Skip to main content
PMC home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2018 Jul 24;27(7):760–786. doi: 10.1111/geb.12729

BioTIME: A database of biodiversity time series for the Anthropocene

Maria Dornelas 1,✉, Laura H Antão 1,2, Faye Moyes 1, Amanda E Bates 3,4, Anne E Magurran 1, Dušan Adam 5, Asem A Akhmetzhanova 6, Ward Appeltans 7, José Manuel Arcos 8, Haley Arnold 1, Narayanan Ayyappan 9, Gal Badihi 1, Andrew H Baird 10, Miguel Barbosa 1,2, Tiago Egydio Barreto 11, Claus Bässler 12, Alecia Bellgrove 13, Jonathan Belmaker 14, Lisandro Benedetti‐Cecchi 15, Brian J Bett 3, Anne D Bjorkman 16, Magdalena Błażewicz 17, Shane A Blowes 14,18, Christopher P Bloch 19, Timothy C Bonebrake 20, Susan Boyd 1, Matt Bradford 21, Andrew J Brooks 22, James H Brown 23, Helge Bruelheide 18,24, Phaedra Budy 25, Fernando Carvalho 26, Edward Castañeda‐Moya 27, Chaolun Allen Chen 28, John F Chamblee 29, Tory J Chase 10,30, Laura Siegwart Collier 31, Sharon K Collinge 32, Richard Condit 33, Elisabeth J Cooper 34, J Hans C Cornelissen 35, Unai Cotano 36, Shannan Kyle Crow 37, Gabriella Damasceno 38, Claire H Davies 39, Robert A Davis 40, Frank P Day 41, Steven Degraer 42,43, Tim S Doherty 40,44, Timothy E Dunn 45, Giselda Durigan 46, J Emmett Duffy 47, Dor Edelist 48, Graham J Edgar 49, Robin Elahi 50, Sarah C Elmendorf 32, Anders Enemar 51, S K Morgan Ernest 52, Rubén Escribano 53, Marc Estiarte 54,55, Brian S Evans 56, Tung‐Yung Fan 57, Fabiano Turini Farah 58, Luiz Loureiro Fernandes 59, Fábio Z Farneda 60,61,62, Alessandra Fidelis 38, Robert Fitt 63, Anna Maria Fosaa 64, Geraldo Antonio Daher Correa Franco 65, Grace E Frank 30, William R Fraser 66, Hernando García 67, Roberto Cazzolla Gatti 68, Or Givan 14, Elizabeth Gorgone‐Barbosa 38, William A Gould 69, Corinna Gries 70, Gary D Grossman 71, Julio R Gutierréz 72,73,74, Stephen Hale 75, Mark E Harmon 76, John Harte 77, Gary Haskins 78, Donald L Henshaw 79, Luise Hermanutz 31, Pamela Hidalgo 53, Pedro Higuchi 80, Andrew Hoey 10, Gert Van Hoey 81, Annika Hofgaard 82, Kristen Holeck 83, Robert D Hollister 84, Richard Holmes 85, Mia Hoogenboom 10,30, Chih‐hao Hsieh 86, Stephen P Hubbell 87, Falk Huettmann 88, Christine L Huffard 89, Allen H Hurlbert 90, Natália Macedo Ivanauskas 65, David Janík 5, Ute Jandt 18,24, Anna Jażdżewska 17, Tore Johannessen 91, Jill Johnstone 92, Julia Jones 93, Faith A M Jones 1, Jungwon Kang 1, Tasrif Kartawijaya 94, Erin C Keeley, Douglas A Kelt 95, Rebecca Kinnear 1,96, Kari Klanderud 97, Halvor Knutsen 91,98, Christopher C Koenig 99, Alessandra R Kortz 1, Kamil Král 5, Linda A Kuhnz 89, Chao‐Yang Kuo 10, David J Kushner 100, Claire Laguionie‐Marchais 101, Lesley T Lancaster 63, Cheol Min Lee 102, Jonathan S Lefcheck 103, Esther Lévesque 104, David Lightfoot 105, Francisco Lloret 55, John D Lloyd 106, Adrià López‐Baucells 60,61,107, Maite Louzao 36, Joshua S Madin 108,109, Borgþór Magnússon 110, Shahar Malamud 14, Iain Matthews 1, Kent P McFarland 106, Brian McGill 111, Diane McKnight 112, William O McLarney 113, Jason Meador 113, Peter L Meserve 114, Daniel J Metcalfe 21, Christoph F J Meyer 60,61,115, Anders Michelsen 116, Nataliya Milchakova 117, Tom Moens 43, Even Moland 91,98, Jon Moore 96,118, Carolina Mathias Moreira 119, Jörg Müller 12,120, Grace Murphy 121, Isla H Myers‐Smith 122, Randall W Myster 123, Andrew Naumov 124, Francis Neat 125, James A Nelson 126, Michael Paul Nelson 76, Stephen F Newton 127, Natalia Norden 67, Jeffrey C Oliver 128, Esben M Olsen 91,98, Vladimir G Onipchenko 6, Krzysztof Pabis 17, Robert J Pabst 76, Alain Paquette 129, Sinta Pardede 94, David M Paterson 1,96, Raphaël Pélissier 130, Josep Peñuelas 54,55, Alejandro Pérez‐Matus 131, Oscar Pizarro 132, Francesco Pomati 133, Eric Post 95, Herbert H T Prins 134, John C Priscu 135, Pieter Provoost 7, Kathleen L Prudic 136, Erkki Pulliainen, B R Ramesh 9, Olivia Mendivil Ramos 137, Andrew Rassweiler 100, Jose Eduardo Rebelo 138, Daniel C Reed 22, Peter B Reich 139,140, Suzanne M Remillard 76, Anthony J Richardson 141,142, J Paul Richardson 143, Itai van Rijn 14, Ricardo Rocha 60,61,144, Victor H Rivera‐Monroy 145, Christian Rixen 146, Kevin P Robinson 78, Ricardo Ribeiro Rodrigues 58, Denise de Cerqueira Rossa‐Feres 147, Lars Rudstam 83, Henry Ruhl 3, Catalina S Ruz 131, Erica M Sampaio 61,148, Nancy Rybicki 149, Andrew Rypel 150, Sofia Sal 151, Beatriz Salgado 67, Flavio A M Santos 152, Ana Paula Savassi‐Coutinho 153, Sara Scanga 154, Jochen Schmidt 37, Robert Schooley 155, Fakhrizal Setiawan 94, Kwang‐Tsao Shao 156, Gaius R Shaver 157, Sally Sherman 158, Thomas W Sherry 159, Jacek Siciński 17, Caya Sievers 1, Ana Carolina da Silva 80, Fernando Rodrigues da Silva 160, Fabio L Silveira 161, Jasper Slingsby 162,163, Tracey Smart 164, Sara J Snell 90, Nadejda A Soudzilovskaia 165, Gabriel B G Souza 166, Flaviana Maluf Souza 167, Vinícius Castro Souza 58, Christopher D Stallings 168, Rowan Stanforth 1, Emily H Stanley 70, José Mauro Sterza 169, Maarten Stevens 170, Rick Stuart‐Smith 49, Yzel Rondon Suarez 171, Sarah Supp 172, Jorge Yoshio Tamashiro 152, Sukmaraharja Tarigan 94, Gary P Thiede 25, Simon Thorn 120, Anne Tolvanen 173, Maria Teresa Zugliani Toniato 174, Ørjan Totland 175, Robert R Twilley 145, Gediminas Vaitkus 176, Nelson Valdivia 177, Martha Isabel Vallejo 67, Thomas J Valone 178, Carl Van Colen 43, Jan Vanaverbeke 42, Fabio Venturoli 179, Hans M Verheye 180,181, Marcelo Vianna 166, Rui P Vieira 3, Tomáš Vrška 5, Con Quang Vu 182, Lien Van Vu 183,184, Robert B Waide 23, Conor Waldock 3, Dave Watts 39, Sara Webb 185,186, Tomasz Wesołowski 187, Ethan P White 188,189, Claire E Widdicombe 190, Dustin Wilgers 191, Richard Williams 192, Stefan B Williams 132, Mark Williamson 193, Michael R Willig 194, Trevor J Willis 195, Sonja Wipf 196, Kerry D Woods 197, Eric J Woehler 49, Kyle Zawada 1,109, Michael L Zettler 198, Thomas Hickler
PMCID: PMC6099392  PMID: 30147447

Abstract

Motivation

The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.

Main types of variables included

The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.

Spatial location and grain

BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).

Time period and grain

BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.

Major taxa and level of measurement

BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.

Software format

.csv and .SQL.

Keywords: biodiversity, global, spatial, species richness, temporal, turnover

1. BACKGROUND

Quantifying changes in biodiversity in the Anthropocene is a key challenge of our time given the paucity of temporal and spatial data for most taxa on Earth. The nature and extent of the reorganization of natural assemblages are currently controversial because conflicting estimates of biodiversity change have been obtained using different methodological approaches and for different regions, time periods and taxa. Some reports suggest alarming and systematic biodiversity loss. For example, estimates of global extinction rates place global losses orders of magnitude above background rates (Pereira, Navarro, & Martins, 2012). In addition, estimates of population trends for vertebrates suggest average declines of the order of 60% in the past 30 years (Collen et al., 2009). Nonetheless, analyses based on spatial variation yield more modest declines in the range of 8% (Newbold et al., 2015). In contrast, some analyses of assemblage time series consistently detect no systematic trend in temporal α‐diversity (such as species richness), on average, across local communities (Brown, Ernest, Parody, & Haskell, 2001; Dornelas et al., 2014; Vellend et al., 2013, 2016), but instead uncover substantial variation in composition (temporal β‐diversity; i.e., temporal turnover), including both losses and gains of species (Dornelas et al., 2014; Magurran, Dornelas, Moyes, Gotelli, & McGill, 2015). Spatially structured gains and losses are also predicted from climate change projections (García Molinos et al., 2016). Some of these discrepancies are a result of differences in the temporal and spatial scales at which analyses were performed (McGill, Dornelas, Gotelli, & Magurran, 2014), whereas other differences may be attributable to the organizational level on which an analysis is focused (e.g., population vs. community). Clearly, more research is needed into how populations, communities and ecosystems are changing in the face of widespread human influence on the planet (Waters et al., 2016). Here, we introduce BioTIME, a curated database of biodiversity time series, with the goal of facilitating and promoting research in this area.

Biodiversity is a multifaceted concept, which can be measured in many different ways. Similar to the approach of essential biodiversity variables (Pereira et al., 2013), we focus on assembling data that maximize the number of metrics that can be calculated. Specifically, BioTIME is composed of species abundance records for assemblages that have been sampled through time with a consistent methodology. The focus on assemblages differentiates BioTIME from population databases, such as the Global Population Dynamics Database (https://www.imperial.ac.uk/cpb/gpdd2/secure/login.aspx) and the Living Planet Index database (http://www.livingplanetindex.org/home/index), and enables users to quantify patterns at different organizational levels, including both the assemblage and the population level. BioTIME complements the PREDICTS database (http://www.predicts.org.uk/) in providing time series rather than space for time comparisons. Moreover, most previous databases have been either terrestrial (e.g., vertebrates, GPDD; vegetation, sPlot; multiple taxa, PREDICTS) or marine (e.g., OBIS), whereas BioTIME includes marine, freshwater and terrestrial realms; hence, it facilitates comparisons across realms. Finally, previous databases are not specifically focused on temporal assemblage data, which means that BioTIME fills an important gap in allowing spatial and temporal comparisons. In addition, coupling BioTIME with additional information will allow analyses of temporal change in phylogenetic diversity and trait diversity alongside taxonomic diversity.

The goals of the BioTIME database are as follows: (a) to assemble and format raw species abundance data for assemblages consistently sampled through time; (b) to encourage re‐use of these data through open‐source access of standardized and curated versions of the data; and (c) to promote appropriate crediting of data sources. These goals are in line with best practice in promoting maximal use of ecological data (Costello et al., 2014; White et al., 2013) and highlight data gaps to funding agencies. In addition, we hope that BioTIME will engage ecologists in the collection, standardization, sharing and quality control of assemblage‐level species abundance data, particularly in poorly sampled parts of the world, and highlight the value of such data to funding agencies.

2. METHODS

The BioTIME database is composed of 11 tables: a main table containing the core observations (records), and 10 tables that provide contextual information as described below and in Supporting Information Figure S1. There are five main levels of organization: record, sample, plot, site and study. A record is our fundamental unit of observation of the abundance of a species in a sample. A sample includes all the records that belong to the same sampling event; for example, a quadrat on the seashore, a single plankton tow or a bird transect. A sample is defined by a single location and a single date. If the exact location has been repeatedly sampled through time, then all the samples that correspond to that location belong to the same plot. Multiple samples and plots can be located in the same area, which we term a site. Finally, the highest observational unit is a study, which is defined by having a regular and consistent sampling methodology. Sources of data in which the sampling methodology changed during the course of the study were classified as separate studies. Every organizational level has contextual variables that are kept either in dedicated tables or are part of the main table (see Supporting Information Figure S1 for a complete list of the fields in each table). In addition, the database also includes tables with information relating to the sampling methodology, and treatments associated with some samples when applicable, citation information, contacts and licenses for each study, and the curation steps performed on each study before it was entered in the database.

2.1. Data acquisition

Searches began in 2010, and data were acquired from a variety of sources: literature searches, large databases [specifically, OBIS (http://www.iobis.org/), GBIF (http://www.gbif.org/) and Ecological Data Wiki (https://ecologicaldata.org/)], through personal networking and through broadcasted data requests at conferences and on social media. We have used four main criteria for data inclusion on BioTIME: (a) abundance observations come from samples of assemblages where all individuals within the sample were counted and identified (i.e., assemblage rather than population data); (b) most of the individuals were identified to species; (c) sampling methods were constant through time; and (d) the time series spans a minimum of 2 years. The last condition was changed relative to the initial criteria because it became apparent that it would allow better spatial representation given the many locations that have been surveyed historically and then resurveyed. Each study is kept separate within the database and has a specific license from the CC spectrum, whose terms must be observed (https://creativecommons.org/). A static version of the database is released with this publication (http://biotime.st-andrews.ac.uk and https://zenodo.org/record/1095627). However, data entry and curation is ongoing (http://biotime.st-andrews.ac.uk/contribute.php), and we expect the database to keep growing in the foreseeable future. We plan to release static updates of the database periodically.

2.2. Data curation and quality control

Before inclusion in the database, data were subjected to standardization in a curation process described specifically for each study in the curation table of the database. Specifically, these were checked for the presence of the following: duplicates within each study and against the entire database; species with zero abundance; and non‐organismal records, all of which were removed. Abundances of zero for a particular population can be inferred from their absence from samples in the study. Additionally, species names were checked for typographic errors and misspellings, and a standardized notation was used for records of morphospecies and species complexes. Most records were included as provided and may not always conform to the latest nomenclature. Furthermore, latitudes and longitudes were checked for their location relative to other descriptors (e.g., country or marine vs. terrestrial). Finally, the grain and extent of each study were calculated from information in the methods where available, or by applying a convex hull algorithm to locations of the samples.

3. DESCRIPTION OF DATA

In total, the version of BioTIME released with this paper includes 8,777,413 records, across 547,161 unique locations, gathered from 361 studies (Figure 1; see Appendix for a full list of citations). These observations span the Poles to the Equator, from depths of c. 5,000 m to elevations of c. 4,000 m above sea level, and include the terrestrial, freshwater and marine realms. The database includes records spanning 21 out of 26 ecoregions [WWF; (http://www.worldwildlife.org/biomes)]. Nonetheless, there are spatial biases in the distribution of sampling locations, with most studies occurring in Europe, North America and Australia. This geographical bias has persisted despite the growth of the database. For example, a comparison between Supporting Information Figure S2 and the data included in the study by Dornelas et al. (2014) displays only small differences, despite the database having more than tripled its size in the interim. It is our hope that this geographical bias will decrease over time via targeted searches and data recruitment.

Figure 1.

Figure 1

Open in a new tab

Top: Geographical locations of all the records included in BioTIME in dark grey, with central points per study shown as circles of different colour and size, according to taxa and number of species. Bottom: Map overlaid with ∼4° grid cells coloured by the length of the full or partial time series contained within each cell

There are 44,440 taxa in BioTIME. The majority of these (88.8%) are species, but some organisms are identified only to coarser taxonomic levels, such as genus. BioTIME includes assemblages across the animal and plant kingdoms, ranging from mammals to microscopic plankton. As with the spatial distribution, there are also taxonomic biases in the data in BioTIME (Figure 2). Almost 70% of records fall into one of four categories: terrestrial plants, birds, fish and marine invertebrates, with fish accounting for 28% of the total database.

Figure 2.

Figure 2

Open in a new tab

Proportion of studies that fall into the different classifications of: Climate, number of years sampled, realm, taxa and biome

BioTIME records span 118 years (from 1874 to 2016), with the longest time series having 97 years and an average duration of 13 years. In more detail, 56.5% of studies contain up to 10 years of data, 42% between 10 and 50 years and 1.4% > 50 years.

4. USAGE NOTES

Version 1.0 of the BioTIME database can be downloaded from https://zenodo.org/record/1095627 or from http://biotime.st-andrews.ac.uk/. The use of data contained in BioTIME should cite original data citations in addition to the present paper. There is considerable variation in the spatial and temporal grain and extent among studies, which must be considered in any analysis of BioTIME data. Moreover, the number of samples was often not constant through time within studies; consequently, we recommend the use of sample‐based rarefaction and provide R code to query the database, implement sample‐based rarefaction and calculate a suite of biodiversity metrics. Specifically, we provide a tutorial guiding users to interact with both formats of the database (.csv and .sql; Allaire et al., 2015; Becker, Wilks, & Brownrigg, 2014; Oksanen et al., 2013; Ooms, James, DebRoy, Wickham, & Horner, 2015; R Development Core Team, 2013; Wickham, 2009; Wickham & Francois, 2015). Please note that for interacting with the .sql version of the database, users will have to set up a connection with the server where they have installed the SQL database. For interacting with the .csv version, users have to download both the data and the metadata csv files, making sure that all the paths to these files are modified accordingly.

The data included in the present paper represent the subset of data within the BioTIME database for which we were able to secure licences to republish. The additional studies held in the full database have been obtained from publicly available data and are listed in Supporting Information Table S1. In total, BioTIME currently holds 387 studies, containing 12,623,386 records from a total of 652,675 distinct geographical locations, and 45,093 species. These records span a total of 124 years from 1858 to 2016 inclusive. We will continue to interact with data providers in order to increase data availability and to recruit additional data. Instructions on how to contribute to future releases can be found here (http://biotime.st-andrews.ac.uk/contribute.php).

