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Literature DB >> 30220729

Combining community resurvey data to advance global change research.

Kris Verheyen¹, Pieter De Frenne², Lander Baeten³, Donald M Waller⁴, Radim Hédl⁵, Michael P Perring⁶, Haben Blondeel⁷, Jörg Brunet⁸, Markéeta Chudomelova⁹, Guillaume Decocq¹⁰, Emiel De Lombaerde¹¹, Leen Depauw¹², Thomas Dirnböck¹³, Tomasz Durak¹⁴, Ove Eriksson¹⁵, Frank S Gilliam¹⁶, Thilo Heinken¹⁷, Steffi Heinrichs¹⁸, Martin Hermy¹⁹, Bogdan Jaroszewicz²⁰, Michael A Jenkins²¹, Sarah E Johnson²², Keith J Kirby²³, Martin Kopecký²⁴, Dries Landuyt²⁵, Jonathan Lenoir²⁶, Daijiang Li²⁷, Martin Macek²⁸, Sybryn Maes²⁹, Frantisek Máliš³⁰, Fraser J G Mitchell³¹, Tobias Naaf³², George Peterken³³, Petr Petřík³⁴, Kamila Reczyńska³⁵, David A Rogers³⁶, Fride Hoistad Schei³⁷, Wolfgang Schmidt³⁸, Tibor Standovár³⁹, Krzystof Świerkosz⁴⁰, Karol Ujházy⁴¹, Hans Van Calster⁴², Mark Vellend⁴³, Ondřej Vild⁴⁴, Kerry Woods⁴⁵, Monika Wulf⁴⁶, Markus Bernhard-Römermann⁴⁷.

Abstract

More and more ecologists have started to resurvey communities sampled in earlier decades to determine long-term shifts in community composition and infer the likely drivers of the ecological changes observed. However, to assess the relative importance of, and interactions among, multiple drivers joint analyses of resurvey data from many regions spanning large environmental gradients are needed. In this paper we illustrate how combining resurvey data from multiple regions can increase the likelihood of driver-orthogonality within the design and show that repeatedly surveying across multiple regions provides higher representativeness and comprehensiveness, allowing us to answer more completely a broader range of questions. We provide general guidelines to aid implementation of multi-region resurvey databases. In so doing, we aim to encourage resurvey database development across other community types and biomes to advance global environmental change research.

Entities: Chemical

Keywords: (quasi-)permanent plots; community ecology; ground layer vegetation; legacy data; temperate forest

Year: 2016 PMID： 30220729 PMCID： PMC6136644 DOI： 10.1093/biosci/biw150

Source DB: PubMed Journal: Bioscience ISSN： 0006-3568 Impact factor: 8.589

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