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Responses of Tropical Forest Canopy Structure and Function to Seasonal and Interannual Variations in Climate

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Authors Smith, Marielle N.

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RESPONSES OF TROPICAL FOREST CANOPY STRUCTURE AND FUNCTION TO SEASONAL AND INTERANNUAL VARIATIONS IN CLIMATE

Marielle N. Smith

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF ECOLOGY AND EVOLUTIONARY BIOLOGY

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR OF PHILOSOPHY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2016

THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Marielle N. Smith, titled ‘Responses of tropical forest canopy structure and function to seasonal and interannual variations in climate’ and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy.

______Date: December 5th 2016 Scott R. Saleska

______Date: December 5th 2016 Travis E. Huxman

______Date: December 5th 2016 Brian J. Enquist

______Date: December 5th 2016 Donald A. Falk

______Date: December 5th 2016 Sean M. McMahon

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

______Date: December 5th 2016 Dissertation Director: Scott R. Saleska

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of the requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided that an accurate acknowledgement of the source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: Marielle N. Smith

ACKNOWLEDGEMENTS

I could not have completed my PhD without the help, support, and guidance of a great many people. Firstly, I am indebted to my advisors, Scott Saleska and Travis Huxman. You have been my biggest advocates along my PhD journey and I am so grateful for that. Scott, you have taught me to be a careful, thorough scientist, and not to be afraid to run with big ideas. Despite being one of the busiest people I know, you regularly made time to discuss my work in depth. In addition, I’ve learnt a lot from you about how to communicate my work in a clear and compelling way. Your lab has been like a family to me over the last six and a half years, providing a fun and supportive working environment. I am so grateful for all the financial support that you have provided, including numerous trips to Brazil. It has always been my dream to do this kind of research in the Amazon, and you made that possible. Travis, you have been such a great mentor. You knew how to advise me right from the start, even when I didn’t know exactly what I wanted to study, and give me the space and guidance to figure it out. You are one of the most strategic people I know, and I admire that immensely. I’m always amazed how I can come to you with a problem, or complex scientific story, and within a short time and not much information, you are able to see exactly how it could be told in a simple, powerful way. I am hugely grateful to my committee—Brian Enquist, Don Falk, and Sean McMahon—who have advised and supported me in varied and complementary ways. Brian, you are always full of creative ideas, and so enthusiastic about my research. I really appreciate that you have encouraged me to be a scholar and to know and gain inspiration from the classic literature. I learnt so much in your Biological Scaling class; tools that I will take with me, and a way of looking at the world and seeing the big, overarching patterns. You also sent me to Colorado in 2011 to work with your lab at RMBL, and that was one of the best summers of my life, where I met some of my closest friends. Don, it has been so wonderful having you on my committee. You have been such a supportive mentor and a dedicated committee member. I really enjoy talking to you about science, and you have given me some great ideas on how to analyse the LiDAR datasets. Sean, having you on my committee has been fantastic. Anyone would find it hard not to be inspired by your constant stream of ideas! Thanks to you and Jess Parker for bringing me out to the SERC forest in Maryland and teaching me how to use the ground-based LiDAR. Your presence on my committee has helped my development as a scientist considerably, and make the whole thing a lot more fun! Thank you to Scott Saleska and Scott Stark for introducing me to LiDAR! I am so excited by its capabilities and this way of looking at tropical forests. I came into

EEB with a lot of ideas, but not really knowing what I wanted to do, and I am leaving with a research programme and plenty of plans for the future. Scott Stark – you laid such important ground in terms of research on forest structure and the LiDAR method. Without that, I wouldn’t have been able to do what I managed to do. In addition to which, you have always made yourself available to help, from collecting data in the field, to writing grants, to helping with coding, so thank you! To my counsellor, Mike Strangstalien—you have been my sixth advisor on this journey, and I really don’t think I would have finished it without your help and support. You went above and beyond to make sure I got here. And, none of this would have been possible without Tyeen Taylor. He was the one who initially suggested that Scott would be a good person to work with, and that Tucson was a pretty cool place. Since then, he has been my partner on many adventures, and supported my scientific development in every possible way. Thank you for believing in me and supporting me; you’re amazing. Thank you to all the wonderful people who contribute to the supportive and inspiring work environment at EEB. Specifically, thank you to the Saleska lab, past and present, for the support, friendship, advice, and encouragement that you have given me: Ty Taylor, Scott Stark, Loren Albert, Joost van Haren, Jin Wu, Laura Meredith, Brad Christoffersen, Sky Dominguez, Rick Wehr, Moira Hough, Neill Prohaska, Luciana Alves, Natalia Restrepo-Coupe, Kenia Wiedemann, Anthony Garnello, Rose Vining, Alejandro Macias, Pilar Vergeli, Veronika Leitold, Tara Woodcock, and Marianne Ritter. Thank you also to present and past members of the Enquist lab: Lindsey Sloat, Julie Messier, Scott Stark, Ben Blonder, Vanessa Buzzard, Amanda Henderson, Sean Michaletz, Brad Boyle, John Donoghue, Christine Lamanna, Colby Sides, Alex Brummer, Naia Morueta Holme, Irena Simova; Chesson lab: Yue (Max) Li, Pacifica Sommers, Nick Kortessis, Simon Stump, Galen Holt; Huxman lab: Henry Adams, Jenny Gremer, Ginny Fitzpatrick, Greg Barron-Gafford; and to my friends and office mates Josh Scholl and Xing-Yue (Monica) Ge. Thank you to the EEB Business Office for helping me in so many ways—Lili Schwartz, LuAnn Cordero, Lauren Harrison, and Barry McCabe—your work is very much appreciated. A big thank you to EEB’s Graduate Program Coordinator, Pennie Rabago, who has kept me on-track with all of the PhD forms and paperwork and has always been so responsive. I feel very fortunate to have developed such good collaborations with researchers in the U.S. and Brazil. In particular, I would like to acknowledge the support and advice of Matt Hayek, Marcos Longo, Michael Keller, Ryan Knox, Raimundo de Oliveira, Rodrigo da Silva, Plinio de Camargo, Mauricio Ferreira, Michela Figueira, Rafael Rosolem, Juliana Schietti, Bruce Nelson, Danilo de Almeida, Diogo Martins Rosa, Flavia Costa, José Luís Camargo, Rita Mesquita, Rafael Assis, and Norberto Emidio.

This work would not have been possible without a great deal of help in the field. Thank you very much to Darlisson Bentes, Cleuton Pereira, Buru, Rupinol, Chico, Luciana Vieira, and Osmaildo. It was a pleasure to work with each of you, and you taught me a lot about the forest in the process. I am very grateful for the many science communication and outreach and teaching opportunities that I have had whilst at the University of Arizona. These were mainly via the Biosphere 2 (B2) Science and Society Fellowship, a research assistant position working on communication materials for B2’s Landscape Evolution Observatory, the Carson Scholar’s Program, the Outreach Scholars STEM Education Preparation Program, and a number of teaching assistantships. Thank you to Steve DeLong, Matt Adamson, Candice Crossey, Kevin Bonine, Shipherd Reed, Jennifer Fields, Shiloe Fontes, William Plant, Pamela Pelletier, Judie Bronstein, and Katrina Dlugosch for helping me to become a better communicator and teacher. Thank you to my funding sources for supporting my research: the UA Graduate/Professional Student Council, Biosphere 2, the UA Arizona Galileo Circle scholarship, EEB Graduate Research Fellowships, NASA Earth and Space Science Fellowship, the National Science Foundation, BDFFP (Biological Dynamics of Forest Fragments Project) Thomas Lovejoy research fellowship, and the Institute of the Environment. I would like to acknowledge three people who played critical roles in getting me to graduate school. My A-level biology teacher, Gilly, inspired me to always aim high and comprehensively prepare for my exams. Rob Thomas introduced me to the joys of field ecology and talking science. Steven Whitfield was my first guide in the magical world of tropical forest ecology, to which I am now truly addicted. Finally, thank you to my wonderful friends and family in the UK and in Tucson. You have shared adventures with me and kept me sane during hard times. Thank you to Maggie Smith for your love and support; you understand the academic path more than anyone else in the family, and that has been so valuable to me. Thank you to my godfather, Reg Ramm, who has been a great spiritual, intellectual, and emotional guide in my life. Thank you to my special friends across the ocean: Laura, Louise, Elsa, Bex, Ez, and Gianna, who have provided me with extensive support (sometimes in the form of packages of tea and Hobnobs). My Tucson adventure buddies and friends have shown me how to properly enjoy the desert, as well as being important confidants: Pacifica, Lindsey, Will, Julie, Loren, Pascal, Brad, Rebekah, Christine, Dave, Max, Ben, Aaron, Neill, Kimberly, Monica, Josh, Dorea, Ed, Jenny, Sky, Erik, Moira, Kristine, Krista, Jeremy, Mimi, Martin, and Martine. Thank you all for helping me on this journey.

