The heterogeneity caused by density distribution affected P. armandii pattern at the upper limit, and heterogeneity of seed dispersal could explain species patterns in both limits. Distinctness of size difference may have an influence on inter-specific species correlations

Spatial Pattern and Ecological Process Difference Analyses of the Boundary Habitats of a Treeline Patch: A Case Study from the Li Mountain, North China

Treeline patches are among Earth’s most sensitive and are important model ecosystems for assessing climate change trends. To explore ecological factors that limit the species’ survival in treelines, the treeline patch of Li Mountain National Nature Reserve was selected as the research site. Pinus armandii (P. armandii), Betula albo-sinensis (B. albo-sinensis), and Betula utilis (B. utilis) were selected as research species based on their dominance. Two 50 m × 50 m plots were established separately from the upper and lower limits of the highest treeline patch for point pattern analysis. Five 10 m × 10 m quadrats per plot were sampled to investigate the flora and environmental factors. The results showed that: (1) Slope and community composition at tree layer in quadrates had significant differences between upper and lower limits. Pinus armandii had a greater population size at the upper limit. Seedling recruitment restricted population development for B. albo-sinensis at the lower limit and B. utilis at the upper limit and less regeneration of B. albo-sinensis at the upper limit. (2) More aggregation scales occurred at the upper limit, and P. armandii had more aggregation scales than the other two species at 0–25 m. The heterogeneity caused by density distribution affected P. armandii pattern at the upper limit, and heterogeneity of seed dispersal could explain species patterns in both limits. Distinctness of size difference may have an influence on inter-specific species correlations

An optical fiber surface plasmon resonance biosensor for wide range detection

An optical fiber surface plasmon resonance biosensor is presented that allows to numerically demonstrate, using transfer matrix method and the finite difference time domain method, the detection range is very wide. Two different structures of graphene photonic crystal multilayer (i.e. sensor I and sensor II) are constructed in the cladding region of single-mode fiber. Graphene is used as the plasma layer instead of the traditional metal. According to the analysis, the properties of graphene can be changed by adjusting the chemical potential µc. In the spectral region of 1.667|µc| < ћω < 2|µc|, the imaginary part of conductivity σ″ becomes negative. Thus the weakly bounded low-less TE-SPR is supported by graphene. The results of the numerical simulation show that the relationship between refractive index and resonant wavelength is linear. The sensor I can detect the refractive index range of 1.33–1.4, and the sensitivity is 1942 nm/RIU. The sensor II can detect the refractive index range of 1.41–1.67, and the sensitivity is up to 2315.4 nm/RIU. Therefore, the detection of wide refractive index range of 1.33–1.67 or simultaneous detection of different biological medium concentration is realized by the sensor

Association between microalbuminuria and subclinical atherosclerosis evaluated by carotid artery intima-media in elderly patients with normal renal function

<p>Abstract</p> <p>Background</p> <p>Moderate to severe renal insufficiency and albuminuria have been shown to be independent risk factors for atherosclerosis. However, little is known about the direct association between subclinical atherosclerosis evaluated by carotid artery intima-media thickness (IMT) and microalbuminuria in elderly patients with normal renal function.</p> <p>Methods</p> <p>Subjects were 272 elderly patients (age  ≥ 60 years) with normoalbuminuria (n = 238) and microalbuminuria (n = 34). Carotid IMT was measured by means of high-resolution B-mode ultrasonography. Estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73 m² was defined as normal renal function. Those who had macroalbuminuria and atherosclerotic vascular disease were not included.</p> <p>Results</p> <p>Compared to subjects with normoalbuminuria, subjects with microalbuminuria had higher mean carotid IMT (1.02 ± 0.38 vs. 0.85 ± 0.28 mm; P < 0.01) and maximal IMT (1.86 ± 0.86 vs. 1.60 ± 0.73 mm; P = 0.06). By a multiple linear regression, microalbuminuria positively correlated with mean carotid IMT after adjusting for traditional cardiovascular disease risk factors including age, sex, hypertension, diabetes, smoking, total cholesterol, pulse pressure, waist circumference, serum uric acid. As a categorical outcome, the prevalence of the highest mean cariotid IMT quartile (increased IMT ≥ 1.05 mm) was compared with the lower three quartiles. After adjusted for potential confounders, microalbuminuria was associated with increased carotid IMT, with an odds ratio of 2.95 [95 % confidence interval, 1.22 – 7.10]. eGFR was not significantly associated with mean carotid IMT in our analysis.</p> <p>Conclusions</p> <p>A slight elevation of albuminuria is a significant determinant of carotid IMT independent of traditional cardiovascular risk factors in our patients. Our study further confirms the importance of intensive examinations for the early detection of atherosclerosis when microalbuminuria is found in elderly patients, although with normal renal function.</p