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High-resolution tropical rain-forest canopy climate data

Published online by Cambridge University Press:  17 October 2022

Monica B. Berdugo Open the ORCID record for Monica B. Berdugo [Opens in a new window] ,
Leander Heyer ,
Karen Yuliana Suarez Contento ,
Jorge Déleg ,
Jörg Bendix Open the ORCID record for Jörg Bendix [Opens in a new window]  and
Maaike Y. Bader
Monica B. Berdugo*
Affiliation:
Ecological Plant Geography, Faculty of Geography, University of Marburg, Marburg, Germany
Leander Heyer
Affiliation:
Ecological Plant Geography, Faculty of Geography, University of Marburg, Marburg, Germany
Karen Yuliana Suarez Contento
Affiliation:
Plant Taxonomy Laboratory, Department of Biology, Federal Rural University of Pernambuco, Recife, Brazil
Jorge Déleg
Affiliation:
Herbario HUTPL, Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Loja, Ecuador
Jörg Bendix
Affiliation:
Laboratory for Climatology and Remote Sensing, Faculty of Geography, University of Marburg, Marburg, Germany
Maaike Y. Bader
Affiliation:
Ecological Plant Geography, Faculty of Geography, University of Marburg, Marburg, Germany
*
*Corresponding author. E-mail: [email protected]

Abstract

Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-min temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.

Keywords

Amazon forestcanopy climateEcuadortree crownsYasuní National Park
Type
Data Paper
Information
Environmental Data Science , Volume 1 , 2022 , e13
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Open Practices
Open data Open materials
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Impact Statement

Climate data from tropical forest canopies are scarce, yet crucial to understanding tropical canopy dynamics. We encourage the use of our high-resolution climate data series as a field-based reference to validate models simulating the microclimate of these canopies. Furthermore, these data series can be used as climate inputs to model small-scale and fast-paced ecological dynamics driven by climate, such as the photosynthesis of canopy-dwelling organisms.

1. Introduction

Forest canopy climate is important for the performance of trees (Luo, Reference Luo2007; O’Grady et al., Reference O’Grady, Tissue and Beadle2011) as well as for sessile and mobile organisms living in the dynamic canopy habitat (Suggitt et al., Reference Suggitt, Platts, Barata, Bennie, Burgess, Bystriakova, Duffield, Ewing, Gillingham, Harper, Hartley, Hemming, Maclean, Maltby, Marshall, Morecroft, Pearce-Higgins, Pearce-Kelly, Phillimore, Price, Pyke, Stewart, Warren and Hill2017). Forest canopies host a wealth of biological diversity (Barker and Pinard, Reference Barker and Pinard2001) and canopy climate factors such as temperature, relative humidity, and light intensity drive and constrain the physiological performance, ecology, and fitness of sporadic and permanent inhabitants (Suggitt et al., Reference Suggitt, Platts, Barata, Bennie, Burgess, Bystriakova, Duffield, Ewing, Gillingham, Harper, Hartley, Hemming, Maclean, Maltby, Marshall, Morecroft, Pearce-Higgins, Pearce-Kelly, Phillimore, Price, Pyke, Stewart, Warren and Hill2017). In addition to hosting sessile organisms, such as plants and lichens (i.e., epiphytes), canopies host mobile organisms ranging in size from microorganisms and arthropods to all groups of vertebrates (Nakamura et al., Reference Nakamura, Kitching, Cao, Creedy, Fayle, Freiberg, Hewitt, Itioka, Koh, Ma, Malhi, Mitchell, Novotny, Ozanne, Song, Wang and Ashton2017). This astonishing diversity is a common descriptor for canopies in mature tropical forests, yet canopy climate records describing the conditions met by these diverse organisms are particularly scarce, despite the expansion of canopy research at tropical latitudes in recent decades (Nakamura et al., Reference Nakamura, Kitching, Cao, Creedy, Fayle, Freiberg, Hewitt, Itioka, Koh, Ma, Malhi, Mitchell, Novotny, Ozanne, Song, Wang and Ashton2017). Here, we present >300 days of canopy climate records with high temporal resolution (each 10 min) obtained in the lowland tropical forest surrounding the Yasuní scientific station in Amazonian Ecuador, an iconic biodiversity hotspot (Bass et al., Reference Bass, Finer, Jenkins, Kreft, Cisneros-Heredia, McCracken, Pitman, English, Swing, Villa, Di Fiore, Voigt and Kunz2010).

The intrinsic difficulty to access forest canopies challenges us while studying canopy climate in forests around the world. Yet, the relevance of canopies for biodiversity maintenance calls for persistence, creativity, and cooperation (Barker and Pinard, Reference Barker and Pinard2001). In addition, forest canopies contribute importantly to vegetation-climate feedback (Lin et al., Reference Lin, Xia and Wan2010; O’Grady et al., Reference O’Grady, Tissue and Beadle2011). The exchange of relevant gases between canopies and the atmosphere can be highly dynamic, in dependence of climatic fluctuations above and inside the canopy (Luo, Reference Luo2007; Lin et al., Reference Lin, Xia and Wan2010). However, high-temporal-resolution field measurements of canopy climate remain scarce and scattered, limiting our ability to understand such dynamics (Nakamura et al., Reference Nakamura, Kitching, Cao, Creedy, Fayle, Freiberg, Hewitt, Itioka, Koh, Ma, Malhi, Mitchell, Novotny, Ozanne, Song, Wang and Ashton2017). This shortage is especially pronounced in tropical forests. Although a modeling approach can supply estimates for the canopy climate of forests in different parts of the world (Maclean and Klinges, Reference Maclean and Klinges2021), the robustness and accuracy of those estimates are hard to evaluate in forest types with no or insufficient available field data. Hence, monitoring canopy climate is critical to improving and validating such climate models. In turn, understanding climatic fluctuations in forest canopies is essential to understand and model vegetation-atmosphere interactions, as well as the functional ecology of trees and the incredible diversity of canopy-dwelling organisms found, in particular, in tropical rainforests.