DEDICATION

I dedicate this dissertation to my father, David Stephen Smith, who always led me to believe that I could do anything I wanted to, and has supported me emotionally and financially through my many years of schooling (without suggesting that I get a

“proper job”!). He instilled in me a great concern for and need to protect the environment, which strongly influenced my decision to pursue ecological research. I also dedicate this to my mother, Deborah Jane Barham Smith, who taught me to be resourceful and to never give up (useful traits for any PhD student!). Finally, I dedicate this work to my sister, Sophie “LB” Alessandra Smith, who, while not entirely understanding why I have spent so much time “counting leaves”, has always been on the end of a phone ready to offer support whenever I needed her. In short, this is not a conventional path in my family, but they have stood by me and been proud of me all the same. Thank you all for your unwavering support.

TABLE OF CONTENTS

ABSTRACT ...... 9

INTRODUCTION ...... 11

PRESENT WORK ...... 14

REFERENCES ...... 17

APPENDIX A: Evidence that tropical forest photosynthesis is not directly limited by high temperature ...... 21

APPENDIX B: Seasonal and El Niño-associated changes in LiDAR-derived LAI and leaf area profiles in an eastern Amazonian forest ...... 69

APPENDIX C: Interannual changes in canopy structure with climate in an evergreen

Amazonian forest ...... 108

ABSTRACT

Understanding how structure and function change across environmental gradients is a fundamental goal of ecology, with important applications in a changing world. In this dissertation, I explore how environmental variations in temperature and precipitation affect three-dimensional canopy structure, and how this, in turn, affects forest function. Characterising how climatic variations affect forest structure and function is particularly important in tropical forests, which are globally important carbon stores that have already shown vulnerability to climate change.

The future of tropical forest carbon stocks is highly uncertain, with plant physiological responses representing the largest source of model uncertainties. As such, my dissertation research comprises empirical investigations into how tropical forests will respond to high temperatures and drought.

Firstly, I examine tropical forest response to high temperature by conducting a comparison of natural forest sites and a tropical forest mesocosm using eddy- covariance data. I present evidence that high temperature declines in tropical forest photosynthesis are not due to direct temperature effects (i.e., that cause damage to the photosynthetic machinery), but instead are predominantly due to indirect temperature effects that result from concurrent increases in vapour pressure deficit

(VPD). While both mechanisms reduce photosynthesis, the impact of increased VPD under future climate may be partly mitigated by enhanced water-use efficiency associated with rising atmospheric CO2 concentrations, suggesting that tropical forests may have opportunities for resilience in the face of global warming.

The second part of my dissertation research examines how tropical forest canopy structure responds to seasonal dry periods and anomalous droughts on seasonal and interannual timescales, using data from ground-based LiDAR (Light

Detection and Ranging). I show that total leaf area index (LAI) does not represent the seasonality of forest structure, since the upper and lower canopy levels exhibit divergent seasonal responses. The seasonal pattern of upper canopy LAI shows good agreement with the seasonal pattern of enhanced vegetation index (EVI) measured from satellites, suggesting that satellites are not capturing the response of the lower canopy. These results indicate that smaller trees are responding to seasonal water limitations and larger trees to light availability.

I found that the response of canopy structure to anomalous (El Niño- induced) drought was similar to seasonal dry periods, but that the trends in LAI and vertical canopy structure were amplified. In particular, I document a delayed loss of

LAI from the upper canopy following extreme drought, which supports the idea that while smaller trees may be more responsive to shorter, less severe dry periods, larger trees are more susceptible to prolonged or more severe droughts.

Finally, I combine a long-term ground-based LiDAR dataset with tree inventory data in order to identify the mechanisms (i.e., changes in leaf area and/or woody biomass) of structural changes caused by droughts. I present evidence that loss of lower canopy LAI following an El Niño-induced drought was due to the mortality of small trees, not loss of leaf area, while an increase in LAI in the upper canopy predominantly resulted from plastic leaf area changes. If small trees are susceptible to drought-induced mortality and the incidence of droughts increases,

10 this could prevent the recovery of tropical forests from drought-induced disturbances.

INTRODUCTION

Tropical forests store the largest portion of carbon out of all forest biomes (~55%), have the highest terrestrial net primary productivity (NPP, ~33%; Bonan 2008; Pan et al. 2011) and are home to the majority of Earth’s species (Dirzo and Raven 2003).

As the largest intact tropical rainforest in the world, the Amazon plays a particularly important role in global climate and biodiversity. Changes to Amazonian forests could affect local and global atmospheric circulation, resulting in altered precipitation patterns (Gedney and Valdes 2000; Werth and Avissar 2002) and atmospheric carbon content.

In the coming century, temperatures in the Amazon are predicted to rise by

2.8–7.2°C and precipitation is projected to decline by 68–87% (Galbraith et al.

2010). However, there is a high degree of uncertainty about how tropical forests will respond to these climatic changes. Some coupled carbon-climate models predict forest biomass reductions of the Amazon and a transition of a large area of rainforest to seasonal forest or savannah by the middle of this century (Cox et al.

2000; Cox et al. 2004; Betts et al. 2004; Huntingford et al. 2008; Malhi et al. 2009).

Other models project biomass increases in the Amazon with similar changes in climate (Good et al. 2013; Huntingford et al. 2013). Discrepancies between model outcomes are predominantly due to uncertainties about how plants will respond to changes in temperature, rainfall, and CO2 (Huntingford et al. 2013), as well as

11 current and future climate scenarios (Malhi et al. 2009; Galbraith et al. 2010; Good et al. 2013). In addition, terrestrial biosphere models exhibit different plant sensitivities to temperature and water stress (Galbraith et al. 2010; Powell et al.

2013; Zhang et al. 2015; Rowland et al. 2015). My dissertation addresses some of the uncertainties with regards to the responses of tropical plants to future climate.

The overall question of my dissertation research is: How do tropical forest canopy structure and function respond to seasonal and interannual variations in climate?

The canopy is the active surface with which forests interact with the atmosphere (Parker 1995) and hence is the principle factor controlling many fundamental ecosystem processes including photosynthesis, transpiration, and energy exchange (Asner et al. 2003). As such, leaf area index (LAI)—defined as the total one-sided leaf area (m2) per unit ground area (m2) (Watson 1947)—is a key parameter in many terrestrial ecosystem and land-surface models that predict forest response to climate (Asner et al. 2003; Jonckheere et al. 2004). Vertical canopy structure and light environments have been mechanistically linked to demographic rates and carbon dynamics (Stark et al. 2012). Given the fundamental interaction of the canopy with the atmosphere, understanding patterns of forest canopy structural change can help identify mechanisms by which forests respond to and influence climate.