DATA ACCESSIBILITY

The BioTIME database is accessible through the BioTIME website (http://biotime.st-andrews.ac.uk) and through the Zenodo repository (https://zenodo.org/record/1095627).

BIOSKETCH.

The BioTIME consortium emerged from the ERC project BioTIME in 2010. The consortium currently includes 271 authors distributed among 35 countries engaged in collecting biodiversity time series data and committed to sharing it for wider use. We hope that the BioTIME database allows analysis of large‐scale patterns of biodiversity change and contributes to giving credit to the data collectors, without whom synthesis would not be possible.

Supporting information

Additional Supporting Information may be found online in the supporting information tab for this article.

Supporting Information

Click here for additional data file. (641.1KB, pdf)

Supporting Information

Click here for additional data file. (1.1MB, pdf)

Supporting Information

Click here for additional data file. (13.3KB, csv)

ACKNOWLEDGMENTS

European Research Council and EU: A.E.M., M.D. and F.M. are grateful for the support of the ERC grants BioTIME [AdG‐250189] and BioCHANGE [PoC‐727440]. J.P. and M.E. acknowledge the financial support from the ERC Synergy grant ERC‐SyG‐2013‐306 610028 IMBALANCE‐P, Spanish CGL2016‐79835‐P and Catalan SGR‐2017‐1005. Long‐term sampling of Calafuria rocky shores (L.B.‐C.) has been supported by various E.U. projects, in addition to the University of Pisa and the Census of Marine Life. Natural Environmental Research Council: C.W. is grateful for the support of the Natural Environmental Research Council [grant number NE/L002531/1]. The Porcupine Abyssal Plain Sustained Observatory is funded by the U.K. Natural Environment Research Council. We thank the Atlantic Meridional Program (supported by the U.K. Natural Environment Research Council through the Atlantic Meridional Transect consortium) and the L4 programme (funded under the U.K. NERC Oceans 2025 programme as part of Theme 10, Sustained Observations). C.E.W. thanks the U.K.'s Natural Environment Research Council for funding the Western Channel Observatory's plankton time‐series through the National Capability programme. National Science Foundation (NSF): S.K.M.E. acknowledges the U.S. National Science Foundation for funding data collection. S.R.S. was supported by NSF grant 1400911. The research of F.P.D. was funded by NSF grant DEB‐1237733. D.A.K. thanks the National Science Foundation (most recently DEB‐1456729) for their support. This material (R.D.H.) is based upon work supported by the National Science Foundation under Grant No. 9714103, 0632263, 0856516, and 1432277. K.D.W. thanks the U.S. Forest Service, National Science Foundation and Andrew W. Mellon Foundation. Research (M.W., R.B.W. and C.B.) was supported by grants DEB‐9705814, BSR‐8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447, DEB‐1546686 and DEB‐129764 from the National Science Foundation to the Department of Environmental Science, University of Puerto Rico, and to the International Institute of Tropical Forestry USDA Forest Service, as part of the Luquillo Long‐Term Ecological Research Program. The U.S. Forest Service (Department of Agriculture) and the University of Puerto Rico gave additional support. J.E.D. thanks the U.S. National Science Foundation for support with grants OCE 95‐21184, OCE‐0099226, OCE 03–52343, OCE‐0623874, OCE‐1031061 and OCE‐1336206. Data compilation and cleaning by A.H.H., B.S.E. and S.J.S. was funded by NSF grant DEB‐1354563 to A.H.H. W.A.G. thanks the AON ITEX program (awards 1432982, 0856710 and 1504381). All research at the U.S. Forest Service International Institute of Tropical Forestry is done in collaboration with the University of Puerto Rico. National Science Foundation (LTER): Jornada LTER, Research Site Manager – New Mexico State University. Datasets were provided by the Jornada Basin Long‐Term Ecological Research (LTER) project. Funding for these data was provided by the U.S. National Science Foundation (Grant DEB‐1235828). C.G. was supported under Cooperative Agreement #DEB‐1440297, NTL LTER. Support for A.L.R. was provided under Cooperative Agreement #DEB‐1440297, NTL‐LTER. Data collection (E.H.S.) was supported by the National Science Foundation #DEB‐1440297, NTL LTER. J.J. is grateful for funding to the H. J. Andrews Long‐Term Ecological Research program from the U.S. National Science Foundation; U.S. Forest Service support of the H. J. Andrews Experimental Forest. V.H.R.‐M. and R.R.T. thank NSF‐Florida Coastal Everglades Long‐Term Ecological Research (FCE‐LTER) program (grant nos DBI‐0620409, DEB‐9910514 and DEB‐1237517). D.C.R. is grateful for support from the NSF's LTER Program. D.C.R. thanks the U.S. National Science Foundation for supporting the Santa Barbara Coastal Long‐Term Ecological Research (SBC‐LTER) program. Data (A.J.B. and R.C.) were provided by the Moorea Coral Reef Long‐Term Ecological Research Program (OCE‐0417412, OCE‐1026851, OCE‐1236905 and OCE‐1637396). D.L. thanks the Jornada Basin LTER Program and the Sevilleta LTER Program. Data (J.J., M.N. and S.M.R.) were provided by the H. J. Andrews Experimental Forest research program, funded by the NSF's LTER Program (DEB‐1440409), U.S. Forest Service Pacific Northwest Research Station and Oregon State University. The authors are grateful to the LTER program for the data they provide. This includes material based upon work supported under Cooperative Agreements DEB‐0832652 and DEB‐0936498, and by grants from the LTER including DEB‐0620652 and DEB‐1234162; further support was provided by the Cedar Creek Ecosystem Science Reserve and the University of Minnesota. J.F.C. acknowledges funding from NSF (DEB‐0823293) from the LTER to the Coweeta LTER Program at the University of Georgia. Other funding: L.H.A. was supported by Fundação para a Ciência e Tecnologia, Portugal (POPH/FSE SFRH/BD/90469/2012). A.R.K. is funded by Ciências sem Fronteiras and Coordenação de Pessoal de Nível Superior (CAPES, Brazil), Grant/Award Number: 1091/13‐1. R.E. is grateful for support by Instituto Milenio de Oceanografía IC120019. A.H.B. is grateful for ARC Centre of Excellence (Grant CE0561432). R.P.V. is currently supported by a doctoral grant from Fundação para a Ciência e Tecnologia, Portugal (SFRH/BD/84030/2012). J.J. is grateful for funding for data collection from NSERC Canada. J.R.G. is grateful for the support from CONICYT/FONDECYT no. 1160026, ICM PO5‐002, CONICYT/FPB‐23. A.P.M. is grateful for the support of FONDECYT Grants 11110351 and 1151094. A.A. and V.O. thank RSF (14‐50‐00029). E.P.W. is supported by the Gordon and Betty Moore Foundation's Data‐Driven Discovery Initiative Grant GBMF4563. J.M.A. was supported by FI/FIAP (1998FI‐00596) and BE (2001BEAI200208) fellowships from the Catalan Government (DURSI) during the fieldwork and by a MECD‐Post‐doctoral fellowship (EX2002‐0022), a Marie Curie Individual Fellowship (QLK5‐CT2002‐51518) and Marie Curie project MARIBA (MERG‐CT‐2004‐022065) afterwards. A.F. receives a scholarship from CNPq (306170/2015‐9); G.D. receives a scholarship from FAPESP (2015/10714‐6); projects to collect data received financial support from FAPESP (São Paulo Research Foundation) (2015/06743‐0, 2008/10049‐9), CNPq (475434/2010‐2) and DFG (German Research Foundation, Project PF 120/10‐2). F.L. acknowledges support from EU CLIMOOR ENV4‐CT97–0694, VULCAN EVK2‐CT‐2000‐00094), Spanish REN2000‐0278/CCI and REN2001‐003/GLO, and Catalonian AGAUR 2014 SGR 453. Y.R.S. is grateful for funding from FUNDECT, CNPq. F.R.S. is grateful for the support of the São Paulo Research Foundation (FAPESP, Proc. 2013/50714‐0). D.A.K. is grateful for the support of FONDECYT (most recently, no. 1070808). E.L. acknowledges funding from NSERC and logistical support from Polar Continental Shelf Program. P.H. is grateful for the support of FONDECYT no. 1130511. G.B.G.S. and M.V. thank the staff who assisted in the field and laboratory research from the Laboratory of Fishery Biology and Technology. This study was part of the programme ‘Environmental Assessment of Guanabara Bay’ coordinated and funded by CENPES – PETROBRAS, which has given permission for the publication of the results. This study was also supported by the Long Term Ecological Programme (PELD programme – CNPq 403809/2012‐6) and by FAPERJ (Thematic Programme, process E‐26/110.114/2013). G.B.G.S. was funded by CAPES/Brazil. C.M. is grateful for the support of the German Academic Exchange Service (DAAD) and the German Research Foundation (DFG). H.B. and U.J. acknowledge the support of sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig. C.F.J.M., R.R. and A.L.‐B. acknowledge funding from Fundação para a Ciência e Tecnologia, Portugal (PTDC/BIA‐BIC/111184/2009, SFRH/BD/80488/2011 and PD/BD/52597/2014, respectively). F.Z.F. was funded by CAPES/Brazil. T.J.W. acknowledges support from the New Zealand Department of Conservation. General acknowledgments: Bioinformatics and Computational Biology analyses were supported by the University of St Andrews Bioinformatics Unit, which is funded by a Wellcome Trust ISSF award (grant 105621/Z/14/Z). We would like to acknowledge Richard Osman for his work in the Woods Hole study, and the Smithsonian Atherton Seidell Fund, which provides funds within the Smithsonian to make old studies and publications more available. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This study (P.B.) was performed under the auspices of Utah State University IACUC protocol number 1539. L.V.V. thanks Earthwatch Institute and their volunteers. R.A.D. and T.S.D. thank the Botanic Gardens and Parks Authority for the financial and logistical support, without which their reptile monitoring project would not be possible. R.S.S. and G.E. thank the Reef Life Survey volunteer divers. P.H. thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). F.H. acknowledges the EWHALE laboratory, Biology and Wildlife Department, Institute of Arctic Biology. H.K. thanks the Ministry of Trade, Industry and Fisheries. A.H. is supported by the Research Council of Norway. J.S. thanks the many researchers and field assistants who over the years contributed to the collection and curation of data for the studies presented in this database. S.K.C. thanks the City of Boulder Department of Open Space and Mountain Parks. N.A. thanks Karnataka Forest Department and IFP staff Messrs. S. Aravajy, S. Ramalingam, N. Barathan, G. Orukaimani, G. Jayapalan, K. Anthapa Gowda, Obbaya Gowda and Manoj Gowda. M.L. and J.M.A. wish to thank all participants in the MEDITS series cruises on board R/V Cornide de Saavedra, both scientists and crew (Spanish Institute of Oceanography), for all their help and support, and especially Pere Abelló and Luis Gil de Sola. Thanks to Daniel Oro and the Population Ecology research team at the Institut Mediterrani d'Estudis Avançats (IMEDEA, CSIC‐UIB). M.L. was supported by a fellowship of Conselleria de Innovació, Hisenda i Economia (Govern de les Illes Balears). R.K., D.P. and J.M. acknowledge SOTEAG (Shetland Oil Terminal Environmental Advisory Group) for providing access to the dataset. We thank SOTEAG (Shetland Oil Terminal Advisory group) for providing data from the long term rocky shore monitoring programme after dataset. We thank Jake Goheen and Rob Pringle for providing data from the UHURU herbivore‐exclusion experiment in central Kenya. J.S.M. thanks the Australian Research Council. J.S.M. thanks the staff of Lizard Island Research Station. F.P. would like to thank the Wasserversorgung Zurich for collecting and allowing access to the data. Data (C.H.D.) was sourced from the Integrated Marine Observing System (IMOS); IMOS is a national collaborative research infrastructure, supported by the Australian Government. J.M.A. would like to thank the Spanish Institute of Oceanography (IEO), Pere Abelló, Luis Gil de Sola and Daniel Oro. M.T.Z.T. thanks Dr Ary Teixeira de Oliveira‐Filho. I.H.M.‐S. thanks the Herschel Island‐Qikiqtaruk Territorial Park management and, in particular, Cameron D. Eckert, Catherine Kennedy, Dorothy Cooley and Jill F. Johnstone for establishing the ITEX protocols for plant composition monitoring on Qikiqtaruk. We thank the Herschel Island‐Qikiqtaruk Territorial Park rangers for data collection logistical support, including in particular Richard Gordon, Edward McLeod, Sam McLeod, Ricky Joe, Paden Lennie, Deon Arey and LeeJohn Meyook. We thank the researchers and field assistants who helped with data collection, including Haydn Thomas, Sandra Angers‐Blondie, Jakob Assmann, Meagan Grabowski, Catherine Henry, Annika Trimble, Louise Beveridge, Clara Flintrop, Santeri Lehtonen, Joe Boyle, John Godlee and Eleanor Walker. Funding was provided by the Yukon Government Herschel Island‐Qikiqtaruk Territorial Park and the U.K. Natural Environment Research Council ShrubTundra Grant NE/M016323/1. We thank the Inuvialuit People for the opportunity to conduct research on their traditional lands. L.H. and L.S.C. are grateful for the support of IPY, Memorial University and ArcticNet for funding. T.J.C. thanks the LIRS Trimodal Mapping Study. M.H. thanks the staff of Lizard Island Research Station. R.R.S. would like to thank E. E. de Assis, Santa Genebra and E. E. Caetetus and acknowledges funding from FAPESP (projetos temáticos: 1999/09635‐0 and 2013/50718‐5) and CNPq (Processo: 561897/2010). F.C. thanks SIBELCO Ltda. of Brazil for the logistic support in the accomplishment of the field work. We acknowledge the thousands of U.S. and Canadian volunteers who annually perform the North American Breeding Bird survey, as well as those who manage the program at the U.S. Geological Survey (USGS). The term ‘Anthropocene’ is not formally recognized by the USGS as a description of geological time. We use it here informally. We hope that data providers will continue to share their data (and any new updates) with OBIS and GBIF and encourage them to correct any errors identified by BioTIME. D.A., D.J., K.K., T.V. acknowledges support from Czech Science Foundation, project No. 16‐18022S and from Czech Ministry of Environment, project No. 170368. We thank Jan Wittoeck and other colleagues who assisted in the sampling and compilation of the macrobenthic data and the Belgian Federal Science Policy Office who funded MACROBEL through the programme ‘Sustainable management of the North Sea’ (SPSD I MN/02/96). M.B., A.J., K.P., J.S. received financial support from internal funds of University of Lódź. W.R.F. thanks the National Science Foundation for support through award OPP‐1440435. N.V. thanks CONICYT grants FONDECYT 1141037 and FONDAP 15150003 (IDEAL).

