Ellenberg-type indicator values for European vascular plant species

Aims: Ellenberg-type indicator values are expert-based rankings of plant species according to their ecological optima on main environmental gradients. Here we extend the indicator-value system proposed by Heinz Ellenberg and co-authors for Central Europe by incorporating other systems of Ellenberg-type indicator values (i.e., those using scales compatible with Ellenberg values) developed for other European regions. Our aim is to create a harmonized data set of Ellenberg-type indicator values applicable at the European scale. Methods: We collected European data sets of indicator values for vascular plants and selected 13 data sets that used the nine-, ten- or twelve-degree scales defined by Ellenberg for light, temperature, moisture, reaction, nutrients and salinity. We compared these values with the original Ellenberg values and used those that showed consistent trends in regression slope and coefficient of determination. We calculated the average value for each combination of species and indicator values from these data sets. Based on species’ co-occurrences in European vegetation plots, we also calculated new values for species that were not assigned an indicator value. Results: We provide a new data set of Ellenberg-type indicator values for 8908 European vascular plant species (8168 for light, 7400 for temperature, 8030 for moisture, 7282 for reaction, 7193 for nutrients, and 7507 for salinity), of which 398 species have been newly assigned to at least one indicator value. Conclusions: The newly introduced indicator values are compatible with the original Ellenberg values. They can be used for large-scale studies of the European flora and vegetation or for gap-filling in regional data sets. The European indicator values and the original and taxonomically harmonized regional data sets of Ellenberg-type indicator values are available in the Supporting Information and the Zenodo repository

Climatic predictors of species distributions neglect biophysiologically meaningful variables

This is the final version. Available on open access from Wiley via the DOI in this record.Aim: Species distribution models (SDMs) have played a pivotal role in predicting how species might respond to climate change. To generate reliable and realistic predictions from these models requires the use of climate variables that adequately capture physiological responses of species to climate and therefore provide a proximal link between climate and their distributions. Here, we examine whether the climate variables used in plant SDMs are different from those known to influence directly plant physiology. Location: Global. Methods: We carry out an extensive, systematic review of the climate variables used to model the distributions of plant species and provide comparison to the climate variables identified as important in the plant physiology literature. We calculate the top ten SDM and physiology variables at 2.5 degree spatial resolution for the globe and use principal component analyses and multiple regression to assess similarity between the climatic variation described by both variable sets. Results: We find that the most commonly used SDM variables do not reflect the most important physiological variables and differ in two main ways: (i) SDM variables rely on seasonal or annual rainfall as simple proxies of water available to plants and neglect more direct measures such as soil water content; and (ii) SDM variables are typically averaged across seasons or years and overlook the importance of climatic events within the critical growth period of plants. We identify notable differences in their spatial gradients globally and show where distal variables may be less reliable proxies for the variables to which species are known to respond. Main conclusions: There is a growing need for the development of accessible, fine-resolution global climate surfaces of physiological variables. This would provide a means to improve the reliability of future range predictions from SDMs and support efforts to conserve biodiversity in a changing climate

Cutting improves the productivity of lucerne-rich stands used in the revegetation of degraded arable land in a semi-arid environment

Understanding the relationships between vegetative and environmental variables is important for revegetation and ecosystem management on the Loess Plateau, China. Lucerne (Medicago sativa L.) has been widely used in the region to improve revegetation, soil and water conservation, and to enhance livestock production. However, there is little information on how environmental factors influence long-term succession in lucerne-rich vegetation. Our objective was to identify the main environmental variables controlling the succession process in lucerne-rich vegetation such that native species are not suppressed after sowing on the Loess Plateau. Vegetation and soil surveys were performed in 31 lucerne fields (three lucerne fields without any management from 2003-2013 and 28 fields containing 11-year-old lucerne with one cutting each year). Time after planting was the most important factor affecting plant species succession. Cutting significantly affected revegetation characteristics, such as aboveground biomass, plant density and diversity. Soil moisture content, soil organic carbon, soil available phosphorus and slope aspect were key environmental factors affecting plant species composition and aboveground biomass, density and diversity. Long-term cutting can cause self-thinning in lucerne, maintain the stability of lucerne production and slow its degradation. For effective management of lucerne fields, phosphate fertilizer should be applied and cutting performed

General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues

Markerless motion capture algorithms require a 3D body with properly personalized skeleton dimension and/or body shape and appearance to successfully track a person. Unfortunately, many tracking methods consider model personalization a different problem and use manual or semi-automatic model initialization, which greatly reduces applicability. In this paper, we propose a fully automatic algorithm that jointly creates a rigged actor model commonly used for animation - skeleton, volumetric shape, appearance, and optionally a body surface - and estimates the actor's motion from multi-view video input only. The approach is rigorously designed to work on footage of general outdoor scenes recorded with very few cameras and without background subtraction. Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy. For reconstruction, 3D body shape is approximated as Gaussian density field. For pose and shape estimation, we minimize a new edge-based alignment energy inspired by volume raycasting in an absorbing medium. We further propose a new statistical human body model that represents the body surface, volumetric Gaussian density, as well as variability in skeleton shape. Given any multi-view sequence, our method jointly optimizes the pose and shape parameters of this model fully automatically in a spatiotemporal way

Micro, Meso, and Macro Data Collection and Analysis, as a Method for Speculative and Artistic Exploration

In this work, an attempt is made to explore the emerging computationally-enhanced private and public environments by analyzing their ecological transitions and its implications on practical, aesthetic, and speculative dimensions. The author has decided to methodologically dissect the multiplicity of information that exists on many possible-to-detect scales (micro, meso, macro), and utilize this extraction as a tool for experimentation and redefinition. With the use of custom-made hardware and software utilities (sensor devices, sentiment analysis algorithms, online APIs, and many more), a vast amount of data is collected and used as a multidimensional layered architecture that constantly shifts and transforms. The extracted and analyzed content of the collection becomes the essence of the work that is shaped and refined through digital and physical making – middleware, recursion, mapping – and by utilizing technological objects within the physical space, the creative process is augmented and amplified, exploring not only new practices and novel applications, but rather redefining behavior, thought-process, and context

European Vegetation Archive (EVA): An integrated database of European vegetation plots

© 2016 International Association for Vegetation Science. The European Vegetation Archive (EVA) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation- plot databases on a single software platform. Data storage in EVA does not affect on-going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the database management software TURBOVEG 3 developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their corresponding names on a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA, contributing in total 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large-scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database