Biogeography of intraspecific trait variability in matgrass (Nardus stricta): High phenotypic variation at the local scale exceeds large scale variability patterns

2020 ◽  
Vol 46 ◽  
pp. 125555
Author(s):  
Pavel Dan Turtureanu ◽  
Ceres Barros ◽  
Stéphane Bec ◽  
Bogdan-Iuliu Hurdu ◽  
Amélie Saillard ◽  
...  
Keyword(s):  
Phenotypic Variation ◽  
Large Scale ◽  
Local Scale ◽  
Nardus Stricta ◽  
Intraspecific Trait Variability ◽  
Trait Variability

scholarly journals Diversity of parental environments increases phenotypic variation in Arabidopsis populations more than genetic diversity but similarly affects productivity

2020 ◽  
Author(s):  
Javier Puy ◽  
Carlos P Carmona ◽  
Hana Dvořáková ◽  
Vít Latzel ◽  
Francesco de Bello
Keyword(s):  
Genetic Diversity ◽  
Environmental Conditions ◽  
Phenotypic Variation ◽  
Ecosystem Functioning ◽  
Phenotypic Variability ◽  
Population Type ◽  
Diversity Effect ◽  
Environmental Fluctuations ◽  
Intraspecific Trait Variability ◽  
Trait Variability

Abstract Background and Aims The observed positive diversity effect on ecosystem functioning has rarely been assessed in terms of intraspecific trait variability within populations. Intraspecific phenotypic variability could stem both from underlying genetic diversity and from plasticity in response to environmental cues. The latter might derive from modifications to a plant’s epigenome and potentially last multiple generations in response to previous environmental conditions. We experimentally disentangled the role of genetic diversity and diversity of parental environments on population productivity, resistance against environmental fluctuations and intraspecific phenotypic variation. Methods A glasshouse experiment was conducted in which different types of Arabidopsis thaliana populations were established: one population type with differing levels of genetic diversity and another type, genetically identical, but with varying diversity levels of the parental environments (parents grown in the same or different environments). The latter population type was further combined, or not, with experimental demethylation to reduce the potential epigenetic diversity produced by the diversity of parental environments. Furthermore, all populations were each grown under different environmental conditions (control, fertilization and waterlogging). Mortality, productivity and trait variability were measured in each population. Key Results Parental environments triggered phenotypic modifications in the offspring, which translated into more functionally diverse populations when offspring from parents grown under different conditions were brought together in mixtures. In general, neither the increase in genetic diversity nor the increase in diversity of parental environments had a remarkable effect on productivity or resistance to environmental fluctuations. However, when the epigenetic variation was reduced via demethylation, mixtures were less productive than monocultures (i.e. negative net diversity effect), caused by the reduction of phenotypic differences between different parental origins. Conclusions A diversity of environmental parental origins within a population could ameliorate the negative effect of competition between coexisting individuals by increasing intraspecific phenotypic variation. A diversity of parental environments could thus have comparable effects to genetic diversity. Disentangling the effect of genetic diversity and that of parental environments appears to be an important step in understanding the effect of intraspecific trait variability on coexistence and ecosystem functioning.

scholarly journals Changes in local-scale intraspecific trait variability of dominant species across contrasting island ecosystems

Ecosphere ◽  
2014 ◽  
Vol 5 (3) ◽  
pp. art26 ◽  
Author(s):  
Bright B. Kumordzi ◽  
Marie-Charlotte Nilsson ◽  
Michael J. Gundale ◽  
David A. Wardle
Keyword(s):  
Dominant Species ◽  
Local Scale ◽  
Island Ecosystems ◽  
Intraspecific Trait Variability ◽  
Trait Variability

Intraspecific trait variability of a typical tree species of riverine forests in southern Brazil