APPENDIX 1. DATA SOURCES

  1. “A transect survey of small land carnivore and red fox populations on a subarctic fell in Finnish forest Lapland over 13 winters”. NERC Centre for Population Biology, Imperial College, The Global Population Dynamics Database v2.0. Available at: http://www3.imperial.ac.uk/cpb/databases/gpdd, accessed 2012.
  2. “Animal Demography Unit ‐ Coordinated Waterbird Counts (CWAC) ‐ AfrOBIS”. Available at http://www.iobis.org/, accessed 2012.
  3. “Bahamas Marine Mammal Research Organisation Opportunistic Sightings ‐ OBIS SEAMAP”. Available at: http://www.iobis.org, accessed 2012.
  4. “Baltic Seabirds Transect Surveys”, Institute of Ecology of Vilnius University ‐ OBIS‐SEAMAP. Available at: http://www.emodnet-biology.eu/component/imis/?module=dataset&dasid=1971, accessed 2012.
  5. “CMarZ (Census of Marine Zooplankton)‐Asia Database”. Accessed through OBIS‐ SCAR‐MarBIN. Available at: http://www.iobis.org/mapper/?dataset=1500, accessed 2012.
  6. “CRED Rapid Ecological Assessments of Coral Population in the Pacific Ocean 2007–2010”. (2011) Coral Reef Ecosystem Division (CRED), Pacific Island Fisheries Sciences Center, National Marine Fisheries Service. Available at: http://www.iobis.org/mapper/?dataset=1578, accessed 2012.
  7. “CRED REA Algal Quadrate Images in the Pacific Ocean 2002–2008”. (2011) Coral Reef Ecosystem Division (CRED), Pacific Island Fisheries Sciences Center, National Marine Fisheries Service. Available at: http://www.iobis.org/mapper/?dataset=1577, accessed 2012.
  8. “CRED Towed‐Diver Fish Biomass Surveys in the Pacific Ocean 2000–2010”. (2011) Coral Reef Ecosystem Division (CRED), Pacific Island Fisheries Sciences Center, National Marine Fisheries Service. Available at: http://www.iobis.org/mapper/?dataset=1581, accessed 2012.
  9. “CSIRO Marine Data Warehouse ‐ OBIS Australia”, CSIRO Division of Marine and Atmospheric Research (CMAR), Australia. Available at http://www.iobis.org, accessed 2012.
  10. “East Coast North America Strategic Assessment Project, Groundfish Atlas for the East Coast of North America”. Available at: http://www.iobis.org, accessed 2012.
  11. “EPA'S EMAP Database”. U.S. Environmental Protection Agency through its Environmental Monitoring and Assessment Program (EMAP). Available at: http://www.iobis.org, accessed 2012. Also available at https://archive.epa.gov/emap/archive-emap/web/html/index-21.html
  12. “Fluctuations and long‐term trends in the relative densities of tetraonid populations in Finland, 1964‐77.” NERC Centre for Population Biology, Imperial College. The Global Population Dynamics Database v2.0. Available at: https://www.imperial.ac.uk/cpb/gpdd2/secure/register.aspx, accessed 2012.
  13. “Great Barrier Reef Seabed Biodiversity Study 2003–2006” (CMAR_ANACC), accessed through OBIS‐Australia. Available at http://www.iobis.org, accessed 2012.
  14. “Marine and Coastal Management ‐ Copepod Surveys ‐ AfrOBIS”. Available at: http://www.iobis.org/, accessed 2012.
  15. “Marine Biological Sample Database, JAMSTEC” (OBIS_JAPAN). Available at: http://www.iobis.org/mapper/?dataset=2289, accessed 2012.
  16. “MEDITS seabird surveys 1999 / 2000 / 2002” (2011). Mediterranean Institute for Advanced Studies (IMEDEA) In: OBIS‐SEAMAP. Available at: http://www.emodnet-biology.eu/component/imis/?module=dataset&dasid=1979, accessed 2012.
  17. “National Benthic Infaunal Database (NBID)” (2003). NOAA/NOS/NCCOS/CCEHBR/Coastal Ecology Program. NOAA's Ocean Service, National Centers for Coastal Ocean Science (NCCOS). Available at: https://data.noaa.gov/dataset/national-benthic-infaunal-database-nbid, accessed 2012.
  18. “NEFSC Benthic Database (OBIS‐USA)”, (2010) Northeast Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce. Available at: http://www.iobis.org/mapper/?dataset=1694, accessed 2012.
  19. “Northeast Fisheries Science Center Bottom Trawl Survey Data (OBIS‐USA).” (2005) NOAA's National Marine Fisheries Service (NMFS) Northeast Fisheries Science Center. Woods Hole, Massachusetts, USA. Available at: http://www.iobis.org/mapper/?dataset=1435, accessed 2013.
  20. “PIROP Northwest Atlantic 1965–1992 ‐ OBIS SEAMAP”. Available at: http://www.iobis.org/mapper/?dataset=2245, accessed 2012.
  21. “POPA cetacean, seabird, and sea turtle sightings in the Azores area 1998–2009 ‐ OBIS SEAMAP”. Available at: http://www.iobis.org/mapper/?dataset=4257, accessed 2012.
  22. “Previous_fisheries_REVIZEE_Program”, accessed through Tropical and Subtropical Western South Pacific OBIS. Available at: http://www.iobis.org/mapper/?dataset=2411, accessed 2012.
  23. “REVIZEE South Score / Pelagic and Demersal Fish Database II”. Available at: http://www.iobis.org/mapper/?dataset=105, accessed 2012.
  24. “Southeast Fisheries Science Center, National Oceanic and Atmospheric Administration. NOAA Southeast Fishery Science Center (SEFSC) Fisheries Log Book System (FLS) Commercial Pelagic Logbook Data”. Available at: http://www.iobis.org/mapper/?dataset=1496, accessed 2012.
  25. “South TX Outer Continental Shelf and MI, AL, and FL Outer Continental Shelf benthic organism sampling 1974–1978”. US National Oceanographic Data Center, Silver Spring, Maryland, USA (2011). Available at http://www.usgs.gov/obis-usa/data_search_and_access/participants.html, accessed 2012.
  26. “South Western Pacific Regional OBIS Data Asteroid Subset”, NIWA (National Institute of Water and Atmospheric Research ‐ New Zealand) MBIS (Marine Biodata Information System) accessed through South Western Pacific OBIS. Available at: http://www.iobis.org/mapper/?dataset=219, accessed 2012.
  27. “South Western Pacific Regional OBIS Data Bryozoan Subset”. Accessed through South Western Pacific OBIS. Available at: http://www.iobis.org/mapper/?dataset=221, accessed 2012.
  28. “South Western Pacific Regional OBIS Data provider for the NIWA Marine Biodata Information System”. Ocean Biogeographic Information System. Occurrence Dataset. Available at: 10.15468/zuuiyu, accessed 2012. [DOI]
  29. “St. Croix, USVI Fish Assessment and Monitoring Data (2002 ‐ Present)”, (2007) Silver Spring, MD Publisher: NOAAs Ocean Service, National Centers for Coastal Ocean Science (NCCOS). National Oceanic and Atmospheric Association (NOAA)‐National Ocean Service (NOS)‐National Centers for Coastal Ocean Science (NCCOS)‐Center for Coastal Monitoring and Assessment (CCMA)‐Biogeography Team. Available at: http://www.iobis.org/mapper/?dataset=1673, accessed 2012.
  30. “St. John, USVI Fish Assessment and Monitoring Data (2002 ‐ Present)”, (2007) Silver Spring, MD Publisher: NOAAs Ocean Service, National Centers for Coastal Ocean Science (NCCOS). National Oceanic and Atmospheric Association (NOAA)‐National Ocean Service (NOS)‐National Centers for Coastal Ocean Science (NCCOS)‐Center for Coastal Monitoring and Assessment (CCMA)‐Biogeography Team. Available at: http://www.iobis.org/mapper/?dataset=1672, accessed 2012.
  31. “The Observer Program database”, accessed through the OBIS‐USA North Pacific Groundfish Observer (North Pacific Research Board). Available at: http://www.iobis.org, accessed 2012.
  32. “Whale Catches in Southern Ocean”. OBIS ‐ Australian Antarctic Data Centre. Available at: http://www.iobis.org, accessed 2013.
  33. “Zooplankton in the Bay of Biscay (1995–2004, yearly DEPM surveys)”. Available at: http://www.emodnet-biology.eu/component/imis/?module=dataset&dasid=2774, accessed 2012.
  34. “The Main Cropping System Experiment (MCSE)”. KBS LTER, Kellogg Biological Station. Available at: http://lter.kbs.msu.edu/research/long-term-experiments/main-cropping-system-experiment/, accessed 2016.
  35. Addinck, W. & de Kluijver, M. (2003) North Sea observations of Crustacea, Polychaeta, Echinodermata, Mollusca and some other groups between 1986 and 2003. Expert Centre for Taxonomic Idenditification (ETI), the Netherlands. Available at: http://www.emodnet-biology.eu/data-catalog?module=dataset&dasid=1037, accessed 2012.
  36. Adler, P. B. , Tyburczy, W. R. & Lauenroth, W. K. (2007) Long‐Term Mapped Quadrats From Kansas Prairie: Demographic Information For Herbaceous Plants. Ecology, 88, 2673 https://doi.org/10.1890/0012–9658(2007)88[2673:LMQFKP]2.0.CO;2. [Google Scholar]
  37. Amorim, P. , Figueiredo, M. , Machete, M. , Morato, T. , Martins, A. & Serrão Santos, R. (2008) Spatial variability of seabird distribution associated with environmental factors: a case study of marine Important Bird Areas in the Azores. ICES Journal of Marine Science, 66, 29–40. [Google Scholar]
  38. Anderson, J. , Vermeire, L. & Adler, P. B. (2011) Fourteen years of mapped, permanent quadrats in a northern mixed prairie, USA. Ecology, 92, 1703. [Google Scholar]
  39. Bakker, C. , Herman, P. & Vink, M. (1994) A new trend in the development of the phytoplankton in the Oosterschelde (SW Netherlands) during and after the construction of a storm‐surge barrier. The Oosterschelde Estuary (The Netherlands): a Case‐Study of a Changing Ecosystem, pp. 79–100. Springer.
  40. Bakker, C. , Herman, P.M.J. & Vink, M. (1990) Changes in seasonal succession of phytoplankton induced by the storm‐surge barrier in the Oosterschelde (S.W. Netherlands). Journal of Plankton Research, 12, 947–972. [Google Scholar]
  41. Bakker, C. & Herman, P.M.J. (1990) Phytoplankton in the Oosterschelde before, during and after the storm‐surge barrier (1982–1990). Netherlands Institute of Ecology; Centre for Estuarine and Marine Ecology, Netherlands. EurOBIS Data. Available at: http://www.iobis.org/mapper/?dataset=505, accessed 2013.
  42. Barceló, C. , Ciannelli, L. , Olsen, E. M. , Johannessen, T. & Knutsen, H. (2016) Eight decades of sampling reveal a contemporary novel fish assemblage in coastal nursery habitats. Global Change Biology, 22, 1155–1167. 10.1111/gcb.13047. [DOI] [PubMed] [Google Scholar]
  43. Barreto, T. E. Patterns and processes affecting the dynamics and structure of seasonally semi‐deciduous forests in SE of Brazil.University of Campinas, PhD thesis. Accessed 2016.
  44. Battles, J. J. , Fahey, T. & Cleavitt, N. (2003) “Forest Inventory of a Whole Tree Harvest: Hubbard Brook Experimental Forest Watershed 5, 1982, pre‐harvest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=36, accessed 2012.
  45. Battles, J. J. , Fahey, T. & Cleavitt, N. (2013) “Forest Inventory of a Whole Tree Harvest: Hubbard Brook Experimental Forest Watershed 5, 1990, 7 years post‐harvest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=37, accessed 2016.
  46. Battles, J. J. , Fahey, T. & Cleavitt, N. (2013b) “Forest Inventory of a Whole Tree Harvest: Hubbard Brook Experimental Forest Watershed 5, 1994, 10 years post‐harvest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=38, accessed 2016.
  47. Battles, J. J. , Fahey, T. & Cleavitt, N. (2013c) “Forest Inventory of a Whole Tree Harvest: Hubbard Brook Experimental Forest Watershed 5, 1999, 15 years post‐harvest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=39, accessed 2016.
  48. Battles, J. J. , Fahey, T. & Cleavitt, N. “Forest Inventory of a Northern Hardwood Forest: Watershed 6 1965, Hubbard Brook Experimental Forest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=29, accessed 2016.
  49. Battles, J. J. , Fahey, T. & Cleavitt, N. “Forest Inventory of a Northern Hardwood Forest: Watershed 6 1977, Hubbard Brook Experimental Forest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=30, accessed 2016.
  50. Battles, J. J. , Fahey, T. & Cleavitt, N. “Forest Inventory of a Northern Hardwood Forest: Watershed 6 1982, Hubbard Brook Experimental Forest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=31, accessed 2016.
  51. Battles, J. J. , Fahey, T. & Cleavitt, N. “Forest Inventory of a Northern Hardwood Forest: Watershed 6 1987, Hubbard Brook Experimental Forest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=32, accessed 2016.
  52. Battles, J. J. , Fahey, T. & Cleavitt, N. “Forest Inventory of a Northern Hardwood Forest: Watershed 6 1992, Hubbard Brook Experimental Forest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=33, accessed 2016.
  53. Battles, J. J. , Fahey, T. & Cleavitt, N. “Forest Inventory of a Northern Hardwood Forest: Watershed 6 1997, Hubbard Brook Experimental Forest.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=34, accessed 2016.
  54. Battles, J. J. , Johnson, C. , Hamburg, S. , Fahey, T. , Driscoll, C. & Likens, G. (2003) “Forest Inventory of a Northern Hardwood Forest: Watershed 6 2002.” The Hubbard Brook Ecosystem Study LTER Program. Available at: http://www.hubbardbrook.org/data/dataset.php?id=35, accessed 2012.
  55. Belmaker, J. , Ziv, Y. & Shashar, N. (2011) The influence of connectivity on richness and temporal variation of reef fishes. Landscape Ecology, 26, 587–597. [Google Scholar]
  56. Benedetti‐Cecchi, L. “Calafuria Low‐shore Intertidal Dataset (1991–2014)”. Department of Biology, University of Pisa. Accessed 2016.
  57. Benedetti‐Cecchi, L. “Calafuria Mid‐shore Intertidal Dataset (1991–2014)”. Department of Biology, University of Pisa. Accessed 2016.
  58. Berezovikov, N. N. (2004) The birds of settlements in Markakol Depression (Southern Altai). Russian Ornithological Journal, 249, 3–15. [Google Scholar]
  59. Bernardes, R.Á. (2005) Peixes da Zona Econômica Exclusiva da região sudeste‐sul do Brasil: levantamento com armadilhas, pargueiras e rede de arrasto de fundo. Edusp.
  60. Bernardes, R.A. , Rodrigues, A.R. , Rossi‐Wongtschowski, C.L. , dos Santos, A.P. , Vieira, R.C. & Wahrlich, R. (2005) Prospecção pesqueira de recursos demersais com armadilhas e pargueiras na Zona Econômica Exclusiva da Região Sudeste‐Sul do Brasil. Instituto Oceanográfico.
  61. Billett, D. S. M. , Bett, B. J. , Reid, W. D. K. , Boorman, B. & Priede, I. G. (2010) Long‐term change in the abyssal NE Atlantic: The “Amperima Event” revisited. Deep‐Sea Research Part II: Topical Studies in Oceanography, 57, 1406–1417. [Google Scholar]
  62. Billett, D. S. M. , Bett, B. J. , Rice, A. L. , Thurston, M. H. , Galéron, J. , Sibuet, M. & Wolff, G. A. (2001) Long‐term change in the megabenthos of the Porcupine Abyssal Plain (NE Atlantic). Progress in Oceanography, 50, 325–348. [Google Scholar]
  63. Björnberg, T. K. S. (1963) On the marine free‐living copepods off Brazil. Boletim do Instituto Oceanogràfico, 13, 03–142. [Google Scholar]
  64. Bonebrake, T.C. , Pickett, E.J. , Tsang, T.P. , Tak, C.Y. , Vu, M.Q. & Van Vu, L. (2016) Warming threat compounds habitat degradation impacts on a tropical butterfly community in Vietnam. Global Ecology and Conservation, 8, 203–211. [Google Scholar]
  65. Boutillier, J. A. (2007) “Pacific Shrimp Trawl Survey”. In: ShrimpTrawl Bio database, Fisheries and Oceans Canada PBS Shellfish Data Unit. OBIS Canada Digital Collections, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada. Available at: http://www.iobis.org, accessed 2012.
  66. Bradford, M. G. , Murphy, H. T. , Ford, A. J. , Hogan, D. L. & Metcalfe, D. J. (2014) Long‐term stem inventory data from tropical rain forest plots in Australia. Ecology, 95, 2362 10.1890/14-0458R.1. [DOI] [Google Scholar]
  67. Brook, B. W. , Traill, L. W. & Bradshaw, C. J. A. (2006) Minimum viable population sizes and global extinction risk are unrelated. Ecology Letters, 9, 375–382. 10.1111/j.1461-0248.2006.00883.x. [DOI] [PubMed] [Google Scholar]
  68. Brooks, A. J. of Moorea Coral Reef LTER. (2016) MCR LTER: Coral Reef: Long‐term Population and Community Dynamics: Fishes. Available at: knb‐lter‐mcr.6.54 10.6073/pasta/d688610e536f54885a3c59d287f6c4c3, accessed 2012. [DOI]
  69. Brooks, A. J. “MCR LTER: Coral Reef: Long‐term Population and Community Dynamics: Fishes”. Moorea Coral Reef LTER; Long Term Ecological Research Network. Available at: 10.6073/pasta/85e08a1ea6548ac2eaf808a70ce3eeb2, accessed 2012. [DOI]
  70. Brown, R.G. , Nettleship, D.N. , Germain, P. , Tull, C.E. & Davis, T. (1975) Atlas of eastern Canadian seabirds.
  71. Budy, P. , Luecke, C. & O'Brien, J. “Zooplankton density for lake samples collected near Toolik Lake Arctic LTER in the summer from 1983 to 1992”. Arctic Long‐Term Ecological Research. Available at: http://arc-lter.ecosystems.mbl.edu/1983-1992arclterzoops, accessed 2016.
  72. Budy, P. , Luecke, C. & O'Brien, J. “Zooplankton density for lake samples collected near Toolik Lake Arctic LTER in the summers between 1993–2002”. Arctic Long‐Term Ecological Research. Available at: http://arc-lter.ecosystems.mbl.edu/1993-2002arcticlterzoops, accessed 2016.
  73. Carpenter, R. (2015) “MCR LTER: Coral Reef: Long‐term Population and Community Dynamics: Other Benthic Invertebrates, ongoing since 2005”. Moorea Coral Reef LTER, knb‐lter‐mcr.7.28. Available at: 10.6073/pasta/8e7b3a0c7a8bf315739921861cc79d10, accessed 2016. [DOI]
  74. Carvalho, F. , Zocche, J. J. & Mendonça, R. A. (2009) Morcegos, (Mammalia, Chiroptera) em restinga no municıpio de Jaguaruna, sul de Santa Catarina, Brasil). Biotemas, 22, 193–201. [Google Scholar]
  75. Cavender‐Bares, J. & Reich, P. B. (2012) Shocks to the system: community assembly of the oak savanna in a 40‐year fire frequency experiment. Ecology, 93, S52–S69. [Google Scholar]
  76. Chen, H. , Liao, Y.‐C. , Chen, C.‐Y. , Tsai, J.‐I. , Chen, L.‐S. & Shao, K.‐T. (2015) Long‐term monitoring dataset of fish assemblages impinged at nuclear power plants in northern Taiwan. Scientific Data, 2, 150071. 10.1038/sdata.2015.71. [DOI] [PMC free article] [PubMed]
  77. Clark, D. & Branton, B. (2007) “DFO Maritimes Research Vessel Trawl Surveys, OBIS Canada Digital Collections”. Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada, OBIS Canada. Available at: http://www.iobis.org, accessed 2012.
  78. Condit, R. (1998) Tropical forest census plots: methods and results from Barro Colorado Island, Panama and a comparison with other plots. Springer‐Verlag, Berlin.
  79. Condit, R. , Chisholm, R.A. & Hubbell, S.P. (2012) Thirty years of forest census at Barro Colorado and the importance of immigration in maintaining diversity. PloS one, 7, e49826. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Condit, R. “Sherman Forest Dynamics Plot, Panama.” The Center for Tropical Forest Science. Smithsonian Tropical Research Institute. Available at: http://www.ctfs.si.edu/site/Sherman/, accessed 2013.
  81. Condit, R. , Ashton, P. , Bunyavejchewin, S. , Dattaraja, H. S. , Davies, S. , Esufali, S. , Ewango, C. , Foster, R. , Gunatilleke, I. A. U. N. , Gunatilleke, C. V. S. , Hall, P. , Harms, K. E. , Hart, T. , Hernandez, C. , Hubbell, S. , Itoh, A. , Kiratiprayoon, S. , Lafrankie, J. , de Lao, S. L. , Makana, J.‐R. , Noor, M. N. S. , Kassim, A. R. , Russo, S. , Sukumar, R. , Samper, C. , Suresh, H. S. , Tan, S. , Thomas, S. , Valencia, R. , Vallejo, M. , Villa, G. & Zillio, T. (2006) The importance of demographic niches to tree diversity. Science, 313, 98–101. [DOI] [PubMed] [Google Scholar]
  82. Danis, B. , Van de Putte, A. , Youdjou, N. & Segers, H. (Editors) “The Antarctic Biodiversity Information Facility”. Available at: http://www.biodiversity.aq, accessed 2013.
  83. Dapporto, L. (2009) Core and satellite butterfly species on Elba island (Tuscan Archipelago, Italy). A study on persistence based on 120 years of collection data. Journal of Insect Conservation, 13, 421–428. [Google Scholar]
  84. Davies, C. H. , Armstrong, A. J. , Baird, M. , Coman, F. , Edgar, S. , Gaughan, D. , Greenwood, J. , Gusmão, F. , Henschke, N. , Koslow, J. A. , Leterme, S. C. , McKinnon, A. D. , Miller, M. , Pausina, S. , Palomino, J. U. , Roennfeldt, R.‐L. , Rothlisberg, P. , Slotwinski, A. , Strzelecki, J. , Suthers, I. M. , Swadling, K. M. , Talbot, S. , Tonks, M. , Tranter, D. H. , Young, J. W. & Richardson, A. J. (2014) Over 75 years of zooplankton data from Australia. Ecology, 95, 3229 10.1890/14-0697.1. [DOI] [Google Scholar]
  85. Davies, C. H. , Coughlan, A. , Hallegraeff, G. , Ajani, P. , Armbrecht, L. , Atkins, N. , Bonham, P. , Brett, S. , Brinkman, R. , Burford, M. , Clementson, L. , Coad, P. , Coman, F. , Davies, D. , Dela‐Cruz, J. , Devlin, M. , Edgar, S. , Eriksen, R. , Furnas, M. , Hassler, C. , Hill, D. , Holmes, M. , Ingleton, T. , Jameson, I. , Leterme, S. C. , Lønborg, C. , McLaughlin, J. , McEnnulty, F. , McKinnon, A. D. , Miller, M. , Murray, S. , Nayar, S. , Patten, R. , Pritchard, T. , Proctor, R. , Purcell‐Meyerink, D. , Raes, E. , Rissik, D. , Ruszczyk, J. , Slotwinski, A. , Swadling, K. M. , Tattersall, K. , Thompson, P. , Thomson, P. , Tonks, M. , Trull, T.W. , Uribe‐Palomino, J. , Waite, A. M. , Yauwenas, R. , Zammit, A. & Richardson, A. J. (2016) A database of marine phytoplankton abundance, biomass and species composition in Australian waters. Scientific Data, 3, 160043 http://10.1038/sdata.2016.43. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Davis, R. A. & Doherty, T. S. (2015) Rapid Recovery of an Urban Remnant Reptile Community following Summer Wildfire. PLoS ONE 10(5), e0127925 10.1371/journal.pone.0127925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Day, F. (2010) “Long‐term N‐fertilized vegetation plots on Hog Island, Virginia Coastal Barrier Islands, 1992–2014.” Virginia Coast Reserve Long‐Term Ecological Research Project. Available at: http://www.vcrlter.virginia.edu/cgi-bin/showDataset.cgi?docid=knb-lter-vcr.106, accessed 2013.
  88. Day, F. P. , Conn, C. , Crawford, E. & Stevenson, M. (2004) Long‐term effects of nitrogen fertilization on plant community structure on a coastal barrier island dune chronosequence. Journal of Coastal Research, 20, 722–730. [Google Scholar]
  89. Degraer, S. , Wittoeck, J. , Appeltans, W. , Cooreman, K. , Deprez, T. , Hillewaert, H. , Hostens, K. , Mees, J. , Vanden Berghe, E. & Vincx, M. (2006) “Macrobel: Long term trends in the macrobenthos of the Belgian Continental Shelf.” Oostende, Belgium. Available at: http://www.emodnet-biology.eu/data-catalog?module=dataset&dasid=145, accessed 2013.
  90. Derezuyk, N. “Phytoplankton of the Ukrainian Black Sea shelf (1985–2005)”. Available at: http://www.emodnet-biology.eu/component/imis/?module=dataset&dasid=2694, accessed 2012.
  91. Diamond, A. , Gaston, A. & Brown, R. (1986) Converting PIROP Counts of Seabirds at Sea to Absolute Densities. Progress Notes No 164. Canadian Wildlife Service, Ottawa.
  92. Dickson, J.G. , Conner, R.N. & Williamson, J.H. (1993) Neotropical migratory bird communities in a developing pine plantation. Procedings on the Annual Conference. SEAFWA, 47, 439–446. [Google Scholar]
  93. Douglass, J.G. , France, K.E. , Richardson, J.P. & Duffy, J.E. (2010) Seasonal and interannual change in a Chesapeake Bay eelgrass community: Insights into biotic and abiotic control of community structure. Limnology and Oceanography, 55, 1499–1520. [Google Scholar]