Canopy climate parameters can be estimated from macroclimate with mechanistic models (Maclean, Reference Maclean2020), but akin to other models, calibration and validations demand field measurements (Lembrechts et al., Reference Lembrechts, Aalto, Ashcroft, De Frenne, Kopecký, Lenoir, Luoto, Maclean, Roupsard, Fuentes-Lillo, García, Pellissier, Pitteloud, Alatalo, Smith, Björk, Muffler, Ratier Backes, Cesarz, Gottschall, Okello, Urban, Plichta, Svátek, Phartyal, Wipf, Eisenhauer, Pușcaș, Turtureanu, Varlagin, Dimarco, Jump, Randall, Dorrepaal, Larson, Walz, Vitale, Svoboda, Finger Higgens, Halbritter, Curasi, Klupar, Koontz, Pearse, Simpson, Stemkovski, Jessen Graae, Vedel Sørensen, Høye, Fernández Calzado, Lorite, Carbognani, Tomaselli, Forte, Petraglia, Haesen, Somers, Van Meerbeek, Björkman, Hylander, Merinero, Gharun, Buchmann, Dolezal, Matula, Thomas, Bailey, Ghosn, Kazakis, de Pablo, Kemppinen, Niittynen, Rew, Seipel, Larson, Speed, Ardö, Cannone, Guglielmin, Malfasi, Bader, Canessa, Stanisci, Kreyling, Schmeddes, Teuber, Aschero, Čiliak, Máliš, De Smedt, Govaert, Meeussen, Vangansbeke, Gigauri, Lamprecht, Pauli, Steinbauer, Winkler, Ueyama, Nuñez, Ursu, Haider, Wedegärtner, Smiljanic, Trouillier, Wilmking, Altman, Brůna, Hederová, Macek, Man, Wild, Vittoz, Pärtel, Barančok, Kanka, Kollár, Palaj, Barros, Mazzolari, Bauters, Boeckx, Benito Alonso, Zong, Di Cecco, Sitková, Tielbörger, van den Brink, Weigel, Homeier, Dahlberg, Medinets, Medinets, De Boeck, Portillo-Estrada, Verryckt, Milbau, Daskalova, Thomas, Myers-Smith, Blonder, Stephan, Descombes, Zellweger, Frei, Heinesch, Andrews, Dick, Siebicke, Rocha, Senior, Rixen, Jimenez, Boike, Pauchard, Scholten, Scheffers, Klinges, Basham, Zhang, Zhang, Géron, Fazlioglu, Candan, Sallo Bravo, Hrbacek, Laska, Cremonese, Haase, Moyano, Rossi and Nijs2020). Recently, a decided leadership and the contribution of a wealth of field measurements resulted in a body of global estimates of high temporal and spatial resolution microclimate for soils and near the soil surface (Lembrechts et al., Reference Lembrechts, van den Hoogen, Aalto, Ashcroft, De Frenne, Kemppinen, Kopecký, Luoto, Maclean, Crowther, Bailey, Haesen, Klinges, Niittynen, Scheffers, Van Meerbeek, Aartsma, Abdalaze, Abedi, Aerts, Ahmadian, Ahrends, Alatalo, Alexander, Allonsius, Altman, Ammann, Andres, Andrews, Ardö, Arriga, Arzac, Aschero, Assis, Assmann, Bader, Bahalkeh, Barančok, Barrio, Barros, Barthel, Basham, Bauters, Bazzichetto, Marchesini, Bell, Benavides, Benito Alonso, Berauer, Bjerke, Björk, Björkman, Björnsdóttir, Blonder, Boeckx, Boike, Bokhorst, Brum, Brůna, Buchmann, Buysse, Camargo, Campoe, Candan, Canessa, Cannone, Carbognani, Carnicer, Casanova-Katny, Cesarz, Chojnicki, Choler, Chown, Cifuentes, Čiliak, Contador, Convey, Cooper, Cremonese, Curasi, Curtis, Cutini, Dahlberg, Daskalova, de Pablo, Della Chiesa, Dengler, Deronde, Descombes, Di Cecco, Di Musciano, Dick, Dimarco, Dolezal, Dorrepaal, Dušek, Eisenhauer, Eklundh, Erickson, Erschbamer, Eugster, Ewers, Exton, Fanin, Fazlioglu, Feigenwinter, Fenu, Ferlian, Fernández Calzado, Fernández-Pascual, Finckh, Higgens, Forte, Freeman, Frei, Fuentes-Lillo, García, García, Géron, Gharun, Ghosn, Gigauri, Gobin, Goded, Goeckede, Gottschall, Goulding, Govaert, Graae, Greenwood, Greiser, Grelle, Guénard, Guglielmin, Guillemot, Haase, Haider, Halbritter, Hamid, Hammerle, Hampe, Haugum, Hederová, Heinesch, Helfter, Hepenstrick, Herberich, Herbst, Hermanutz, Hik, Hoffrén, Homeier, Hörtnagl, Høye, Hrbacek, Hylander, Iwata, Jackowicz-Korczynski, Jactel, Järveoja, Jastrzębowski, Jentsch, Jiménez, Jónsdóttir, Jucker, Jump, Juszczak, Kanka, Kašpar, Kazakis, Kelly, Khuroo, Klemedtsson, Klisz, Kljun, Knohl, Kobler, Kollár, Kotowska, Kovács, Kreyling, Lamprecht, Lang, Larson, Larson, Laska, le Maire, Leihy, Lens, Liljebladh, Lohila, Lorite, Loubet, Lynn, Macek, Mackenzie, Magliulo, Maier, Malfasi, Máliš, Man, Manca, Manco, Manise, Manolaki, Marciniak, Matula, Mazzolari, Medinets, Medinets, Meeussen, Merinero, Mesquita, Meusburger, Meysman, Michaletz, Milbau, Moiseev, Moiseev, Mondoni, Monfries, Montagnani, Moriana-Armendariz, Morra di Cella, Mörsdorf, Mosedale, Muffler, Muñoz-Rojas, Myers, Myers-Smith, Nagy, Nardino, Naujokaitis-Lewis, Newling, Nicklas, Niedrist, Niessner, Nilsson, Normand, Nosetto, Nouvellon, Nuñez, Ogaya, Ogée, Okello, Olejnik, Olesen, Opedal, Orsenigo, Palaj, Pampuch, Panov, Pärtel, Pastor, Pauchard, Pauli, Pavelka, Pearse, Peichl, Pellissier, Penczykowski, Penuelas, Petit Bon, Petraglia, Phartyal, Phoenix, Pio, Pitacco, Pitteloud, Plichta, Porro, Portillo-Estrada, Poulenard, Poyatos, Prokushkin, Puchalka, Pușcaș, Radujković, Randall, Ratier Backes, Remmele, Remmers, Renault, Risch, Rixen, Robinson, Robroek, Rocha, Rossi, Rossi, Roupsard, Rubtsov, Saccone, Sagot, Sallo Bravo, Santos, Sarneel, Scharnweber, Schmeddes, Schmidt, Scholten, Schuchardt, Schwartz, Scott, Seeber, Segalin de Andrade, Seipel, Semenchuk, Senior, Serra-Diaz, Sewerniak, Shekhar, Sidenko, Siebicke, Siegwart Collier, Simpson, Siqueira, Sitková, Six, Smiljanic, Smith, Smith-Tripp, Somers, Sørensen, Souza, Souza, Souza Dias, Spasojevic, Speed, Spicher, Stanisci, Steinbauer, Steinbrecher, Steinwandter, Stemkovski, Stephan, Stiegler, Stoll, Svátek, Svoboda, Tagesson, Tanentzap, Tanneberger, Theurillat, Thomas, Thomas, Tielbörger, Tomaselli, Treier, Trouillier, Turtureanu, Tutton, Tyystjärvi, Ueyama, Ujházy, Ujházyová, Uogintas, Urban, Urban, Urbaniak, Ursu, Vaccari, Van de Vondel, van den Brink, Van Geel, Vandvik, Vangansbeke, Varlagin, Veen, Veenendaal, Venn, Verbeeck, Verbrugggen, Verheijen, Villar, Vitale, Vittoz, Vives-Ingla, von Oppen, Walz, Wang, Wang, Way, Wedegärtner, Weigel, Wild, Wilkinson, Wilmking, Wingate, Winkler, Wipf, Wohlfahrt, Xenakis, Yang, Yu, Yu, Zellweger, Zhang, Zhang, Zhao, Ziemblińska, Zimmermann, Zong, Zyryanov, Nijs and Lenoir2021). This integrative research highlighted the divergence between soil microclimate and macroclimate, summarized consistent patterns across biomes, and incentivized using microclimate to tackle ecological research in the frame imposed by global change. For the canopy, a recent mechanistic model can estimate climate conditions from a climate input and from a series of canopy descriptors (Maclean and Klinges, Reference Maclean and Klinges2021), however remote and under-described forests configure challenging applications of such models. While extrapolated and modeled macroclimate estimates have supported a generation of studies assessing the effects of climate on organisms and ecosystems (Suggitt et al., Reference Suggitt, Platts, Barata, Bennie, Burgess, Bystriakova, Duffield, Ewing, Gillingham, Harper, Hartley, Hemming, Maclean, Maltby, Marshall, Morecroft, Pearce-Higgins, Pearce-Kelly, Phillimore, Price, Pyke, Stewart, Warren and Hill2017), the divergence between macroclimate and climate of specific habitats (Lembrechts and Lenoir, Reference Lembrechts and Lenoir2020) can obscure the efforts to interpret the impact of climate change on organismal responses (Suggitt et al., Reference Suggitt, Platts, Barata, Bennie, Burgess, Bystriakova, Duffield, Ewing, Gillingham, Harper, Hartley, Hemming, Maclean, Maltby, Marshall, Morecroft, Pearce-Higgins, Pearce-Kelly, Phillimore, Price, Pyke, Stewart, Warren and Hill2017; Lembrechts and Lenoir, Reference Lembrechts and Lenoir2020). Therefore, high-resolution microclimate records constitute a relevant input to unveil ecological nuances of the habitat where they were gathered.