Until recently, we lacked the methods and long-term datasets required to quantitatively assess the dynamics of complex tropical forest canopies at scales relevant to understanding ecosystem-scale carbon dynamics. LiDAR (Light

Detection and Ranging) provides unprecedented three-dimensional information on canopy structure (Lefsky et al. 2002). The LiDAR instrument fires high frequency laser pulses at the forest canopy and a sensor measures the amount of time it takes for a pulse to leave the instrument, intercept vegetation, and return to the sensor.

That time is then converted into a distance to the target. The growing availability of multi-temporal LiDAR data affords unique opportunities to measure canopy structure change at sufficient spatial and temporal scales to study ecosystem dynamics.

In this dissertation, I explore how tropical forests may respond to future climate via an experimental manipulation (Appendix A), using seasonal variations as proxies for extreme events (Appendix B), and long-term forest monitoring in order to capture anomalous climate events and recovery-disturbance cycles (Appendix C).

Specifically, I ask the following main questions in each of the chapters: How will tropical forest photosynthesis respond to high temperatures (Appendix A)?

How do LAI and vertical canopy structure change seasonally and does the seasonal response of canopy structure predict its response to anomalous drought (Appendix

B)? How does forest structure respond to interannual climate variability in old- growth tropical forests (Appendix C)?

PRESENT WORK

The manuscripts for my dissertation research are presented in Appendices A-C, which contain the introduction, methods, results, and discussion sections for each chapter. Here, I have provided a summary of the main findings of each study.

Summary of Appendix A: “Evidence that tropical forest photosynthesis is not directly limited by high temperature”

This article examines how tropical forest photosynthesis responds to high temperatures. To do so, we compared the temperature response of gross ecosystem productivity (GEP) in a climate controlled, 0.2 ha artificial tropical forest (the

Biosphere 2 Tropical Forest Biome, B2-TF) to the eddy-covariance measured response of natural tropical forest sites in the Brazilian Amazon and Mexico. We found that rather than being directly limited by high temperatures, photosynthesis in the B2-TF and in situ tropical forests was indirectly limited via stomatal closures that occur due to concurrent increases in vapour pressure deficits (VPD). This article is in preparation for submission to the Proceedings of the National Academy of Sciences of the United States of America.

Summary Appendix B: “Seasonal and El Niño-associated changes in LiDAR- derived LAI and leaf area profiles in an eastern Amazonian forest”

This work is the first ground-based study to investigate seasonal changes in vertical canopy structure in a tropical forest. This is a potentially important dynamic that may improve the ability of ecosystem models to accurately reproduce the seasonal

14 cycle of tropical forest photosynthesis. In addition, the study explores whether the seasonal response of canopy structure is able to predict its response to anomalous drought. We made almost three years of monthly measurements of LAI and canopy profiles using a ground-based LiDAR instrument. While seasonal variations in total

LAI were normally small, variations in the LAI of different canopy layers were more dynamic. During the dry season, leaf area increased in the upper canopy and decreased in the lower canopy, and these trends reversed at the onset of the wet season. Seasonal patterns of LAI and vertical canopy structure were similar during the El Niño year, but trends were amplified. Total forest LAI declined dramatically at the height of the 2015-2016 El Niño, with the majority of leaf area being lost (85%) from the low canopy, while there was a small increase in leaf area in the upper canopy. The seasonal pattern of the upper canopy is consistent with the seasonality of satellite-derived canopy greenness (enhanced vegetation index, EVI), indicating that satellite metrics are predominantly capturing structural changes at the top of the canopy. Another important conclusion of this article is that the lower canopy appears to be most responsive to seasonal and anomalous droughts, perhaps due to the shallower roots of understory trees.

Summary of Appendix C: “Interannual changes in canopy structure with climate in an evergreen Amazonian forest”

In this study, we used a long-term multi-temporal dataset of ground-based LiDAR measurements made at the Tapajós National Forest, near Santarém, Brazil, to answer the following questions: (1) How does forest structure respond to

15 interannual climate variability in old-growth tropical forests? (2) What are the long- term structural dynamics of disturbance and recovery? The timeseries of LiDAR- derived measurements of forest structure encompassed two different climatic regimes—a drier period, during which annual precipitation increased, and a wetter period, during which annual precipitation decreased and there were two large drought events. Structural changes during the first (wetter) period were consistent with recovery from a disturbance prior to measurements began, while those during the second (drier) period indicated a response to a drying trend and drought- induced disturbances. As in the previous study on seasonal patterns of canopy structure, we observed divergent trends in the upper and lower canopy levels. In this paper, we were able to attribute increases in the lower canopy during the first period to woody biomass growth of smaller trees, and reductions in the lower canopy during the second period to mortality. In contrast, more plastic changes in leaf area dominated the upper canopy.

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“The Role of Ecosystem-Atmosphere Interactions in Simulated Amazonian

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Williams, and Patrick Meir. 2010. “Multiple Mechanisms of Amazonian Forest

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APPENDIX A: Evidence that tropical forest photosynthesis is not directly

limited by high temperature

Prepared for submission in: Proceedings of the National Academy of Sciences of the United States of America

Authors: Marielle N. Smitha, Tyeen C. Taylora, Joost van Harenb, Rafael Rosolemc,d,

Natalia Restrepo-Coupea,e, Jin Wuf, Raimundo C. de Oliveirag, Rodrigo da Silvah,

Alessandro C. de Araujoi,j, Plinio B. de Camargok, Travis E. Huxmanl, Scott R.

Saleskaa,1

Author affiliations: aDepartment of Ecology and Evolutionary Biology, University of

Arizona, Tucson, AZ 85721, USA; bBiosphere 2, University of Arizona, 32540 S.

Biosphere Road, Oracle, AZ 85623, USA; cDepartment of Civil Engineering,

University of Bristol, Bristol, UK; dCabot Institute, University of Bristol, Bristol, UK; ePlant Functional Biology and Climate Change Cluster, University of Technology

Sydney, Sydney, NSW, Australia; fEnvironmental and Climate Sciences Department,

Brookhaven National Laboratory, Upton, NY, 11973, USA; gEmbrapa Amazônia

Oriental, 68035-110 Santarém, Pará, Brazil; hDepartment of Environmental Physics,

University of Western Pará (UFOPA), Santarém, Pará, Brazil; iInstituto Nacional de

Pesquisas da Amazônia (INPA), Manaus, Amazonas, Brazil; jEmbrapa Amazônia

Oriental, Belém, Pará, Brazil; kLaboratório de Ecologia Isotópica, Centro de Energia

Nuclear na Agricultura (CENA), Universidade de São Paulo, 13400-970 Piracicaba,

São Paulo, Brazil; lEcology and Evolutionary Biology & Center for Environmental

Biology, University of California, Irvine, CA 92629, USA.