  94. Durigan, G. “Brazil Dataset 2”, Instituto Florestal, Floresta Estadual de Assis. Accessed 2016.
  95. Durigan, G. “Brazil Dataset 3”, Instituto Florestal, Floresta Estadual de Assis. Accessed 2016.
  96. Durigan, G. “Brazil Dataset 4”, Instituto Florestal, Floresta Estadual de Assis. Accessed 2016.
  97. Durigan, G. “Brazil Dataset 5”, Instituto Florestal, Floresta Estadual de Assis. Accessed 2016.
  98. Edelist, D. , Rilov, G. , Golani, D. , Carlton, J.T. & Spanier, E. (2013) Restructuring the Sea: profound shifts in the world's most invaded marine ecosystem. Diversity and Distributions, 19, 69–77. [Google Scholar]
  99. Edgar, G. J. & Stuart‐Smith, R. D. (2014) Systematic global assessment of reef fish communities by the Reef Life Survey program. Nature Scientific Data 1, 140007 10.1038/sdata.2014.7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Elmendorf, S.C. , Henry, G.H. , Hollister, R.D. , Björk, R.G. , Bjorkman, A.D. , Callaghan, T.V. , Collier, L.S. , Cooper, E.J. , Cornelissen, J.H. & Day, T.A. (2012a) Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time. Ecology letters, 15, 164–175. [DOI] [PubMed] [Google Scholar]
  101. Elmendorf, S.C. , Henry, G.H. , Hollister, R.D. , Björk, R.G. , Boulanger‐Lapointe, N. , Cooper, E.J. , Cornelissen, J.H. , Day, T.A. , Dorrepaal, E. & Elumeeva, T.G. (2012b) Plot‐scale evidence of tundra vegetation change and links to recent summer warming. Nature Climate Change, 2, 453–457. [Google Scholar]
  102. Elmendorf, S.C. , Henry, G.H. , Hollister, R.D. , Fosaa, A.M. , Gould, W.A. , Hermanutz, L. , Hofgaard, A. , Jónsdóttir, I.S. , Jorgenson, J.C. & Lévesque, E. (2015) Experiment, monitoring, and gradient methods used to infer climate change effects on plant communities yield consistent patterns. Proceedings of the National Academy of Sciences, 112, 448–452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Elmendorf, S.C. (2012) Global Tundra Vegetation Change −30 years of plant abundance data from unmanipulated and experimentally‐warmed plots. Available at: http://www.polardata.ca, accessed 2017. CCIN reference number 10786.
  104. Enemar, A. , Sjöstrand, B. E. , Andersson, G. Ö. & von Proschwitz, T. (2004) The 37‐year dynamics of a subalpine passerine bird community, with special emphasis on the influence of environmental temperature and Epirrita autumnata cycles. Ornis Svecica, 14, 63–106. [Google Scholar]
  105. Ernest, S. K. M. , Valone, T. J. & Brown, J. H. (2009) Long‐term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA. Ecology, 90, 1708. [DOI] [PubMed] [Google Scholar]
  106. Escribano, R. , Manríquez, K. & Godoy, F. (2006) “Copepoda‐COPAS Center (COPAS_CPD1) ‐ Planktonic copepods from the Chilean Humboldt Current System ‐ Eastern South Pacific Regional Node of OBIS (ESPOBIS)”. Available at: http://www.iobis.org, accessed 2012.
  107. ESRI (2015) ArcGIS Desktop: Release 10.1
  108. Farah, F. T. , Rodrigues, R. R. , Santos, F. A. M. , Tamashiro, J. Y. , Shepherd, G. J. , Siqueira, T. , Batista, J. L. F. & Manly, B. J. F. (2014) Forest destructuring as revealed by the temporal dynamics of fundamental species – Case study of Santa Genebra Forest in Brazil. Ecological Indicators, 37, 40–44. 10.1016/j.ecolind.2013.09.011. [DOI] [Google Scholar]
  109. Farneda, F. Z., Rocha, R., López‐Baucells, A., Sampaio, E. M., Palmeirim, J. M., Bobrowiec, P. E., Grelle, C. E. & Meyer, C. F. (2018) Functional recovery of Amazonian bat assemblages following secondary forest succession. Biological Conservation, 218, 192–199. [Google Scholar]
  110. Fefilova, E. B. , Baturina, M. A. , Kononova, O. N. , Loskutova, O. A. , Khokhlova, L. G. & Dubovskaya, O. P. (2014) Long‐Term Changes of Aquatic Communities in the Kharbeyskie Lakes. Journal of Siberian Federal University. Biology, 3 240–266. [Google Scholar]
  111. Fidelis, A. & Damasceno, G. “Brazil Dataset 7”, Lab of Vegetation Ecology, Universidade Estadual Paulista. Accessed 2016.
  112. Fidelis, A. “Brazil Dataset 8”, Lab of Vegetation Ecology, Universidade Estadual Paulista. Accessed 2016.
  113. Fidelis, A. , Blanco, C. C. , Müller, S. C. , Pillar, V. D. & Pfadenhauer, J. (2012) Short‐term changes caused by fire and mowing in Brazilian Campos grasslands with different long‐term fire histories. Journal of Vegetation Science, 23, 552–562. 10.1111/j.1654-1103.2011.01364.x [DOI] [Google Scholar]
  114. Fletcher, C. & Kassim, A.R. Pasoh Forest Dynamics Plot Data. Available at: http://www.ctfs.si.edu/site/Pasoh/, accessed 2013.
  115. Foster, D. , Von Holle, B. & Parshall, T. (2006) “Land Use on the Southern New England and New York Coasts 1600–2001. Harvard Forest Data Archive: HF044.” The Harvard Forest Long Term Ecological Research Program. Available at: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf044, accessed 2013.
  116. Fraser, W. (2014) “At‐sea seabird censuses. Data on the species encountered (including marine mammals), their abundance, distribution and behavior. Data collected aboard cruises off the coast of the Western Antarctic Peninsula, 1993 ‐ present”. Palmer Station Antarctica LTER. Available at: 10.6073/pasta/e3871e749fa737dd94d5a269ac90e8ce, accessed 2016. [DOI]
  117. Friggens, M. (2008) “Sevilleta LTER Small Mammal Population Data”, Albuquerque, NM: Sevilleta Long Term Ecological Research Site Database: SEV008. Available at: http://sev.lternet.edu/data/sev-8, accessed 2012.
  118. Gaiser, E. (2010) Macrophyte count data collected from Northeast Shark Slough, Everglades National Park (FCE) from September 2006 to Present. Environmental Data Initiative. Available at: 10.6073/pasta/effd9e98134913af21b670febebd6233, accessed 2012. [DOI]
  119. Gavrilov, G. M. & Glebov, I. A. (2013) The composition and community structure of benthic fish in the economic zone of Russia Bering sea on the results of studies of “TINRO centrе” in 2005–2012 years. Modern problems of science and education, 11, 37–49. [Google Scholar]
  120. Gido, K. B. “Fish population on selected watersheds at Konza Prairie ‐ CFP01.” Konza Prairie LTER Program. Available at: http://www.konza.ksu.edu/KNZ/pages/data/Knzdsdetail.aspx?datasetCode=CFP01, accessed 2012.
  121. Goheen, J.R. , Palmer, T.M. , Charles, G.K. , Helgen, K.M. , Kinyua, S.N. , Maclean, J.E. , Turner, B.L. , Young, H.S. & Pringle, R.M. (2013) Piecewise disassembly of a large‐herbivore community across a rainfall gradient: the UHURU experiment. PLoS One, 8, e55192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  122. Grant, P. R. (1976) An 11‐year study of small mammal populations at Mont St. Hilaire, Quebec. Canadian Journal of Zoology, 54, 2156–2173. [Google Scholar]
  123. Grant, P. R. (1976) “An 11‐year study of small mammal populations at Mont St. Hilaire, Quebec”. NERC Centre for Population Biology, Imperial College. The Global Population Dynamics Database v2.0. Available at: http://www3.imperial.ac.uk/cpb/databases/gpdd, accessed 2012.
  124. Grossman, G. D. (1982) Dynamics and Organization of a Rocky Intertidal Fish Assemblage: The Persistence and Resilience of Taxocene Structure. The American Naturalist, 119, 611–637. http://www.jstor.org/stable/2461182. [Google Scholar]
  125. Grossman, G. D. (2007) “Stream fish assemblage stability in a southern Appalachian stream at the Coweeta Hydrologic Laboratory from 1984 to 1995”. Coweeta Long Term Ecological Research Program. Available at: 10.6073/pasta/f0baf5f59c89f670e04f537f5cc05290, accessed 2016. [DOI]
  126. Grossman, G. D. , Moyle, P. B. & Whitaker Jr, J. O. (1982) Stochasticity in structural and functional characteristics of an Indiana stream fish assemblage: a test of community theory. The American Naturalist, 1, 423–454. [Google Scholar]
  127. Hale, S. S. , Hughes, M. M. , Strobel, C. J. , Buffum, H. W. , Copeland, J. L. & Paul, J. F. (2002) Coastal ecological data from the Virginian Biogeographic Province, 1990–1993. Ecology, 83, 2942. [Google Scholar]
  128. Hall, G. A. (1984) A Long‐Term Bird Population Study in an Appalachian Spruce Forest. The Wilson Bulletin, 96, 228–240. [Google Scholar]
  129. Halpin, P.N. , Read, A.J. , Fujioka, E. , Best, B.D. , Donnelly, B. , Hazen, L.J. , Kot, C. , Urian, K. , LaBrecque, E. & Dimatteo, A. (2009) OBIS‐SEAMAP: The world data center for marine mammal, sea bird, and sea turtle distributions. Oceanography, 22, 104–115. [Google Scholar]
  130. Harmon, M. & Franklin, J. (2012) “Long‐term growth, mortality and regeneration of trees in permanent vegetation plots in the Pacific Northwest, 1910 to present.” Long‐Term Ecological Research. Forest Science Data Bank, Corvallis. Available at: http://andrewsforest.oregonstate.edu/data/abstract.cfm?dbcode=TV010, accessed 2012.
  131. Hartnett, D.C. & Collins, S.L. (2016) PVC02 Plant Species Composition on Selected Watersheds at Konza Prairie. Environmental Data Initiative. Available at: 10.6073/pasta/7b6df00de4d0fcecfd344c02de9f9c62, accessed 2017. [DOI]
  132. Hidalgo, P. , Escribano, R. , Vergara, O. , Jorquera, E. , Donoso, K. & Mendoza, P. (2010) Patterns of copepod diversity in the Chilean coastal upwelling system. Deep Sea Research Part II: Topical Studies in Oceanography, 57, 2089–2097. [Google Scholar]
  133. Hirche, H.‐J. , Kosobokova, K. , Gaye‐Haake, B. , Harms, I. , Meon, B. & Nöthig, E.‐M. (2006) Structure and function of contemporary food webs on Arctic shelves: A panarctic comparison: The pelagic system of the Kara Sea–Communities and components of carbon flow. Originator: B. Meon and E.‐M. Nöthig. Available at: http://www.iobis.org/mapper/?dataset=4396, accessed 2012.
  134. Hirche, H.‐J. , Kosobokova, K. , Gaye‐Haake, B. , Harms, I. , Meon, B. & Nöthig, E.‐M. (2006) Structure and function of contemporary food webs on Arctic shelves: A panarctic comparison: The pelagic system of the Kara Sea–Communities and components of carbon flow. Progress in Oceanography, 71, 288–313. [Google Scholar]
  135. Hoey, A. “Aceh WCS fish 2010‐16”. Accessed 2016.
  136. Hoey, A. “Aceh WCS fish surveys”. Accessed 2016.
  137. Hoey, A. “Karimunjawa WCS fish data”. Accessed 2016.
  138. Hogstad, O. (1993) Structure and dynamics of a passerine bird community in a spruce‐dominated boreal forest. A 12‐year study. Annales Zoologici Fennici, 30, 43–54. [Google Scholar]
  139. Hollister, R.D. , May, J.L. , Kremers, K.S. , Tweedie, C.E. , Oberbauer, S.F. , Liebig, J.A. , Botting, T.F. , Barrett, R.T. & Gregory, J.L. (2015) Warming experiments elucidate the drivers of observed directional changes in tundra vegetation. Ecology and Evolution, 5, 1881–1895. [DOI] [PMC free article] [PubMed] [Google Scholar]
  140. Holmes, R. T. & Sherry, T. W. (2001) Thirty‐year bird population trends in an unfragmented temperate deciduous forest: the importance of habitat change. The Auk, 118, 589–610. [Google Scholar]
  141. Holmes, R. T. & Sturges, F. W. (1975) Bird community dynamics and energetics in a northern hardwoods ecosystem. Journal of Animal Ecology, 44, 175–200. [Google Scholar]
  142. Holmes, R. T. & Sherry, T. W. (1988) Assessing population trends of New Hampshire forest birds: Local versus regional patterns. The Auk, 105, 756–768. [Google Scholar]
  143. Holmes, R. T. , Sherry, T. W. & Sturges, F. W. (1986) Bird Community Dynamics in a Temperate Deciduous Forest: Long‐Term Trends at Hubbard Brook. Ecological Monographs, 56, 201–220. [Google Scholar]
  144. Hopky, G.E. , Lawrence, M.J. & Chiperzak, D.B. “Beaufort Sea NOGAP1 Zooplankton ‐ NOGAP B1, Zooplankton Data from the Canadian Beaufort Sea Shelf, 1984–1985”. Arctic Ocean Diversity, University of Alaska Fairbanks, Fairbanks. Available at: http://www.arcodiv.org/Database/Plankton_datasets, accessed 2012.
  145. Hopky, G.E. , Lawrence, M.J. & Chiperzak, D.B. “Beaufort Sea NOGAP2 Zooplankton ‐ NOGAP B2, Zooplankton Data from the Canadian Beaufort Sea Shelf, 1987–1988”. Arctic Ocean Diversity, University of Alaska Fairbanks, Fairbanks. Available at: http://www.arcodiv.org/Database/Plankton_datasets, accessed 2012.
  146. Hopky, G.E. , Lawrence, M.J. & Chiperzak, D.B. “Beaufort Sea NOGAP3 Zooplankton ‐ NOGAP B3, Zooplankton Data from the Canadian Beaufort Sea Shelf, 1986”. Arctic Ocean Diversity, University of Alaska Fairbanks, Fairbanks. Available at: http://www.arcodiv.org/Database/Plankton_datasets, accessed 2012.
  147. How, R.A. (1998) Long‐term sampling of a herpetofaunal assemblage on an isolated urban bushland remnant, Bold Park, Perth. Journal of the Royal Society of Western Australia, 81, 143–148. [Google Scholar]
  148. Hsieh, C.‐H. “Icthyoplankton data collected from Yenliao Bay in 6 stations northeast of Taiwan (1995–2000)”. Ecoinformatics Lab, Institute of Oceanography National Taiwan University. Accessed 2016.
  149. Hubbell, S. P. , Condit, R. & Foster, R. B. (2005) “Barro Colorado Forest Census Plot Data”. Available at: https://ctfs.arnarb.harvard.edu/webatlas/datasets/bci, accessed 2012.
  150. Huettmann, F. (1998) An ecological GIS research application for the northern Atlantic‐The PIROP database software, environmental data sets and the role of the internet. In: Riekert W.‐F. and Tochtermann K. (Eds.) Hypermedia im Umweltschutz Proceedings of Deutsche Gesellschaft für Informatik (GI) and Forschungsinstitut für anwendungsorientierte Wissensverarbeitung (FAW) Ulm. Umwelt‐Informatik aktuell; Bd.17, Metropolis Verlag/Marburg. pp. 213–217
  151. Hundt, R. (2001) Ökologisch‐geobotanische Untersuchungen an den mitteldeutschen Wiesengesellschaften unter besonderer Berücksichtigung ihres Wasserhaushaltes und ihrer Veränderung durch die Intensivbewirtschaftung im Rahmen der Großflächenproduktion. Biosphärenreservat Rhön, Thüringen. Monografie, 3, 366. [Google Scholar]
  152. Institute of Agricultural and Fisheries research (ILVO), Belgium (2015) Epibenthos and demersal fish monitoring at long‐term monitoring stations in the Belgian part of the North Sea. Available at: 10.14284/54, accessed 2016. [DOI]
  153. Institute of Agricultural and Fisheries research (ILVO), Belgium (2016) Macrobenthos monitoring at long‐term monitoring stations in the Belgian part of the North Sea between 1979 and 1999. Available at: 10.14284/201, accessed 2016. [DOI]
  154. Institute of Agricultural and Fisheries research (ILVO), Belgium (2016) Macrobenthos monitoring at long‐term monitoring stations in the Belgian part of the North Sea from 2001 on. Available at: 10.14284/202, accessed 2016. [DOI]
  155. Jahncke, J. & Rintoul, C. (2006) “CalCOFI and NMFS Seabird and Marine Mammal Observation Data, 1987–2006”. California Cooperative Oceanic Fisheries Investigations (CalCOFI) and National Marine Fisheries Service (NMFS) cruises, 1987–2006 ‐ OBIS SEAMAP. Available at: http://www.iobis.org, accessed 2012.
  156. Jalilov, A. B. , Andreychev, A. V. & Kuznetsov, V. A. (2014) Monitoring and conservation of medium and large mammals in Chamzinsky District of the Republic of Mordovia.Vestnik of Lobachevsky University of Nizhni Novgorod, 4(1), 222–227. [Google Scholar]
  157. Jandt, U. & Bruelheide, H. (2012) German vegetation reference database (GVRD). Biodiversity & Ecology, 4, 355–355. [Google Scholar]
  158. Joern, A. (2016) CGR02 Sweep Sampling of Grasshoppers on Konza Prairie LTER watersheds (1982‐present). Environmental Data Initiative. Available at: 10.6073/pasta/7060b2c244229a37e3bfc8c18f14ad02, accessed 2016. [DOI]
  159. Jonas, J.L. & Joern, A. (2007) Grasshopper (Orthoptera: Acrididae) communities respond to fire, bison grazing and weather in North American tallgrass prairie: a long‐term study. Oecologia, 153, 699–711. [DOI] [PubMed] [Google Scholar]
  160. Jones, J. & Miller, J. “Spatial and temporal distribution and abundance of moths in the Andrews Experimental Forest, 1994 to 2008”. H. J. Andrews Experimental Forest. Forest Science Data Bank, Corvallis. Available at: http://andrewsforest.oregonstate.edu/data/abstract.cfm?dbcode=SA015, accessed 2013.
  161. Karatayev, V. A. , Karatayev, A. Y. , Burlakova, L. E. & Rudstam, L. G. (2014) Eutrophication and Dreissena Invasion as Drivers of Biodiversity: A Century of Change in the Mollusc Community of Oneida Lake. PLoS ONE, 9(7), e101388 10.1371/journal.pone.0101388. [DOI] [PMC free article] [PubMed] [Google Scholar]
  162. Kartzinel, T.R. , Goheen, J.R. , Charles, G.K. , DeFranco, E. , Maclean, J.E. , Otieno, T.O. , Palmer, T.M. & Pringle, R.M. (2014) Plant and small‐mammal responses to large‐herbivore exclusion in an African savanna: five years of the UHURU experiment. Ecology, 95, 787–787. 10.1890/13-1023R.1. [DOI] [Google Scholar]
  163. Kaufman, D.W. Seasonal summary of numbers of small mammals on 14 LTER traplines in prairie habitats at Konza Prairie. Konza Prairie Long‐Term Ecological Research. Available at: http://lter.konza.ksu.edu/content/csm01-seasonal-summary-numbers-small-mammals-14-lter-traplines-prairie-habitats-konza, accessed 2016.
  164. Kelt, D. A. , Meserve, P. L. , Gutiérrez, J. R. , Milstead, W. B. & Previtali, M. A. (2013) Long‐term monitoring of mammals in the face of biotic and abiotic influences at a semiarid site in north‐central Chile. Ecology, 94, 977 10.1890/12-1811.1. [DOI] [Google Scholar]
  165. Kendeigh, S. C. (1982) Bird populations in east central Illinois: Fluctuations, variations, and development over a half‐century. University of Illinois Press.
  166. Kennedy, M. & Spry, J. (2011) Atlantic Zone Monitoring Program Maritimes Region plankton datasets. Fisheries and Oceans Canada‐BioChem archive. OBIS Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada.
  167. Kenner, M. C. , Estes, J. A. , Tinker, M. T. , Bodkin, J. L. , Cowen, R. K. , Harrold, C. , Hatfield, B. B. , Novak, M. , Rassweiler, A. & Reed, D. C. (2013) A multi‐decade time series of kelp forest community structure at San Nicolas Island, California (USA). Ecology, 94, 2654 10.1890/13-0561R.1. [DOI] [Google Scholar]
  168. Khoruzhiy, A. A. & Naydenko, S. V. (2014) Species structure and year‐to‐year dynamics of nekton biomass in the upper epipelagic layer of the Pacific waters at Kuril Islands in summer periods of the 2000s. Izv. TINRO, 176, 16–36. [Google Scholar]
  169. Knops, J. & Tilman, D. Successional Dynamics on a Resampled Chronosequence ‐ Experiment 014. Cedar Creek Ecosystem Science Reserve. Available at http://www.cedarcreek.umn.edu/research/data/dataset?ghe014, accessed 2016.
  170. Kosobokova, K. “White Sea Zooplankton”. Shirshov Institute of Oceanology, Moscow; Arctic Ocean Diversity, University of Alaska Fairbanks, Fairbanks. Available at: http://www.iobis.org/mapper/?dataset=4488, accessed 2012.
  171. Krefting, L. W. & Ahlgren, C. E. (1974) Small Mammals and Vegetation Changes After Fire in a Mixed Conifer‐Hardwood Forest. Ecology, 65, 1391–1398. [Google Scholar]
  172. Krivenko, V. G. (1991) Waterfowl and their protection. Moscow, Agropromizdat Publishers, 271 p. [Google Scholar]
  173. Kuhnz, L. A. , Ruhl, H. A. , Huffard, C. L. & Smith, K. L. (2014) Rapid changes and long‐term cycles in the benthic megafaunal community observed over 24 years in the abyssal northeast Pacific. Progress in Oceanography, 124, 1–11. [Google Scholar]
  174. Kuo, C.‐Y. , Yuen, Y.S. , Meng, P.‐J. , Ho, P.‐H. , Wang, J.‐T. , Liu, P.‐J. , Chang, Y.‐C. , Dai, C.‐F. , Fan, T.‐Y. , Lin, H.‐J. , Baird, A. H. & Chen, C. A. (2012) Recurrent Disturbances and the Degradation of Hard Coral Communities in Taiwan. PLoS ONE, 7, e44364. [DOI] [PMC free article] [PubMed] [Google Scholar]
  175. Kuo, C.‐Y. “Scleractinian Coral Dataset”, ARC Centre of Excellence for Coral Reef Studies, James Cook University, Australia. Accessed 2016.
  176. Kuo, C.‐Y. “unpublished data from a tropical coral reef in Taiwan”. Accessed 2016.
  177. Kushner, D. J. , Rassweiler, A. , McLaughlin, J. P. & Lafferty, K. D. (2013). A multi‐decade time series of kelp forest community structure at the California Channel Islands. Ecology, 94, 2655 10.1890/13-0562.1. [DOI] [Google Scholar]
  178. Laguionie‐Marchais, C. , Billett, D. S. M. , Paterson, G. L. D. , Ruhl, H. A. , Soto, E. H. , Smith, J. L. & Thatje, S. (2013) Inter‐annual dynamics of abyssal polychaete communities in the North East Pacific and North East Atlantic‐A family‐level study. Deep‐Sea Research Part I: Oceanographic Research Papers, 75, 175–186. [Google Scholar]
  179. Laguionie‐Marchais, C. , Paterson, G. L. J. , Bett, B. J. , Smith, K. L. & Ruhl, H. A. (2016) Inter‐annual species‐level variations in an abyssal polychaete assemblage (Sta. M, NE Pacific, 4000 m). Progress in Oceanography, 140, 43–53. [Google Scholar]
  180. Lancaster, L. T. & Fitt, R. “unpublished Damselfly abundance data”, University of Aberdeen. Accessed 2016.
  181. Landis, D. & Gage, S. (2014) Insect Populations via Sticky Traps at KBS‐LTER. Available at: http://lter.kbs.msu.edu/datatables/67, accessed 2016.
  182. Lathrop, R. (2000) “Madison Wisconsin Lakes Zooplankton 1976 ‐ 1994.” North Temperate Lakes Long Term Ecological Research Program, Center for Limnology, University of Wisconsin‐Madison. Available at: http://lter.limnology.wisc.edu/dataset/madison-wisonsin-lakes-zooplankton-1976-1994, accessed 2013.
  183. Lee, C.M. , Kim, S.‐S. & Kwon, T.‐S. (2016) Butterfly fauna in Mount Gariwang‐san, Korea. Journal of Asia‐Pacific Biodiversity, 9, 198–204. [Google Scholar]
  184. Lefcheck, J. S. (2015) The use of functional traits to elucidate the causes and consequences of biological diversity. The College of William & Mary, PhD thesis. Available at: http://gradworks.umi.com/36/62/3662989.html, accessed 2016.
  185. Lightfoot, D. (2007) “Jornada Grasshopper Data”. Jornada Basin LTER. Available at: http://jornada.nmsu.edu/lter/dataset/49712/view, accessed 2016.
  186. Lightfoot, D. & Schooley, R. L. “SMES rodent trapping data, Small Mammal Exclosure Study”. Jornada LTER. Available at: http://jornada.nmsu.edu/sites/jornada.nmsu.edu/files/data_files/JornadaStudy_086_smes_rodent_trapping_data_0.csv, accessed 2016.
  187. Lightfoot, D. (2013) “Lizard pitfall trap data (LTER‐II, LTER‐III)”. Jornada Basin LTER. Available at: http://jornada.nmsu.edu/lter/dataset/49821/view, accessed 2016.
  188. Lightfoot, D. “Small Mammal Exclosure Study (SMES)”. Sevilleta Long Term Ecological Research Program. Available at: http://sev.lternet.edu/content/small-mammal-exclosure-study-smes-0, accessed 2016.
  189. Lightfoot, D. (2011) “Small Mammal Exclosure Study (SMES) Vegetation Data from the Chihuahuan Desert Grassland and Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (2006–2009)”. Long Term Ecological Research Network. Available at: 10.6073/pasta/d80d5e2196cd11ef79df23ebe5a77c19, accessed 2016. [DOI]