Tropical regions host vast forested areas, like the Amazon, yet detailed canopy climate records for these forests remain scattered (e.g., Löbs et al., Reference Löbs, Walter, Barbosa, Brill, Alves, Cerqueira, de Oliveira Sá, de Araújo, de Oliveira, Ditas, Moran-Zuloaga, Pires Florentino, Wolff, Godoi, Kesselmeier, Mota de Oliveira, Andreae, Pöhlker and Weber2020). Here, we present a set of climate measurements with high temporal resolution obtained in the canopy of the forest in Yasuní National Park, Ecuador, a global biodiversity hotspot (Bass et al., Reference Bass, Finer, Jenkins, Kreft, Cisneros-Heredia, McCracken, Pitman, English, Swing, Villa, Di Fiore, Voigt and Kunz2010). Data were collected within the crowns of eight canopy trees, in the Johansson (Reference Johansson1974) zone corresponding to the middle canopy. We monitored temperature, relative humidity, photosynthetically active radiation (PAR), and leaf wetness, and we derived the vapor pressure deficit (VPD). In selected crowns, we additionally registered precipitation. Solar radiation, and wind speed and direction. Despite several data gaps due to the harsh conditions for electronics in the forest coupled with logistical challenges, we compiled canopy climate series with more than 300 days of data.

We anticipate that these data series will enrich the view and methodological possibilities of ecologists studying the wealth of organisms dwelling in the canopy of this and other tropical lowland forests. Even if the structure of this canopy remains relatively stable despite recent climate warming trends (Nabe-Nielsen and Valencia, Reference Nabe-Nielsen and Valencia2020), the biology, ecology, and phenology of its inhabitants may lead to new ecological dynamics and emergent properties (Pincebourde et al., Reference Pincebourde, Murdock, Vickers and Sears2016). We encourage using these data series to assess organismal responses of specific organism groups to short-term canopy climate. It is likely that a modeling approach can assess how relevant are fine temporal resolution climate patterns for organismal responses (Lembrechts et al., Reference Lembrechts, Nijs and Lenoir2019).

The section below corresponds to the Metadata of our climate data series. Given the potential use of these data in ecological studies, the metadata follows the standard descriptors suggested by Michener et al. (Reference Michener, Brunt, Helly, Kirchner and Stafford1997), excluding non-applicable fields to avoid redundancies while maintaining the suggested numbering system.

2. Metadata

2.1. Class I. Data set descriptors

  1. A. Data set identity:

    High-resolution tropical rain-forest canopy climate data

  2. B. Data set identification codes:

    Suggested Data set Identify Codes.

    “S1R_Canopy_Climate_2019–20.csv,” “S1V_Canopy_Climate_2019–20.csv,”

    “S2R_Canopy_Climate_2019–20.csv,” “S2V_Canopy_Climate_2019–20.csv,”

    “S3R_Canopy_Climate_2019–20.csv,” “S3V_Canopy_Climate_2019–20.csv,”

    “S4R_Canopy_Climate_2019–20.csv,” “S4V_Canopy_Climate_2019–20.csv,”

    “Ridges_Canopy_Climate.csv,” “Valleys_Canopy_Climate.csv,” and “Yasuni_Canopy_Climate.csv” (DataS1).

  3. C. Data set description

    1. I. Originators:

      1. 1. Monica B. Berdugo

        University of Marburg, Faculty of Geography, Ecological Plant Geography, Deutschhausstraße 10, D-35032, Marburg, Germany

      2. 2. Maaike Bader

        University of Marburg, Faculty of Geography, Ecological Plant Geography, Deutschhausstraße 10, D-35032, Marburg, Germany

      3. 3. Jörg Bendix

        University of Marburg, Faculty of Geography, Laboratory for Climatology and Remote Sensing, Deutschhausstrasse 12, D-35032 Marburg, Germany.

2.2. Class II. Research origin descriptors

  1. A. Overall project description:

    1. 1. Identity:

      A global approach to analyze the extent of the newly detected Tropical Lowland Cloud Forest (TLCF) based on a large-scale analysis of fog frequency and epiphyte growth, with a special focus on South America

    2. 2. Originators:

      The TLCF project was coordinated by Maaike Bader, lead of the Ecological Plant Geography laboratory, and Jörg Bendix, lead of the Laboratory for Climatology and Remote Sensing; both associated with the Faculty of Geography at the University of Marburg.

    3. 3. Period of study:

      The project started in 2018 and will end in 2022.

    4. 4. Objectives:

      Mapping and validating the occurrence of fog and modeled abundance patterns of epiphytes in tropical lowland forest areas using remote sensing, field observations, and modeling.

    5. 5. Source of funding:

      This research was funded by the German Research Foundation – DFG, grants BA 3843/7–1, BE 1780/48–1, and LE 3990/1–1. Deutsche Forschungsgemeinschaft e.V. Kennedyallee 40, 53175 Bonn, Germany.

  2. B. Specific subproject description

    1. 1. Site description

      1. a. Site type: Well-preserved tropical rainforest.

      2. b. Geography: Sites were located near the Yasuní Scientific Station (YSS) in Amazonian Ecuador (Figure 1). The YSS is located in the Orellana province (0⁰40′27′′S 76⁰23′50′′W, ~230 m a.s.l., ~90 ha) to the South of the Tiputini river.

        Figure 1. Study area and stratified study design used to monitor climate with a 10-min temporal resolution in the tropical rainforest canopy of the Yasuní National Park, Ecuador. Large circles represent study sites located at different distances from the Tiputini river (black indicates the largest distance and the lightest grey indicates the shorter distance). Two canopy climate stations were established in each site, one in a valley (blue dot) and the other in a ridge (orange dot) as highlighted by colour-keyed altitudinal belts.

      3. c. Habitat: Medium crown of eight canopy trees (Table 1).

        Table 1. Identity and dimensions of the trees supporting the canopy climate stations instrumented a given sensor set to monitor the canopy climate of the tropical rain-forest of the Yasuní National Park, Ecuador.

        Note. Coloured bars refer to the stratified study design where S indicates sites and T indicates topography (blue represents valley and orange, ridges). Crown radius is an average calculated from three to four measurements. Canopy cover statistics derived processing 10 pictures taken with a mobile phone and processed in Gap Light Analyzer (GLA Version 2.0; Frazer et al., Reference Frazer, Canham and Lertzman2008). Crown depth calculated as the difference between Tree height and Fist branch height.

        Abbreviations: DBH, diameter at the breast height; Wind, digital anemometer; LW, leaf wetness; PAR, photosynthetic active radiation; PRE, Generic rain gauge; RAD, Global radiation; RH, relative humidity; T, temperature.