Corresponding author: 1Scott R. Saleska, Department of Ecology and Evolutionary

Biology, University of Arizona, 1041 E Lowell Street, Biological Sciences West Room

310, Tucson, AZ 85721, USA. Tel: (520) 626-1500. Email: [email protected]

Keywords: Amazon, Biosphere 2, climate change, photosynthesis, tropical forests, temperature threshold

Abstract

Tropical forests may be vulnerable to future climate change especially if photosynthesis in tropical trees is near a high temperature threshold. High temperatures may directly impact photosynthetic metabolism (e.g., via increases in photorespiration, or thermally-induced membrane degradation). Alternatively, high temperatures may indirectly limit photosynthetic leaf gas exchange (due to stomatal closures in response to concomitant increases in vapor pressure deficit, VPD). The distinction between these mechanisms is important -- the indirect effect (stomatal closure) is a regulatory response expected to be modest under future elevated CO2, whereas direct temperature responses more likely indicate a threshold above which permanent photosynthetic declines will substantially impair forest function -- but they are difficult to distinguish empirically in natural settings because VPD is highly

22 correlated with temperature. We investigated these non-mutually-exclusive hypotheses, first using a climate controlled, 0.2 ha tropical forest mesocosm

(Biosphere 2 Tropical Forest) to separate temperature from VPD responses. We found that photosynthesis (gross ecosystem productivity, GEP) was strongly reduced by increasing VPD, but not by increasing temperature (even up to 38°C). Second, we analyzed flux tower-derived GEP from three evergreen forest sites in the Brazilian

Amazon and a Mexican tropical dry forest, using binned regressions that partitioned changes in photosynthesis between temperature and VPD. We found that high- temperature forest GEP declines were mainly due to increases in VPD, not higher temperatures. Future climate-induced increases in VPD may be mitigated by higher atmospheric CO2 and water-use efficiency, suggesting that tropical forests may have a greater margin of resilience under climate change.

Significance statement

Observations of reduced tropical forest photosynthesis during warm periods have produced concerns that this critical habitat is near a high-temperature limit. If true, tropical forest carbon uptake will be strongly reduced with continuing climate change. Here we present an analysis of carbon exchange data from natural tropical forests and a tropical forest mesocosm. We found that this apparent temperature limit is actually caused by temperature-associated increases in the difference in humidity between the atmosphere and leaf air (vapor pressure deficit, VPD), not by high temperatures themselves. This is important, because the effects of higher VPD

(but not of higher temperature) could be ameliorated by higher atmospheric CO2

23 concentrations in the future, suggesting that tropical forests have a safety margin under climate change.

Introduction

Tropical forests store 55% (471 + 93 Pg C) of global forest carbon and constitute the largest sink within the world’s forests, sequestering ~4.0 Pg C year-1 (70% of the global forest sink) (Pan et al. 2011). Climate-driven forest mortality or reductions in productivity could compromise the status of this important carbon sink, leading to increased atmospheric CO2 concentrations and further biosphere-climate feedbacks

(Cox et al. 2000). In particular, it has been suggested that significant biomass loss, or

‘die-back’ of the world’s largest tropical forest, the Amazon, may constitute a

“tipping element” that could lead to large, long-term changes in the Earth’s climate system (Lenton et al. 2008).

In the coming century, temperatures in the Amazon are predicted to rise by

2.8–7.2°C and precipitation is projected to decline by 68–87% (Galbraith et al.

2010). However, there is a high degree of uncertainty about how tropical forests will respond to different climate scenarios because the principle global change factors

(e.g. air temperature, precipitation, and atmospheric CO2) could affect plants via multiple mechanisms (Lewis et al. 2009; Galbraith et al. 2010). In response to changes in these drivers, some coupled carbon-climate models predict forest biomass reductions of the Amazon and a transition of a large area of rainforest to deciduous forest or savannah by the middle of this century (Cox et al. 2000; Cox et al. 2004; Betts et al. 2004; Huntingford et al. 2008; Malhi et al. 2009). Other models

24 project biomass increases in the Amazon with similar changes in climate (Good et al.

2013; Huntingford et al. 2013). Discrepancies between model outcomes are predominantly due to uncertainties about how plants will respond to changes in temperature, rainfall, and CO2 (Huntingford et al. 2013), as well as current and future climate scenarios (Malhi et al. 2009; Galbraith et al. 2010; Good et al. 2013).

In addition, terrestrial biosphere models exhibit different plant sensitivities to temperature and water stress (Galbraith et al. 2010; Powell et al. 2013; Zhang et al.

2015; Rowland et al. 2015). Even where the response of gross primary production

(GPP) to temperature or precipitation between models is similar, the underlying mechanisms may differ (Galbraith et al. 2010; Rowland et al. 2015).

Large-scale in situ drought experiments in the Amazon (Nepstad et al. 2007;

Brando et al. 2008; da Costa et al. 2010) are helping to improve model projections of tropical forest resilience (Powell et al. 2013) but equivalent warming experiments have not been carried out in field settings in the tropics (Wood et al. 2012; Galbraith et al. 2010; Cavaleri et al. 2015; Corlett 2011). While a number of leaf and branch- level thermal tolerance studies have been conducted (reviewed in Wood et al.

2012), results at this spatial scale may overestimate canopy-level thermal tolerance.

For instance, studies by Griffin et al. (2002) and Doughty and Goulden (2008) both found that stand- or canopy-level gas exchange was more sensitive to warming than leaf-level gas exchange. This illustrates the importance of experimentation with the capacity to understand warming at the right spatial scale - canopy-level gas exchange. Outcomes of such efforts will be valuable for the accurate parameterization of ecosystem models.

Two important hypotheses about how net photosynthesis in tropical forests responds to high temperatures are direct biochemical degradation and stomatal limitation. In the first (H1), high temperatures directly, negatively impact the biochemistry of photosynthetic carbon fixation, resulting from a combination of the temperature sensitivities of involved enzymes (Rubisco and other enzymes associated with the Calvin Cycle and RuBP regeneration) and membrane destabilization leading to loss of electron transport capacity (Lloyd and Farquhar,

2008). This last feature is presumed to bring about lasting restrictions on photosynthetic function due to significant protein deformation and oxidant accumulation requiring active processes to repair or replace system features.

Consistent with H1, Doughty and Goulden (2008) observed that net ecosystem carbon uptake, measured with the eddy covariance technique, declined precipitously above an air temperature of 28°C in the Tapajós National Forest

(TNF), an evergreen Amazonian forest in Pará, Brazil. Given the current frequency of occurrence of such canopy temperatures, these authors inferred that tropical forests are approaching a high temperature threshold and that future increases will have a negative effect.

In the second hypothesis (H2), photosynthetic decline with rising temperatures results as a function of stomatal closure from increased vapor pressure deficit (VPD) occurring as a result of the known temperature-driven change in water-holding capacity of air and associated change in potential energy. A rise in VPD reflects an increase in the driving force for water loss from the leaf, of which terrestrial plants have evolved sensitivity (Zeiger et al. 1987). Stomatal

26 closure allows leaves to conserve water but also limits CO2 assimilation (Farquhar and Sharkey 1982; Cowan 1982). Modeling work by Lloyd and Farquhar (2008) supported the notion that declines in carbon exchange from leaves, branches, and canopies at high temperature occur primarily from the increase in diffusive resistance of CO2 associated with closing stomata to mitigate substantial water loss.

A study of eddy covariance measurements in a peatland in New Zealand also found that GPP at high temperatures was more likely limited by VPD-induced stomatal closures than direct temperature effects on photosynthetic capacity (Goodrich et al.

2015).

These two hypotheses are not mutually exclusive, but temperature- associated changes in carbon balance in Land Surface Schemes (LSS) and Earth

System Models (ESM) are dominated by either direct (H1) (Galbraith et al. 2010) or indirect temperature influence on photosynthetic metabolism (H2) (Rowland et al.

2015). Differences among model equations and schemes, conflicting conclusions from both empirical and modeling studies, and the first order concern over potential forest impact, suggest a need to evaluate the relative importance of temperature versus VPD in controlling tropical forest gross ecosystem productivity (GEP). How will these forests respond to future temperature regimes and how close are they to critical thresholds that would result in significant changes to ecosystem structure and function?