  190. Lindén, H. & Rajala, P. (1981) Fluctuations and long‐term trends in the relative densities of tetraonid populations in Finland, 1964‐77. Finnish Game Research, 39, 13–34. [Google Scholar]
  191. Ling, S. D. , Scheibling, R. E. , Rassweiler, A. , Johnson, C. R. , Shears, N. , Connell, S. D. , Salomon, A. K. , Norderhaug, K. M. , Pérez‐Matus, A. , Hernández, J. C. , Clemente, S. , Blamey, L. K. , Hereu, B. , Ballesteros, E. , Sala, E. , Garrabou, J. , Cebrian, E. , Zabala, M. , Fujita, D. & Johnson, L. E. (2014) Global regime shift dynamics of catastrophic sea urchin overgrazing. Philosophical Transactions of the Royal Society B: Biological Sciences, 370, 20130269. [Google Scholar]
  192. Lloret, F. , Peñuelas, J. & Estiarte, M. (2004) Experimental evidence of reduced diversity of seedlings due to climate modification in a Mediterranean‐type community. Global Change Biology, 10, 248–258. 10.1111/j.1365-2486.2004.00725.x. [DOI] [Google Scholar]
  193. Louzao, M. , Hyrenbach, K.D. , Arcos, J.M. , Abelló, P. , Sola, L.G. & Oro, D. (2006) Oceanographic habitat of an endangered Mediterranean procellariiform: implications for marine protected areas. Ecological Applications, 16, 1683–1695. [DOI] [PubMed] [Google Scholar]
  194. Machete, M. & Santos, R. (2007) Azores Fisheries Observer Program (POPA): a case study of the multidisciplinary use of observer data. Proceedings of the 5th International Fisheries Observer Conference, pp. 15–18.
  195. Mack, M. C. , Schuur, E. A. G. , Bret‐Harte, M. S. , Shaver, G. R. & Chapin, F. S. (2004) Ecosystem carbon storage in arctic tundra reduced by long‐term nutrient fertilization. Nature, 431, 440–443. [DOI] [PubMed] [Google Scholar]
  196. Madin, J.S. , Hoogenboom, M. , Chase, T.J. , Dornelas, M. , Frank, G.E. , Pizarro, O. , Williams, S.B. & Zawada, K. “Lizard Island Trimodal Coral 2015 ‐ 2016”. Accessed 2017.
  197. Madin, L. & Horgan, E. (2006) Zooplankton Sampled with 10m2MOCNESS Net in Georges Bank 1995–1999. Woods Hole Oceanographic Institution, USA: U.S. GLOBEC Data Management Office. Available at: http://www.iobis.org/mapper/?dataset=2339, accessed 2012.
  198. Malyshev, Y. S. (2011) On the diagnostic techniques of ranks of the number dynamics cycles of small mammals. Baikal Zoological Journal, 1(6), 92–106. [Google Scholar]
  199. Markhaseva, E.L. , Golikov, A.A. , Agapova, T.A. & Beig, A.A. (1985) Archives of the Arctic Seas Zooplankton. Available at: http://www.iobis.org/mapper/?dataset=4470, accessed 2012.
  200. McKnight, D. , Priscu, J. , Keeley, E. & Tursich, N. “Summer Phytoplankton Densities 1992–2001”. McMurdo Dry Valleys LTER. Available at: http://mcm.lternet.edu/content/summer-phytoplankton-densities-1992-2001, accessed 2016.
  201. McLarney, W. O. , Meador, J. & Chamblee, J. (2010) “Upper Little Tennessee River Biomonitoring Program Database.” Coweeta Long Term Ecological Research Program. Available at: https://coweeta.uga.edu/dbpublic/dataset_details.asp?accession=4045, accessed 2012.
  202. Melnikov, Y. I. , Melnikova, N. & Pronkevich, V. V. (2000) Migration of birds of prey in the mouth of the river Irkut. Russian Ornithological Journal, 108, 3–17. [Google Scholar]
  203. Mendenhall, C.D. , Karp, D.S. , Meyer, C.F. , Hadly, E.A. & Daily, G.C. (2014) Predicting biodiversity change and averting collapse in agricultural landscapes. Nature, 509, 213. [DOI] [PubMed] [Google Scholar]
  204. Merritt, J. (1999) Long Term Mammal Data from Powdermill Biological Station 1979–1999. Environmental Data Initiative. Available at: 10.6073/pasta/83c888854e239a79597999895bb61cfe, accessed 2016. [DOI]
  205. Meyer, C.F. & Kalko, E.K. (2008) Assemblage‐level responses of phyllostomid bats to tropical forest fragmentation: land‐bridge islands as a model system. Journal of Biogeography, 35, 1711–1726. [Google Scholar]
  206. Meyer, C.F. & Kalko, E.K. (2008) Bat assemblages on Neotropical land‐bridge islands: nested subsets and null model analyses of species co‐occurrence patterns. Diversity and Distributions, 14, 644–654. [Google Scholar]
  207. Meyer, C.F. , Fründ, J. , Lizano, W.P. & Kalko, E.K. (2008) Ecological correlates of vulnerability to fragmentation in Neotropical bats. Journal of Applied Ecology, 45, 381–391. [Google Scholar]
  208. Milchakova, N. A. , Ryabogina, V. G. & Chernyshova, E. B. (2011) “Macroalgae of the Crimean coastal zone (the Black Sea, 1967–2007)”. Sevastopol, IBSS. Available at: http://www.emodnet-biology.eu/component/imis/?module=dataset&dasid=2690, accessed 2012.
  209. Mitchell, P. I. , Newton, S. F. , Ratcliffe, N. & Dunn, T. E. Seabird 2000. Joint Nature Conservation Committee, Peterborough, UK. Available at: http://www.iobis.org/, accessed 2012.
  210. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2013) “Monitoring site 1000 Shorebird Survey” (ShorebirdsDatapackage2012.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  211. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2014) “Monitoring site 1000 Forest and grassland research ‐ Bird survey data” (BirdData2009‐B_ver20120328.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  212. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2014) “Monitoring site 1000 Forest and grassland research ‐ Surface wandering beetles survey data” (GBDataPackage2014ver1.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  213. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2014) “Monitoring site 1000 Village survey ‐ Bird survey data (2005–2012)” (SAT02.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  214. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2014) “Monitoring site 1000 Village survey ‐ Medium and large mammal survey data (2006–2012)” (SAT03zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  215. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2015) “Monitoring site 1000 Alpine research ‐ Bumblebee Survey” (KOZ08.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  216. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2015) “Monitoring site 1000 Alpine research ‐ Butterfly Survey” (KOZ06.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  217. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2015) “Monitoring site 1000 Alpine research ‐ Surface wandering beetles” (KOZ07zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  218. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2015) “Monitoring site 1000 Coastal zone research ‐ Algae survey” (MOB01.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index_file_algalbeds.html). Accessed 2016.
  219. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2015) “Monitoring site 1000 Coastal zone research ‐ Algae survey” (MOB02.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index_file_algalbeds.html). Accessed 2016.
  220. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2015) “Monitoring site 1000 Coastal zone research ‐ Tidal flat survey” (HIG01.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index_file_tidalflats.html). Accessed 2016.
  221. Monitoring Site 1000 Project, Biodiversity Center, Ministry of Environment of Japan (2015) “Monitoring site 1000 Land waters research ‐ Lakes: benthic animal research” (LKbenthosDatapackage2014ver1.zip, downloaded from http://www.biodic.go.jp/moni1000/findings/data/index.html). Accessed 2016.
  222. Moore, J. J. & Howson, C. M. “Survey of the rocky shores in the region of Sullom Voe, Shetland, A report to SOTEAG from Aquatic Survey & Monitoring Ltd”, Cosheston, Pembrokeshire. 29 p. Available at: http://www.soteag.org.uk, accessed 2013.
  223. Moore, N. W. (1991) “The development of dragonfly communities and the consequences of territorial behaviour: A 27‐year study on small ponds at Woodwalton Fen, Cambridgeshire, United Kingdom”. NERC Centre for Population Biology, Imperial College. The Global Population Dynamics Database Version 2.0. Available at: http://www3.imperial.ac.uk/cpb/databases/gpdd, accessed 2012.
  224. Moore, N. (1991) The development of dragonfly communities and the consequences of territorial behaviour: A 27 year study on small ponds at Woodwalton Fen, Cambridgeshire, United Kingdom. Odonatologica, 20, 203–231. [Google Scholar]
  225. Morato, T. , Varkey, D.A. , Damaso, C. , Machete, M. , Santos, M. , Prieto, R. , Santos, R.S. & Pitcher, T.J. (2008) Evidence of a seamount effect on aggregating visitors. Marine Ecology Progress Series, 357, 23–32. [Google Scholar]
  226. Muldavin, E. “Pinon Juniper Net Primary Production Quadrat Data from the Sevilleta National Wildlife Refuge, New Mexico: 1999–2001.” Sevilleta Long Term Ecological Research Program. Available at: http://sev.lternet.edu/data/sev-187, accessed 2013.
  227. Muldavin, E. “Pinon‐Juniper (Core Site) Quadrat Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico (2003‐Present).” Sevilleta Long Term Ecological Research Program. Available at: http://sev.lternet.edu/node/1718, accessed 2013.
  228. Muldavin, E. & Collins, S. (2003) Prescribed Burn Effect on Chihuahuan Desert Grasses and Shrubs at the Sevilleta National Wildlife Refuge, New Mexico: Species Composition Study 2004 to present. Sevilleta LTER. Available at: http://sev.lternet.edu/data/sev-166, accessed 2016.
  229. Myster, R.W. “Flooded forest plot sampling in Peru”. Luquillo LTER. Available at: http://luq.lternet.edu/data/luqmetadata169, accessed 2016.
  230. Naumov, A. “Benthos of the White Sea. A database”. White Sea Biological Station, Zoological Institute RAS. Available at: http://www.emodnet-biology.eu/component/imis/?module=dataset&dasid=2769, accessed 2012.
  231. Neat, F. & Campbell, N. (2011) Demersal fish diversity of the isolated Rockall plateau compared with the adjacent west coast shelf of Scotland. Biological Journal of the Linnean Society, 104, 138–147. 10.1111/j.1095-8312.2011.01699.x. [DOI] [Google Scholar]
  232. Nedosekin, V. Y. (2015) Long‐term dynamics of the population and the quantity of small mammals under conditions of the reserve “Galichya Gora”. Proceedings of National Nature Reserve Prisursky, 30, 87–90. [Google Scholar]
  233. NERC (2010) “The Global Population Dynamics Database Version 2”. Centre for Population Biology, Imperial College. Available at: http://www.sw.ic.ac.uk/cpb/cpb/gpdd.html, accessed 2016.
  234. NIWA “The New Zealand Freshwater Fish Database”. Available at: https://www.niwa.co.nz/our-services/online-services/freshwater-fish-database, accessed 2016.
  235. Norden, N. , García, H. & Salgado, B. “La Planada Forest Dynamics Plot”. The Center for Tropical Forest Science. Smithsonian Tropical Research Institute. Available at: http://www.ctfs.si.edu/site/La+Planada, accessed 2016.
  236. NTL LTER “NTLFI02 North Temperate Lakes LTER: Fish Abundance 1981 ‐ current”. North Temperate Lakes Long Term Ecological Research program, NSF. Center for Limnology, University of Wisconsin‐Madison. Available at: https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-fish-abundance-1981-current, accessed 2012.
  237. NTL LTER (2011) “North Temperate Lakes LTER: Zooplankton ‐ Madison Lakes Area 1997 ‐ current.” North Temperate Lakes Long Term Ecological Research Program, Center for Limnology, University of Wisconsin‐Madison. Available at: http://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-zooplankton-madison-lakes-area-1997-current, accessed 2013.
  238. NTL LTER “North Temperate Lakes LTER: Phytoplankton ‐ Madison Lakes Area 1995 ‐ current.” North Temperate Lakes Long Term Ecological Research Program, Center for Limnology, University of Wisconsin‐Madison. Available at: https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-phytoplankton-madison-lakes-area-1995-current, accessed 2013.
  239. NTL LTER “North Temperate Lakes LTER: Zooplankton ‐ Trout Lake Area 1982 ‐ current.” NorthTemperate Lakes Long Term Ecological Research Program, Center for Limnology, University of Wisconsin‐Madison. Available at: https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-zooplankton-trout-lake-area-1982-current, accessed 2013.
  240. Ohmart, R. , Pearson, D. , Hostetler, M. , Katti, M. & Hulen, T. (2003) “Transect bird survey with data synthesis from multiple transects in the central Arizona‐Phoenix area: period 1998 to 2000.” Central Arizona‐Phoenix Long‐Term Ecological Research. Global Institute for Sustainability, Arizona State University. Available at: https://caplter.asu.edu/data/data-catalog/?id=43, accessed 2012.
  241. Oliver, J. C. , Prudic, K. L. & Collinge, S. K. (2006) Boulder County Open Space butterfly diversity and abundance. Ecology, 87, 1066. [Google Scholar]
  242. Olsen, E. M. , Carlson, S. M. , Gjøsæter, J. & Stenseth, N. C. (2009) Nine decades of decreasing phenotypic variability in Atlantic cod. Ecology Letters, 12, 622–631. 10.1111/j.1461-0248.2009.01311.x [DOI] [PubMed] [Google Scholar]
  243. Ostler, R. “Marine Nature Conservation Review (MNCR) and associated benthic marine data held and managed by JNCC ‐ EurOBIS”. Joint Nature Conservation Committee, Centre for Ecology and hydrology, Aberdeenshire, UK. Available at: http://www.emodnet-biology.eu/data-catalog/??module=dataset&dasid=621, accessed 2012.
  244. Paquette, A. , Laliberté, E. , Bouchard, A. , de Blois, S. , Legendre, P. & Brisson, J. (2007) Lac Croche understory vegetation data set (1998–2006). Ecology, 88, 3209 10.1890/07-0513.1 [DOI] [Google Scholar]
  245. Pautzke, C.G. “Arctic Copepods T3 & AIDJEX Ice Islands”. Available at: http://www.arcodiv.org/Database/Plankton_datasets, accessed 2012.
  246. Pautzke, C.G. “Copepoda collected from the Canada Basin Arctic Ocean; Fletcher's Ice Island (T‐3) 1970–1972 and AIDJEX, 1975”. Phytoplankton Primary Production Below Arctic Ocean Pack Ice: An Ecosystems Analysis. PhD Thesis University of Washington; Arctic Ocean Diversity, University of Alaska Fairbanks, Fairbanks. Accessed 2012.
  247. Pélissier, R. , Pascal, J.‐P. , Ayyappan, N. , Ramesh, B. R. , Aravajy, S. & Ramalingam, S. R. (2011) Tree demography in an undisturbed Dipterocarp permanent sample plot at Uppangala, Western Ghats of India. Ecology, 92, 1376. [Google Scholar]
  248. Pérez‐Matus, A. & Ruz, C. S. “Conicyt‐Fondecyt Fish and Turf Algae Data”, Chile, Subtidal Ecology Laboratory, Estacion Costera de Investigaciones Marinas, Facultad de Ciencias Biológicas. Pontificia Universidad Catolica de Chile, accessed 2016.
  249. Pollard, E. (1991) Monitoring butterfly numbers In: Goldsmith F. B. (ed) Monitoring for Conservation and Ecology. Chapman and Hall. [Google Scholar]
  250. Pomati, F. , Matthews, B. , Jokela, J. , Schildknecht, A. & Ibelings, B. W. (2012) Effects of Re‐Oligotrophication and Climate Warming on Plankton Richness and Community Stability in a Deep Mesotrophic Lake. Oikos, 121, 1317–1327. [Google Scholar]
  251. Pomati, F. , Tellenbach, C. , Matthews, B. , Venail, P. , Ibelings, B. W. & Ptacnik, R. (2015) Challenges and Prospects for Interpreting Long‐Term Phytoplankton Diversity Changes in Lake Zurich (Switzerland). Freshwater Biology, 60, 1052–1059. 10.1111/fwb.12416 [DOI] [Google Scholar]
  252. Pombo, L. & Rebelo, J. E. (2002) Spatial and temporal organization of a coastal lagoon fish community‐Ria de Aveiro, Portugal. Cybium, 26(3), 185–196. [Google Scholar]
  253. Pombo, L. , Elliott, M. I. & Rebelo, J. E. (2005) Environmental influences on fish assemblage distribution of an estuarine coastal lagoon, Ria de Aveiro (Portugal). Scientia Marina, 69(1), 143–159. [Google Scholar]
  254. Pombo, L. , Rebelo, J. E. & Elliott, M. (2007) The structure, diversity and somatic production of the fish community in an estuarine coastal lagoon, Ria de Aveiro (Portugal). Hydrobiologia, 587(1), 253–268. [Google Scholar]
  255. Preston, F. W. (1960) Time and Space and the Variation of Species. Ecology, 41, 611–627. [Google Scholar]
  256. Prins, H. H. T. & Douglas‐Hamilton, I. (1990) Stability in a Multi‐Species Assemblage of Large Herbivores in East Africa. Oecologia, 83, 392–400. [DOI] [PubMed] [Google Scholar]
  257. Privett, S. , Cowling, R. & Taylor, H. (2001) Thirty years of change in the fynbos vegetation of the Cape of Good Hope Nature Reserve, South Africa. Bothalia, 31(1), 99–115. 10.4102/abc.v31i1.509. [DOI] [Google Scholar]
  258. Pugh, P. Discovery Collections Midwater Database. National Oceanography Centre, Southampton, UK. Available at: http://www.iobis.org/mapper/?dataset=12, accessed 2012.
  259. Pulliainen, E. (1981) A transect survey of small land carnivore and red fox populations on a subarctic fell in Finnish Forest Lapland over 13 winters. Annales Zoologici Fennici, 18, 270–278. [Google Scholar]
  260. Ratkova, T. N. “Phytoplankton of the White Sea, Barents Sea, Amundsen & Nansen Basins”. Institute of Oceanology, Academy of Sciences of Russia, Moscow, Russia; Arctic Ocean Diversity, University of Alaska Fairbanks, Fairbanks. Available at: http://www.arcodiv.org/Database/Plankton_datasets, accessed 2012.
  261. Read, A. , Halpin, P. , Crowder, L. , Best, B. & Fujioka, E. (2010) OBIS‐SEAMAP: mapping marine mammals, birds and turtles. World Wide Web electronic publication. http://seamap.env.duke.edu
  262. Reagan, D.P. (1991) The response of Anolis lizards to hurricane‐induced habitat changes in a Puerto Rican rain forest. Biotropica, 23, 468–474. [Google Scholar]
  263. Rebelo, J. E. (1992) The ichthyofauna and abiotic hydrological environment of the Ria de Aveiro, Portugal. Estuaries, 15(3), 403–413. [Google Scholar]
  264. Reed, D. C. (2014a) “SBC LTER: Reef: Kelp forest community dynamics: Abundance and size of giant kelp (Macrocystis pyrifera), ongoing since 2000”. Santa Barbara Coastal LTER. Available at: http://sbc.lternet.edu/cgi-bin/showDataset.cgi?docid=knb-lter-sbc.18, accessed 2016. 10.6073/pasta/d90872297e30026b263a119d4f5bca9f [DOI]
  265. Reed, D. C. (2014b) “SBC LTER: Reef: Kelp forest community dynamics: Fish abundance”. Santa Barbara Coastal LTER. Available at: http://sbc.lternet.edu/cgi-bin/showDataset.cgi?docid=knb-lter-sbc.17, accessed 2016. 10.6073/pasta/e37ed29111b2fddffc08355252b8b8c7. [DOI]
  266. Reed, D. C. (2014c) “SBC LTER: Reef: Kelp forest community dynamics: Invertebrate and algal density”. Santa Barbara Coastal LTER. Available at: http://sbc.lternet.edu/cgi-bin/showDataset.cgi?docid=knb-lter-sbc.19, accessed 2016. 10.6073/pasta/cd4cf864efecd69891dfe1d73b9ac9c3. [DOI]
  267. Reed, D. C. (2014d) “SBC LTER: Reef: Kelp forest community dynamics: Cover of sessile organisms, Uniform Point Contact”. Santa Barbara Coastal LTER. Available at: http://sbc.lternet.edu/cgi-bin/showDataset.cgi?docid=knb-lter-sbc.15, accessed 2016. 10.6073/pasta/f906c91e98c2a5fe752dfa0ccdc8895f. [DOI]
  268. Reed, D. C. (2014) “SBC LTER: Reef: Kelp Forest Community Dynamics: Fish abundance”. Santa Barbara Coastal LTER. Available at: 10.6073/pasta/e37ed29111b2fddffc08355252b8b8c7, accessed 2016. [DOI]
  269. Rees, H. , Pendle, M. , Waldock, R. , Limpenny, D. & Boyd, S. (1999) “A comparison of benthic biodiversity in the North Sea, English Channel and Celtic Seas ‐ Epifauna”. Centre for Environment, Fisheries and Aquaculture Science; Burnham Laboratory, UK. Available at: http://www.iobis.org/mapper/?dataset=51, accessed 2012.
  270. Rees, H. , Pendle, M. , Waldock, R. , Limpenny, D. & Boyd, S. (1999) A comparison of benthic biodiversity in the North Sea, English Channel, and Celtic Seas. ICES Journal of Marine Science, 56, 228–246. [Google Scholar]
  271. Reich, P. , Wedin, D. , Hobbie, S. & Davis, M. “Experiment 133 ‐ Effect of Burning Patterns on Vegetation in the Fish Lake Burn Compartments ‐ Shrub Survey”. Cedar Creek Ecosystem Science Reserve. Available at: http://www.cedarcreek.umn.edu/research/data/experiment?e133, accessed 2012.
  272. Reichert, M. (2009) “MARMAP Chevron Trap Survey 1990–2009”. SCDNR/NOAA MARMAP Program, SCDNR MARMAP Aggregate Data Surveys, The Marine Resources Monitoring, Assessment, and Prediction (MARMAP) Program, Marine Resources Research Institute, South Carolina Department of Natural Resources U.S.A. Available at: http://www.usgs.gov/obis-usa/data_search_and_access/participants.html, accessed 2012.
  273. Reichert, M. (2009) “MARMAP Florida Antillean Trap Survey 1990–2009”. SCDNR/NOAA MARMAP Program, SCDNR MARMAP Aggregate Data Surveys, The Marine Resources Monitoring, Assessment, and Prediction (MARMAP) Program, Marine Resources Research Institute, South Carolina Department of Natural Resources U.S.A. Available at: http://www.usgs.gov/obis-usa/data_search_and_access/participants.html, accessed 2012.
  274. Reichert, M. (2010) “MARMAP Neuston Nets 1990–2009”. SCDNR/NOAA MARMAP Program, SCDNR MARMAP Aggregate data surveys, The Marine Resources Monitoring, Assessment, and Prediction (MARMAP) Program, Marine Resources Research Institute, South Carolina Department of Natural Resources U.S.A. Available at: http://www.usgs.gov/obis-usa/data_search_and_access/participants.html, accessed 2012.
  275. Reichert, M. (2010) “MARMAP Fly Net 1990–2009”. SCDNR/NOAA MARMAP Program, SCDNR MARMAP Aggregate Data Surveys, The Marine Resources Monitoring, Assessment, and Prediction (MARMAP) Program, Marine Resources Research Institute, South Carolina Department of Natural Resources USA. Available at: http://www.usgs.gov/obis-usa/, accessed 2013.
  276. Reichert, M. (2010) “MARMAP Yankee Trawl 1990–2009”. SCDNR/NOAA MARMAP Program, SCDNR MARMAP Aggregate data surveys, The Marine Resources Monitoring, Assessment, and Prediction (MARMAP) Program, Marine Resources Research Institute, South Carolina Department of Natural Resources USA. Available at: http://www.usgs.gov/obis-usa, accessed 2013.
  277. Reichert, M. (2010) “MARMAP Blackfish Trap Survey 1990–2009”. SCDNR/NOAA MARMAP Program. SCDNR MARMAP Aggregate Data Surveys. The Marine Resources Monitoring. Assessment. and Prediction (MARMAP) Program. Marine Resources Research Institute. South Carolina Department of Natural Resources USA. Available at: http://www.usgs.gov/obis-usa/. accessed 2013.
  278. Richardson, B.A. (1999) The bromeliad microcosm and the assessment of faunal diversity in a neotropical forest. Biotropica, 31, 321–336. [Google Scholar]
  279. van Rijn, I. “Trawl fisheries in the Israeli Mediterranean 1980–2013”. Accessed 2016.
  280. Rinnan, R. , Stark, S. & Tolvanen, A. (2009) Responses of vegetation and soil microbial communities to warming and simulated herbivory in a subarctic heath. Journal of Ecology, 97, 788–800. [Google Scholar]
  281. Rintoul, C. , Schlagenhauf‐Langabeer, B. , Hyrenbach, K. D. , Morgan, K. H. & Sydeman, W. J. (2006) Atlas of California Current Marine Birds and Mammals: Version 1. Unpublished report, PRBO Conservation Science, Petaluma, California.
