Received: 21 September 2021 | Revised: 21 January 2022 | Accepted: 25 January 2022 DOI: 10.1111/gcb.16130 RESEARCH REPORT MASTREE+: Time-­series of plant reproductive effort from six continents Andrew Hacket-­Pain1 | Jessie J. Foest1 | Ian S. Pearse2 | Jalene M. LaMontagne3 | 4 5 6,7 8 Walter D. Koenig | Giorgio Vacchiano | Michał Bogdziewicz | Thomas Caignard | 6 9 10 Paulina Celebias | Joep van Dormolen | Marcos Fernández-­Martínez | 11 12 13 Jose V. Moris | Ciprian Palaghianu | Mario Pesendorfer | Akiko Satake14 | 15 16 17 Eliane Schermer | Andrew J. Tanentzap | Peter A. Thomas | 11 18 19 Davide Vecchio | Andreas P. Wion | Thomas Wohlgemuth | Tingting Xue20 | Katharine Abernethy21,22 | Marie-­Claire Aravena Acuña23 | Marcelo Daniel Barrera24 | 3 25 26 23 Jessica H. Barton | Stan Boutin | Emma R. Bush | Sergio Donoso Calderón | 27 28 23 Felipe S. Carevic | Carolina Volkmer de Castilho | Juan Manuel Cellini | 29,30,31,32 33,34 35 Colin A. Chapman | Hazel Chapman | Francesco Chianucci | 36 37 35 38 Patricia da Costa | Luc Croisé | Andrea Cutini | Ben Dantzer | 39 40 40 R. Justin DeRose | Jean-Thoussaint Dikangadissi | Edmond Dimoto | 41 42 13 Fernanda Lopes da Fonseca | Leonardo Gallo | Georg Gratzer | David F. Greene43 | Martín A. Hadad44 | Alejandro Huertas Herrera45,46 | Kathryn J. Jeffery21 | Jill F. Johnstone47 | Urs Kalbitzer48,49 | Władysław Kantorowicz50 | 51 52 53 Christie A. Klimas | Jonathan G. A. Lageard | Jeffrey Lane | Katharina Lapin54 | 55 3 56 Mateusz Ledwoń | Abigail C. Leeper | Maria Vanessa Lencinas | 57 3 42 Ana Cláudia Lira-­Guedes | Michael C. Lordon | Paula Marchelli | 58 26 59 Shealyn Marino | Harald Schmidt Van Marle | Andrew G. McAdam | 60 37 Ludovic R. W. Momont | Manuel Nicolas | Lúcia Helena de Oliveira Wadt61 | 62 56 63 Parisa Panahi | Guillermo Martínez Pastur | Thomas Patterson | Pablo Luis Peri64 | 65 66 26 Łukasz Piechnik | Mehdi Pourhashemi | Claudia Espinoza Quezada | 67,68 26 56 Fidel A. Roig | Karen Peña Rojas | Yamina Micaela Rosas | 54 65 56 Silvio Schueler | Barbara Seget | Rosina Soler | Michael A. Steele58 | Mónica Toro-­Manríquez45,46 | Caroline E. G. Tutin21 | Tharcisse Ukizintambara69 | Lee White21,22,70 | Biplang Yadok34,71 | John L. Willis72 | Anita Zolles54 | 65 11 Magdalena Żywiec | Davide Ascoli 1 Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, UK 2 U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado, USA This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd. Glob Change Biol. 2022;00:1–17.  wileyonlinelibrary.com/journal/gcb | 1 2 | HACKET-­PAIN et al. 3 Department of Biological Sciences, DePaul University, Chicago, Illinois, USA 4 Hastings Reservation, University of California Berkeley, Carmel Valley, California, USA 5 Department of Agricultural and Environmental Sciences, University of Milan, Milan, Italy 6 Faculty of Biology, Institute of Environmental Biology, Adam Mickiewicz University, Poznań, Poland 7 INRAE, LESSEM, University Grenoble Alpes, Grenoble, France 8 Univ. Bordeaux, INRAE, BIOGECO, Pessac, France 9 Department of Computing, University of London, London, UK 10 CREAF, Barcelona, Catalonia, Spain 11 Department of Agricultural, Forest and Food Sciences (DISAFA), University of Torino, Torino, Italy 12 Stefan cel Mare Univ Suceava, Forestry Fac, Appl Ecol Lab, Suceava, Romania 13 Department of Forest and Soil Sciences, Institute of Forest Ecology, University of Natural Resources and Life Sciences Vienna, Vienna, Austria 14 Kyushu University, Fukuoka, Japan 15 Aix Marseille Univ, Avignon Université, CNRS, IRD, IMBE, Marseille, France 16 Ecosystems and Global Change Group, Department of Plant Sciences, University of Cambridge, Cambridge, UK 17 School of Life Sciences, Keele University, Staffordshire, UK 18 Graduate Degree Program in Ecology and The Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, Colorado, USA 19 Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland 20 College of Civil and Architecture and Engineering, Chuzhou University, China 21 Faculty of Natural Sciences, University of Stirling, Stirling, UK 22 Institut de Recherche en Ecologie Tropicale, CENAREST, Libreville, Gabon 23 Facultad de Ciencias Forestales y de la Conservación de la Naturaleza (FCFCN), Universidad de Chile, Santiago, Chile 24 Universidad Nacional de la Plata (UNLP), Buenos Aires, Argentina 25 Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada 26 Royal Botanic Garden Edinburgh, Edinburgh, UK 27 Facultad de Recursos Naturales Renovables, Universidad Arturo Prat, Iquique, Chile 28 Brazilian Agricultural Research Corporation, Embrapa Roraima, Boa Vista, Brazil 29 Wilson Center, Washington, District of Columbia, USA 30 Department of Anthropology, George Washington University, Washington, District of Columbia, USA 31 School of Life Sciences, University of KwaZulu-­Natal, Pietermaritzburg, South Africa 32 Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi'an, China 33 School of Biological Sciences, University of Canterbury, Canterbury, New Zealand 34 Nigerian Montane Forest Project (NMFP), Yelway Village, Nigeria 35 CREA—­Research Centre for Forestry and Wood, Arezzo, Italy 36 Brazilian Agricultural Research Corporation, Embrapa Meio Ambiente, Jaguariúna, Brazil 37 Département Recherche-­Développement-­Innovation, Office National des Forêts, Fontainebleau, France 38 Department of Psychology, Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA 39 Department of Wildland Resources and Ecology Center, Utah State University, Logan, Utah, USA 40 Agence Nationale des Parcs Nationaux (ANPN), Libreville, Gabon 41 Brazilian Agricultural Research Corporation, Embrapa Acre, Rio Branco, AC, Brazil 42 Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB) (INTA—­CONICET, Instituto Nacional de Tecnología Agropecuaria—­Consejo Nacional de Investigaciones Científicas y Técnicas, Bariloche, Argentina 43 Department of Forestry and Wildland Resources, Humboldt State University, Arcata, California, USA 44 Laboratorio de Dendrocronología de Zonas Áridas, CIGEOBIO (CONICET-­UNSJ), Rivadavia, Argentina 45 Centro de Investigación en Ecosistemas de la Patagonia (CIEP), Coyhaique, Chile 46 Ulterarius Consultores Ambientales y Científicos Ltda, Punta Arenas, Chile 47 Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, USA 48 Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Radolfzell, Germany 49 Department of Biology, University of Konstanz, Konstanz, Germany 50 Department of Silviculture and Genetics of Forest Trees, Forest Research Institute, Raszyn, Poland 51 Environmental Science and Studies Department, DePaul University, Chicago, Illinois, USA 52 Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK 53 Department of Biology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada HACKET-­PAIN et al. | 3 54 Austrian Research Centre for Forests BFW, Vienna, Austria 55 Institute of Systematics and Evolution of