      4. d. Geology, landform: Modestly undulating terrain, where local valleys and ridges differ by less than 100 m of elevation, with poor and clayey soils originated from weathering of dominant materials of the intersection between two geological shields, Andes and Brazilian (stratified clays and sediments of the Curaray formation from the tertiary; Tschopp, Reference Tschopp1953).

      5. e. Site history: Forest protected within the Yasuní National Park and distant from active oil well extraction, with the influence of cultural practices of the local indigenous communities belonging to the ethnic group Waorani.

      6. f. Climate: The region corresponds to the Holdridge Life Zone of tropical wet forest (Holdridge, Reference Holdridge1947, Reference Holdridge1964) with a wet equatorial climate with imperceptible seasonality (Bailey, Reference Bailey2014), a mean annual temperature of ~25⁰C and an average annual precipitation of ~2,240 mm.

    2. 2. Experimental or sampling design

      1. a. Design characteristics: To monitor canopy climate, eight canopy climate stations were established in a stratified sampling design targeting ridges (four stations) and valleys (four stations). Nearby valley and ridge trees correspond to a site (Table 1). Air temperature, relative humidity, PAR, and leaf wetness were recorded by all canopy climate stations, while precipitation, wind, and global radiation were recorded by selected canopy climate stations (Table 1).

      2. b. Data collection period, frequency, etc.: All canopy climate stations measured climate parameters every 30 s and recorded data every 10 min (mean, minimum, maximum, or sampling values of the 20 measurements) between April 2019 and February 2020. However, the different stations and sensors have gaps within this period (Figure 2 and Table 2).

        Figure 2. Continuity of the climate series recorded by eight canopy climate station established in the tropical rainforest of the Yasuní National Park, Ecuador. For brevity, we aggregated the data at daily resolution. Power failure corresponded to complete gaps in the series while NA occurred for some of the climate parameters. See descriptive statistics in Table 2.

        Table 2. Percentage of NA due to sensor failure (Failure) or due to values out of the variable or sensor range (Outlier) in the climate series recorded by eight canopy climate station established in the tropical rainforest of the Yasuní National Park, Ecuador.

        Note. No NA was identified for station S3V, neither for precipitation, recorded with PRE.

        Abbreviations: Wind, digital anemometer; LW, leaf wetness; PAR, photosynthetic active radiation; PRE, generic rain gauge; RAD, global radiation; RH, relative humidity; T, temperature.

    3. 3. Research methods

      1. a. Field/laboratory: We installed eight climate stations, each in the crown of a tree, using adapted climbing techniques (Perry Reference Perry1978) in the second half of April 2019. These trees belonged to seven species distributed in six botanical families and averaged 64 ± 25.6 cm (mean ± SD) for DBH, 26 ± 2.7 m of height, and 7.2 ± 1.5 m of crown radius (Table 1); we targeted canopy trees, that is, those immersed in the canopy stratum of the forest—excluding sub-canopy and emergent trees. In each tree, the sensor set (Figure 3) was established in the medium section and upper side of crown branches (i.e., in the middle canopy sensu Johansson Reference Johansson1974). We verified the performance of the climate stations in the lab before installation, and from the ground via WIFI after installation, using the interface provided by the datalogger manufacturer (Device Configuration Utility, 2.21.16 by Campbell scientific). Verifications and data downloading from the ground were performed 1 week, 4 months, and 10 months after installation. After the last verification, we removed the climate stations.

        Figure 3. Set of sensors used to monitor climate with a 10-min temporal resolution in the tropical rainforest canopy of Yasuní National Park, Ecuador. In each station, a radiation shield (a) protected the sensor for temperature and relative humidity from direct sunshine and rain. The leaf wetness sensor (b) and the sensor for photosynthetic active radiation (c) were placed on the upper side of the medium section of a crown branch. Selected canopy climate stations were instrumented with a generic rain gauge (d), a sensor for global radiation (e), or a digital anemometer (f), as indicated in Table 1. Instrument details are provided in Table 3.

      2. b. Instrumentation: Each canopy climate station consisted of a logger (80 MB CPU drive and 30 MB serial flash storage, configured to host a Wi-Fi network) and its sensor set (Table 1) powered by a solar panel (50 Wp/12VDC, Resun model RSM-50P) and a backup battery (12 V, SunBrigth Battery model 6-FM-20). A controller (Morningstar corporation, model SHS-10) protected the climate station against short circuit, overcurrent, high voltage, and lightning, and set reverse current at night, that is, shifted the energy source from the solar panel to the battery, charged during light hours. Loggers and sensors (Table 3) were set, programmed, and tested using ShortCut 4.1. (2018) and Device Configuration Utility version 2.21.16 (2019), both by Campbell Scientific. The recording program consisted of registering the different climate parameters every 30 s and logging the data every 10 min. Thus, average, maximum, minimum, standard deviation, and sample values were obtained from 20 measurements.

        Table 3. Instrumentation used to monitor the canopy climate of the tropical rain-forest of the Yasuní National Park, Ecuador.

      3. c. Permit history: The data presented here were collected in the frame of the research permit 003–2019-IC- PNY- DPAO—PUCE issued by the Ministry of Environment of Ecuador in February 2019 and expired in February 2020.

  3. C. Project personnel:

    Monica Berdugo (University of Marburg, Faculty of Geography, Ecological Plant Geography, Marburg, Germany) implemented the ecological work package of the TLCF project, in which the climate was recorded, with the support of Karen Suárez (Federal University of Pernambuco, Biology Department, Laboratory of Plant Taxonomy, Recife, Brazil) and Jorge Déleg (Universidad Técnica Particular de Loja, Departamento de Ciencias Naturales, Herbario HUTPL, Loja, Ecuador); canopy climate installation was supported from the ground by Louise Guérot (University of Marburg, Faculty of Geography, Ecological Plant Geography, Marburg, Germany). Jorge Déleg verified the station performance 4 months after installation. Monica Berdugo removed the climate stations with the support of Ricardo Jaramillo Terán (Universidad San Francisco de Quito, College of Biological and Environmental Sciences, Quito, Ecuador) and Diego Moreno (Universidad Pedagógica y Tecnológica de Colombia, Escuela de Biología, Tunja, Colombia) and developed data curation, verification, and visualization with the support of Leander Heyer (University of Marburg, Faculty of Geography, Ecological Plant Geography).

2.3. Class III. Data set status and accessibility

  1. A. Status

    1. 1. Latest update:

      December 2021

    2. 2. Latest archive date:

      February 2020

    3. 3. Metadata status:

      August 2020

    4. 4. Data verification:

      Data were checked in by the authors. In each data series, a variable of each climate parameter was plotted to identify gaps and suspicious data in the records, suggesting partial or definitive sensor failure. This data exploration was supported by field notes taken at the time of removing the stations (e.g., “rain gauge clogged with canopy debris,” “rain gauge broken; the rain collector is absent,” “climate station box colonized by termites,” “logger under water within the climate station box”). To ease comparisons among stations and sites, data series were aligned, and suspicious data were labeled as NA (R script Data_ Compilation.Rmd). By the end of the data verification process, the data series contained only trustable data (Figure 4).

      Figure 4. Example of a multiplot after the data verification process, corresponding to station S2V, recording climate every 10-min in the tropical rainforest canopy of the Yasuní National Park, Ecuador. Following the suggestions by the sensor maker (Dielectric leaf wetness sensor, Campbell Scientific), we set the threshold <274 mV to indicate dry and > = 284 to indicate wet leaves.

