Adaptive plasticity and niche expansion in an invasive thistle

AGAP : équipe GE²popPhenotypic differentiation in size and fecundity between native and invasive populations of a species has been suggested as a causal driver of invasion in plants. Local adaptation to novel environmental conditions through a micro-evolutionary response to natural selection may lead to phenotypic differentiation and fitness advantages in the invaded range. Local adaptation may occur along a stress tolerance trade-off, favoring individuals that, in benign conditions, shift resource allocation from stress tolerance to increased vigor and fecundity and, therefore, invasiveness. Alternately, the typically disturbed invaded range may select for a plastic, generalist strategy, making phenotypic plasticity the main driver of invasion success. To distinguish between these hypotheses, we performed a field common garden and tested for genetically based phenotypic differentiation, resource allocation shifts in response to water limitation, and local adaptation to the environmental gradient which describes the source locations for native and invasive populations of diffuse knapweed (Centaurea diffusa). Plants were grown in an experimental field in France (naturalized range) under water addition and limitation conditions. After accounting for phenotypic variation arising from environmental differences among collection locations, we found evidence of genetic variation between the invasive and native populations for most morphological and life-history traits under study. Invasive C.diffusa populations produced larger, later maturing, and therefore potentially fitter individuals than native populations. Evidence for local adaptation along a resource allocation trade-off for water limitation tolerance is equivocal. However, native populations do show evidence of local adaptation to an environmental gradient, a relationship which is typically not observed in the invaded range. Broader analysis of the climatic niche inhabited by the species in both ranges suggests that the physiological tolerances of C.diffusa may have expanded in the invaded range. This observation could be due to selection for plastic, general-purpose genotypes with broad environmental tolerances

Using MODIS land surface temperatures and the Crocus snow model to understand the warm bias of ERA-Interim reanalyses at the surface in Antarctica

Moderate-Resolution Imaging spectroradiometer (MODIS) land surface temperatures in Antarctica were processed in order to produce a gridded data set at 25 km resolution, spanning the period 2000-2011 at an hourly time step. The Aqua and Terra orbits and MODIS swath width, combined with frequent clear-sky conditions, lead to very high availability of quality-controlled observations: on average, hourly data are available 14 h per day at the grid points around the South Pole and more than 9 h over a large area of the Antarctic Plateau. Processed MODIS land surface temperatures, referred to hereinafter as MODISTs values, were compared with in situ hourly measurements of surface temperature collected over the entirety of the year 2009 by seven stations from the Baseline Surface Radiation Network (BSRN) and automatic weather stations (AWSs). In spite of an occasional failure in the detection of clouds, MODISTs values exhibit a good performance, with a bias ranging from ĝ̂'1.8 to 0.1 °C and errors ranging from 2.2 to 4.8 °C root mean square at the five stations located on the plateau. These results show that MODISTs values can be used as a precise and accurate reference to test other surface temperature data sets. Here, we evaluate the performance of surface temperature in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis known as ERA-Interim reanalysis. During conditions detected as cloud free by MODIS, ERA-Interim shows a widespread warm bias in Antarctica in every season, ranging from +3 to +6 °C on the plateau. This confirms a recent study which showed that the largest discrepancies in 2 m air temperature between ERA-Interim and the global temperature data set HadCRUT4 compiled by the Met Office Hadley Centre and the University of East Anglia's Climatic Research Unit occur in Antarctica. A comparison with in situ surface temperature shows that this bias is not strictly limited to clear-sky conditions. A detailed comparison with stand-alone simulations by the Crocus snowpack model, forced by ERA-Interim, and with the ERA-Interim/land simulations, shows that the warm bias may be due primarily to an overestimation of the surface turbulent fluxes in very stable conditions. Numerical experiments with Crocus show that a small change in the parameterization of the effects of stability on the surface exchange coefficients can significantly impact the snow surface temperature. The ERA-Interim warm bias appears to be likely due to an overestimation of the surface exchange coefficients under very stable conditions. © Author(s) 2014. CC Attribution 3.0 License

Modified choice function heuristic selection for the multidimensional knapsack problem

Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristics are able to solve many combinatorial optimisation problems, including the multidimensional 0-1 knapsack problem (MKP). The traditional framework for iterative selection hyper-heuristics relies on two key components, a heuristic selection method and a move acceptance criterion. Existing work has shown that a hyper-heuristic using Modified Choice Function heuristic selection can be effective at solving problems in multiple problem domains. Late Acceptance Strategy is a hill climbing metaheuristic strategy often used as a move acceptance criteria in selection hyper-heuristics. This work compares a Modified Choice Function - Late Acceptance Strategy hyper-heuristic to an existing selection hyper-heuristic method from the literature which has previously performed well on standard MKP benchmarks

Combinatorial Auctions, Knapsack Problems, and Hill-climbing Search

. This paper examines the performance of hill-climbing algorithms on standard test problems for combinatorial auctions (CAs). On single-unit CAs, deterministic hill-climbers are found to perform well, and their performance can be improved significantly by randomizing them and restarting them several times, or by using them collectively. For some problems this good performance is shown to be no better than chancel; on others it is due to a well-chosen scoring function. The paper draws attention to the fact that multi-unit CAs have been studied widely under a different name: multidimensional knapsack problems (MDKP). On standard test problems for MDKP, one of the deterministic hill-climbers generates solutions that are on average 99% of the best known solutions. 1 Introduction Suppose there are three items for auction, X, Y, and Z, and three bidders, B1, B2, and B3. B1 wants any one of the items and will pay $5, B2 wants two items -- X and one of Y or Z -- and will pay$9, an..