We utilized an experimental tropical forest with significant climate control within the Biosphere 2 (B2) facility, near Tucson (Arizona, USA) to understand future forest photosynthetic responses. Specifically, we compared the response of

GEP to air temperature and VPD in the B2 Tropical Forest Biome (B2-TF) to three evergreen forest sites in the Brazilian Amazon (K34, K67, and K83) that form part of the Large-scale Biosphere-Atmosphere Experiment in Amazonia (LBA project, Keller et al. 2004) and a tropical dry forest in Mexico (Tesopaco, Perez-Ruiz et al. 2010).

We analyzed ecosystem carbon exchange data from eddy covariance towers at the natural forest sites and from a tower in the B2-TF, which estimates net ecosystem exchange (NEE) by measuring the rate of change of CO2 concentrations inside the biome (Lin et al. 1998). The B2-TF is a 0.2 ha enclosed mesocosm with a complex vertical canopy structure including mature trees up to 13-17 m in height, comprising a phylogenetically diverse assemblage of species typical of lowland tropical rainforests in Southern and Central America (Leigh et al. 1999). The B2-TF allows assessment of the temperature sensitivity of tropical forest photosynthesis up maximum temperatures that are ~10°C higher than those recorded at the

Amazonian sites. In addition, we were able to reduce the sensitivity of VPD to temperature by supplying water to the system through small rainfall events and humidifiers to maintain relative humidity. As a result, the B2-TF exhibited a similar range of VPD to the sites in the Brazilian Amazon (0-3.4 kPa, compared to 0-2.9 kPa for Amazon sites), while temperature was manipulated over a much larger range

(19-42°C, compared to 23-33°C for Amazon sites). This allowed us to experimentally force mean VPD for a given temperature bin to a lower value than at any of the natural forest sites (Fig. 1).

We compared the response of GEP to temperature and VPD under two different VPD-temperature regimes to test whether high temperature declines in

GEP are predominantly due to direct (H1) or indirect (H2) temperature effects. VPD increased rapidly with temperature at the in situ sites (K34, K67, K83, and

Tesopaco), while VPD increased more gradually with temperature in the B2-TF.

With these two different VPD-temperature regimes in mind, we made the following predictions about how tropical forest photosynthesis will respond to high temperatures.

(1) If declines in photosynthesis at high temperatures are predominantly due to

direct temperature effects that alter photosynthetic biochemistry (H1),

photosynthesis will show the same response to increasing temperature

irrespective of the VPD-temperature regime. Hence, we would expect GEP to

respond similarly to temperature in natural forests and the B2-TF.

(2) If declines in photosynthesis at high temperatures are driven by indirect

temperature effects, comprising VPD-induced stomatal closures (H2),

photosynthesis will continue up to higher temperatures only when VPD

remains low. Given this, we would expect GEP to continue up to higher

temperatures in the B2-TF, where VPD remains low despite increases in

temperature.

We also examined the independent effects of temperature and VPD on GEP by performing separate regressions between GEP and VPD and GEP and temperature, binning by temperature and VPD, respectively (“binned regressions”,

Materials and Methods). Identifying the dominant driver (VPD or temperature) of photosynthetic reductions at high temperatures will improve the formulation of vegetation models and inform predictions of tropical forest response to climate.

Results and Discussion

Light-saturated photosynthesis (estimated by GEP) was maintained at higher temperatures in the B2-TF, in contrast to natural tropical forests (Fig. 2A). Whereas

GEP declined at temperatures above 27°C at the Amazon forest sites (K34, K67, and

K83) and 28°C at the seasonally dry tropical forest (Tesopaco), GEP did not decrease in the B2-TF until temperatures exceeded 38°C. For a given temperature, B2 experiences lower VPD than natural sites, consequently avoiding reductions in stomatal conductance that normally occur at high temperatures at natural sites where temperature and VPD are stronger covariates (Figs. 1 and S5). These findings are consistent with the hypothesis that indirect temperature effects (i.e., VPD rather than temperature) are the main driver of high temperature declines in photosynthesis (H2). While GEP in the B2-TF was much less sensitive to temperature than natural forest sites (Fig. 2A), GEP was more responsive to increasing VPD, which resulted in an almost identical response to natural forest sites (Fig. 2B). This provides further evidence in support of the indirect temperature hypothesis (H2), since GEP was more sensitive to VPD than to temperature.

However, we should note that our experimental design did not allow us to distinguish the interactive effects of VPD and temperature on GEP.

Across all sites, the high temperature response (> 28°C) of GEP to VPD within individual 1°C temperature bins was negative (mean slope values for GEP x VPD regressions were significantly different from zero at p < 0.05 based on one-tailed

Student’s t-tests, except for K83, Fig. 3) while GEP showed no response, or in two cases, a positive response to temperature within individual 0.2 kPa VPD bins (mean

30 slope values for GEP x temperature regressions were not significantly different from zero based on one-tailed Student’s t-tests, except for Tesopaco where p = 0.014 and the B2-TF where p = 0.04). This suggests that, in common with the B2-TF, VPD is the major control on GEP at high temperatures at natural forest sites. These results are in accordance with the findings of Wu et al. (2016), who found that GEP corrected by VPD showed little response to temperature, while GEP corrected by temperature declined significantly with VPD. Here, we extend their analysis to four additional sites to show that VPD is the main driver of high temperature declines in GEP at a range of tropical forests.

Our results indicate that direct effects of temperature on photosynthesis do not manifest until much higher temperatures than the optimum values observed in natural tropical forests. This is in agreement with the findings of Lloyd and

Farquhar (2008), whose leaf-level photosynthesis model showed that declines in photosynthesis at ~30°C are almost entirely due to indirect effects of temperature.

Direct, irreversible effects of temperature that damage the photosynthetic machinery tend to occur well above 35°C (e.g., ~50°C for the tropical species studied by Cunningham and Read (2006)), although the exact value depends upon the growth temperature regime (Berry and Bjorkman 1980). This is important information, because direct temperature effects can permanently damage leaf biochemistry, while indirect effects lead to reversible changes in stomatal conductance that allow photosynthesis to continue when temperatures decline

(Rowland et al. 2015; Berry and Bjorkman 1980).

The relative effects of VPD and temperature on photosynthesis have rarely been assessed at the leaf-level (Slot and Winter 2016). Vargas and Cordero (2013) measured the response of leaf-level photosynthesis to temperature (20-45°C) for two tropical tree species whilst maintaining VPD < 2.0 kPa, thus avoiding indirect temperature effects. The optimum temperature (Topt) was 28°C for both species, similar to studies where VPD was not controlled (e.g., field measurements made by

Slot et al. 2016). However, the reduction in photosynthesis above this point appears to be more gradual than in studies which did not hold VPD constant, perhaps indicating, as we find here, that indirect temperature effects are the main driver of photosynthetic declines at high temperatures. While this study does suggest that maintaining low VPD levels can reduce the sensitivity of photosynthesis to temperature, what is needed is to compare leaf-level temperature responses when

VPD is allowed to increase and when it is maintained below a critical level, but this work does not yet appear to have been done.

An earlier study took the reciprocal approach, assessing the leaf-level response on a cool-coastal herb (this time, in terms of stomatal conductance) to increasing VPD, whilst holding temperature constant at 20°C and then 30°C

(Osmond et al. 1980). Temperature positively affected (increased) stomatal conductance, while VPD negatively affected it. This appears to be in-line with our findings; however, different species and in particular, tropical forest trees, may exhibit highly varied stomatal sensitivities to VPD (Osmond et al. 1980).

Furthermore, it is not clear whether 30°C represents the Topt for the species, or not.