  2. B. Accessibility

    1. 1. Storage location and medium:

      Data files and R scripts of the developed open-source tools are stored in Dryad (https://datadryad.org/stash/share/saMwQoZF81_wT5Gyxm_W2QGfzqGiaeG9nHOVjvfwMT4). In addition, raw data are stored in the data warehouse of the Laboratory for Climatology and Remote Sensing (LCRS) at the Faculty of Geography of the University of Marburg.

    2. 2. Contact person(s):

      Monica B. Berdugo, University of Marburg, Faculty of Geography, Ecological Plant Geography, Deutschhausstraße 10, D-35032, Marburg, Germany, phone: +4,906,421 2,824,187, and .

      Maaike Bader, University of Marburg, Faculty of Geography, Ecological Plant Geography, Deutschhausstraße 10, D-35032, Marburg, Germany, phone: +4,906,421 2,828,952,

    3. 3. Copyright restrictions:

      None

    4. 4. Proprietary restrictions:

      There is no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used.

  3. C. Costs:

    None.

2.4. Class IV. Data structural descriptors

  1. A. Data set file

    1. 1. Identity:

      S1V_Canopy_Climate_2019–20.csv.

      S1R_Canopy_Climate_2019–20.csv.

      S2V_Canopy_Climate_2019–20.csv.

      S2R_Canopy_Climate_2019–20.csv.

      S3V_Canopy_Climate_2019–20.csv.

      S3R_Canopy_Climate_2019–20.csv.

      S4V_Canopy_Climate_2019–20.csv.

      S4R_Canopy_Climate_2019–20.csv.

      Yasuni_Canopy_Climate_2019_20.csv.

      Ridges_Canopy_Climate_2019_20.csv.

      Valleys_Canopy_Climate_2019_20.csv.

      Data_Compilation.Rmd.

      Dashboard_CanopyClimate.Rmd.

      Dashboard_CanopyClimate.html.

      Filtering_ CanopyClimate.Rmd.

    2. 2. Size:

      Data size details are presented in Table 4.

      Table 4. Data structural descriptor.

      Note. Size of the data series of canopy climate of the tropical rain-forest of the Yasuní National Park, Ecuador along to the size of their companion Markdown scripts.

    3. 3. Format and storage mode:

      Comma-separated values (.csv), R Markdown scripts (.Rmd) and outputs (.html).

    4. 4. Header information:

      Data series of stations in the same site (e.g., S1V and S1R) share headers (See Table 4 in Section B, Variable information)

    5. 5. Alphanumeric attributes:

      Mixed (See Table 4 in Section B, Variable information).

  2. B. Variable information

    The details of the climatic variables included in the data are summarized in Table 5.

    Table 5. Variable information for the canopy climate data series of the tropical rain-forest of the Yasuní National Park, Ecuador.

    Abbreviations: RCC, derived data series aggregating climate records for ridges; VCC, derived data series aggregating climate records for valleys; YCC, derived data series aggregating climate records for the forest.

    Figure 5. Appearance of a box station (S2R) used to monitor canopy climate with a 10-min temporal resolution in the tropical rainforest canopy of the Yasuní National Park, Ecuador. Tables 2 and 4 detail equipment and station components.

  3. C. Data anomalies:

    In the original data series, absent data were filled with NA either by the data logger or during the data verification process, where suspicious data points were replaced by NA. In the derived data series, absent data were filled with NA during the data compilation process.

2.5. Class V. Supplemental descriptors

  1. A. Data acquisition

    Automated data loggers.

  2. B. Quality assurance/quality control procedures:

    The original data series went through a data verification process while the derived data series resulted from a data compilation process.

    The data verification, including the calculation of derived variables, consisted of four steps: (a) visualization and diagnosis of suspicious data, that is, those exceeding the possible values for the climate parameter in the locality or those resulting from sensor failure (for instance, PAR that was above zero during night hours), resulting from malfunctioning of specific sensors; (b) replacement of suspicious data with NA; (c) calculation of derived climate variables (Table 5), average relative humidity as the mean value of the recorded relative humidity variables (maximum and minimum), VPD derived from average relative humidity and average air temperature following Fenton and Frego (Reference Fenton and Frego2005), calculated dew point (DewPtC) derived from average air temperature and average relative humidity following Lawrence (Reference Lawrence2005); and transformation of solar radiation data, recorded in kWm2, to W/m2, because this is the most commonly used unit; and (d) creation of fields used to filter data, that is, Date, Hour, and DateHour (Table 4), by using functions of R base (trunc; R Core Team, 2020), and “lubridate”(hour; Grolemund and Wickham, Reference Grolemund and Wickham2011) and “chron” (as.chron; James and Hornik, Reference James and Hornik2020) R packages.

    The data compilation consisted of three steps: (a) temporally aligning series for each climate parameter; (b) assigning a value for each climate parameter to each timestep, by either copying the value recorded by a single station or calculating a mean value when two or more stations registered that parameter; absent data were filled with NA; and (c) writing the aggregated data series as an output (in format csv file).

  3. C. Related materials:

    None

  4. D. Computer programs and data-processing algorithms:

    Data were downloaded using the logger software (Campbell Scientific), while data verification and processing were performed using R (see details in literal B, Quality assurance/quality control procedures, above and in the R scripts: Data_Compilation.Rmd, Dashboard_CanopyClimate.Rmd, and Filtering_CanopyClimate.Rmd).

  5. E. Archiving

    1. 1. Archival procedures: Description of how data are archived for long-term storage and access

    2. 2. Redundant archival sites: Locations and procedures followed

  6. F. Publications and results:

    None.

  7. G. History of data set usage

    1. 1. Data request history:

      None.

    2. 2. Data set update history:

      First update in August 2019, second update in February 2020.

    3. 3. Review history:

      None.

    4. 4. Questions and comments from secondary users:

      None.

3. Conclusion

The main practical lesson from this data collection and compilation is that sustaining cooperation with local researchers is critical to strengthen our capabilities toward understanding ecological processes occurring in tropical canopies, as likewise suggested by Haelewaters et al. (Reference Haelewaters, Hofmann and Romero-Olivares2021). This lesson highlights the need for budgeting qualified partners to ensure proper equipment maintenance. Then, relocating a part of the available budget toward qualified colleagues rather than to equipment would likely result in a more complete and clean raw data series. We observed that equipment becomes less reliable as forest growth occurs in or around critical components of sensors and electronics, including the power supply.

Abbreviations

PAR

photosynthetically active radiation

VPD

vapor pressure deficit

Acknowledgments

The authors thank the invaluable logistical support by Carlos Padilla, David Lasso, and the Yasuní Scientific Station staff of the Pontifical Catholic University of Ecuador and by Glenda Mendieta-Leiva and Nada Nikolić, members of the Ecological Plant Geography group of the Faculty of Geography at the University of Marburg. Special acknowledgements also for the excellent tree climbing training provided by Vicki Tough and Jeremy Davies of Sylvana ALTA Ltd, and for the cartographic work by Christiane Enderle.

Data Availability Statement

Data files and R scripts of the developed open-source tools are stored in Dryad (https://datadryad.org/stash/share/saMwQoZF81_wT5Gyxm_W2QGfzqGiaeG9nHOVjvfwMT4).

Author Contributions

Conceptualization: M.B.B., K.Y.S.C., M.Y.B.; Data curation: M.B.B., L.H., J.D.; Data visualization: M.B.B., L.H.; Funding acquisition: M.Y.B., J.B.; Investigation: M.B.B., K.Y.S.C., M.Y.B.; Methodology: M.B.B., K.Y.S., M.Y.B.; Project administration: M.B.B., M.Y.B.; Resources and supervision: M.Y.B.; Software: M.B.B., L.H.; Writing original draft: M.B.B. All authors approved the final submitted draft.