Flora ◽  
2021 ◽  
Vol 279 ◽  
pp. 151806
Author(s):  
Edilvane Inês Zonta ◽  
Guilherme Krahl de Vargas ◽  
João André Jarenkow
Keyword(s):  
Tree Species ◽  
Southern Brazil ◽  
Riverine Forests ◽  
Intraspecific Trait Variability ◽  
Trait Variability

scholarly journals Statistical Downscaling of Daily Wind Speed Variations

2014 ◽  
Vol 53 (3) ◽  
pp. 660-675 ◽  
Author(s):  
Megan C. Kirchmeier ◽  
David J. Lorenz ◽  
Daniel J. Vimont
Keyword(s):  
Wind Speed ◽  
Gamma Distribution ◽  
Large Scale ◽  
Probabilistic Method ◽  
Probabilistic Approach ◽  
Local Scale ◽  
Scale Parameters ◽  
Wind Speeds ◽  
Distribution Shape ◽  
Realistic Information

AbstractThis study presents the development of a method to statistically downscale daily wind speed variations in an extended Great Lakes region. A probabilistic approach is used, predicting a daily-varying probability density function (PDF) of local-scale daily wind speed conditioned on large-scale daily wind speed predictors. Advantages of a probabilistic method are that it provides realistic information on the variance and extremes in addition to information on the mean, it allows the autocorrelation of downscaled realizations to be tuned to match the autocorrelation of local-scale observations, and it allows flexibility in the use of the final downscaled product. Much attention is given to fitting the proper functional form of the PDF by investigating the observed local-scale wind speed distribution (predictand) as a function of the decile of the large-scale wind (predictor). It is found that the local-scale standard deviation and the local-scale shape parameter (from a gamma distribution) are nonconstant functions of the large-scale predictor. As such, a vector generalized linear model is developed to relate the large-scale and local-scale wind speeds. Maximum likelihood and cross validation are used to fit local-scale gamma distribution shape and scale parameters to the large-scale wind speed. The result is a daily-varying probability distribution of local-scale wind speed, conditioned on the large-scale wind speed.

scholarly journals Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach

2015 ◽  
Vol 6 (1) ◽  
pp. 61-81 ◽  
Author(s):  
L. Gerlitz ◽  
O. Conrad ◽  
J. Böhner
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
Large Scale ◽  
Local Scale ◽  
Predictor Variables ◽  
High Mountain ◽  
Target Area ◽  
Monthly Precipitation ◽  
Highly Nonlinear

Abstract. The heterogeneity of precipitation rates in high-mountain regions is not sufficiently captured by state-of-the-art climate reanalysis products due to their limited spatial resolution. Thus there exists a large gap between the available data sets and the demands of climate impact studies. The presented approach aims to generate spatially high resolution precipitation fields for a target area in central Asia, covering the Tibetan Plateau and the adjacent mountain ranges and lowlands. Based on the assumption that observed local-scale precipitation amounts are triggered by varying large-scale atmospheric situations and modified by local-scale topographic characteristics, the statistical downscaling approach estimates local-scale precipitation rates as a function of large-scale atmospheric conditions, derived from the ERA-Interim reanalysis and high-resolution terrain parameters. Since the relationships of the predictor variables with local-scale observations are rather unknown and highly nonlinear, an artificial neural network (ANN) was utilized for the development of adequate transfer functions. Different ANN architectures were evaluated with regard to their predictive performance. The final downscaling model was used for the cellwise estimation of monthly precipitation sums, the number of rainy days and the maximum daily precipitation amount with a spatial resolution of 1 km2. The model was found to sufficiently capture the temporal and spatial variations in precipitation rates in the highly structured target area and allows for a detailed analysis of the precipitation distribution. A concluding sensitivity analysis of the ANN model reveals the effect of the atmospheric and topographic predictor variables on the precipitation estimations in the climatically diverse subregions.

scholarly journals Evaluating Predictor Strategies for Regression-Based Downscaling with a Focus on Glacierized Mountain Environments

