The role of plant functional trade-offs for biodiversity changes and biome shifts under scenarios of global climatic change

The global geographic distribution of biodiversity and biomes is determined by species-specific physiological tolerances to climatic constraints. Current vegetation models employ empirical bioclimatic relationships to predict present-day vegetation patterns and to forecast biodiversity changes and biome shifts under climatic change. In this paper, we consider trade-offs in plant functioning and their responses under climatic changes to forecast and explain changes in plant functional richness and shifts in biome geographic distributions. <br><br> The Jena Diversity model (JeDi) simulates plant survival according to essential plant functional trade-offs, including ecophysiological processes such as water uptake, photosynthesis, allocation, reproduction and phenology. We use JeDi to quantify changes in plant functional richness and biome shifts between present-day and a range of possible future climates from two SRES emission scenarios (A2 and B1) and seven global climate models using metrics of plant functional richness and functional identity. <br><br> Our results show (i) a significant loss of plant functional richness in the tropics, (ii) an increase in plant functional richness at mid and high latitudes, and (iii) a pole-ward shift of biomes. While these results are consistent with the findings of empirical approaches, we are able to explain them in terms of the plant functional trade-offs involved in the allocation, metabolic and reproduction strategies of plants. We conclude that general aspects of plant physiological tolerances can be derived from functional trade-offs, which may provide a useful process- and trait-based alternative to bioclimatic relationships. Such a mechanistic approach may be particularly relevant when addressing vegetation responses to climatic changes that encounter novel combinations of climate parameters that do not exist under contemporary climate

Will Remote Sensing Shape the Next Generation of Species Distribution Models?

Two prominent limitations of species distribution models (SDMs) are spatial biases in existing occurrence data and a lack of spatially explicit predictor variables to fully capture habitat characteristics of species. Can existing and emerging remote sensing technologies meet these challenges and improve future SDMs? We believe so. Novel products derived from multispectral and hyperspectral sensors, as well as future Light Detection and Ranging (LiDAR) and RADAR missions, may play a key role in improving model performance. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling. We hope this interdisciplinary perspective will motivate ecologists, remote sensing experts and modelers to work together for developing a more refined SDM framework in the near future

Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery

Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden) and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77) than one using the full set of wavebands (77%, Kappa statistic value = 0.65). Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages

GATE : a simulation toolkit for PET and SPECT

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols, and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at the address http://www-lphe.epfl.ch/GATE/