Ethics Statement

The research meets all ethical guidelines, including adherence to the legal requirements of the study country.

Funding Statement

This data paper was funded by the German Research Foundation –DFG– (BA 3843/7–1, BE 1780/48–1 & LE 3990/1–1).

Competing Interests

The authors declare no competing interests exist.

Footnotes

This research article was awarded Open Data and Open Materials badges for transparent practices. See the Data Availability Statement for details.

References

Bailey, RG (2014) Ecoregions, 2nd Edn. New York: Springer. https://doi.org/10.1007/978-1-4939-0524-9CrossRefGoogle Scholar
Barker, MG and Pinard, MA (2001) Forest canopy research: Sampling problems, and some solutions. Plant Ecology 153, 23–38.CrossRefGoogle Scholar
Bass, MS, Finer, M, Jenkins, CN, Kreft, H, Cisneros-Heredia, DF, McCracken, SF, Pitman, NC, English, PH, Swing, K, Villa, G, Di Fiore, A, Voigt, CC and Kunz, TH (2010) Global conservation significance of Ecuador’s Yasuní National Park. PLoS ONE 5, e8767. https://doi.org/10.1371/journal.pone.0008767CrossRefGoogle ScholarPubMed
Fenton, N. J., & Frego, K. A. (2005). Bryophyte (moss and liverwort) conservation under remnant canopy in managed forests. Biological Conservation, 122(3), 417–430. https://doi.org/10.1016/j.biocon.2004.09.003CrossRefGoogle Scholar
Frazer, GW, Canham, CD and Lertzman, KP (2008) Gap Light Analyzer (GLA): Imaging Software to Extract Canopy Structure and Gap Light Transmission Indices from True-Colour Fisheye Photographs, User’s Manual and Program Documentation. Burnaby: Simon Fraser University. Available at http://www.rem.sfu.ca/forestry/downloads/Files/GLAV2UsersManual.pdf (accessed 28 February 2019).Google Scholar
Grolemund, G and Wickham, H (2011) Dates and times made easy with lubridate. Journal of Statistical Software 40(3), 1–25. Available at https://www.jstatsoft.org/v40/i03/.CrossRefGoogle Scholar
Haelewaters, D, Hofmann, TA and Romero-Olivares, AL (2021) Ten simple rules for global north researchers to stop perpetuating helicopter research in the Global South. PLoS Computational Biology 17, 1–8. https://doi.org/10.1371/journal.pcbi.1009277CrossRefGoogle ScholarPubMed
Holdridge, LR (1947) Determination of world plant formations from simple climatic data. Science 105, 367–368. https://doi.org/10.1126/science.105.2727.367CrossRefGoogle ScholarPubMed
Holdridge, LR (1964) Life zone ecology. In Life zone ecology. rev. ed.Google Scholar
James, D and Hornik, K (2020) chron: Chronological Objects which Can Handle Dates and Times. R package version 2.3–56.Google Scholar
Johansson, D (1974) Ecology of vascular epiphytes in West African rain forests. Acta Phytogeographica Suecica 59, 136.Google Scholar
Lawrence, MG (2005) The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bulletin of the American Meteorological Society 86, 225–234. https://doi.org/10.1175/BAMS-86-2-225CrossRefGoogle Scholar
Lembrechts, JJ, Aalto, J, Ashcroft, MB, De Frenne, P, Kopecký, M, Lenoir, J, Luoto, M, Maclean, IMD, Roupsard, O, Fuentes-Lillo, E, García, RA, Pellissier, L, Pitteloud, C, Alatalo, JM, Smith, SW, Björk, RG, Muffler, L, Ratier Backes, A, Cesarz, S, Gottschall, F, Okello, J, Urban, J, Plichta, R, Svátek, M, Phartyal, SS, Wipf, S, Eisenhauer, N, Pușcaș, M, Turtureanu, PD, Varlagin, A, Dimarco, RD, Jump, AS, Randall, K, Dorrepaal, E, Larson, K, Walz, J, Vitale, L, Svoboda, M, Finger Higgens, R, Halbritter, AH, Curasi, SR, Klupar, I, Koontz, A, Pearse, WD, Simpson, E, Stemkovski, M, Jessen Graae, B, Vedel Sørensen, M, Høye, TT, Fernández Calzado, MR, Lorite, J, Carbognani, M, Tomaselli, M, Forte, TGW, Petraglia, A, Haesen, S, Somers, B, Van Meerbeek, K, Björkman, MP, Hylander, K, Merinero, S, Gharun, M, Buchmann, N, Dolezal, J, Matula, R, Thomas, AD, Bailey, JJ, Ghosn, D, Kazakis, G, de Pablo, MA, Kemppinen, J, Niittynen, P, Rew, L, Seipel, T, Larson, C, Speed, JDM, Ardö, J, Cannone, N, Guglielmin, M, Malfasi, F, Bader, MY, Canessa, R, Stanisci, A, Kreyling, J, Schmeddes, J, Teuber, L, Aschero, V, Čiliak, M, Máliš, F, De Smedt, P, Govaert, S, Meeussen, C, Vangansbeke, P, Gigauri, K, Lamprecht, A, Pauli, H, Steinbauer, K, Winkler, M, Ueyama, M, Nuñez, MA, Ursu, TM, Haider, S, Wedegärtner, REM, Smiljanic, M, Trouillier, M, Wilmking, M, Altman, J, Brůna, J, Hederová, L, Macek, M, Man, M, Wild, J, Vittoz, P, Pärtel, M, Barančok, P, Kanka, R, Kollár, J, Palaj, A, Barros, A, Mazzolari, AC, Bauters, M, Boeckx, P, Benito Alonso, JL, Zong, S, Di Cecco, V, Sitková, Z, Tielbörger, K, van den Brink, L, Weigel, R, Homeier, J, Dahlberg, CJ, Medinets, S, Medinets, V, De Boeck, HJ, Portillo-Estrada, M, Verryckt, LT, Milbau, A, Daskalova, GN, Thomas, HJD, Myers-Smith, IH, Blonder, B, Stephan, JG, Descombes, P, Zellweger, F, Frei, ER, Heinesch, B, Andrews, C, Dick, J, Siebicke, L, Rocha, A, Senior, RA, Rixen, C, Jimenez, JJ, Boike, J, Pauchard, A, Scholten, T, Scheffers, B, Klinges, D, Basham, EW, Zhang, J, Zhang, Z, Géron, C, Fazlioglu, F, Candan, O, Sallo Bravo, J, Hrbacek, F, Laska, K, Cremonese, E, Haase, P, Moyano, FE, Rossi, C and Nijs, I (2020) SoilTemp: A global database of near-surface temperature. Global Change Biology 26, 6616–6629. https://doi.org/10.1111/gcb.15123CrossRefGoogle ScholarPubMed
Lembrechts, JJ and Lenoir, J (2020) Microclimatic conditions anywhere at any time! Global Change Biology 26, 337–339. https://doi.org/10.1111/gcb.14942CrossRefGoogle ScholarPubMed
Lembrechts, JJ, Nijs, I and Lenoir, J (2019) Incorporating microclimate into species distribution models. Ecography 42, 1267–1279. https://doi.org/10.1111/ecog.03947Google Scholar
Lembrechts, JJ, van den Hoogen, J, Aalto, J, Ashcroft, MB, De Frenne, P, Kemppinen, J, Kopecký, M, Luoto, M, Maclean, IMD, Crowther, TW, Bailey, JJ, Haesen, S, Klinges, DH, Niittynen, P, Scheffers, BR, Van Meerbeek, K, Aartsma, P, Abdalaze, O, Abedi, M, Aerts, R, Ahmadian, N, Ahrends, A, Alatalo, JM, Alexander, JM, Allonsius, CN, Altman, J, Ammann, C, Andres, C, Andrews, C, Ardö, J, Arriga, N, Arzac, A, Aschero, V, Assis, RL, Assmann, JJ, Bader, MY, Bahalkeh, K, Barančok, P, Barrio, IC, Barros, A, Barthel, M, Basham, EW, Bauters, M, Bazzichetto, M, Marchesini, LB, Bell, MC, Benavides, JC, Benito Alonso, JL, Berauer, BJ, Bjerke, JW, Björk, RG, Björkman, MP, Björnsdóttir, K, Blonder, B, Boeckx, P, Boike, J, Bokhorst, S, Brum, BNS, Brůna, J, Buchmann, N, Buysse, P, Camargo, JL, Campoe, OC, Candan, O, Canessa, R, Cannone, N, Carbognani, M, Carnicer, J, Casanova-Katny, A, Cesarz, S, Chojnicki, B, Choler, P, Chown, SL, Cifuentes, EF, Čiliak, M, Contador, T, Convey, P, Cooper, EJ, Cremonese, E, Curasi, SR, Curtis, R, Cutini, M, Dahlberg, CJ, Daskalova, GN, de Pablo, MA, Della Chiesa, S, Dengler, J, Deronde, B, Descombes, P, Di Cecco, V, Di Musciano, M, Dick, J, Dimarco, RD, Dolezal, J, Dorrepaal, E, Dušek, J, Eisenhauer, N, Eklundh, L, Erickson, TE, Erschbamer, B, Eugster, W, Ewers, RM, Exton, DA, Fanin, N,Fazlioglu, F, Feigenwinter, I, Fenu, G, Ferlian, O, Fernández