Animals, Polish Academy of Sciences, Kraków, Poland 56 Centro Austral de Investigaciones Científicas (CADIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ushuaia, Argentina 57 Brazilian Agricultural Research Corporation, Embrapa Amapá, Macapá, Brazil 58 Department of Biology and Institute of the Environment, Wilkes University, Wilkes-­Barre, Pennsylvania, USA 59 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA 60 Independent researcher, Saint-­Maur-­des-­Fossés, France 61 Brazilian Agricultural Research Corporation, Embrapa Rondônia, Porto Velho, Brazil 62 Botany Research Division, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization, Tehran, Iran 63 School of Biological, Environmental, and Earth Sciences, The University of Southern Mississippi, Hattiesburg, Mississippi, USA 64 Instituto Nacional de Tecnología Agropecuaria (INTA), Universidad Nacional de la Patagonia Austral (UNPA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Río Gallegos, Argentina 65 W. Szafer Institute of Botany, Polish Academy of Sciences, Kraków, Poland 66 Forest Research Division, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization, Tehran, Iran 67 Laboratorio de Dendrocronología e Historia Ambiental, IANIGLA—­CONICET-­Universidad Nacional de Cuyo, Mendoza, Argentina 68 Facultad de Ciencias, Hémera Centro de Observación de la Tierra, Escuela de Ingeniería Forestal, Universidad Mayor, Santiago, Chile 69 Stony Brook University, Long Island, New York, USA 70 Ministère des Eaux, des Forêts, de la Mer, de l'Environnement chargé du Plan Climat, des Objectifs de Development Durable et du Plan d'Affectation des Terres, Boulevard Triomphale, Libreville, Gabon 71 Biosecurity NZ, Ministry for Primary Industries, Wellington, New Zealand 72 USDA Forest Service, Auburn, Alabama, USA Correspondence Andrew Hacket-­Pain, Department of Abstract Geography and Planning, School of Significant gaps remain in understanding the response of plant reproduction to envi- Environmental Sciences, University of Liverpool, Liverpool, UK. ronmental change. This is partly because measuring reproduction in long-­lived plants Email: andrew.hacket-pain@liverpool.ac.uk requires direct observation over many years and such datasets have rarely been made Funding information publicly available. Here we introduce MASTREE+, a data set that collates reproduc- Embrapa, Grant/Award Number: SEG tive time-­series data from across the globe and makes these data freely available to 12.13.07.007.00.00; National Natural Science Foundation of China, Grant/ the community. MASTREE+ includes 73,828 georeferenced observations of annual Award Number: 32001310; Natural reproduction (e.g. seed and fruit counts) in perennial plant populations worldwide. Environment Research Council, Grant/ Award Number: NE/S00713X/1 and These observations consist of 5971 population-­level time-­series from 974 species in NE/S007857/1; U.S. Forest Service, 66 countries. The mean and median time-­series length is 12.4 and 10 years respec- Grant/Award Number: RJVA-­PNW-­ 01-­JV-­11261952-­231; National Science tively, and the data set includes 1122 series that extend over at least two decades Foundation, Grant/Award Number: (≥20 years of observations). For a subset of well-­studied species, MASTREE+ includes DEB-­0 080609, DEB-­0 423442, DEB-­ 0620579, DEB-­1026415, DEB-­1636476, extensive replication of time-­series across geographical and climatic gradients. Here DEB-­8702629, DEB-­9211769 and we describe the open-­access data set, available as a.csv file, and we introduce an as- DEB-­9810217; Polish National Science Foundation, Grant/Award Number: sociated web-­based app for data exploration. MASTREE+ will provide the basis for 2019/33/B/NZ8/01345; Austrian Science improved understanding of the response of long-­lived plant reproduction to environ- Fund, Grant/Award Number: P30381; Klima-­und Energiefonds, Grant/Award mental change. Additionally, MASTREE+ will enable investigation of the ecology and Number: KR16AC0K13339 evolution of reproductive strategies in perennial plants, and the role of plant repro- duction as a driver of ecosystem dynamics. KEYWORDS demography, flowering, general flowering, masting, plant reproduction, recruitment, regeneration Resumen Aún existen importantes vacíos en la comprensión de la respuesta reproductiva de las plantas al cambio medioambiental, en parte, porque su monitoreo en especies de 4 | HACKET-­PAIN et al. plantas longevas requiere una observación directa durante muchos años, y estos con- juntos de datos rara vez han estado disponibles. Aquí presentamos a MASTREE +, una base de datos que recopila series de tiempo de la reproducción de las plantas de todo el planeta, poniendo a disposición estos datos de libre acceso para la comuni- dad científica. MASTREE + incluye 73.828 puntos de observación de la reproducción anual georreferenciados (ej. conteos de semillas y frutos) en poblaciones de plantas perennes en todo el mundo. Estas observaciones consisten en 5971 series temporales a nivel de población provenientes de 974 especies en 66 países. La mediana de la duración de las series de tiempo es de 10 años (media = 12.4 años) y el conjunto de datos incluye 1.122 series de al menos dos décadas (≥20 años de observaciones). Para un subconjunto de especies bien estudiadas, MASTREE +incluye un amplio conjunto de series temporales replicadas en gradientes geográficos y climáticos. Describimos el conjunto de datos de acceso abierto disponible como un archivo.csv y presentamos una aplicación web asociada para la exploración de datos. MASTREE+ proporcion- ará la base para mejorar la comprensión sobre la respuesta reproductiva de plantas longevas al cambio medioambiental. Además, MASTREE+ facilitará los avances en la investigación de la ecología y la evolución de las estrategias reproductivas en plantas perennes y el papel de la reproducción vegetal como determinante de la dinámica de ecosistemas. 