STUDI DESKRIPTIF LEVEL BERPIKIR GEOMETRI VAN HIELE SISWA DI SMP NEGERI PERCONTOHAN DI LEMBANG

Geometri sekolah mempunyai peluang besar untuk dipahami oleh siswa dibandingkan dengan cabang ilmu matematika yang lainnya. Hal ini dikarenakan pengenalan konsep dasar geometri sudah dikenal oleh siswa sejak usia dini, seperti mengenal bangun-bangun geometri. Namun beberapa penelitian menunjukkan bahwa masih banyak siswa yang mengalami kesulitan dalam belajar geeometri, khususnya pada tingkat SMP. Oleh karena itu diperlukan penelitian terhadap level berpikir geometri siswa. Penelitian ini bertujuan untuk mengetahui: (1) level berpikir geometri siswa di SMP Negeri percontohan di Lembang, dan (2) menelaah apakah pembelajaran geometri yang berlangsung di sekolah menerapkan tahapan pembelajaran Van Hiele atau tidak. Metode dalam penelitian ini merupakan studi deskriptif dengan subjek penelitian adalah siswa kelas IX dari dua sekolah menengah pertama di Lembang. Instrumen dalam penelitian ini terdiri dari: (1) instrumen tes, yaitu tes level berpikir geometri Van Hiele pada materi bangun datar. Hasil dari tes ini dianalisis dengan kategori level berpikir sebagai berikut: level 0 adalah tahap pengenalan; level 1 adalah tahap analisis; level 2 adalah tahap pengurutan; level 3 adalah tahap deduksi formal; dan level 4 adalah tahap akurasi. (2) Instrumen non tes, yaitu berupa wawancara terhadap guru dan murid. Berdasarkan hasil penelitian diperoleh kesimpulan bahwa: (1) secara keseluruhan siswa SMP telah memasuki tahap berpikir geometri Van Hiele. Sebagian besar siswa berada pada tahap pengenalan (level 0) yaitu 81,16%, sedangkan sisanya telah memasuki tahap analisis (level 1) sebesar 17,39% dan tahap pengurutan (level 2) sebesar 1,45%. (2) Pembelajaran geometri di sekolah kurang memperhatikan tahapan pembelajaran geometri Van Hiele---------- Student has a big opportunity to understand geometry because the basic concept has early familiar, such as know the geometry’s objects. However, some of the research were show that many student difficult to learn geometry, specifically for junior high school. Because of that, it necessary to research about the geometry level thinking. The goal of the research are to know: (1) student geometry level thinking at the model of junior high school in Lembang, and (2) observe the lesson geometry at school by use the phase of Van Hiele geometry learning. The method is descriptive study with the subject are the student from IX class of two junior high school in Lembang. The instrument is: (1) test instrument, is Van Hiele geometry level test. The result will be analysis by categories of Van Hiele: level 0 is visualization; level 1 is analysis; level 2 is informal deduction; level 3 is deduction; and level 4 is rigor. (2) Non-test instrument, is interview to the teacher and student. Base of the research, the conclusion are: (1) by and large the student has include the Van Hiele geometry level. Student at level 0 is 81, 16%, at level 1 is 17,3% and at level 2 is 1,45%. (2) School did’nt use the phase of Van Hiele geometry learning

Current measures of metabolic heterogeneity within cervical cancer do not predict disease outcome

<p>Abstract</p> <p>Background</p> <p>A previous study evaluated the intra-tumoral heterogeneity observed in the uptake of F-18 fluorodeoxyglucose (FDG) in pre-treatment positron emission tomography (PET) scans of cancers of the uterine cervix as an indicator of disease outcome. This was done via a novel statistic which ostensibly measured the spatial variations in intra-tumoral metabolic activity. In this work, we argue that statistic is intrinsically <it>non</it>-spatial, and that the apparent delineation between unsuccessfully- and successfully-treated patient groups via that statistic is spurious.</p> <p>Methods</p> <p>We first offer a straightforward mathematical demonstration of our argument. Next, we recapitulate an assiduous re-analysis of the originally published data which was derived from FDG-PET imagery. Finally, we present the results of a principal component analysis of FDG-PET images similar to those previously analyzed.</p> <p>Results</p> <p>We find that the previously published measure of intra-tumoral heterogeneity is intrinsically non-spatial, and actually is only a surrogate for tumor volume. We also find that an optimized linear combination of more canonical heterogeneity quantifiers does not predict disease outcome.</p> <p>Conclusions</p> <p>Current measures of intra-tumoral metabolic activity are not predictive of disease outcome as has been claimed previously. The implications of this finding are: clinical categorization of patients based upon these statistics is invalid; more sophisticated, and perhaps innately-geometric, quantifications of metabolic activity are required for predicting disease outcome.</p

Quantitative Modeling of Cerenkov Light Production Efficiency from Medical Radionuclides

There has been recent and growing interest in applying Cerenkov radiation (CR) for biological applications. Knowledge of the production efficiency and other characteristics of the CR produced by various radionuclides would help in accessing the feasibility of proposed applications and guide the choice of radionuclides. To generate this information we developed models of CR production efficiency based on the Frank-Tamm equation and models of CR distribution based on Monte-Carlo simulations of photon and β particle transport. All models were validated against direct measurements using multiple radionuclides and then applied to a number of radionuclides commonly used in biomedical applications. We show that two radionuclides, Ac-225 and In-111, which have been reported to produce CR in water, do not in fact produce CR directly. We also propose a simple means of using this information to calibrate high sensitivity luminescence imaging systems and show evidence suggesting that this calibration may be more accurate than methods in routine current use