  282. Robinson, K.P. , Baumgartner, N. , Eisfeld, S.M. , Clark, N.M. , Culloch, R.M. , Haskins, G.N. , Zapponi, L. , Whaley, A.R. , Weare, J.S. & Tetley, M.J. (2007) The summer distribution and occurrence of cetaceans in the coastal waters of the outer southern Moray Firth in northeast Scotland (UK). Lutra, 50, 19. [Google Scholar]
  283. Robinson, K. P. (2010) “CRRU (Cetacean Research and Rescue Unit) Cetacean sightings in Scotland waters”. Available at: http://www.emodnet-biology.eu/component/imis/?module=dataset&dasid=2819, accessed 2012.
  284. Rocha, R. (2017) Tropical forest fragmentation: effects on the spatio‐temporal dynamics of its bat communities. PhD Thesis, University of Lisbon, Lisbon, Portugal.
  285. Rocha, R. , López‐Baucells, A. , Farneda, F.Z. , Groenenberg, M. , Bobrowiec, P.E.D. , Cabeza, M. , Palmeirim, J.M. & Meyer, C.F.J. (2017) Consequences of a large‐scale fragmentation experiment for Neotropical bats: disentangling the relative importance of local and landscape‐scale effects. Landscape Ecology, 32, 31–45. [Google Scholar]
  286. Rocha, R., Ovaskainen, O., López‐Baucells, A., Farneda, F. Z., Sampaio, E. M., Bobrowiec, P. E., Cabeza, M., Palmeirim, J. M. & Meyer, C. F. (2018) Secondary forest regeneration benefits old‐growth specialist bats in a fragmented tropical landscape. Scientific reports, 8, 3819. [DOI] [PMC free article] [PubMed] [Google Scholar]
  287. Rogers, L. A. , Stige, L. C. , Olsen, E. M. , Knutsen, H. , Chan, K.‐S. & Stenseth, N. C. (2011) Climate and population density drive changes in cod body size throughout a century on the Norwegian coast. Proceedings of the National Academy of Sciences, 108(5), 1961–1966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  288. Rossa‐Feres, D. C. (1997) Community ecology of anura amphibia at Northwest region of Sao Paulo state, Brazil: microhabitat, seasonality, diet and multidimensional niche. State University of São Paulo, PhD thesis. Accessed 2016.
  289. Rudstam, L. (2008) “Zooplankton survey of Oneida Lake, New York, 1964 – 2012”, KNB Data Repository. Available at: https://knb.ecoinformatics.org/#view/kgordon.17.56, accessed 2016.
  290. Ruhl, H. A. & Rybicki, N. B. (2010) Long‐term reductions in anthropogenic nutrients link to improvements in Chesapeake Bay habitat. Proceedings of the National Academy of Sciences, 107(38), 16566–16570. 10.1073/pnas.1003590107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  291. Ruhl, H. A. & Smith, K. L. (2004) Shifts in Deep‐Sea Community Structure Linked to Climate and Food Supply. Science, 305, 513–515. [DOI] [PubMed] [Google Scholar]
  292. Ruiz, G.M. , Fofonoff, P.W. , Steves, B. , Huber, T. , Larson, K. , McCann, L. , Hitchcock, N.G. , Hines, A.H. & Carlton, J.T. (2005) North American Sessile Marine Invertebrate Survey. Available at: http://www.iobis.org/mapper/?dataset=106, accessed 2012.
  293. Sackett, W. & University of Texas. University of Texas ‐ Austin; Marine Science Institute (2011). Benthic organism data from the South Texas Outer Continental Shelf (STOCS) and the Mississippi, Alabama, and Florida (MAFLA) Outer Continental Shelf studies from 16 May 1974 to 20 February 1978 (NODC Accession 8500179). Version 2.2. National Oceanographic Data Center, NOAA, accessed 2012.
  294. Sal, S. , López‐Urrutia, Á. , Irigoien, X. , Harbour, D. S. & Harris, R. P. (2013) Marine microplankton diversity database. Ecology, 94, 1658 10.1890/13-0236.1. [DOI] [Google Scholar]
  295. Salami, B. , Higuchi, P. , Silva, A. C. , Ferreira, T. S. , Marcon, A. K. , Buzzi Jr, F. & Bento, M. A. (2014) Influência de variáveis ambientais na dinâmica do componente arbóreo em um fragmento de Floresta Ombrófila Mista em Lages, SC. Scientia Forestalis (IPEF), 42, 197–207. [Google Scholar]
  296. Sampaio, E.M. , Kalko, E.K. , Bernard, E. , Rodríguez‐Herrera, B. & Handley, C.O. (2003) A biodiversity assessment of bats (Chiroptera) in a tropical lowland rainforest of Central Amazonia, including methodological and conservation considerations. Studies on Neotropical fauna and environment, 38, 17–31. [Google Scholar]
  297. Sandercock, B. K. “Variable distance line‐transect sampling of bird population numbers in different habitats on Konza Prairie (1981–2009)”. Konza Prairie Long Term Ecological Research Program. Available at: http://www.konza.ksu.edu/knz/pages/data/KnzEntity.aspx?id=CBP011, accessed 2016.
  298. Sanquetta, C. R. (2008) “Monitoring experiences at the Atlantic rainforest biome using permanent plots. (Experiencias de monitoramento no bioma mata atlantica com uso de parcelas permanentes)”, Universidade Federal Do Parana. Accessed 2016.
  299. Scott, D. , Metts, B. & Lance, S. “The Rainbow Bay Long‐term Study”. Available at: http://srelherp.uga.edu/projects/rbay.htm, accessed 2016.
  300. Scott, D.A. & English, T.S. “Arctic copepod Zooplankton Ice Island T3”. Available at: http://www.arcodiv.org/Database/Data_overview.html, accessed 2012.
  301. Scott, D.A. & English, T.S. (1969) Copepoda collected from Fletcher's Ice Island (T‐3) in the Canadian Basin of the Arctic Ocean. Technical Report No. 240, Reference M69‐62. University of Washington and Arctic Ocean Diversity, University of Alaska Fairbanks, Fairbanks.
  302. Shao, K. T. , Lin, J. , Wu, C.‐H. , Yeh, H.‐M. & Cheng, T.‐Y. (2012) “A dataset from bottom trawl survey around Taiwan”. Available at: http://www.iobis.org, accessed 2013. [DOI] [PMC free article] [PubMed]
  303. Shaver, G. (2015) “Above ground plant biomass a moist acidic tussock tundra experimental site, 1984, Artic LTER, Toolik Lake, Alaska”. Available at: 10.6073/pasta/08a91cb2697f7cdc82d654e82b53c5c5, accessed 2016. [DOI]
  304. Shaver, G. R. & Chapin, F. S. (1991) Production: biomass relationships and element cycling in contrasting arctic vegetation types. Ecological Monographs, 61(1), 1–31. [Google Scholar]
  305. Sheftel, B. I. , Samiya, R. , Aleksandrov, D.Y. , Tserendavaa, R. , Tamir, M. & Mühlenberg, M. (2010) Population dynamics of small mammals at Western Khentey during ten years. Proceedings of the international conference Ecological consequences of biosphere processes in the ecotone zone of southern Siberia, vol. I. Oral reports, 230–233.
  306. Sherman, S. (2010) “Maine Department of Marine Resources Inshore Trawl Survey, 2000 – 2009”. Maine Department of Marine Resources, Maine. Available at: http://www.usgs.gov/obis-usa/data_search_and_access/datasets.html, accessed 2012.
  307. Shi, Z. , Sherry, R. , Xu, X. , Hararuk, O. , Souza, L. , Jiang, L. , Xia, J. , Liang, J. & Luo, Y. (2015) Evidence for long‐term shift in plant community composition under decadal experimental warming. Journal of Ecology, 103, 1131–1140. [Google Scholar]
  308. Shochat, E. , Katti, M. & Warren, P. (2004) “Point count bird censusing: long‐term monitoring of bird distribution and diversity in central Arizona‐Phoenix: period 2000 to 2011”. Central Arizona‐Phoenix Long‐Term Ecological Research. Global Institute for Sustainability, Arizona State University. Available at: https://caplter.asu.edu/data/data-catalog/?id=46, accessed 2012.
  309. Sicinski, J. & Bamber, R. (2008) “Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida”. Available at: http://www.scarmarbin.be, accessed 2012.
  310. Sicinski, J. & Blazewicz‐Paszkowycz, M. (2008) “Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea”. Available at: site http://www.scarmarbin.be, accessed 2012.
  311. Sicinski, J. & Blazewicz‐Paszkowycz, M. (2008) “Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea”. Available at: http://www.scarmarbin.be, accessed 2012.
  312. Siferd, T. (2010) Central and Arctic Multi‐Species Stock Assessment Surveys. OBIS Canada Digital Collections. Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada (OBIS Canada). Available at: http://www.iobis.org/mapper/?dataset=2293, accessed 2012.
  313. Silva, F. R. “Brazil Dataset 1”, Universidade Federal de São Carlos. Accessed 2016.
  314. Silveira, F. L. & Lopes, R. M. (2008) “On the Marine Free‐Living Copepods off Brazil ‐WSAOBIS”. Western South Atlantic OBIS, São Paulo. Available at: http://www.iobis.org, accessed 2012.
  315. Souza, G.B.G. & Vianna, M. “Demersal fish hauls from Guanabara Bay, Brazil 2005–2015”. Accessed 2017.
  316. Stallings, C. D. , Mickle, A. , Nelson, J. A. , McManus, M. G. & Koenig, C. C. (2015) Faunal communities and habitat characteristics of the Big Bend seagrass meadows, 2009–2010. Ecology, 96, 304. [Google Scholar]
  317. Stapp, P. (2013) SGS‐LTER Long‐Term Monitoring Project: Small Mammals on Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1994 −2006, ARS Study Number 118. Environmental Data Initiative. Available at: 10.6073/pasta/2e311b4e40fea38e573890f473807ba9, accessed 2017. [DOI]
  318. Steinberg, D. (2017) Zooplankton collected with a 2‐m, 700‐um net towed from surface to 120 m, aboard Palmer Station Antarctica LTER annual cruises off the western antarctic peninsula, 2009 ‐ 2016. Environmental Data Initiative. Available at: 10.6073/pasta/fb658789188724be5f27c81a634647d5, accessed 2017. [DOI]
  319. Stenseth, N. C. , Bjørnstadf, O. N. , Falck, W , Fromentin, J. M. , Gjøsieter, J. & Gray, J. S. (1999) Dynamics of coastal cod populations: intra‐and intercohort density dependence and stochastic processes. Proceedings of the Royal Society of London B: Biological Sciences, 266(1429), 1645–1654. [Google Scholar]
  320. Sterza, J. M. & Fernandes, L. L. (2006) Zooplankton community of the Vitoria Bay estuarine system (Southeastern Brazil): Characterization during a three‐year study. Brazilian Journal of Oceanography, 54(2–3), 95–105. . [DOI] [Google Scholar]
  321. Stevens, M. (2010) “Trekvis ‐ Migratory fishes in the river Scheldt.” Research Institute for Nature and Forest (INBO). Available at: http://www.gbif.org/dataset/b2d0f29e-4614-4001-93c8-f651878a86d2, accessed 2014.
  322. Suarez, Y. R. “Brazil Dataset 6”, Mato Gosso do Sul State University. Accessed 2016.
  323. Sukumar, R. Mudumalai Forest Dynamics Plot Data. Available at: http://www.ctfs.si.edu/site/Mudumalai/, accessed 2013.
  324. Sumner, F. B. , Osborn, R. C. , Cole, L. J. & Davis, B. M. “A biological survey of the waters of Woods Hole and vicinity”. Available at: http://www.iobis.org, accessed 2012.
  325. Sumner, F. B. , Osborn, R. C. , Cole, L. J. & Davis, B. M. (1911) A biological survey of the waters of Woods Hole and vicinity. Bulletin of the U.S. Bureau of Fisheries, 31, 1.
  326. Svensson, S. (2006) Species composition and population fluctuations of alpine bird communities during 38 years in the Scandinavian mountain range. Ornis Svecica, 16(4), 183–210. [Google Scholar]
  327. Svensson, S. , Thorner, A. M. & Nyholm, N. E. I. (2010) Species trends, turnover and composition of a woodland bird community in southern Sweden during a period of 57 years. Ornis Svecica 20, 31–44. [Google Scholar]
  328. Sweatman, H.P.A. , Cheal, A.J. , Coleman, G.J. , Delean, S. , Fitzpatrick, B.M. , Miller, I.R. , Ninio, R. , Osborne, K. , Page, C.M. & Thompson, A.A. (2001) Long‐Term Monitoring of the Great Barrier Reef. Status Report Number 5. Available at: http://www.iobis.org/mapper/?dataset=233, accessed 2017.
  329. Taylor, H. (1984) A vegetation survey of the Cape of Good Hope Nature Reserve. I. The use of association‐analysis and Braun‐Blanquet methods. Bothalia, 15, 245–258. [Google Scholar]
  330. Thomsen, P.F. , Jørgensen, P.S. , Bruun, H.H. , Pedersen, J. , Riis‐Nielsen, T. , Jonko, K. , Słowińska, I. , Rahbek, C. & Karsholt, O. (2016) Resource specialists lead local insect community turnover associated with temperature – analysis of an 18‐year full‐seasonal record of moths and beetles. Journal of Animal Ecology, 85, 251–261. [DOI] [PubMed] [Google Scholar]
  331. Thorn, S. , Bässler, C. , Bernhardt‐Römermann, M. , Cadotte, M. , Heibl, C. , Schäfer, H. , Seibold, S. & Müller, J. (2016) Changes in the dominant assembly mechanism drive species loss caused by declining resources. Ecology Letters, 19, 163–170. [DOI] [PubMed] [Google Scholar]
  332. Thorn, S. , Bässler, C. , Gottschalk, T. , Hothorn, T. , Bussler, H. , Raffa, K. & Müller, J. (2014) New insights into the consequences of post‐windthrow salvage logging revealed by functional structure of saproxylic beetles assemblages. PLoS ONE, 9, e101757. [DOI] [PMC free article] [PubMed] [Google Scholar]
  333. Thorn, S. , Werner, S. A. , Wohlfahrt, J. , Bässler, C. , Seibold, S. , Quillfeldt, P. & Müller, J. (2016) Response of bird assemblages to windstorm and salvage logging ‐ Insights from analyses of functional guild and indicator species. Ecological Indicators, 65, 142–148. [Google Scholar]
  334. Thuiller, W. , Slingsby, J. A. , Privett, S. D. J. & Cowling, R. M. (2007) Stochastic Species Turnover and Stable Coexistence in a Species‐Rich, Fire‐Prone Plant Community. PLoS ONE, 2(9), e938 10.1371/journal.pone.0000938 [DOI] [PMC free article] [PubMed] [Google Scholar]
  335. Tkachenko, K. S. , Wu, B.‐J. , Fang, L.‐S. & Fan, T.‐Y. (2007) Dynamics of a coral reef community after mass mortality of branching Acropora corals and an outbreak of anemones. Marine Biology, 151, 185–194. [Google Scholar]
  336. Tomiałojć, L. & Wesołowski, T. (1994) Die Stabilität der Vogelgemeinschaft in einem Urwald der gemässigten Zone: Ergebnisse einer 15jährigen Studie aus dem Nationalpark von Białowieża (Polen). Beob, 91, 73–110. [Google Scholar]
  337. Tomiałojć, L. & Wesołowski, T. (1996) Structure of a primaeval forest bird community during 1970s and 1990s (Białowieża National Park, Poland). Acta Ornithologica, 31, 133–154. [Google Scholar]
  338. Tomiałojć, L. , Wesołowski, T. & Walankiewicz, W. (1984) Breeding bird community of a primaeval temperate forest (Białowieża National Park, Poland). Acta Ornithologica, 20, 241–310. [Google Scholar]
  339. Tremblay, J.M. & Branton, B. (2007) DFO Maritimes Research Vessel Trawl Surveys, OBIS Canada Digital Collections. Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada, OBIS Canada.
  340. Trexler, J. (2007) “Consumer Stocks: Fish, Vegetation, and other Non‐physical Data from Everglades National Park (FCE), South Florida from February 2000 to Present.” Florida Coastal Everglades LTER Program. Available at: http://fcelter.fiu.edu/data/core/metadata/EML/?datasetid=LT_CD_Trexler_001, accessed 2012.
  341. Twilley, R. , Rivera‐Monroy, V. H. & Castaneda, E. (2005) “Mangrove Forest Growth from the Shark River Slough, Everglades National Park (FCE), South Florida from January 1995 to Present”. Florida Coastal Everglades LTER. 10.6073/pasta/bec6c029df692768f349106c69162df7. Available at: http://fcelter.fiu.edu/data/core/metadata/?datasetid=LT_PP_Rivera_002, accessed 2016. [DOI]
  342. USFS “Landbird Monitoring Program (UMT‐LBMP).” US Forest Service. Available at: http://www.avianknowledge.net/, accessed 2012.
  343. USGS Patuxent Wildlife Research Center “North American Breeding Bird Survey” http://ftp data set, version 2014.0. Available at: http://ftp://ftpext.usgs.gov/pub/er/md/laurel/BBS/DataFiles/, accessed 2013.
  344. Vanholder, B. (1997) “Belgian Migrating Lepidoptera”. NERC Centre for Population Biology, Imperial College. The Global Population Dynamics Database v2.0. Available at: https://www.imperial.ac.uk/cpb/gpdd2/secure/register.aspx, accessed 2012.
  345. Venturoli, F. , Felfili, J. M. & Fagg, C. W. (2011) Temporal evaluation of natural regeneration in a semideciduous secondary forest in Pirenopolis, Goias, Brazil. Revista Arvore, 35(3), 473–483. [DOI] [Google Scholar]
  346. Vermont Center for Ecostudies , Lambert, J. D. & Hart, J. (2015) “Mountain Birdwatch 1.0”. KNB Data Repository, 10.5063/F1DN430G, accessed 2016.
  347. Vickery, W. L. & Nudds, T. D. (1984) Detection of Density‐Dependent Effects in Annual Duck Censuses. Ecology, 65, 96. [Google Scholar]
  348. Viereck, L.A. , Van Cleve, K. , Chapin, F.S. , Ruess, R.W. & Hollingsworth, T.N. (2005) Vegetation Plots of the Bonanza Creek LTER Control Plots: Species Count (1975 ‐ 2004). Environmental Data Initiative. Available at: 10.6073/pasta/8dd0e1ac48e2f82b51adabfbd3c62ae2, accessed 2012. [DOI]
  349. Vrška, T. , Král, K. , Janík, D. & Adam, D. “Natural Forests of the Czech Republic”. Available at: http://naturalforests.cz/research, accessed 2016.
  350. Vu, L. V. (2009) Diversity and similarity of butterfly communities in five different habitat types at Tam Dao National Park, Vietnam. Journal of Zoology, 277, 15–22. 10.1111/j.1469-7998.2008.00498.x [DOI] [Google Scholar]
  351. Wacasey, J. , Atkinson, E. , Derick, L. & Weinstein, A. “Zoobenthos data from the southern Beaufort Sea”. Available at: http://www.iobis.org/mapper/?dataset=4461, accessed 2012.
  352. Wacasey, J. , Atkinson, E. , Derick, L. & Weinstein, A. (1977) Zoobenthos data from the southern Beaufort Sea. Fisheries and Environment Canada. Fisheries and Marine Service Data report 41, Arctic Biological Station fisheries and Marine Service Department of Fisheries and the Environment; Arctic Ocean Diversity, University of Alaska Fairbanks, Fairbanks.
  353. Wade, E. (2011) Snow crab research trawl survey database (Southern Gulf of St. Lawrence, Gulf region, Canada) from 1988 to 2010. OBIS Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada,
  354. Wagner, R. , Marxsen, J. , Zwick, P. & Cox, E. J. (2011) Central European Stream Ecosystems: The Long Term Study of the Breitenbach. John Wiley & Sons.
  355. Waide, R. B. (2010) “Bird abundance ‐ point counts”. Long Term Ecological Research Network. Available at: 10.6073/pasta/0d96957379936a038ebbbcc6135b2fab, accessed 2012. [DOI]
  356. Waide, R. B. “Bird abundance ‐ point counts, El Verde Field Station, Puerto Rico: Luquillo Long Term Ecological Research Site Database: Data Set 23”. Available at: http://luq.lternet.edu/data/luqmetadata23, accessed 2012.
  357. Webb, S. L. & Scanga, S. E. (2001) Windstorm Disturbance without Patch Dynamics: Twelve Years of Change in a Minnesota Forest. Ecology, 82, 893–897. [Google Scholar]
  358. Wesołowski, T. , Mitrus, C. , Czeszczewik, D. & Rowiński, P. (2010) Breeding Bird Dynamics in a Primeval Temperate Forest Over Thirty‐Five Years: Variation and Stability in the Changing World. Acta Ornithologica, 45(2), 209–232. [Google Scholar]
  359. Wesołowski, T. , Tomiałojć, L. , Mitrus, C. , Rowiński, P. & Czeszczewik, D. (2002) The breeding bird community of a primaeval temperate forest (Białowieża National Park, Poland) at the end of the 20th century. Acta ornithologica, 37, 27–45. [Google Scholar]
  360. Wesołowski, T. , Rowiński, P. , Mitrus, C. & Czeszczewik, D. (2006) Breeding bird community of a primeval temperate forest (Białowieża National Park, Poland) at the beginning of the 21st century. Acta Ornithologica, 41, 55–70. [Google Scholar]
  361. Wesołowski, T. , Czeszczewik, D. , Hebda, G. , Maziarz, M. , Mitrus, C. & Rowiński, P. (2015) 40 years of breeding bird community dynamics in a primeval temperate forest (Białowieża National Park, Poland). Acta Ornithologica, 50, 95–120. [Google Scholar]
  362. Widdicombe, C. E. , Eloire, D. , Harbour, D. , Harris, R. P. & Somerfield, P. J. (2010) Long‐term phytoplankton community dynamics in the Western English Channel. Journal of Plankton Research, 32, 643–655. 10.1093/plankt/fbp127 [DOI] [Google Scholar]
  363. Widdicombe, C. E. , Eloire, D. , Harbour, D. , Harris, R. P. & Somerfield, P. J. (2010) Time series of phyto‐ and microzooplankton abundance and composition at station L4 in the English Channel from 1988 to 2009. 10.1594/PANGAEA.758061 [DOI]
  364. Wiley, R. H. “Population estimates of Appalachian salamanders”. Coweeta LTER. Available at: http://coweeta.uga.edu/eml/1044.xml, accessed 2016.
  365. Wilgers, D. J. , Horne, E. A. , Sandercock, B. K. & Volkmann, A. W. (2006) Effects of rangeland management on community dynamics of the herpetofauna of the tallgrass prairie. Herpetologica, 62, 378–388. [Google Scholar]
  366. Williams, D. “Pelagic Fish Observations 1968–1999.” Australian Antarctic Data Centre. Available at: http://www.gbif.org/dataset/85b0a82a-f762-11e1-a439-00145eb45e9a, accessed 2012.
  367. Williamson, M. (1983) The Land‐Bird Community of Skokholm: Ordination and Turnover. Oikos, 41, 378–384. [Google Scholar]
  368. Willig, M. R. & Bloch, C. P. (2016) “El Verde Grid long‐term invertebrate data: Luquillo Long Term Ecological Research Site Database: Data Set 107”. Available at:http://luq.lternet.edu/data/luqmetadata107/7427, accessed 2016.
  369. Willis, T. “Hahei marine dataset (1997–2002), New Zealand fish”. Institute of Marine Sciences, University of Portsmouth. Accessed 2016.
  370. Woehler, E. “Seabirds of the Southern and South Indian Ocean ‐ Australian Antarctic Data Centre”. Available at: http://www.iobis.org, accessed 2012.
  371. Woods, K. D. (2009) Multi‐decade, spatially explicit population studies of canopy dynamics in Michigan old‐growth forests. Ecology, 90, 3587. [Google Scholar]
  372. Woods, K. D. (2014) Multi‐decade biomass dynamics in an old‐growth hemlock‐northern hardwood forest, Michigan, USA. PeerJ, 2, e598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  373. Yen, P. P. W. , Sydeman, W. J. & Hyrenbach, K. D. (2004) Marine bird and cetacean associations with bathymetric habitats and shallow‐water topographies: Implications for trophic transfer and conservation. Journal of Marine Systems, 50, 79–99. [Google Scholar]
  374. Yen, P. P. W. , Sydeman, W. J. , Bograd, S. J. & Hyrenbach, K. D. (2006) Spring‐time distributions of migratory marine birds in the southern California Current: Oceanic eddy associations and coastal habitat hotspots over 17 years. Deep‐Sea Research Part II: Topical Studies in Oceanography, 53, 399–418. [Google Scholar]
  375. Ylänne, H. , Stark, S. & Tolvanen, A. (2015) Vegetation shift from deciduous to evergreen dwarf shrubs in response to selective herbivory offsets carbon losses: evidence from 19 years of warming and simulated herbivory in the subarctic tundra. Global Change Biology, 21, 3696–3711. [DOI] [PubMed] [Google Scholar]
  376. Zachmann, L. , Moffet, C. & Adler, P. (2010) Mapped quadrats in sagebrush steppe: long‐term data for analyzing demographic rates and plant–plant interactions. Ecology, 91, 3427. [Google Scholar]
  377. Zakharov, V. D. (1998) Biodiversity of bird population of terrestrial habitats in Southern Ural. Miass: IGZ, Ural Branch of Russian Academy of Sciences,158 p.
  378. Zettler, M.L. (2005) Macrozoobenthos baltic sea (1980–2005) as part of the IOW‐Monitoring. Institut für Ostseeforschung Warnemünde, Germany. Available at: http://www.iobis.org/mapper/?dataset=2289, accessed 2012.

