Suchergebnisse

Although many climate research experiments are providing valuable data, long-term measurements are not always affordable. In the last decades, several facilities have secured long-term experiments, but few studies have incorporated spatial and scale effects. Most of them have been implemented in experimental agricultural fields but none for ecological studies. Scale effects can be assessed using remote sensing images from space or airborne platforms. Unmanned aerial vehicles (UAVs) are contributing to an increased spatial resolution, as well as becoming the intermediate scale between ground measurements and satellite/airborne image data. In this paper we assess the applicability of UAV-borne multispectral images to provide complementary experimental data collected at point scale (field sampling) in a long-term rain manipulation experiment located at the Kiskun Long-Term Socio-Ecological Research (LTSER) site named ExDRain to assess the effects on grassland vegetation. Two multispectral sensors were compared at different scales, the Parrot Sequoia camera on board a UAV and the portable Cropscan spectroradiometer. The NDVI values were used to assess the effect of plastic roofs and a proportional reduction effect was found for Sequoia-derived NDVI values. Acceptable and significant positive relationships were found between both sensors at different scales, being stronger at Cropscan measurement scale. Differences found at plot scale might be due to heterogeneous responses to treatments. Spatial variability analysis pointed out a more homogeneous response for plots submitted to severe and moderate drought. More investigation is needed to address the possible effect of species abundance on NDVI at plot scale contributing to a more consistent representation of ground measurements. The feasibility of carrying out systematic UAV flights coincident or close to ground campaigns will certainly reveal the consistency of the observed spatial patterns in the long run. ; This research was funded by the European Union's Horizon 2020 Research and Innovation Program under grant agreement No. 654359 (eLTER Horizon 2020 project). Gy. K-D. was supported by the National Research, Development and Innovation Fund (NRDI Fund) of Hungary (Nos. K112576, K129068) ; We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI) ; Peer reviewed

Questions. Compensatory dynamics are described as one of the main mechanisms that increase community stability, e.g., where decreases of some species on a year‐to‐year basis are offset by an increase in others. Deviations from perfect synchrony between species (asynchrony) have therefore been advocated as an important mechanism underlying biodiversity effects on stability. However, it is unclear to what extent existing measures of synchrony actually capture the signal of year‐to‐year species fluctuations in the presence of long‐term directional trends in both species abundance and composition (species directional trends hereafter). Such directional trends may lead to a misinterpretation of indices commonly used to reflect year‐to‐year synchrony. Methods. An approach based on three‐term local quadrat variance (T3) which assesses population variability in a three‐year moving window, was used to overcome species directional trend effects. This "detrending" approach was applied to common indices of synchrony across a worldwide collection of 77 temporal plant community datasets comprising almost 7,800 individual plots sampled for at least six years. Plots included were either maintained under constant "control" conditions over time or were subjected to different management or disturbance treatments. Results. Accounting for directional trends increased the detection of year‐to‐year synchronous patterns in all synchrony indices considered. Specifically, synchrony values increased significantly in ~40% of the datasets with the T3 detrending approach while in ~10% synchrony decreased. For the 38 studies with both control and manipulated conditions, the increase in synchrony values was stronger for longer time series, particularly following experimental manipulation. Conclusions. Species' long‐term directional trends can affect synchrony and stability measures potentially masking the ecological mechanism causing year‐to‐year fluctuations. As such, previous studies on community stability might have overemphasised the role of compensatory dynamics in real‐world ecosystems, and particularly in manipulative conditions, when not considering the possible overriding effects of long‐term directional trends. ; We thank multiple entities for the financial support necessary to obtain the different databases: the U.S. National Science Foundation under grant numbers DEB‐8114302, DEB‐8811884, DEB‐9411972, DEB‐0080382, DEB‐0620652, DEB‐1234162, DEB‐9707477, DEB‐0316402, DEB‐08‐16453, and DEB‐12‐56034, DEB‐0618210, the Nutrient Network (http://www.nutnet.org) experiment from the National Science Foundation Research Coordination Network (NSF‐DEB‐1042132), the New Zealand National Vegetation Survey Databank, the Spanish MINECO (Project CGL2014‐53789‐R), the Madrid Regional Government (Projects REMEDINAL‐3 and REMEDINAL‐TE), the European Research Council Synergy grant 610028 (IMBALANCE‐P), the Institute on the Environment (DG‐0001‐13), the SOERE‐ACBB financed through French National Agency for Research (ANAEE‐F, ANR‐11‐INBS‐0001), the Estonian Research Council (IUT 20‐28, IUT 20‐29), Czech Science Foundation (GAČR 17‐05506S and 19‐28491X), the European Regional Development Fund (Centre of Excellence EcolChange), the German Federal Environmental Foundation (DBU) for a grant to the NABU Hamburg (management experiment Calamagrostis epigejos), and the German Federal Ministry of Education and Research within the framework of the project BIOTA Southern Africa (promotion numbers 01LC0024, 01LC0024A and 01LC0624A2), Task 159 of SASSCAL (promotion number 01LG1201) and the Scottish Government's Rural and Environmental Science and Analytical Services division. Acknowledgement Data owned by NERC© Database Right/Copyright NERC. Further support was provided by the Jornada Basin Long‐Term Ecological Research (LTER) project, Cedar Creek Ecosystem Science Reserve and the University of Minnesota. We also thank the Lawes Agricultural Trust and Rothamsted Research for data from the e‐RA database. The Rothamsted Long‐term Experiments National Capability (LTE‐NCG) is supported by the UK Biotechnology and Biological Sciences Research Council (Grant BBS/E/C/000J0300) and the Lawes Agricultural Trust. ; Peer reviewed