1 | I NTRO D U C TI O N Recent analysis of long-­term data sets indicates that seed pro- duction may be sensitive to climate change. Where increases in Climate change and other anthropogenic drivers are altering plant temperature favour reproduction, warming is linked to increased demographics, with reported changes in plant mortality, growth seed production (Bogdziewicz et al., 2020; Buechling et al., 2016; and reproduction (Allen et al., 2010; McDowell et al., 2020; Pearse Caignard et al., 2017), whereas in drought-­limited populations seed et al., 2017; Senf et al., 2018). These demographic shifts are production has declined in association with warming (Redmond et al., changing the composition and structure of vegetation, with far-­ 2012). Additionally, environmental change may alter the interannual reaching effects on ecosystem functioning and services, includ- variability and spatial synchrony of reproduction (Hacket-­Pain & ing complex effects on biodiversity and terrestrial carbon sinks Bogdziewicz, 2021; Pearse et al., 2017). These shifts in reproduction (Carnicer et al., 2011; Chen et al., 2019; Clark et al., 2016; Ruiz-­ have consequences for recruitment and wider ecosystem dynamics Benito et al., 2017). In most plant species, seed production is a key (Pau et al., 2018; Redmond et al., 2012; Schupp et al., 2019). For process limiting sexual reproduction. However, our understanding example, long-­term reductions in tropical rainforest fruit production of climate-­driven changes in seed production lags behind other have been linked with declining vitality of herbivorous megafauna key demographic processes such as growth and mortality (Clark (Bush et al., 2020), and low seed availability can limit forest recovery et al., 2021), where inventory data, tree-­r ing networks and remote after large-­scale mortality events (Redmond et al., 2018). Beyond sensing have transformed understanding of responses to environ- changes in mean seed and fruit production, shifts in the spatiotem- mental change (Buras et al., 2020; Changenet et al., 2021; Klesse poral variability of flowering and fruiting (i.e. masting) will also have et al., 2020). Reproduction and other processes associated with impacts on key ecosystem services and habitat management (Pearse plant recruitment require direct and intensive field-­b ased obser- et al., 2021) including commercial and subsistence food crops vation over many years. However, there have been few previous (Calama et al., 2011; Ladio & Lozada, 2004; Shelef et al., 2017), seed-­ attempts to collate, archive and make available original data from eating animal population dynamics (Touzot et al., 2020), and human long-­term monitoring studies across taxa and wide geographic health through the trophic interactions that drive vector-­borne zoo- areas (Ascoli, Maringer, et al., 2017; Koenig & Knops, 2000; Pearse notic disease dynamics (Bennett et al., 2010; Bregnard et al., 2020). et al., 2020). Consequently, the response of plant reproduction to However, the direction and magnitude of reported changes in mast- ongoing environmental change remains poorly understood, and ing are inconsistent, and this variability in response remains poorly paucity of data compromises the parameterisation of reproduc- understood (Hacket-­Pain & Bogdziewicz, 2021). tion in models used to predict future vegetation dynamics (Fisher As the magnitude of plant reproduction is highly variable et al., 2018; Vacchiano et al., 2018). across time and space (Figure 1), multi-­d ecadal time-­s eries of plant HACKET-­PAIN et al. | 5 Pinus sylvestris Carya floridana Empetrum nigrum Fagus sylvatica Chionochloa macra 350 50 8 400 25 flowers/individual seeds/individual seeds/individual seeds/m2 g/m2 0 0 0 0 0 1980 1990 2000 1970 1980 1990 1985 1995 2005 1995 2005 2015 1990 1995 2000 2005 Year Year Year Year Year F I G U R E 1 Examples of population-­level time-­series of reproductive effort from MASTREE+. For five diverse plant species, data from several local populations are plotted to illustrate the range of spatiotemporal variation in reproduction that is typical in long-­lived plants. Note that axis scales and units vary between plots reproductive effort with high replication and sampling across en- scale. We also include ordinal time-­series, which record annual re- vironmental gradients are needed to derive meaningful inferences production output according to an ordered categorical scale (e.g. fail- and predictions from modelling efforts (Clark et al., 2021; Pearse ure/partial/full crop) which can be successfully used to investigate et al., 2021; Pennekamp et al., 2019; Vacchiano et al., 2018). The the variability and synchrony of plant reproduction (Bogdziewicz availability of such data will enable robust estimates of the re- et al., 2021). sponse of plant reproduction to recent environmental change, The current version of MASTREE+ currently includes 5971 and through identification of the underlying drivers, prediction species-­specific and georeferenced time-­series representing 73,828 of future trends. MASTREE+ provides these time-­s eries of plant annual observations of reproductive effort in perennial plant pop- reproductive effort, and will enable testing of changes in masting ulations, and the project is designed to continue to assemble and patterns associated with recent environmental change across mul- update records (see Sections 4 and 5). Mean and median time-­series tiple species and geographical regions (Hacket-­P ain & Bogdziewicz, length are 12.6 and 10 years respectively. 2846 series are based on 2021; LaMontagne et al., 2021; Pearse et al., 2017). Such data sets continuous measures of reproductive effort, and 3125 are ordinal will also enable new insights into the ecology and evolution of pe- series. Ordinal series originate mainly from Europe. Importantly, rennial plant reproduction (Dale et al., 2021), and the role of plant MASTREE+ contains 1122 time-­series ⋝20 years, of which 187 reproduction as a driver of other ecological processes including time-­series exceed 40 years of observations. Such records will en- plant recruitment and animal population dynamics (Brumme et al., able quantification of recent changes in plant reproduction, includ- 2021; Connell & Green, 2000; Curran & Leighton, 2000; Schupp ing mean reproductive effort and spatiotemporal variability, and the et al., 2019). identification of key drivers of change. In total, 974 species are represented, drawn from 136 families across the plant Tree of Life. This increases species representation 2 | M A S TR E E+ by 168% compared with the largest previously available compila- tion (Pearse et al., 2020), which is incorporated into MASTREE+. Here we introduce a project to collate data of perennial plant re- This expands the potential to quantify reproductive traits that de- productive time-­series. Time-­series originate from diverse sources, scribe the spatiotemporal variability of reproduction (i.e. masting) including 17th century European forestry records of seed produc- with other life-­history traits to better understand the evolution tion (‘mast years’) (Ascoli, Vacchiano, et al., 2017), data from ongoing of plant reproductive strategies (Dale et al., 2021; Fernandez-­ plant reproductive biology and phenology monitoring programmes Martinez et al., 2019; Pesendorfer et al., 2021). For example, we (e.g. RENECOFOR, LTER, California Acorn Survey), and projects have 67 species overlap with the plant demographic database studying the dynamics of ecosystems including the relationships COMPADRE (Salguero-­G omez et al., 2015), 442 species overlap between seed production and animal demographics (Boutin et al., with seed mass data from the Kew Seed Information Database 2006). Many of these data sets record the number or mass of flow- (Royal