This is a pre-copyedited, author-produced PDF of an article accepted for publication in Tree physiology (Ed. OUP) following peer review. The version of record Poyatos, R. et al. "SAPFLUXNET: towards a global database of sap flow measurements" in Tree physiology, vol. 36, issue 12 (Dec. 2016), p. 1449-1455 is available online at: DOI 10.1093/treephys/tpw110 1 SAPFLUXNET: towards a global database of sap flow measurements 2 Rafael Poyatos1,2, Víctor Granda1, Roberto Molowny-Horas1, Maurizio Mencuccini1,3,4, 3 Kathy Steppe2, Jordi Martínez-Vilalta1,5 4 1 CREAF, Cerdanyola del Vallès 08193, Spain 5 2 Laboratory of Plant Ecology, Faculty of Bioscience Engineering, Ghent University, 6 Ghent, Belgium 7 3 ICREA, Barcelona, Spain 8 4 School of GeoSciences, University of Edinburgh, Edinburgh, UK 9 5 Univ Autònoma Barcelona, Cerdanyola del Vallès 08193, Spain 10 Corresponding author: 11 Rafael Poyatos 12 r.poyatos@creaf.uab.es 13 +34 93 581 46 76 14 Keywords: drought, evapotranspiration, functional traits, plant water use, plant 15 hydraulics, sap flux, transpiration Page 1 of 23 16 Abstract 17 Plant transpiration is the main evaporative flux from terrestrial ecosystems, it controls 18 land surface energy balance, determines catchment hydrological responses and 19 influences regional and global climate. Transpiration regulation by plants is a key 20 (and still not completely understood) process that underlies vegetation drought 21 responses and land evaporative fluxes under global change scenarios. 22 Thermometric methods of sap flow measurement have now been widely used to 23 quantify whole-plant and stand transpiration in forests, shrublands and orchards 24 around the world. A large body of research has applied sap flow methods to analyse 25 seasonal and diurnal patterns of transpiration and to quantify their responses to 26 hydroclimatic variability, but syntheses of sap flow data at regional to global scales 27 are extremely rare. Here we present the SAPFLUXNET initiative, aimed at building 28 the first global database of plant-level sap flow measurements. A preliminary 29 metadata survey launched in December 2015, showed an encouraging response by 30 the sap flow community, with sap flow datasets from field studies representing >160 31 species and >120 globally distributed sites. The main goal of SAPFLUXNET is to 32 analyse the ecological factors driving plant- and stand-level transpiration. 33 SAPFLUXNET will open promising research avenues at an unprecedented global 34 scope, namely: (i) exploring the spatiotemporal variability of plant transpiration and 35 its relationship with plant and stand attributes (ii) summarising physiological 36 regulation of transpiration by means of few water use traits, usable for land surface 37 models, (iii) improving our understanding of the coordination between gas exchange 38 and plant-level traits (e.g. hydraulics) and (iv) analysing the ecological factors 39 controlling stand transpiration and evapotranspiration partitioning. Finally, Page 2 of 23 40 SAPFLUXNET can provide a benchmark to test models of physiological controls of 41 transpiration, contributing to improve the accuracy of individual water stress 42 responses, a key element to obtain robust predictions of vegetation responses to 43 climate change. 44 Keywords: drought, evapotranspiration, functional traits, plant water use, plant 45 hydraulics, sap flux, transpiration Page 3 of 23 46 Plant transpiration is a key component of land evaporative fluxes, but it is hard to 47 measure 48 Plant transpiration is the main evaporative flux from terrestrial ecosystems 49 (Schlesinger and Jasechko 2014), it controls land surface energy balance, 50 determines catchment hydrological responses and influences regional and global 51 climate (Bonan 2008). Because of the central role played by their water economy, 52 plants adjust physiologically and structurally to maintain their hydraulic integrity and 53 metabolic function under drought at various time and organisational scales (Maseda 54 and Fernández 2006). These adjustments determine how plants regulate 55 transpiration under fluctuating conditions of water supply and demand, resulting in 56 multiple water use strategies within and across ecosystems. The diversity of water 57 use strategies underlie plant drought responses, and these responses are major 58 drivers of demographic rates (McDowell et al. 2008) community dynamics 59 (Silvertown et al. 2015) and ecosystem function. Transpiration regulation by plants is 60 thus a key (and still not completely understood) process that underlies vegetation 61 drought responses (McDowell et al. 2013) and land evaporative fluxes (Oishi et al. 62 2010, Miralles et al. 2011) under global change scenarios. 