Calzado, MR, Fernández-Pascual, E, Finckh, M, Higgens, RF, Forte, TGW, Freeman, EC, Frei, ER, Fuentes-Lillo, E, García, RA, García, MB, Géron, C, Gharun, M, Ghosn, D, Gigauri, K, Gobin, A, Goded, I, Goeckede, M, Gottschall, F, Goulding, K, Govaert, S, Graae, BJ, Greenwood, S, Greiser, C, Grelle, A, Guénard, B, Guglielmin, M, Guillemot, J, Haase, P, Haider, S, Halbritter, AH, Hamid, M, Hammerle, A, Hampe, A, Haugum, SV, Hederová, L, Heinesch, B, Helfter, C, Hepenstrick, D, Herberich, M, Herbst, M, Hermanutz, L, Hik, DS, Hoffrén, R, Homeier, J, Hörtnagl, L, Høye, TT, Hrbacek, F, Hylander, K, Iwata, H, Jackowicz-Korczynski, MA, Jactel, H, Järveoja, J, Jastrzębowski, S, Jentsch, A, Jiménez, JJ, Jónsdóttir, IS, Jucker, T, Jump, AS, Juszczak, R, Kanka, R, Kašpar, V, Kazakis, G, Kelly, J, Khuroo, AA, Klemedtsson, L, Klisz, M, Kljun, N, Knohl, A, Kobler, J, Kollár, J, Kotowska, MM, Kovács, B, Kreyling, J, Lamprecht, A, Lang, SI, Larson, C, Larson, K, Laska, K, le Maire, G, Leihy, RI, Lens, L, Liljebladh, B, Lohila, A, Lorite, J, Loubet, B, Lynn, J, Macek, M, Mackenzie, R, Magliulo, E, Maier, R, Malfasi, F, Máliš, F, Man, M, Manca, G, Manco, A, Manise, T, Manolaki, P, Marciniak, F, Matula, R, Mazzolari, AC, Medinets, S, Medinets, V, Meeussen, C, Merinero, S, Mesquita, RCG, Meusburger, K, Meysman, FJR, Michaletz, ST, Milbau, A, Moiseev, D, Moiseev, P, Mondoni, A, Monfries, R, Montagnani, L, Moriana-Armendariz, M, Morra di Cella, U, Mörsdorf, M, Mosedale, JR, Muffler, L, Muñoz-Rojas, M, Myers, JA, Myers-Smith, IH, Nagy, L, Nardino, M, Naujokaitis-Lewis, I, Newling, E, Nicklas, L, Niedrist, G, Niessner, A, Nilsson, MB, Normand, S, Nosetto, MD, Nouvellon, Y, Nuñez, MA, Ogaya, R, Ogée, J, Okello, J, Olejnik, J, Olesen, JE, Opedal, ØH, Orsenigo, S, Palaj, A, Pampuch, T, Panov, AV, Pärtel, M, Pastor, A, Pauchard, A, Pauli, H, Pavelka, M, Pearse, WD, Peichl, M, Pellissier, L, Penczykowski, RM, Penuelas, J, Petit Bon, M, Petraglia, A, Phartyal, SS, Phoenix, GK, Pio, C, Pitacco, A, Pitteloud, C, Plichta, R, Porro, F, Portillo-Estrada, M, Poulenard, J, Poyatos, R, Prokushkin, AS, Puchalka, R, Pușcaș, M, Radujković, D, Randall, K, Ratier Backes, A, Remmele, S, Remmers, W, Renault, D, Risch, AC, Rixen, C, Robinson, SA, Robroek, BJM, Rocha, AV, Rossi, C, Rossi, G, Roupsard, O, Rubtsov, AV, Saccone, P, Sagot, C, Sallo Bravo, J, Santos, CC, Sarneel, JM, Scharnweber, T, Schmeddes, J, Schmidt, M, Scholten, T, Schuchardt, M, Schwartz, N, Scott, T, Seeber, J, Segalin de Andrade, AC, Seipel, T, Semenchuk, P, Senior, RA, Serra-Diaz, JM, Sewerniak, P, Shekhar, A, Sidenko, NV, Siebicke, L, Siegwart Collier, L, Simpson, E, Siqueira, DP, Sitková, Z, Six, J, Smiljanic, M, Smith, SW, Smith-Tripp, S, Somers, B, Sørensen, MV, Souza, JJLL, Souza, BI, Souza Dias, A, Spasojevic, MJ, Speed, JDM, Spicher, F, Stanisci, A, Steinbauer, K, Steinbrecher, R, Steinwandter, M, Stemkovski, M, Stephan, JG, Stiegler, C, Stoll, S, Svátek, M, Svoboda, M, Tagesson, T, Tanentzap, AJ, Tanneberger, F, Theurillat, JP, Thomas, HJD, Thomas, AD, Tielbörger, K, Tomaselli, M, Treier, UA, Trouillier, M, Turtureanu, PD, Tutton, R, Tyystjärvi, VA, Ueyama, M, Ujházy, K, Ujházyová, M, Uogintas, D, Urban, AV, Urban, J, Urbaniak, M, Ursu, TM, Vaccari, FP, Van de Vondel, S, van den Brink, L, Van Geel, M, Vandvik, V, Vangansbeke, P, Varlagin, A, Veen, GF, Veenendaal, E, Venn, SE, Verbeeck, H, Verbrugggen, E, Verheijen, FGA, Villar, L, Vitale, L, Vittoz, P, Vives-Ingla, M, von Oppen, J, Walz, J, Wang, R, Wang, Y, Way, RG, Wedegärtner, REM, Weigel, R, Wild, J, Wilkinson, M, Wilmking, M, Wingate, L, Winkler, M, Wipf, S, Wohlfahrt, G, Xenakis, G, Yang, Y, Yu, Z, Yu, K, Zellweger, F, Zhang, J, Zhang, Z, Zhao, P, Ziemblińska, K, Zimmermann, R, Zong, S, Zyryanov, VI, Nijs, I and Lenoir, J (2022). Global maps of soil temperature. Global Change Biology 28, 3110–3144. https://doi.org/10.1111/gcb.16060CrossRefGoogle ScholarPubMed
Lin, D, Xia, J and Wan, S (2010) Climate warming and biomass accumulation of terrestrial plants: A meta-analysis. New Phytologist 188, 187–198. https://doi.org/10.1111/j.1469-8137.2010.03347.xCrossRefGoogle ScholarPubMed
Löbs, N, Walter, D, Barbosa, CGG, Brill, S, Alves, RP, Cerqueira, GR, de Oliveira Sá, M, de Araújo, AC, de Oliveira, LR, Ditas, F, Moran-Zuloaga, D, Pires Florentino, AP, Wolff, S, Godoi, RHM, Kesselmeier, J, Mota de Oliveira, S, Andreae, MO, Pöhlker, C and Weber, B (2020) Microclimatic conditions and water content fluctuations experienced by epiphytic bryophytes in an Amazonian rain forest. Biogeosciences 17, 5399–5416. https://doi.org/10.5194/bg-17-5399-2020Google Scholar
Luo, Y (2007) Terrestrial carbon–cycle feedback to climate warming. Annual Review of Ecology, Evolution, and Systematics 38, 683–712. https://doi.org/10.1146/annurev.ecolsys.38.091206.095808CrossRefGoogle Scholar
Maclean, IMD (2020) Predicting future climate at high spatial and temporal resolution. Global Change Biology 26, 1003–1011. https://doi.org/10.1111/gcb.14876CrossRefGoogle ScholarPubMed
Maclean, IMD and Klinges, DH (2021) Microclimc: A mechanistic model of above, below and within-canopy microclimate. Ecological Modelling 451, 109567. https://doi.org/10.1016/j.ecolmodel.2021.109567CrossRefGoogle Scholar
Michener, WK, Brunt, JW, Helly, JJ, Kirchner, TB and Stafford, SG (1997) Nongeospatial metadata for the ecological sciences. Ecological Applications 7, 330–342. https://doi.org/10.1890/1051-0761(1997)007[0330:NMFTES]2.0.CO;2CrossRefGoogle Scholar
Nabe-Nielsen, J and Valencia, R (2020) Canopy structure and forest understory conditions in a wet Amazonian forest—No change over the last 20 years. Biotropica 52, 1121–1126. https://doi.org/10.1111/btp.12872CrossRefGoogle Scholar
Nakamura, A, Kitching, RL, Cao, M, Creedy, TJ, Fayle, TM, Freiberg, M, Hewitt, CN, Itioka, T, Koh, LP, Ma, K, Malhi, Y, Mitchell, A, Novotny, V, Ozanne, CMP, Song, L, Wang, H and Ashton, LA (2017) Forests and their canopies: Achievements and horizons in canopy science. Trends in Ecology and Evolution 32, 438–451. https://doi.org/10.1016/j.tree.2017.02.020CrossRefGoogle ScholarPubMed
O’Grady, PA, Tissue, DT and Beadle, CL (2011) Canopy processes in a changing climate. Tree Physiology 31(9), 887–892. https://doi.org/10.1093/treephys/tpr096CrossRefGoogle Scholar
Perry, D. (1978). A method of access into the crowns of emergent and canopy trees. Biotropica (10):155–157.CrossRefGoogle Scholar
Pincebourde, S, Murdock, CC, Vickers, M and Sears, MW (2016) Fine-scale microclimatic variation can shape the responses of organisms to global change in both natural and urban environments. Integrative and Comparative Biology 56, 45–61. https://doi.org/10.1093/icb/icw016Google ScholarPubMed
R Core Team (2020) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/.Google Scholar
Suggitt, AJ, Platts, PJ, Barata, IM, Bennie, JJ, Burgess, MD, Bystriakova, N, Duffield, S, Ewing, SR, Gillingham, PK, Harper, AB, Hartley, AJ, Hemming, DL, Maclean, IMD, Maltby, K, Marshall, HH, Morecroft, MD, Pearce-Higgins, JW, Pearce-Kelly, P, Phillimore, AB, Price, JT, Pyke, A, Stewart, JE, Warren, R and Hill, JK (2017) Conducting robust ecological analyses with climate data. Oikos 126, 1533–1541. https://doi.org/10.1111/oik.04203CrossRefGoogle Scholar
Tschopp, HJ (1953) Oil explorations in the orient of Ecuador, 1938-1950. Bulleting of the American Association of Petroleum Geologists 37, 2303–2347.Google Scholar
Figure 0