2017 ◽  
Vol 56 (6) ◽  
pp. 1707-1729 ◽  
Author(s):  
Marlis Hofer ◽  
Johanna Nemec ◽  
Nicolas J. Cullen ◽  
Markus Weber
Keyword(s):  
Large Scale ◽  
Global Radiation ◽  
Local Scale ◽  
Forecast Skill ◽  
Model Complexity ◽  
High Mountain ◽  
European Alps ◽  
Limited Information ◽  
Mountain Environments ◽  
Measuring Site

AbstractThis study explores the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity, and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in close proximity to mountain glaciers: 1) the Vernagtbach station in the European Alps, 2) the Artesonraju measuring site in the tropical South American Andes, and 3) the Mount Brewster measuring site in the Southern Alps of New Zealand. The large-scale dataset being evaluated is the ERA-Interim dataset. In the downscaling procedure, particular emphasis is put on developing efficient yet not overfit models from the limited information in the temporally short (typically a few years) observational records of the high mountain sites. For direct (univariate) predictors, optimum scale analysis turns out to be a powerful means to improve the forecast skill without the need to increase the downscaling model complexity. Yet the traditional (multivariate) predictor sets show generally higher skill than the direct predictors for all variables, sites, and days of the year. Only in the case of large sampling uncertainty (identified here to particularly affect observed precipitation) is the use of univariate predictor options justified. Overall, the authors find a range in forecast skill among the different predictor options applied in the literature up to 0.5 (where 0 indicates no skill, and 1 represents perfect skill). This highlights that a sophisticated predictor selection (as presented in this study) is essential in the development of realistic, local-scale scenarios by means of downscaling.

scholarly journals Influence of the Bermuda High on interannual variability of summertime ozone in the Houston-­Galveston-Brazoria region

2016 ◽  
Author(s):  
Yuxuan Wang ◽  
Beixi Jia ◽  
Sing-Chun Wang ◽  
Mark Estes ◽  
Lu Shen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Interannual Variability ◽  
Large Scale ◽  
Gulf Coast ◽  
Surface Ozone ◽  
Circulation Pattern ◽  
Local Scale ◽  
Metropolitan Region ◽  
Southern Us ◽  
Mlr Model

Abstract. The Bermuda High (BH) quasi-permanent pressure system is the key large-scale circulation pattern influencing summertime weather over the eastern and southern US. Here we developed a multiple linear regression (MLR) model to characterize the effect of the BH on year-to-year changes of monthly-mean maximum daily 8-hour average (MDA8) ozone in the Houston-Galveston-Brazoria (HGB) metropolitan region during June, July and August (JJA). The BH indicators include the longitude of the BH western edge (BH-Lon), and the BH intensity index (BHI) defined as the pressure gradient along its western edge. Both BH-Lon and BHI are selected by MLR as significant predictors (p < 0.05) of the interannual (1990–2015) variability of the HGB-mean ozone throughout JJA, while local-scale meridional wind speed is selected as an additional predictor for August only. Local-scale temperature and zonal wind speed are not identified as important factors for any summer month. The best-fit MLR model can explain 61 %–72 % of the interannual variability of the HGB-mean summertime ozone over 1990–2015 and shows good performance in cross-validation (R2 higher than 0.48). The BH-Lon is the most important factor, which alone explains 38 %–48 % of such variability. The location and strength of the Bermuda High appears to control whether or not low-ozone maritime air from the Gulf of Mexico can enter southeastern Texas and affect air quality. This mechanism also applies to other coastal urban regions along the Gulf Coast (e.g. New Orleans, LA; Mobile, AL; and Pensacola, FL), suggesting that the BH circulation pattern can affect surface ozone variability through a large portion of the Gulf Coast.

scholarly journals Multi-Scale Evaluation of the TSEB Model over a Complex Agricultural Landscape in Morocco