63 The measurement of plant transpiration and its drivers is deeply rooted in the history 64 of quantitative experiments in plant biology but most of the experimental techniques 65 developed before the 1950’s were intrusive and unsuitable for the investigation of the 66 driving factors of transpiration of living plants, especially under field conditions 67 (Wullschleger et al. 1998). The advent of gas exchange systems allowed the 68 development of automated cuvettes to measure transpiration from leaves to whole Page 4 of 23 69 plants, but the upscaling of leaf-to-plant transpiration is highly labour-intensive and 70 error-prone, while chamber-based measurements of whole-plant transpiration are 71 logistically demanding can and alter canopy microclimate (Goulden and Field 1994). 72 Micrometeorological techniques, stand or catchment water balances and remote 73 sensing approaches provide evapotranspiration estimates, but neither of these 74 methods is suitable to study water use strategies of individual plants across the 75 entire spatiotemporal domain. 76 Sap flow methods: development, limitations and contribution to progress in 77 understanding tree and ecosystem physiology 78 Thermometric methods of sap flow measurement were devised during the 1930s and 79 refined in the late 1950s (Marshall 1958). The main methodological families were 80 already established by the mid-1980s and many improvements and new derivations 81 have continued to appear (Čermák et al. 2004, Vandegehuchte and Steppe 2013 82 and references therein). Sap flow methods measure either total sap flow through a 83 plant’s organ or sap flux density, the flow rate per unit conducting area 84 (Vandegehuchte and Steppe 2013). They use pulsed or continuous heating and track 85 temperature changes caused by convective heat transport by moving sap in different 86 plant organs along the transpiration pathway (Goldstein et al. 1998, Burgess et al. 87 2000). Given the easy replication and automation of sap flow probes, they have been 88 widely used on main, woody stems to quantify tree and stand transpiration (Granier 89 et al. 1996). 90 Each of the sap flow methods has distinct advantages and methodological 91 constraints. No single method is able to measure the entire range of sap flow rates Page 5 of 23 92 and all of them are subject to one or more of the following issues (Vandegehuchte 93 and Steppe 2013): incomplete contact of the probe with the sapwood, natural 94 temperature gradients, the need for species-specific calibrations and parameters, 95 uncertainty in baseline flow estimates, wounding effects and sensor drift. These 96 potential artefacts may be aggravated by within-method differences in sensor design 97 among manufacturers and laboratories (Lu et al. 2004). Unsurprisingly, validation 98 studies have raised concerns about the potential biases incurred by most sap flow 99 methods (Steppe et al. 2010). Additional uncertainty results from upscaling from 100 sensors to trees and to stands (Oren et al. 1998, Čermák et al. 2004). Nevertheless, 101 all methods yield reasonable temporal patterns of plant transpiration and sap flow- 102 derived transpiration has often compared well with independent measurements 103 (McCulloh et al. 2007) and with evapotranspiration at larger scales (Wilson et al. 104 2001). 105 Sap flow methods have greatly contributed to addressing fundamental questions of 106 (mostly woody) plant and ecosystem physiology, such as age/height- (Hubbard et al. 107 1999) and drought-related variations in the water transport capacity of plants 108 (Cochard et al. 1996), the mechanisms of stem water storage dynamics (Goldstein et 109 al. 1998) or foliar water uptake (Burgess and Dawson 2004). Sap flow methods have 110 also been successfully applied to study broader ecological questions, such as the 111 effects of tree diversity on ecosystem functioning (Forrester et al. 2010). At a more 112 applied level, sap flow is frequently measured in studies designed to guide 113 silvicultural or agronomic practices (Jones 2004, Aranda et al. 2012). A large body of 114 research has applied sap flow methods to analyse seasonal and diurnal patterns of 115 transpiration and canopy (stomatal) conductance (Gs) (Phillips and Oren 1998) and Page 6 of 23 116 to quantify their responses to hydroclimatic variability. 