Dornelas M, Antão LH, Moyes F, et al. BioTIME: A database of biodiversity time series for the Anthropocene. Global Ecol Biogeogr. 2018;27:760–786. 10.1111/geb.12729

Funding information European Research Council and EU, Grant/Award Number: AdG‐250189, PoC‐727440 and ERC‐SyG‐2013‐610028; Natural Environmental Research Council, Grant/Award Number: NE/L002531/1; National Science Foundation, Grant/Award Number: DEB‐1237733, DEB‐1456729, 9714103, 0632263, 0856516, 1432277, DEB‐9705814, BSR‐8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447, DEB‐1546686, DEB‐129764, OCE 95‐21184, OCE‐ 0099226, OCE 03‐52343, OCE‐0623874, OCE‐1031061, OCE‐1336206 and DEB‐1354563; National Science Foundation (LTER) , Grant/Award Number: DEB‐1235828, DEB‐1440297, DBI‐0620409, DEB‐9910514, DEB‐1237517, OCE‐0417412, OCE‐1026851, OCE‐1236905, OCE‐1637396, DEB 1440409, DEB‐0832652, DEB‐0936498, DEB‐0620652, DEB‐1234162 and DEB‐0823293; Fundação para a Ciência e Tecnologia, Grant/Award Number: POPH/FSE SFRH/BD/90469/2012, SFRH/BD/84030/2012, PTDC/BIA‐BIC/111184/2009; SFRH/BD/80488/2011 and PD/BD/52597/2014; Ciência sem Fronteiras/CAPES, Grant/Award Number: 1091/13‐1; Instituto Milenio de Oceanografía, Grant/Award Number: IC120019; ARC Centre of Excellence, Grant/Award Number: CE0561432; NSERC Canada; CONICYT/FONDECYT, Grant/Award Number: 1160026, ICM PO5‐002, CONICYT/FONDECYT, 11110351, 1151094, 1070808 and 1130511; RSF, Grant/Award Number: 14‐50‐00029; Gordon and Betty Moore Foundation, Grant/Award Number: GBMF4563; Catalan Government; Marie Curie Individual Fellowship, Grant/Award Number: QLK5‐CT2002‐51518 and MERG‐CT‐2004‐022065; CNPq, Grant/Award Number: 306170/2015‐9, 475434/2010‐2, 403809/2012‐6 and 561897/2010; FAPESP (São Paulo Research Foundation), Grant/Award Number: 2015/10714‐6, 2015/06743‐0, 2008/10049‐9, 2013/50714‐0 and 1999/09635‐0 e 2013/50718‐5; EU CLIMOOR, Grant/Award Number: ENV4‐CT97‐0694; VULCAN, Grant/Award Number: EVK2‐CT‐2000‐00094; Spanish, Grant/Award Number: REN2000‐0278/CCI, REN2001‐003/GLO and CGL2016‐79835‐P; Catalan, Grant/Award Number: AGAUR SGR‐2014‐453 and SGR‐2017‐1005; DFG, Grant/Award Number: 120/10‐2; Polar Continental Shelf Program; CENPES – PETROBRAS; FAPERJ, Grant/Award Number: E‐26/110.114/2013; German Academic Exchange Service; sDiv; iDiv; New Zealand Department of Conservation; Wellcome Trust, Grant/Award Number: 105621/Z/14/Z; Smithsonian Atherton Seidell Fund; Botanic Gardens and Parks Authority; Research Council of Norway; Conselleria de Innovació, Hisenda i Economia; Yukon Government Herschel Island‐Qikiqtaruk Territorial Park; UK Natural Environment Research Council ShrubTundra Grant, Grant/Award Number: NE/M016323/1; IPY; Memorial University; ArcticNet. DOI: 10.13039/50110000027. Netherlands Organization for Scientific Research in the Tropics NWO, grant W84‐194. Ciências sem Fronteiras and Coordenação de Pessoal de Nível Superior (CAPES, Brazil), Grant/Award Number: 1091/13‐1. National Science foundation (LTER), Award Number: OCE‐9982105, OCE‐0620276, OCE‐1232779. FCT ‐ SFRH / BPD / 82259 / 2011. U.S. Fish and Wildlife Service/State Wildlife federal grant number T‐15. Australian Research Council Centre of Excellence for Coral Reef Studies (CE140100020). Australian Research Council Future Fellowship FT110100609. M.B., A.J., K.P., J.S. received financial support from internal funds of University of Lódź. NSF DEB 1353139. Catalan Government fellowships (DURSI): 1998FI‐00596, 2001BEAI200208, MECD Post‐doctoral fellowship EX2002‐0022. National Science Foundation Award OPP‐1440435. FONDECYT 1141037 and FONDAP 15150003 (IDEAL). CNPq Grant 306595‐2014‐1