This is important to know, because it is likely that temperature has a strong negative effect on stomatal conductance when it is greater than Topt.

Finally, leaf-level work by Slot and Winter (2016) also supports our findings in terms of VPD effects being the main constraint on photosynthesis at high temperatures. They found that Topt increased with growth temperature, while the assimilation rate at Topt (Aopt) decreased. Critically, this pattern was reversed when relative humidity was increased from ~45 to ~90%, leading to an increase in Aopt.

This suggests that VPD-induced stomatal closures were limiting Aopt for treatments in which humidity was not elevated.

There is an apparent mismatch between leaf- and canopy-level temperature responses; specifically, canopy-level photosynthesis has been shown to be more sensitive to temperature than at the leaf-level (Doughty and Goulden 2008).

Doughty & Goulden (2008) suggest that this is because canopy gas exchange is dominated by sunlit leaves of the upper canopy, which commonly experience temperatures above the Topt, therefore making canopy measurements very sensitive to high temperatures. Detailed monitoring of leaf temperatures by Slot et al. (2016) confirms that for much of the day (50%), sun-exposed leaves experience temperatures greater than Topt. However, a more pressing reason for this discrepancy is due to the way in which we measure canopy air temperature—from sensors mounted above the canopy—which do not represent the great variation of leaf temperatures experienced within the canopy and especially the extreme temperatures of sun-exposed leaves (e.g. Fig. 6, Doughty & Goulden 2008). An important future challenge will be to somehow include leaf temperatures in the

33 assessment of canopy-level gas exchange, in order to accurately scale leaf- to canopy-level temperature responses (e.g., using thermal infrared cameras, as in

Jones et al. 2009).

The response of GEP in the B2-TF to VPD was equivalent to natural forests.

Our results indicate that the B2-TF showed greater tolerance to temperature than natural sites because VPD remained low. Thermal acclimation and community assembly change may also have contributed to the forest’s enhanced thermal tolerance and could comprise important mechanisms of resilience for tropical forests in the future. We do not consider evolutionary adaptation because the B2-TF contains only one generation of trees. In addition, a previous modeling study showed that the use of a land surface model typically parameterized for Amazon forests (i.e., the Simple Biosphere Model 3, SiB3; Baker et al. 2003; Baker et al. 2008) was only able to accurately reproduce NEE dynamics inside the B2-TF once key parameters related to thermal tolerance were modified through multi-objective optimization (Rosolem et al. 2010). The mechanism of enhanced thermal tolerance was hypothetically attributed to high-temperature acclimation or shifts in plant community assembly. However, the study did not explicitly examine the VPD- temperature regime or compare it to natural sites.

Photosynthesis can acclimate to warming via an increase in Topt (reviewed in

Drake et al. 2016). However, the evidence for tropical species is mixed, with some studies showing support for acclimation of photosynthesis in seedlings

(Cunningham and Read 2002; Cunningham and Read 2003) and another finding no

34 evidence for photosynthetic acclimation in fully developed leaves and branches of adult canopy trees (Doughty 2011).

Under a changing environment, differential species performance can lead to rapid community assembly shifts (Enquist and Enquist 2011; Feeley et al. 2013), which could alter the aggregate response of the forest to climate (Tilman et al. 1997;

Moorcroft 2006). In the B2-TF, of the 528 trees (115 species) planted, 100 trees (53 species) survived to the year 2000. Hence, species-specific thermal tolerances may have shaped the outcome of competition and differential mortality among trees, and thereby the overall forest response to temperature.

Whilst we cannot determine whether the contrasting response of GEP to temperature in the B2-TF is due to lower VPD levels, thermal acclimation, or community assembly changes, the fact that GEP can continue up to high temperatures does show that tropical forest photosynthesis is not directly limited by temperature, since it can continue above the normal Topt identified at natural forest sites (28°C). Furthermore, we found VPD to be the dominant control on GEP at high temperatures in the natural forest sites.

A number of modeling studies identify temperature increases as a major driver of Amazonian biomass loss or reduced productivity (Galbraith et al, 2010,

Huntingford et al. 2013; Rowland et al. 2015), but few explicitly examine whether the mechanism is predominantly via direct or indirect temperature effects. This is an important avenue to pursue, especially since indirect effects intersect the pathways of temperature and water stress in plants. Of those studies that do separate indirect and direct effects, all five of the vegetation models assessed by

Rowland et al. (2015) supported H2, since declines in net photosynthesis at high temperatures were driven by reductions in stomatal conductance, rather than biochemical controls. Galbraith et al. (2010) found that direct temperature effects dominated the temperature-associated reductions in Amazonian vegetation carbon in two dynamic global vegetation models (DGVMs: the Top-down Representation of

Interactive Foliage and Flora Including Dynamics, TRIFFID, and Lund–Potsdam–

Jena, LPJ), but that indirect effects were more important in a third model (Hyland).

They attributed the higher sensitivity of Hyland to indirect temperature effects to its

Amazonian plant functional types (PFTs) having a higher Topt compared to the other models (32°C for Hyland, 21°C for LPJ, and 28°C for TRIFFID) and its use of the

Jarvis-Stewart stomatal conductance scheme, which appears more sensitive to changes in humidity.

No ecosystem-scale warming manipulations currently exist for tropical forests (Wood et al. 2012; Corlett 2011; Cavaleri et al. 2015). Here, we utilized the large-scale tropical forest mesocosm at the B2-TF, which uniquely allowed us to expose a tropical forest to temperatures ~10°C higher than maximum values currently experienced by Amazon forests and to alter the relationship between VPD and temperature. In so doing, we were able to assess the response of GEP to temperature and VPD under a regime where VPD was highly sensitive to temperature (natural forest sites) and a regime where VPD had a lower sensitivity to temperature (B2-TF), in an attempt to partially decouple these variables. Our results provide compelling evidence that tropical forest photosynthesis is not directly limited by temperature, and that, in the absence of high levels of VPD, it can

36 continue up to high temperatures. Furthermore, through binned regression analyses, we show that VPD and not temperature is the major control on photosynthesis at natural forest sites.

Our findings imply that tropical forests are not currently close to a high temperature threshold, as some authors have suggested (Doughty and Goulden

2008; Clark et al. 2010) and may be more resilient to future warming. VPD, and hence VPD-induced stomatal closures, are likely to increase with the increased temperatures projected for tropical regions. Plant water stress under high temperatures may be mitigated by increased water-use efficiency (WUE) under elevated CO2 concentrations. However, whether WUE will in fact increase with CO2 is a matter of current debate. Modeling studies (Zhang et al. 2015; Swann et al.

2016) and evidence from stable carbon isotopes and tree rings (Peñuelas et al.

2011; van der Sleen et al. 2015) found support for this mechanism, but mixed conclusions have been obtained from eddy-covariance data (Keenan et al. 2013; Tan et al. 2015) and Free- Air Carbon dioxide Enrichment (FACE) experiments (Leakey et al. 2009; Gray et al. 2016). The majority of our understanding comes from temperate zones, and hence, the forthcoming FACE experiment in the Amazon should provide valuable insights on this issue (Norby et al. 2015).

Our findings help to resolve an outstanding debate in the literature concerning the mechanism by which temperature limits photosynthesis, which we hope will improve predictions of tropical forest response to climate change. With this endeavor in mind, we encourage the reevaluation of Topt and stomatal conductance schemes used in ecosystem models, both of which are critical in

37 determining the sensitivity of tropical forest vegetation to warming and the underlying mechanism (direct versus indirect). However, more work needs to be done to understand the combined physiological effects of elevated temperatures and CO2 on tropical forests.