117 The time is ripe for a global database of sap flow measurements: the SAPFLUXNET 118 initiative 119 Sap flow probes are relatively affordable and easy to deploy in the field, but they 120 have logistic requirements that preclude the long-term maintenance of multiple 121 experimental sites across broad ecological gradients. Data syntheses of sap flow (or 122 Gs) responses to hydroclimatic factors across species have either focused on 123 responses to single variables (Oren et al. 1999) or they have been limited to a few 124 closely-related species (Duursma et al. 2008), likely reflecting limitations in data 125 availability in relatively homogeneous and comparable formats. Indeed, a recent 126 global synthesis of sap flow focusing on pine stands’ response to variation in leaf 127 area index, soil and climate variables, required digitizing published data (Tor-ngern et 128 al. 2016), introducing additional, unnecessary errors. Only a few studies have 129 analysed responses of sap-flow and/or Gs across a diverse group of tree species 130 (Granier et al. 2000) or along wide climatic gradients within a species (Poyatos et al. 131 2007). Other quantitative syntheses have typically analysed maximum values of sap 132 flow or Gs, either measured during short periods or retrieved from published sources. 133 These studies have mostly dealt with how maximum sap flow rates vary with 134 allometry (Meinzer et al. 2005), plant hydraulics (Manzoni et al. 2013) or wood traits 135 (Kallarackal et al. 2013). 136 Increasing data sharing and reuse promotes progress in ecology by extending the 137 value of data beyond individual studies (Wolkovich et al. 2012). Global and regional 138 networks of biosphere-atmosphere carbon and water exchange curate regional and Page 7 of 23 139 global datasets, improve data interoperability and enable collaborations and data 140 syntheses (Papale et al. 2012). Recent methodological progress in data 141 management, database design (Boden et al. 2013), and open-source software tools 142 have occurred in concert with the increasing availability of global databases. 143 Functional and ecosystem ecologists are already producing global syntheses on 144 vegetation controls of water balance and on plant’s drought vulnerability (Choat et al. 145 2012, Williams et al. 2012). However, the sap flow community has not yet fully 146 embraced this challenge; for instance, sap flow datasets are mostly absent from the 147 main data repositories for ecological or Earth system sciences (one dataset for 148 canopy transpiration in try-db.org; one dataset, with peak values only, in 149 datadryad.org; none in pangaea.de, re3data.org or zenodo.org; all repositories 150 accessed May 2016). 151 The sap flow-related literature is already substantial: a Scopus search ("sap flow" 152 OR "sapflow" OR "sap flux" OR "sapflux" in title, abstract or keywords) returned 153 >2500 papers, whose keywords included the scientific names of >250 plant species, 154 according to The Plant List (The Plant List (2013). Version 1.1. ; 155 http://www.theplantlist.org/).These figures likely overestimate the number of potential 156 data sources, but they nevertheless suggest that an ecologically relevant compilation 157 is feasible. We searched the title, abstract and keywords of these papers to retrieve 158 the sap flow method(s) used in each paper, and we show that the heat dissipation 159 method (Granier 1985) is the most popular, by far (Figure 1). The SAPFLUXNET 160 initiative (http://sapfluxnet.creaf.cat/) is building the first global database of plant-level 161 sap flow measurements (obtained using thermometric methods) to analyse the 162 environmental and physiological factors driving tree- and stand-level transpiration. A Page 8 of 23 163 preliminary metadata survey launched in December 2015, showed an encouraging 164 response by the sap flow community. Potentially contributed sap flow datasets from 165 field studies represent >160 species and >120 globally distributed sites (Figure 2). 166 We have now started the data collection stage, and we welcome processed sap flow 167 datasets measured at the stem or whole-plant level (but not at the branch level), 168 under field conditions and at sub-daily intervals. Please see the SAPFLUXNET wiki 169 https://github.com/sapfluxnet/sapfluxnet-public/wiki for further details on minimum 170 requirements for sap flow, environmental and ancillary data. Our ultimate goal is that, 171 by the end of the project (September 2018), a first version of the SAPFLUXNET 172 database is made available to data contributors. After an embargo period, the 173 database will be made publicly available to the scientific community. 