REFERENCES

  1. Allaire, J. , Cheng, J. , Xie, Y. , McPherson, J. , Chang, W. , Allen, J. , … Hyndman, R. (2015). rmarkdown: Dynamic documents for R (R package version 0.5.1). Available at: https://rmarkdown.rstudio.com/
  2. Becker, R. A. , Wilks, A. R. , & Brownrigg, R. (2014). mapdata: Extra map databases Available at: https://CRAN.R-project.org/package=mapdata
  3. Brown, J. H. , Ernest, S. M. , Parody, J. M. , & Haskell, J. P. (2001). Regulation of diversity: Maintenance of species richness in changing environments. Oecologia, 126, 321–332. [DOI] [PubMed] [Google Scholar]
  4. Collen, B. E. N. , Loh, J. , Whitmee, S. , McRae, L. , Amin, R. , & Baillie, J. E. M. (2009). Monitoring change in vertebrate abundance: The living planet index. Conservation Biology, 23, 317–327. [DOI] [PubMed] [Google Scholar]
  5. Costello, M. J. , Appeltans, W. , Bailly, N. , Berendsohn, W. G. , de Jong, Y. , Edwards, M. , … Bisby, F. A. (2014). Strategies for the sustainability of online open‐access biodiversity databases. Biological Conservation, 173, 155–165. [Google Scholar]
  6. Dornelas, M. , Gotelli, N. J. , McGill, B. , Shimadzu, H. , Moyes, F. , Sievers, C. , & Magurran, A. E. (2014). Assemblage time series reveal biodiversity change but not systematic loss. Science, 344, 296–299. [DOI] [PubMed] [Google Scholar]
  7. García Molinos, J. , Halpern, B. S. , Schoeman, D. S. , Brown, C. J. , Kiessling, W. , Moore, P. J. , … Burrows, M. T. (2016). Climate velocity and the future global redistribution of marine biodiversity. Nature Climate Change, 6, 83–88. [Google Scholar]
  8. Magurran, A. E. , Dornelas, M. , Moyes, F. , Gotelli, N. J. , & McGill, B. (2015). Rapid biotic homogenization of marine fish assemblages. Nature Communication, 6, 8405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. McGill, B. J. , Dornelas, M. , Gotelli, N. J. , & Magurran, A. E. (2014). Fifteen forms of biodiversity trend in the Anthropocene. Trends in Ecology and Evolution, 30, 104–113. [DOI] [PubMed] [Google Scholar]
  10. Newbold, T. , Hudson, L. N. , Hill, S. L. , Contu, S. , Lysenko, I. , Senior, R. A. , … Puvis, A. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50. [DOI] [PubMed] [Google Scholar]
  11. Oksanen, J. , Blanchet, F. G. , Kindt, R. , Legendre, P. , Minchin, P. R. , O'Hara, R. B. , … Wagner, H. (2013). vegan: Community ecology package (R package version 2.0–7). Available at: http://CRAN.R-project.org/package=vegan
  12. Ooms, J. , James, D. , DebRoy, S. , Wickham, H. , & Horner, J. (2015). RMySQL: Database interface and MySQL driver for R Available at: https://CRAN.R-project.org/package=RMySQL
  13. Pereira, H. M. , Ferrier, S. , Walters, M. , Geller, G. N. , Jongman, R. H. G. , Scholes, R. J. , … Wegmann, M. (2013). Essential biodiversity variables. Science, 339, 277–278. [DOI] [PubMed] [Google Scholar]
  14. Pereira, H. M. , Navarro, L. M. , & Martins, I. S. (2012). Global biodiversity change: The bad, the good, and the unknown. Annual Review of Environment and Resources, 37, 25–50. [Google Scholar]
  15. R Development Core Team . (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  16. Vellend, M. , Baeten, L. , Myers‐Smith, I. H. , Elmendorf, S. C. , Beauséjour, R. , Brown, C. D. , Frenne, D. P., … Wipf, S. (2013). Global meta‐analysis reveals no net change in local‐scale plant biodiversity over time. Proceedings of the National Academy of Sciences USA, 110, 19456–19459. [DOI] [PMC free article] [PubMed]
  17. Vellend, M. , Dornelas, M. , Baeten, L. , Beausejour, R. , Brown, C. D. , De Frenne, P. , … Myers‐Sievers, C. (2016). Estimates of local biodiversity change over time stand up to scrutiny. Ecology, 98, 583–590. [DOI] [PubMed] [Google Scholar]
  18. Waters, C. N. , Zalasiewicz, J. , Summerhayes, C. , Barnosky, A. D. , Poirier, C. , Gałuszka, A. , … Wolfe, A. P. (2016). The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science, 351, aad2622. [DOI] [PubMed] [Google Scholar]
  19. White, E. P. , Baldridge, E. , Brym, Z. T. , Locey, K. J. , McGlinn, D. J. , & Supp, S. R. (2013). Nine simple ways to make it easier to (re) use your data. Ideas in Ecology and Evolution, 6, 1–10. [Google Scholar]
  20. Wickham, H. (2009). ggplot2: Elegant graphics for data analysis. New York, NY: Springer. [Google Scholar]
  21. Wickham, H. , & Francois, R. (2015). dplyr: A grammar of data manipulation Available at: https://dplyr.tidyverse.org/

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional Supporting Information may be found online in the supporting information tab for this article.

Supporting Information

Click here for additional data file. (641.1KB, pdf)

Supporting Information

Click here for additional data file. (1.1MB, pdf)

Supporting Information

Click here for additional data file. (13.3KB, csv)

Data Availability Statement

The BioTIME database is accessible through the BioTIME website (http://biotime.st-andrews.ac.uk) and through the Zenodo repository (https://zenodo.org/record/1095627).

Articles from Global Ecology and Biogeography are provided here courtesy of Wiley

RESOURCES