Analysing temporal patterns in plant communities is extremely important to quantify the extent and the consequences of ecological changes, especially considering the current biodiversity crisis. Long-term data collected through the regular sampling of permanent plots represent the most accurate resource to study ecological succession, analyse the stability of a community over time and understand the mechanisms driving vegetation change. We hereby present the LOng-Term Vegetation Sampling (LOTVS) initiative, a global collection of vegetation time-series derived from the regular monitoring of plant species in permanent plots. With 79 data sets from five continents and 7,789 vegetation time-series monitored for at least 6 years and mostly on an annual basis, LOTVS possibly represents the largest collection of temporally fine-grained vegetation time-series derived from permanent plots and made accessible to the research community. As such, it has an outstanding potential to support innovative research in the fields of vegetation science, plant ecology and temporal ecology. ; The authors acknowledge institutional support as follows. Nicola J. Day: Te Apārangi Royal Society of New Zealand (Rutherford Postdoctoral Fellowship). Jiří Danihelka: Czech Science Foundation (project no. 19-28491X) and Czech Academy of Sciences (project no. RVO 67985939). Francesco de Bello: Spanish Plan Nacional de I+D+i (project PGC2018-099027-B-I00). Eric Garnier: La Fage INRA experimental station. Tomáš Herben: GAČR grant 20-02901S. Anke Jentsch: German Federal Ministry of Education and Research (grant 031B0516C - SUSALPS) and Oberfrankenstiftung (grant OFS FP00237). Norbert Juergens: German Federal Ministry of Education and Research (grant 01LG1201N - SASSCAL ABC). Frédérique Louault and Katja Klumpp: AnaEE-France (ANR-11-INBS-0001). Robin J. Pakeman: Strategic Research Programme of the Scottish Government's Rural and Environment Science and Analytical Services Division. Meelis Pärtel: Estonian Research Council (PRG609) and European Regional Development Fund (Centre of Excellence EcolChange). Josep Peñuelas: Spanish Government (grant PID2019-110521GB-I00), Fundación Ramon Areces (grant ELEMENTAL-CLIMATE), Catalan Government (grant SGR 2017-1005), and European Research Council (Synergy grant ERC-SyG-2013-610028, IMBALANCE-P). Ute Schmiedel: German Federal Ministry of Education and Research (Promotion numbers 01LC0024, 01LC0024A, 01LC0624A2, 01LG1201A, 01LG1201N). Hana Skálová: GAČR grant 20-02901S. Karsten Wesche: International Institute Zittau, Technische Universität Dresden. Susan K. Wiser: New Zealand Ministry for Business, Innovation and Employment's Strategic Science Investment Fund. Ben A. Woodcock: NERC and BBSRC (NE/N018125/1 LTS-M ASSIST - Achieving Sustainable Agricultural Systems). Enrique Valencia: Program for attracting and retaining talent of Comunidad de Madrid (no. 2017-T2/AMB-5406) and Community of Madrid and Rey Juan Carlos University (Young Researchers R&D Project. Ref. M2165 – INTRANESTI). Truman P. Young: National Science Foundation (LTREB DEB 19-31224). ; Peer reviewed