174 Building a harmonized database from highly heterogeneous data sources is a 175 challenging endeavour. We have designed a semi-automated, reproducible and 176 version-controlled, data ingestion process using the tools provided by the R 177 environment (e.g. R language for computing and statistics, interactive R Markdown 178 documents and Shiny apps). Data incorporation to the server and initial data quality 179 control (timestamp and units standardization, species and geographic coordinates 180 validation, gap quantification) is automatically performed by R functions (Figure 3). 181 SAPFLUXNET will compile, curate and disseminate processed data. Since sap flow 182 measurements are affected by a substantial methodological variability sap flow time 183 series in SAPFLUXNET are documented with metadata on sensor characteristics, 184 data processing and upscaling procedures, provided by data contributors. For 185 example, we specify whether species-specific calibrations (Steppe et al. 2010) or Page 9 of 23 186 natural gradient corrections (Do and Rocheteau 2002) have been applied to the raw 187 data. These metadata will allow us to account for variability caused by 188 methodological issues in joint analyses using the database. SAPFLUXNET will thus 189 allow a first quantification of the impacts of methodological variability on sap flow 190 measurements across a large number of species. We expect that these analyses will 191 foster the development of standardized protocols within the sap flow community, as a 192 prerequisite towards a fully coherent database similarly to what FLUXNET has 193 achieved for ecosystem-level CO2 and water fluxes (Papale et al. 2012). There have 194 been recent advances in this direction with the dissemination of code for robust, 195 semi-automatic flux baselining for heat dissipation probes (Oishi et al. 2016) and for 196 generalised integration of radial variation of sap flow in the scaling to whole-tree 197 water use (Berdanier et al. 2016). 198 Unravelling the ecological drivers of plant transpiration to understand drought 199 vulnerability and ecosystem physiology 200 The SAPFLUXNET initiative will provide opportunities to improve our understanding 201 of the ecological determinants of plant water use and drought responses. It will allow 202 describing how annually or seasonally-integrated plant transpiration vary across 203 biomes, plant functional types or taxonomic groups. SAPFLUXNET will open 204 promising research avenues at an unprecedented global scope, namely: (I) exploring 205 the spatiotemporal variability of plant transpiration and its relationship with plant and 206 stand attributes (ii) summarising physiological regulation of transpiration by means of 207 water use traits, representing maximum water transport rates and sensitivity to 208 environmental factors, usable for land surface models, (iii) improving our Page 10 of 23 209 understanding of the coordination between gas exchange and plant-level traits (e.g. 210 hydraulics) and (iv) analysing the ecological factors controlling stand transpiration 211 and evapotranspiration partitioning. 212 SAPFLUXNET will allow the first analyses of biogeographical and climate-driven 213 patterns of plant transpiration. The variation in transpiration with water availability will 214 be assessed using probabilistic, multiscalar drought indices (Vicente-Serrano et al. 215 2010), which include drought responses at multiple temporal scales and allow 216 comparisons across ecosystems with contrasting water availability. Moreover, 217 SAPFLUXNET will extend scaling relationships between sap flux density and plant 218 size and hydraulic architecture (e.g. sapwood area, leaf area), to test for functional 219 convergence across functional types, genera or environmental gradients (Zeppel 220 2013). As stand-level hydraulic adjustments (Whitehead et al. 1984) covary with 221 plant water availability, a relevant question will be to assess how these scaling 222 relationships vary with stand structure (e.g. basal area, density, canopy height). 