Botanic Gardens Kew, 2021) and 82 species overlap with ers, seeds, fruits or cones per individual or unit area on a continuous the seed germination database SylvanSeeds (Fernandez-­P ascual, 6 | HACKET-­PAIN et al. 2021). Reflecting a bias in sampling to temperate forests, woody 3 | A PPLI C ATI O N S O F M A S TR E E+ species from the genera Quercus (60 species), Nothofagus (10), Pinus (25), Abies (13), Acer (13) and Eucalyptus (15) are highly rep- MASTREE+ provides the data sets to establish how fecundity, and resented, but other well-­represented genera include Acacia (11), specifically seed masting, responds to environmental change. It in- Shorea (9) and Chionochloa (11). We include data from 66 coun- cludes the high replication of long time-­series required to isolate tries, six continents (Figure 2), and from all the major vegetated climate change effects on plant reproductive effort (Hacket-­Pain & biomes (Figure 3). Importantly, we increase data representation Bogdziewicz, 2021; Mundo et al., 2021) (Figure 4), while high spatial from regions that have been unrepresented in previous data sets replication across environmental gradients (e.g. Figure 3b) provides (Ascoli, Maringer, et al., 2017; Pearse et al., 2020), including south the opportunity for a complementary space-­for-­time substitution ap- and central America, Africa, and Asia, although these regions re- proach (Wion et al., 2020). The expected response of masting to cli- main strongly under-­represented. mate change remains unresolved, and MASTREE+ will enable testing Sampling intensity varies between species. For example, 71% of of contrasting predictions that masting will be unresponsive to trends species are represented by a single time-­series, but other species in mean temperature (Kelly et al., 2013), or will shift predictably based have high replication, often covering large parts of their geograph- on climate-­driven changes in resource limitation (Bogdziewicz, 2021). ical distribution. 51 species are represented by at least 10 location-­ Resolving this uncertainty is a priority because changes in seed mast- specific time-­series. The most replicated species are Fagus sylvatica ing will impact plant reproductive success, and more widely affect (913 site-­specific time-­series), Picea abies (844), Pinus sylvestris (419), ecosystem services and habitat management (Ida, 2021; Pearse et al., Larix decidua (395), Abies alba (393), Quercus robur (188), Quercus pet- 2021; Touzot et al., 2020). raea (161), Pinus cembra (135) and Picea glauca (108). These and other In systems where seed production limits recruitment, MASTREE+ well-­replicated species include data spanning large climatic gradi- can be utilised to understand the drivers of plant reproduction and ents (Figure 3). These records will enable investigation of intraspe- regeneration (Abraham et al., 2018; Manríquez et al., 2016; Oliva cific variation in plant reproduction across climate, space, and time, et al., 2013). Intraspecific differences in fecundity and masting in- including trends in the spatiotemporal variability of reproduction. fluence regeneration success, determining species composition and It will also enable comprehensive assessments of intraspecific vari- vegetation structure, including during the colonisation of new hab- ability of masting characteristics (i.e. interannual variability, autocor- itats (Joubert et al., 2013), and after natural and anthropogenic dis- relation), including variation with environmental conditions that are turbance (Martin-­DeMoor et al., 2010; Mokake et al., 2018; Peters predicted by theory but have rarely been tested (Pearse et al., 2020; et al., 2005). Masting characteristics of hundreds of species can be Pesendorfer et al., 2021), and analysis of interspecific variation in investigated using MASTREE+, and integration with plant trait and spatial synchrony of reproduction (Dale et al., 2021), in functionally demographic databases will enable deeper integration of masting and diverse plant species. reproductive strategies within life history theory (Salguero-­Gomez (a) 75 (b) 75 2210 50 50 752 25 25 Latitude (°) Latitude (°) 0 0 Count −25 100 −25 N 10 Ordinal −50 −50 Continuous 1 −100 0 100 0 50 100 150 200 250 Longitude (°) Count F I G U R E 2 The geographical distribution of time-­series within MASTREE+. The (a) spatial and (b) latitudinal distribution of species-­specific time-­series. For (b), series are stacked and coloured according to the variable type (Continuous, Ordinal). Plotting of counts for ordinal data in the northern mid-­latitudes are truncated due to high sampling intensity in central Europe. Unprojected map, datum = WGS84 HACKET-­PAIN et al. | 7 (a) Subtropical desert (b) Temperate grassland desert Picea abies Woodland shrubland Temperate forest Picea glauca −10 Boreal forest Abies balsamea Temperate rain forest Tropical rain forest Pinus sylvestris Tropical forest savanna Tundra Betula pubescens (°C) Pinus albicaulis 0 Picea engelmannii Mean annual temperature Fagus crenata Fagus sylvatica Pinus mugo 10 Nothofagus alpina Fagus grandifolia Pinus edulis Quercus rubra 20 Quercus alba Quercus petraea Quercus velutina 30 Quercus ilex 0 100 200 300 400 −5 0 5 10 15 Mean annual precipitation (cm) Mean annual temperature (°C) F I G U R E 3 Distribution of time-­series in MASTREE+ according to local climate (Worldclim v2.1, 30 arcsecond resolution, Mosier et al., 2014). Only time-­series representing reproduction at the stand or patch scale are plotted (regional records are excluded, as local climate data based on coordinates may not be representative). (a) Series plotted according to Whittaker biomes (Whittaker, 1970) and (b) Species with high replication (≥20 species-­specific time-­series), plotted according to local mean annual temperature. Species are labelled according to the first three characters of the genus followed by the first three characters of the species name, and species are ordered according to the sample site with the lowest mean annual temperature. Unfilled points represent ordinal time-­series and filled points represent continuous time-­series et al., 2016). Many ecologically and economically important species identify the drivers of population dynamics, both in seed-­dependent show highly variable investment in reproduction between years, species and further down the trophic cascade (Kleef & Wijsman, and the ability to accurately forecast occasional years of high seed 2015; Lithner & Jönsson, 2002). Where species are well replicated in production is a priority for habitat management, with wide ranging MASTREE+, the spatial synchrony of masting can also be quantified, applications (Chiavetta & Marzini, 2021; Pearse et al., 2021; Pukkala allowing researchers to determine where regional estimates of mast- et al., 2010). Predictive models of masting developed and tested ing