2020 ◽  
Vol 12 (7) ◽  
pp. 1181
Author(s):  
Jamal Elfarkh ◽  
Jamal Ezzahar ◽  
Salah Er-Raki ◽  
Vincent Simonneaux ◽  
Bouchra Ait Hssaine ◽  
...  
Keyword(s):  
Latent Heat ◽  
Complex Terrain ◽  
Large Scale ◽  
Agricultural Landscape ◽  
Local Scale ◽  
Heat Fluxes ◽  
Balance Model ◽  
Modis Data ◽  
Multi Scale ◽  
Basin Scale

An accurate assessment of evapotranspiration (ET) is crucially needed at the basin scale for studying the hydrological processes and water balance especially from upstream to downstream. In the mountains, this term is poorly understood because of various challenges, including the vegetation complexity, plant diversity, lack of available data and because the in situ direct measurement of ET is difficult in complex terrain. The main objective of this work was to investigate the potential of a Two-Source-Energy-Balance model (TSEB) driven by the Landsat and MODIS data for estimating ET over a complex mountain region. The complexity is associated with the type of the vegetation canopy as well as the changes in topography. For validating purposes, a large-aperture scintillometer (LAS) was set up over a heterogeneous transect of about 1.4 km to measure sensible (H) and latent heat (LE) fluxes. Additionally, two towers of eddy covariance (EC) systems were installed along the LAS transect. First, the model was tested at the local scale against the EC measurements using multi-scale remote sensing (MODIS and Landsat) inputs at the satellite overpasses. The obtained averaged values of the root mean square error (RMSE) and correlation coefficient (R) were about 72.4 Wm−2 and 0.79 and 82.0 Wm−2 and 0.52 for Landsat and MODIS data, respectively. Secondly, the potential of the TSEB model for evaluating the latent heat fluxes at large scale was investigated by aggregating the derived parameters from both satellites based on the LAS footprint. As for the local scale, the comparison of the latent heat fluxes simulated by TSEB driven by Landsat data performed well against those measured by the LAS (R = 0.69, RMSE = 68.0 Wm−2), while slightly more scattering was observed when MODIS products were used (R = 0.38, RMSE = 99.8 Wm−2). Based on the obtained results, it can be concluded that (1) the TSEB model can be fairly used to estimate the evapotranspiration over the mountain regions; and (2) medium- to high-resolution inputs are a better option than coarse-resolution products for describing this kind of complex terrain.

scholarly journals Testing biodiversity theory using species richness of reef-building corals across a depth gradient

2019 ◽  
Vol 15 (10) ◽  
pp. 20190493 ◽  
Author(s):  
T. Edward Roberts ◽  
Sally A. Keith ◽  
Carsten Rahbek ◽  
Tom C. L. Bridge ◽  
M. Julian Caley ◽  
...  
Keyword(s):  
Species Richness ◽  
Large Scale ◽  
Coral Species ◽  
Environmental Gradients ◽  
Abiotic Factors ◽  
Empirical Support ◽  
Null Model ◽  
Local Scale ◽  
Small Scale ◽  
Energy Availability

Natural environmental gradients encompass systematic variation in abiotic factors that can be exploited to test competing explanations of biodiversity patterns. The species–energy (SE) hypothesis attempts to explain species richness gradients as a function of energy availability. However, limited empirical support for SE is often attributed to idiosyncratic, local-scale processes distorting the underlying SE relationship. Meanwhile, studies are also often confounded by factors such as sampling biases, dispersal boundaries and unclear definitions of energy availability. Here, we used spatially structured observations of 8460 colonies of photo-symbiotic reef-building corals and a null-model to test whether energy can explain observed coral species richness over depth. Species richness was left-skewed, hump-shaped and unrelated to energy availability. While local-scale processes were evident, their influence on species richness was insufficient to reconcile observations with model predictions. Therefore, energy availability, either in isolation or in combination with local deterministic processes, was unable to explain coral species richness across depth. Our results demonstrate that local-scale processes do not necessarily explain deviations in species richness from theoretical models, and that the use of idiosyncratic small-scale factors to explain large-scale ecological patterns requires the utmost caution.

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