223 One of the main goals of SAPFLUXNET will be to summarise physiological 224 regulation of transpiration by means of few water use traits. For example, semi- 225 empirical responses of sap flow (or canopy conductance) to radiation, vapour 226 pressure deficit and soil moisture can be embedded in dynamic, hierarchical 227 Bayesian models, which can deal with observation, process and parameter 228 uncertainties enabling the analysis of heterogeneous sap flow datasets (Ward et al. 229 2013). Water use traits derived from these models represent complex 230 ecophysiological strategies as emerging relationships, such as the trade-off between 231 maximum stomatal conductance and sensitivity to vapour pressure deficit, which Page 11 of 23 232 arises from stomatal limitation of xylem embolism (Oren et al. 1999). This trade-off, 233 provides a benchmark to investigate variation in physiological regulation of 234 transpiration across biomes, climates (Poyatos et al. 2007), wood anatomy types 235 (Meinzer et al. 2013) or taxonomic groups (Johnson et al. 2012). Moreover, 236 physiological responses to soil moisture, radiation and atmospheric turbulence are 237 virtually unexplored at regional to global scales. 238 Coordination of canopy gas exchange and whole-plant hydraulics has long been 239 proposed (Meinzer 2002), but only recently have global syntheses of hydraulic traits 240 started to provide a broader picture of the ecological patterns and the multiple facets 241 of drought tolerance in plants. Whole-plant water use traits derived from 242 SAPFLUXNET will help complete the worldwide spectrum of hydraulic strategies 243 defined by gas exchange and water potential regulation (Martínez-Vilalta et al. 244 2014), hydraulic safety margins (Choat et al. 2012), capacitance (Meinzer et al. 245 2009) and leaf water relations (Bartlett et al. 2012). Combining global sap flow data 246 with on-site water potentials will also simplify the parameterisation of whole-plant 247 transpiration models (Sperry and Love 2015). 248 At the ecosystem level, SAPFLUXNET can provide globally-distributed values of 249 seasonally- and annually-aggregated stand transpiration, which will improve our 250 understanding of the biophysical controls of vegetation-atmosphere fluxes (Köstner 251 2001) and will help constrain the estimates of transpiration-to-evapotranspiration 252 ratios (Schlesinger and Jasechko 2014). SAPFLUXNET can help to link plant 253 functional traits with ecosystem water and carbon fluxes (Reichstein et al. 2014). 254 Finally, SAPFLUXNET can provide a benchmark to test algorithms of physiological Page 12 of 23 255 controls of transpiration embedded in process-based models (Williams et al. 2001), 256 contributing to improve the accuracy of individual water stress responses, a key 257 element to obtain robust predictions of vegetation responses to climate change 258 (Levine et al. 2015). 259 Acknowledgements 260 We would like to thank Ram Oren and two anonymous reviewers for their 261 constructive comments on the manuscript. The SAPFLUXNET initiative is funded by 262 the Spanish Ministry of Economy and Competitiveness through grant CGL2014- 263 5583-JIN. Page 13 of 23 References Aranda I, Forner A, Cuesta B, Valladares F (2012) Species-specific water use by forest tree species: From the tree to the stand. Agric Water Manag 114:67–77. 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Zeppel M (2013) Convergence of tree water use and hydraulic architecture in water- limited regions: a review and synthesis. Ecohydrology 6:889–900. Page 19 of 23 273 Figure captions 274 Figure 1. Cumulative count of appearances of different sap flow methods in a 275 Scopus search of sap flow-related literature ("sap flow" OR "sapflow" OR "sap 276 flux" OR "sapflux" in title, abstract or keywords). Note that counts represent 277 individual appearances of each method, not papers (e.g. a paper can have multiple 278 methods). HP: heat pulse methods, include Compensation Heat Pulse, Tmax 279 Method, Calibrated Average Gradient and Sapflow+; HD; Heat Dissipation; THB: 280 Tissue Heat Balance; SHB: Stem Heat Balance; HFD: Heat Field Deformation; HRM: 281 Heat Ratio Method. 282 Figure 2. Geographic distribution of potential sap flow data sources, coded by sap 283 flow method, based on a preliminary survey. An interactive summary of this 284 preliminary survey, with further information on the growth condition of the stand, the 285 number of species and trees can be found at 286 http://sapfluxnet.creaf.cat/shiny/preliminary_dashboard/. 287 Figure 3. Data flow from reception to production of level 1 datasets. Initial data and 288 metadata quality control is encapsulated in a R package under continuous 289 development. This semi-automated procedure generates interactive QC reports that 290 are shared with data contributors to resolve any issues with the data. All the 291 individual steps in this procedure and any manual changes on the datasets are 292 traceable because they are logged in structured text files. Beyond Level 1, data 293 processing is under implementation, as depicted by discontinuous lines. Page 20 of 23 294 Figure 1. Page 21 of 23 295 Figure 2 Page 22 of 23 Page 23 of 23