can be appropriately used as indicators of local variability in seed using MASTREE+ data may enable more effective seed collection or fruit availability. The scale of spatial synchrony of masting appears for afforestation and restoration schemes (Fargione et al., 2021; to be highly variable between species (Bogdziewicz et al., 2019), but Kettle et al., 2010), inform wildlife and conservation management this has only been quantified of a handful of species so far (Koenig & (Choquenot & Ruscoe, 2000; Fujiki, 2018; Ida, 2021; O'Donnell & Knops, 2013; LaMontagne et al., 2020). Hoare, 2012), and enable forecasting of periods of elevated infec- In masting species, highly variable allocation to reproduction tion risk from tick-­borne disease, which predictably follow years has wider effects on plant resource allocation, and carbon and nu- of high seed production in many forest ecosystems (Brugger et al., trient cycling through ecosystems, but this remains poorly explored 2018; Cunze et al., 2018; Heyman et al., 2012; Ostfeld et al., 1996). (Brumme et al., 2021; Khanna et al., 2009; Muller-­Haubold et al., The availability of seed and fruit production data sets in 2015). Data in MASTREE+ can be combined with existing field and MASTREE+ will be broadly relevant when paired with existing an- remote-­sensing data sets of plant growth or productivity, and with imal population data sets. The pulses of resources associated with data sets of whole-­ecosystem or soil carbon and nutrient fluxes to large reproductive events are key drivers of the population dynamics understand how variable allocation to reproduction influences car- of seed-­eating insects, mammals and birds, with cascading impacts bon sequestration above and belowground, and how this varies be- through ecosystems (Bouchard et al., 2018; Kanamori et al., 2017; tween species and across environmental gradients (Bajocco et al., Selonen et al., 2016). Time-­series in MASTREE+ can be combined 2021; Nussbaumer et al., 2021; Oddou-­Muratorio et al., 2021; Zhang with existing long time-­series of animal populations and behaviour to et al., 2022). Related work can use MASTREE+ data combined with 8 | HACKET-­PAIN et al. 5971 2846 mature seed, fruit, or cone production. We did not set a minimum time-­series length but prioritised compiling effort on time-­series ≥4 years. All time-­series represent reproductive effort at the pop- ulation level, ranging from local populations with <10 individuals to Ordinal Continuous regional estimates of reproduction, and we recorded information on the number of monitored individuals in each population and the spa- tial scale represented by the time-­series (Table 1). We also included information on the original data collection methods, which included litter traps (19.3% of all records), seed, cone and fruit counts (18.3%), other methods including estimates of cone production using cone or Time-series number fruit scars and categorical classification of seed and fruit crops by wildlife managers or foresters. Data were collected from several sources. We harmonised data from previously published compilations of plant reproductive ef- fort displaying differences in data architecture (Ascoli et al., 2020; Ascoli, Maringer, et al., 2017; Pearse et al., 2020). To identify other time-­series, we searched Google Scholar and Scopus with multi- ple combinations of search terms (see Appendix 2). Spanish-­ and French-­language searches was used to increase data representa- 1 tion from South America and Africa. An initial screen was based on 1950 1970 1990 2010 the title and abstract to exclude irrelevant sources. Then, potential sources were classified based on the inclusion of useful time-­series data of reproductive effort, available as either data tables, figures, 1 descriptions in the text or in supplementary data files or in online 1700 1750 1800 1850 1900 1950 2000 data repositories. Finally, we solicited contributions of previously Year unpublished data sets from our research networks. Time-­series were extracted from the original sources. In the case of values published F I G U R E 4 Timespans covered by species-­specific time-­series in MASTREE+, coloured by data class. Inset plot shows continuous in tables, in the text, or in online data repositories or supplemen- data since 1950 when time-­series replication is highest tary data files, we extracted values directly from the source. In cases where data were contained in figures, we used the WebPlotDigitizer tool (Rohatgi, 2020). Metadata associated with each time-­series was existing or retrospective sampling (e.g. tree-­rings) to address out- also extracted from the sources, or directly from data set contribu- standing question regarding resource allocation between growth, tors, and copies of original sources were archived. reproduction, and defence, particularly how this varies interspecifi- cally and with environmental stress, and how this may shape species and community responses to environmental change (Lauder et al., 4.1 | Data set variables 2019; Redmond et al., 2019). For each monitored population we recorded annual observations of reproductive effort, the units of measurement, the method used 4 | DATA S O U RC E S , ACQ U I S ITI O N A N D to assess reproductive output and the number of monitored indi- CO M PI L ATI O N viduals (Table 1). Where multiple measures of reproductive output were recorded for the same population (e.g. where seeds and cones We collected species-­specific time-­s eries of annual reproductive were recorded separately), this was recorded to enable filtering of effort for terrestrial perennial plant populations, including trees, the data set for pseudoreplicates (Table 1). For ordinal series, we shrubs, herbs and grasses. We included data from unmanaged maintained the original number of classes, but we rescaled to integer and managed populations, but excluded agricultural crop species scales starting at 1 (lowest reproductive output). For continuous se- subject to selective breeding. Where reproduction was monitored ries, where possible we converted data into a common unit (e.g. we under experimentally manipulated conditions (e.g. fertilisation, converted ‘seeds/ha’ to ‘seeds/m2’). Years with missing observations warming, rainfall exclusion), we only included data from control are not recorded, and time-­series that would otherwise have gaps plots. consist of a set of segments. The Start and End year corresponds to Data were collected for reproductive effort at different stages the first and last observation year for each time-­series, respectively, of the reproductive cycle (e.g. flowers or inflorescences, pollen including all segments. Length is the number of observations within abundance, number of fruits, cones or seeds), but 90% of data were each time-­series, and can therefore be lower than the number of HACKET-­PAIN