Materials and Methods

Study sites

B2 is a large-scale Earth science facility near Tucson (Arizona, USA), comprising five biomes, of which the B2-TF is one. The B2-TF provides a controlled environment that can be sealed off from the outside world, allowing researchers to measure forest response to specific environmental variables. Climate conditions are maintained to be broadly similar to Amazonian forest sites (Arain 2000).

Experiments in the B2-TF have already elucidated tropical forest response to increasing CO2 concentrations (Lin et al. 1998; Lin et al. 1999) and drought (Rascher et al. 2004).

Data from the Brazilian sites (K34, K67, and K83) are from the LBA eddy covariance towers, part of the Brazil flux network (Restrepo-Coupe et al. 2013).

Here, we used the most recent version of the K67 eddy flux data, as in Wu et al.

(2016). K67 and K83 are located within the TNF, near Santarém, Pará. The TNF is a terra firme (upland) moist tropical forest, receiving an average rainfall of 1920 mm per year, and mean temperature and humidity are 25°C and 85%, respectively (Rice et al., 2004). The forest experiences a 5-month dry season between July and

November (months when mean rainfall is less than 100 mm per month) (Pyle et al.

2008). The eddy flux tower at K67 has been in operation since 2001, with significant gaps due to tree fall and lightning damage. Data collections at the K83 flux tower site started in July 2000 and ended in March 2004 (Goulden et al. 2004). This site underwent selective logging in 2001, which resulted in only small changes in ecosystem fluxes (Miller et al. 2011). The K34 site, located in the Cuieiras reserve, near Manaus, Amazonas, is the westernmost LBA site is an old-growth terra firme tropical rainforest. This region receives ~2400 mm rainfall per year and has a 3- month dry season from July until September (Araújo et al. 2002; de Gonçalves et al.

2013). K34 is a wetter forest than the TNF and contains greater variation in small- scale topography. It has an eddy flux tower that has been in operation since 1999.

The tropical dry forest site (Tesopaco) is located in Sonora, Mexico. It experiences a

9-month dry season from October until June when the majority of the species lose their leaves (Perez-Ruiz et al. 2010) (unlike the Brazilian sites, which are all evergreen forests). Mean annual temperature is 22.1°C and the annual rainfall is

712 mm (Alvarez-Yepiz et al. 2008).

The atmospheric CO2 concentration was higher in the B2-TF than the natural forest sites (406 ppm, compared to 368 ppm at K34 and 381 ppm at K83, Fig. S7).

However, this difference is likely too small to have had significant physiological effects; as a point of comparison, the CO2 treatment levels of current FACE sites are

550-600 ppm (Norby et al. 2015).

Data selection and environmental drivers

Overlapping NEE, photosynthetically active radiation (PAR), temperature, and VPD data were selected for the B2-TF from a non-gap-filled dataset compiled by Rosolem et al. (2010); this comprised almost 4 months of data from 2000 and 2002. All complete years of overlapping NEE, PAR, temperature, and VPD data were included for the three sites in the Brazilian Amazon (K34, K67, and K83). According to this criteria, 3 years of data were included for K34 (1999-2000 and 2003-2005), 7 years for K67 (2002-2006 and 2008-2011) and 3 years for K83 (2000-2003). We excluded periods when the tropical deciduous forest site in Mexico (Tesopaco) was dormant by using a leaf area index (LAI) threshold of >2.08 (mean growing season LAI, when the growing season is defined as periods when LAI >0.5). As a result, we included data from 7 July to 20 September 2006 in the analyses presented here.

Where PAR data was missing (from the K34 dataset only), we estimated PAR from measurements of incoming short wave radiation (rgs in W m-2), where PAR = 2 x rgs, as in Restrepo-Coupe et al. (2013).

Air temperature measurements were recorded above the canopy at each site.

Flux calculations

See Restrepo-Coupe et al., (2013) and Perez-Ruiz et al. (2010) for site descriptions and CO2 flux calculations for the LBA and Tesopaco sites, respectively. NEE in the

B2-TF is calculated from the rate of change of CO2 inside the biome:

푑[CO ] NEE = 2 푎 푀 + 퐹 + 퐹 (1) d푡 푎 leak conc

40 where d[CO2]a/dt is the rate of change in CO2 concentration in the air inside the mesocosm, Ma is the number of moles of air within the mesocosm per unit ground area (m2), Fleak is the CO2 flux between the B2-TF and the neighboring mesocosms due to air leakage through the partition curtains, and Fconc is the rate of CO2 uptake by the concrete structure due to a carbonation reaction between CO2 and calcium oxide (Lin et al. 1998).

We calculated GEE from hourly (or for Tesopaco, half hourly) NEE measurements, where GEE is NEE minus Reco. Here, we present GEP, calculated as negative GEE. Reco was assumed to equal night-time NEE values; as such, daily Reco values for B2-TF and Tesopaco were calculated as the mean of night-time NEE for each day. Reco for the Brazilian sites was calculated as the mean of night-time NEE within a 5-day window and gap-filled based on a relationship with PAR (Restrepo-

Coupe et al., 2013). Daytime Reco was not estimated by fitting a function to nighttime

NEE and temperature, an approach that is commonly used at higher latitude sites

(Reichstein et al. 2005), because these variables were not correlated within months.

Respiration tends to have a higher temperature sensitivity than photosynthesis, such that the ratio of respiration to photosynthesis exhibits a nonlinear temperature dependence to short-term warming (Drake et al. 2016). If daytime Reco exhibits a similar response to temperature at our sites, we may have underestimated GEP at high temperatures. However, respiration can acclimate to high temperatures (Atkin et al. 2005), especially over longer time-scales (Drake et al. 2016). Furthermore, there is no a priori reason for us to expect that the temperature response of daytime

Reco would be different between sites.

If our assumption of the equivalence of daytime and nighttime Reco is not valid, or if Reco does show a temperature dependence, our GEP estimates would be affected. Recent research partitioned NEE measurements into GEP and daytime Reco using stable carbon isotopic composition in a temperate deciduous forest and found evidence that light inhibits daytime Reco relative to nighttime rates (Wehr et al.

2016). Isotopic flux partitioning methods have not yet been deployed in the tropics, but if tropical daytime Reco exhibits a similar response, then our GEP values would be overestimates. However, respiration does show higher sensitivity to temperature than photosynthesis, in which case, our GEP values, particularly at high temperatures, would be underestimates. However, we do not expect the response of daytime Reco to temperature to be different between sites.

Light saturation curves were plotted for each site in order to estimate the light value at which NEE saturates. These were as follows: 300 W m-2 (global incident radiation) for Tesopaco, 1000 µmol m-2 s-1 (PAR) for K34, K67, and K83, and 200 W m-2 (downward shortwave radiation) for the B2-TF.

Mean values of light-saturated GEP values were calculated for 1°C temperature bins (similar to Doughty and Goulden 2008) and 0.2 kPa VPD bins. We scaled GEP to the maximum GEP value for each forest site to compare the response of canopy-level photosynthesis in the B2-TF with natural forest sites (Fig. 2). This provides a fairer comparison given the younger age of the B2 forest (~25 years), lower light environment (due to light interception of the space-frame), and area restrictions.

Binned regression analyses

To compare the relative influence of temperature versus VPD on GEP, we performed

“binned regressions” of GEP against temperature for individual 0.2 kPa VPD bins and regressions of GEP against VPD for 1°C temperature bins (using a similar method to Wu et al., 2016; Fig. S4). For the next analysis, we selected data > 28°C for each forest site, which represents a high temperature threshold for most of the sites in this study and agrees with previous findings (e.g., Doughty & Goulden 2008). Our goal was to assess the separate effects of temperature and VPD on GEP at high temperatures. Using data at and above the temperature threshold, we plotted the density of slope values for each of the binned regression analyses—(1) GEP versus temperature binned by VPD and (2) GEP versus VPD binned by temperature (Fig. 3).