Figure 1. Study area and stratified study design used to monitor climate with a 10-min temporal resolution in the tropical rainforest canopy of the Yasuní National Park, Ecuador. Large circles represent study sites located at different distances from the Tiputini river (black indicates the largest distance and the lightest grey indicates the shorter distance). Two canopy climate stations were established in each site, one in a valley (blue dot) and the other in a ridge (orange dot) as highlighted by colour-keyed altitudinal belts.

Figure 1

Table 1. Identity and dimensions of the trees supporting the canopy climate stations instrumented a given sensor set to monitor the canopy climate of the tropical rain-forest of the Yasuní National Park, Ecuador.

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Figure 2. Continuity of the climate series recorded by eight canopy climate station established in the tropical rainforest of the Yasuní National Park, Ecuador. For brevity, we aggregated the data at daily resolution. Power failure corresponded to complete gaps in the series while NA occurred for some of the climate parameters. See descriptive statistics in Table 2.

Figure 3

Table 2. Percentage of NA due to sensor failure (Failure) or due to values out of the variable or sensor range (Outlier) in the climate series recorded by eight canopy climate station established in the tropical rainforest of the Yasuní National Park, Ecuador.

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Figure 3. Set of sensors used to monitor climate with a 10-min temporal resolution in the tropical rainforest canopy of Yasuní National Park, Ecuador. In each station, a radiation shield (a) protected the sensor for temperature and relative humidity from direct sunshine and rain. The leaf wetness sensor (b) and the sensor for photosynthetic active radiation (c) were placed on the upper side of the medium section of a crown branch. Selected canopy climate stations were instrumented with a generic rain gauge (d), a sensor for global radiation (e), or a digital anemometer (f), as indicated in Table 1. Instrument details are provided in Table 3.

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Table 3. Instrumentation used to monitor the canopy climate of the tropical rain-forest of the Yasuní National Park, Ecuador.

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Figure 4. Example of a multiplot after the data verification process, corresponding to station S2V, recording climate every 10-min in the tropical rainforest canopy of the Yasuní National Park, Ecuador. Following the suggestions by the sensor maker (Dielectric leaf wetness sensor, Campbell Scientific), we set the threshold <274 mV to indicate dry and > = 284 to indicate wet leaves.

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Table 4. Data structural descriptor.

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Table 5. Variable information for the canopy climate data series of the tropical rain-forest of the Yasuní National Park, Ecuador.

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Figure 5. Appearance of a box station (S2R) used to monitor canopy climate with a 10-min temporal resolution in the tropical rainforest canopy of the Yasuní National Park, Ecuador. Tables 2 and 4 detail equipment and station components.