et al. | 9 TA B L E 1 Overview of the data variables in the MASTREE+ data set. A more detailed description of the variables is included in Appendix 5 Variable Description Alpha_Number Unique code associated with each original source of data, that is, the publication, report or thesis containing extracted data, or the previously unpublished data set included in MASTREE+ Segment Temporal segment of a time-­series containing gaps (note that years with no observations are not recorded). Individual time-­ series can consist of multiple segments Site_number Code to differentiate multiple sites from the same original source (Alpha_Number/Study_ID) Variable_ Code to differentiate multiple measures of reproductive output from the same species-­site combination (e.g. where seeds and number cones were recorded separately) Year Year of observation Species Species identifier, standardised to The Plant List nomenclature. ‘spp.’ is used to indicate a record identified to the genus level only. ‘MIXED’ indicates a non-­species-­specific community-­level estimate of annual reproductive effort Species_code Six-­character species identifier Mono_Poly Monocarpic (semelparous) or Polycarpic (iteroparous) species Value The measured value of annual reproductive output VarType Continuous or ordinal data. Continuous time-­series are recorded on a continuous scale. Ordinal series are recorded on an ordered categorical scale. All ordinal series are rescaled to start at 1 (lowest reproductive effort) and to contain only integer values Unit The unit of measurement, where VarType is continuous Max_Value The maximum value in a time-­series Variable Categorical classification of the measured variable. Options limited to: cone, flower, fruit, seed, pollen, total reproduction organs Collection_ Classification of the method used to measure reproductive effort. Options are limited to: cone count, cone scar count, flower method count, fruit count, fruit scar sound, seed count, seed trap, pollen count, lake sediment pollen count, harvest record, visual crop assessment, other quantification, dendrochronological reconstruction Latitude Latitude of the record, in decimal degrees Longitude Longitude of the record, in decimal degrees Coordinate_flag A flag to indicate the precision of the latitude and longitude. A = coordinates provided in the original source B = coordinates estimated by the compiler based on a map or other location information provided in the original source C = coordinates estimated by the compiler as the approximate centre point of the smallest clearly defined geographical unit provided in the original source (e.g. county, state, island), and potentially of low precision Site A site name or description, based on information in the original source Country The country where the observation was recorded Elevation The elevation of the sample site in metres above sea level, where provided in the original source Spatial_unit Categorical classification of spatial scale represented by the record, estimated by the compiler based on information provided in the original source. stand = <100 ha patch = 100–­10,000 ha region = 10,000–­1,000,000 ha super-­region = >1,000,000 ha No_individuals Either the number of monitored individual plants, or the number of litter traps. NA indicates no information in the original source, and 9999 indicates that while the number of monitored individuals was not specified, the source indicated to the compiler that the sample size was likely ≥10 individuals or litter traps Start The first year of observations for the complete time-­series, including all segments End The final year of observations for the complete time-­series, including all segments Length The number of years of observations. Note that may not be equal to the number of years between the Start and End of the time-­series, due to gaps in the time-­series. Reference Identification for the original source of the data, see Appendix 4 for the complete list of references Record_type Categorisation of the original source. Peer-­reviewed = extracted from peer reviewed literature Grey = extracted from grey literature Unpublished = unpublished data (Continues) 10 | HACKET-­PAIN et al. TA B L E 1 (Continued) Variable Description ID_enterer Identification of the original compiler of the data. AHP, Andrew Hacket-­Pain; ES, Eliane Schermer; JVM, Jose Moris; XTT, Tingting Xue; TC, Thomas Caignard; DV, Davide Vecchio; DA, Davide Ascoli; IP, Ian Pearse; JL, Jalene LaMontagne; JVD, Joep van Dormolen Date_entry Date of data entry into MASTREE+ in the format yyyy-­mm-­dd Note on data Notes on the location of the data within the original source, such as page or figure number location Comments Additional comments Study_ID Unique code associated with each source of data. M_ = series extracted from published literature; A_ = series incorporated from Ascoli et al. (2020), Ascoli, Maringer, et al. (2017) and Ascoli, Vacchiano, et al. (2017); PLK_ = series incorporated from Pearse et al. (2017); D_ = unpublished data sets years between the Start and End. The location (decimal degrees), site shared the same study species and approximate location (using a name, elevation and country of each time-­series were recorded. The ±0.1 decimal degree buffer between pairs of time-­s eries). PDGs spatial scale represented by the time-­series was estimated on a four-­ containing time-­s eries from multiple sources (Alpha_Number) were point scale, from individual stand to region, based on information then inspected further. Suspect pairs of time-­s eries within PDGs contained in the original source. Information on the nature of the were initially identified based on a correlation test (Spearman's source, and reference information was also recorded. Full details of ρ > 0.97), and we then inspected manually for duplication using data variables are listed in Table 1. Each time-­series can be uniquely information including location, units, and collection methods to defined by combining Alpha_Number, Site_number, Variable_number identify possible duplication. To supplement the semi-­automated and Species_code. detection of duplicates, we performed a further manual check, ex- amining groups of time-­s eries that shared the same country and species. Suspect pairs of series might, for example, share match- 4.2 | Technical validation and quality control ing spatial references, matching site descriptions and/or matching author names. A two-­stage approach was adopted to validate time-­series data. Where duplicated series were identified, or where independence Initially, we standardised attribute data and checked for errors and could not be confirmed, we selected a single time-­series for inclu- inconsistencies within time-­series. Species names were checked and sion in MASTREE+. Generally, the longest time-­series was priori- standardised to The Plant List nomenclature, using the ‘Taxonstand’ tised, unless there were clear signs that a shorter time-­series was of package for R (v. 2.3) (Cayuela et al., 2021). Country names were higher quality (e.g. the data were directly shared by the author and converted to the English short name (ISO3166-­1) using the ‘country- not extracted from a graph). code’ package for R (v. 1.2.0) (Arel-­Bundock et al., 2018). Automatic checks were performed to ensure that each time-­series was uniquely identified by the identification variables and that time-­series' obser- 5 | DATA S E T AVA I L A B I LIT Y A N D vations were uniquely identified by Year. Species_code was assigned M A S TR E E+ DATA E X PLO R E R by automatically combining the first three characters from the TPL-­ standardised genus and species names. Where separate species The data set is provided as a csv file in the online supporting in- shared a Species_code, a unique combination was manually created. formation (Appendix 1) and is distributed under a CC-­BY-­4.0 li- The final character of Species_code for populations of a hybrid ori- cence so that it can be freely used, shared and modified so long gin was changed to ‘X’. We ran various automatic checks to ensure as appropriate credit is given. The data set will be expanded and all observations in a time-­series had uniform attribute data where updated over time, so users are encouraged to check for the lat- such uniformity was expected (i.e. within a time-­series, there was est version of the data set on GitHub (https://github.com/JJFoe​ only a single value for variables such as Unit). Interrelated variables st/MASTR​EEplus) and via associated updates to the MASTREE+ were checked to ensure consistency, for example that time-­series Data Explorer. The MASTREE+ Data Explorer allows users to spatial data (Latitude, Longitude) fell within the boundaries of the in- explore the MASTREE+ data set and provides an alternative for dicated Country. Time-­series duration variables (i.e. Segment, Start, downloading the data set, including user-­d efined subsets thereof. End, Length) were directly calculated from time-­series. The MASTREE+ Data Explorer was created using the shiny pack- The second stage involved the detection and removal of du- age in R (Chang et al., 2021) and can be accessed at https://mastr​ plication problems between time-­s eries, that is, series added eeplus.shiny​apps.io/mastr​e eplu​s/. Time-­s eries are plotted on a multiple times, including with partial overlap, usually when data zoomable world map, with updating summary plots showing the were published in more than one source. First, we created ‘po- time-­s eries lengths and species/genera for the selected region, as tential duplication groups’ that contained sets of time-­s eries that well as scaled time-­s eries for initial visualisation of the data within HACKET-­PAIN et al. | 11 F I G U R E 5 Example of the MASTREE+ Shiny Data Explorer, showing data from the South Island of New Zealand. The Data Explorer allows the user to explore data availability within MASTREE+, and download the full or user-­defined subsets of the data set the selected region of interest (Figure 5). Individual time-­s eries allow the user to subset the full data set. An R script is provided in can be selected on the map to reveal associated meta-­data, includ- Appendix 6 that illustrates how to load, manipulate and visualise ing the location, species and original source. Various filter options the data set. 12 | HACKET-­PAIN et al. 6 | C A LL FO R DATA publications to manuel.nicolas@onf.fr. Publications using the Lopé data (Reference = Bush_2021) are requested to cite the original data We have increased taxonomic and geographic representation in set (http://hdl.handle.net/11667/​152), acknowledge The National MASTREE+, but many gaps remain in the coverage of our data set. Parks Agency of Gabon (ANPN) and the University of Stirling, and Our goal is to provide a global platform for sharing data on long-­lived send copies of any resulting publications to science@parcsgabon.ga plant reproduction, and we encourage scientists to submit time-­ and k.a.abernethy@stir.ac.uk. series of annual reproductive effort in perennial plant populations for inclusion in MASTREE+ (Table 2). We will consider all species-­ AC K N OW L E D G E M E N T S specific time-­series of four or more years, including continuous and We thank Mark Green and Esther Dale. This study was funded ordinal observations. We include time-­series data on flower, seed, by the UK Natural Environment Research Council grant no. NE/ fruit and cone production, which are associated with geographical S007857/1 to AHP, AJT and PAT. JJF was supported a PhD student- coordinates. We can include data that represent small local popu- ship under Natural Environment Research Council grant number lations through to large regional-­scale assessments of reproductive NE/S00713X/1. Original data collection was supported by many effort. Note that we only record annual reproductive effort. Where funders, including the Natural Sciences and Engineering Research data are collected at sub-­annual timesteps, this means that repro- Council of Canada (SB, AM, BD, JL) the National Science Foundation duction must be aggregated to annual units (e.g. April–­March). (AM, BD), the Austrian Climate Research Program (ACRP) of the Potential contributors of data are encouraged to search the lat- ‘Klima-­ und Energiefonds’ (9th Call, project: MoreSeedsAdapt—­ est version of the data set to check whether the data are already in- KR16AC0K13339), the Brazilian Agricultural Research Corporation cluded in MASTREE+, either by downloading the latest version from (EMBRAPA) through the Kamukaia III Project ‘Appreciation of GitHub, Dryad (Section 5) or via the MASTREE+ Data Explorer. If non-­timber forest products in the Amazon’ (grant number SEG the data are not already included, potential contributors are encour- 12.13.07.007.00.00), the W. Szafer Institute of Botany of the Polish aged to contact the corresponding author to discuss arrangements Academy of Sciences and the Polish National Science Foundation for sharing data. The minimum data requirements are included in (2019/33/B/NZ8/01345), the French ministry of Agriculture and Table 2. Food, the French ministry and the French agency for Ecological Transition, the French national forest office, and the European Commission (under successive regulations from No 1091/94 until 6.1 | Data licence No 2152/2003). As the French part of the ICP Forests inten- sive (Level II) monitoring programme, the RENECOFOR network MASTREE+ is published under a CC-­BY-­4.0 licence, which ena- has benefited from its scientific framework and shared expertise. bles users to copy and redistribute, adapt and modify the data set Data from Bonanza Creek LTER come from a partnership between in any medium or format and for any purpose, including commer- the University of Alaska Fairbanks and the U.S. Forest Service, cial. You must give appropriate credit by citing this publication, with funding from the National Science Foundation Long-­Term provide a link to the license and indicate if changes were made Ecological Research program (NSF Grant numbers DEB-­1636476, (see https://creat​iveco​mmons.org/licen​ses/by/4.0/ for further de- DEB-­1026415, DEB-­0620579, DEB-­0 423442, DEB-­0 080609, DEB-­ tails). Data can be accessed via Github: https://github.com/JJFoe​ 9810217, DEB-­9211769, DEB-­8702629) and by the USDA Forest st/MASTR​EEplus, Dryad: https://doi.org/10.5061/dryad.18931​ Service, Pacific Northwest Research Station (Agreement # RJVA-­ zd02, or via the MASTREE+ Siny App. Publications using the PNW-­01-­JV-­11261952-­231). The contribution of TX was supported RENECOFOR data (Reference = RENECOFOR_2020) are requested by the National Natural Science Foundation (Grant No. 32001310) to acknowledge the RENECOFOR network, and send copies of and Chuzhou University Start-­up Foundation for Research (2021qd05). Data from Italian Central Apennines were available, TA B L E 2 Minimum data requirements for submissions to thanks to the support of the research project ‘Monitoraggio della MASTREE+. For further details see Table 1 produzione di seme di specie forestali, rinnovazione naturale e Minimum data requirements and metadata relazioni con la fauna selvatica (Pasciona)’ funded by the Foreste Minimum of four consecutive measurements of annual reproductive Casentinesi, Monte Falterona e Campigna National Park. The contri- output bution from RJD would not have been possible without the effort of Measurement at the population level (local population through Dr. Theodore W. (‘Doc’) Daniel. Doc Daniel had the long-­term vision regional scale estimates acceptable) to commence and oversee the annual cone counts for hundreds of Species name according to The Plant List. Records identified to the trees from 1947-­1981 on the Utah State Agricultural College (now genus level are acceptable, and measurements of non-­species-­ Utah State University) School Forest, later named in his honour as specific community reproductive effort may be included. the T.W. Daniel Experimental Forest. The Rothwald data were col- Spatial coordinates of the monitored population lected as part of the research project ‘Sporadic seed production in Details of the method used to measure reproductive effort (e.g. mast seeding trees’, P30381 of the Austrian Science Fund (FWF). litter traps, seed counts, visual crop estimate, see Table 1) We thank Charley Krebs, Rudy Boonstra, Dennis Murray for sharing HACKET-­PAIN et al. | 13 unpublished data, and we gratefully acknowledge the scientists re- Tutin, Tharcisse Ukizintambara, Lee White, Biplang Yadok, John sponsible for the following open-­access data sets that were incorpo- L. Willis, Anita Zolles, Magdalena Żywiec, Davide Ascoli. Writing—­ rated into MASTREE+: https://doi.org/10.5061/dryad.4qrfj​6q9m, original draft: Andrew Hacket-­Pain. Writing—­Review and editing: All https://doi.org/10.5061/dryad.1s625, https://doi.org/10.5061/ authors. Supervision and Project administration: Andrew Hacket-­Pain. dryad.v6wwp​zgrb, https://doi.org/10.5061/dryad.772g3, https:// Funding acquisition: Andrew Hacket-­Pain, Andrew J. Tanentzap, doi.org/10.5061/dryad.pv608, https://doi.org/10.5061/dryad. Peter A. Thomas. stqjq​2c0c, https://doi.org/10.5061/dryad.61m318c, https://doi. org/10.5061/dryad.75v7c. We thank the many collaborators, stu- DATA AVA I L A B I L I T Y S TAT E M E N T dents, friends and family who have helped to support long-­term The data that supports the findings of this study are available in the data collection. Any use of trade, firm, or product names is for de- supplementary material of this article, and are openly available via scriptive purposes only and does not imply endorsement by the U.S. Github: https://github.com/JJFoe​st/MASTR​EEplus, Dryad: https:// Government. The findings and conclusions of this publication are doi.org/10.5061/dryad.18931​zd02, or via the MASTREE+ Data those of the author(s) and should not be construed to represent an Explorer: https://mastr​eeplus.shiny​apps.io/mastr​eeplus official USDA, Forest Service, or United States Government deter- mination or policy. ORCID Andrew Hacket-­Pain https://orcid.org/0000-0003-3676-1568 C O N FL I C T O F I N T E R E S T Jessie J. Foest https://orcid.org/0000-0002-8236-4646 The authors declare no conflict of interest. Ian S. Pearse https://orcid.org/0000-0001-7098-0495 Jalene M. LaMontagne https://orcid.org/0000-0001-7713-8591 AU T H O R C O N T R I B U T I O N S Walter D. Koenig https://orcid.org/0000-0001-6207-1427 Conceptualisation: Andrew Hacket-­Pain, Ian S. Pearse, Walter D. Giorgio Vacchiano https://orcid.org/0000-0001-8100-0659 Koenig, Giorgio Vacchiano, Michał Bogdziewicz, Mario Pesendorfer, Michał Bogdziewicz https://orcid.org/0000-0002-6777-9034 Akiko Satake, Andrew J. Tanentzap, Peter A. Thomas, Thomas Thomas Caignard https://orcid.org/0000-0001-5009-4613 Wohlgemuth, Davide Ascoli. Methodology, including literature search, Paulina Celebias https://orcid.org/0000-0001-9916-2904 source classification, data extraction and compilation: Andrew Hacket-­ Marcos Fernández-­Martínez https://orcid. Pain, Jessie J. Foest, Ian S. Pearse, Jalene M. LaMontagne, Walter D. org/0000-0002-5661-3610 Koenig, Giorgio Vacchiano, Michał Bogdziewicz, Thomas Caignard, Jose V. Moris https://orcid.org/0000-0003-0241-7910 Paulina Celebias, Joep van Dormolen, Marcos Fernández-­Martínez, Ciprian Palaghianu https://orcid.org/0000-0003-2496-2135 Jose V. Moris, Ciprian Palaghianu, Mario Pesendorfer, Akiko Mario Pesendorfer https://orcid.org/0000-0002-7994-7090 Satake, Eliane Schermer, Andrew J. Tanentzap, Peter A. Thomas, Akiko Satake https://orcid.org/0000-0002-0831-8617 Davide Vecchio, Andreas P. Wion, Thomas Wohlgemuth, Tingting Eliane Schermer https://orcid.org/0000-0001-7302-2241 Xue, Davide Ascoli. Methodology, including data validation: Andrew Andrew J. Tanentzap https://orcid.org/0000-0002-2883-1901 Hacket-­Pain, Jessie F. Foest. Data Explorer: Jessie J. Foest. Data con- Peter A. Thomas https://orcid.org/0000-0003-3115-3301 tribution: Andrew Hacket-­Pain, Ian S. Pearse, Jalene M. LaMontagne, Andreas P. Wion https://orcid.org/0000-0002-0701-2843 Walter D. Koenig, Michał Bogdziewicz, Peter A. Thomas, Katharine Thomas Wohlgemuth https://orcid.org/0000-0002-4623-0894 Abernethy, Marcelo Daniel Barrera, Jessica H. Barton, Stan Boutin, Katharine Abernethy https://orcid.org/0000-0002-0393-9342 Emma R. Bush, Sergio Donoso Calderón, Felipe S. Carevic, Carolina Marie-­Claire Aravena Acuña https://orcid. Volkmer de Castilho, Juan Manuel Cellini, Hazel Chapman, Colin org/0000-0002-4493-4396 A. 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