Finally, we performed one-tailed Student’s t-tests to determine whether the mean slope values for each type of binned regression and site were significantly different from zero.

Acknowledgments

This work was supported by the National Science Foundation’s Partnerships for

International Research and Education (PIRE) (#OISE-0730305), and the

Philecological Foundation, with additional support from the Department of Energy

(GoAmazon), the National Aeronautics and Space Administration (NASA) (LBA-

DMIP project, award #NNX09AL52G), and the UA’s Agnese Nelms Haury Program in

Environment and Social Justice. MNS and JW were supported by the NASA Earth and

Space Science Fellowship (NESSF) program (grant #NNX14AK95H). Meteorological

43 data collection and quality control analysis of the B2-TF dataset by RR were also supported by the NESSF program (grant #NNX09AO33H).

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Figures

Figure 1. Tukey boxplots and regression lines (with standard error) showing the relationship between vapor pressure deficit (VPD) and temperature according to five temperature bins for the B2-TF (red), a seasonally dry tropical forest (Tesopaco, green), and Amazon forest sites (K34, K67, and K83, blue). Horizontal lines in the center of the boxplots are median values, and at the ends are first and third quartiles, while vertical lines extending from the boxplots (whiskers) show the data that lies within 1.5 interquartile range (IQR) of the lower and upper quartiles, and data points at the end of the whiskers represent outliers.

Figure 2. Light-saturated gross ecosystem productivity (GEP) versus (A) temperature and (B) vapor pressure deficit (VPD) for the B2-TF (red), a seasonally dry tropical forest (Tesopaco, green), and Amazon forest sites (K34, K67, and K83, blue). (A)

Points show the average GEP for each 1°C temperature bin, scaled to the maximum GEP value for each forest site; (B) Points show the average light-saturated GEP for each 0.2 kPa VPD bin, scaled to the maximum GEP value for each site. Error bars are standard errors. See Fig. S1 for unscaled data and Fig. S2 for unscaled data showing each Amazonian site separately.

Figure 3. Distributions of GEP sensitivity to temperature (µmol CO2 m-2 s-1 / °C, red lines) and to VPD (µmol CO2 m-2 s-1 / kPa, blue lines) derived from binned regressions (Fig. S4) between GEP and temperature and between GEP and VPD.

Data >28°C have been selected for each site to examine which is the driving factor of high temperature declines in GEP. Dashed lines show the mean slope value for each type of regression. Stars indicate mean slopes that are significantly different from zero (p < 0.05, one-tailed Student’s t-tests).

Supporting Information

1.1 Contrasting VPD-temperature regimes within the B2-TF

In the main text, we present data for all B2-TF data combined. However, the B2-TF was actually operated under two different VPD-temperature regimes: 1) a low VPD- temperature covariance regime (“B2-TF low VT cov”), in which we reduced the correlation between VPD and temperature by supplying water to the system in small rainfall events to maintain VPD at low levels while temperature varied, and 2) a high VPD-temperature covariance regime (“B2-TF high VT cov”), where the facility was operated in such a way as to allow VPD and temperature to covary in a similar way to natural settings (Fig. S3).

GEP was maintained up to much higher temperatures that natural forest sites under both VPD-temperature regimes (Fig. S2A). However, photosynthetic rates were higher for the B2 low VT cov than the B2 high VT cov, consistent with the notion that GEP is higher when VPD is low (H2). GEP for B2 low VT cov showed little response to VPD presumably because VPD remained low, but the B2 high VT cov regime showed a similar response to natural forest sites (Fig. S2B).

Figure S1. Unscaled version of Fig. 2. Light-saturated gross ecosystem productivity (GEP) versus (A) temperature and (B) vapor pressure deficit (VPD) for the B2-TF (red), a seasonally dry tropical forest (Tesopaco, green), and Amazon forest sites

(K34, K67, and K83, blue). GEP is averaged within (A) 1°C temperature bins and (B) 0.2 kPa VPD bins. Error bars are standard errors.

Figure S2. Unscaled version of Fig. 2 showing separate lines for all Amazonian sites and the B2-TF contrasting VPD- temperature regimes (see SI section 1.1). Light-saturated gross ecosystem productivity (GEP) versus (A) temperature and (B) vapor pressure deficit (VPD) for B2 high VT cov (red), B2 low VT cov (yellow), a seasonally dry tropical forest (Tesopaco, green), and Amazon forest sites (K34, K67, and K83: light blue, purple, and dark blue, respectively). GEP is averaged within (A)

1°C temperature bins and (B) 0.2 kPa VPD bins. Error bars are standard errors.

Figure S3. Tukey boxplots and regression lines (with standard error) showing the relationship between vapor pressure deficit (VPD) and temperature according to five temperature bins for the B2 high VT cov (red), B2 low VT cov (yellow), a seasonally dry tropical forest (Tesopaco, green), and Amazon forest sites (K34, K67, and K83, blue). See SI section 1.1 for an explanation of the contrasting VPD- temperature regimes in the B2-TF (B2 high and low VT cov). See Fig. 1 caption for description of what boxplots whiskers represent.

Figure S4. Linear regressions between (A) light-saturated gross ecosystem productivity (GEP) and temperature for 0.2 kPa vapor pressure deficit (VPD) bins and (B) light-saturated GEP and VPD for 1°C temperature bins for the B2-TF, a seasonally dry tropical forest (Tesopaco, second panels), and Amazon forest sites (K34, K67, and K83). Here all data is included, not just data >28°C (as in Fig. 3).

Figure S5. Raw values of light-saturated gross ecosystem productivity (GEP) versus temperature with points colored by vapor pressure deficit (VPD) values for the B2-

TF, a seasonally dry tropical forest (Tesopaco, second panels), and Amazon forest sites (K34, K67, and K83).

Figure S6. Ecosystem respiration (Reco) as a function of temperature for the B2-TF

(red), a seasonally dry tropical forest (Tesopaco, green), and Amazon forest sites

(K34, K67, and K83: light blue, purple, and dark blue, respectively). Error bars are standard errors.

Figure S7. Tukey boxplots of atmospheric CO2 concentrations in natural tropical forests in the Amazon (K34 and K83: red and yellow, respectively) and in the B2-TF

(B2 high VT cov, B2 low VT cov, and all B2 data: green, blue, and purple, respectively). Mean CO2 concentrations for each site are as follows: K34: 368 ppm,

K83: 381 ppm, B2 high VT cov: 407.9 ppm, B2 low VT cov 407.1 ppm, B2 (all data):

406 ppm. See Fig. 1 caption for description of what boxplots whiskers represent.

APPENDIX B: Seasonal and El Niño-associated changes in LiDAR-derived LAI

and leaf area profiles in an eastern Amazonian forest

Authors: Marielle N. Smith1, Scott C. Stark2, Tara Woodcock1, Mauricio Ferreira3,

Eronaldo de Oliveira4, Luciana F. Alves5, Michela Figueira4, Natalia Restrepo-

Coupe1,6, Luiz Aragao7, Plinio B. de Camargo3, Raimundo C. de Oliveira8, Donald A.

Falk9,10, Sean M McMahon11,12, Travis Huxman13, Scott R. Saleska1

Author affiliations: (1) Ecology & Evolutionary Biology, University of Arizona,

Tucson, AZ, USA; (2) Department of Forestry, Michigan State University, East

Lansing, MI 48824, USA; (3) CENA, University of São Paulo, Brazil; (4) Universidade

Federal do Oeste do Pará (UFOPA), Santarém, Pará, Brazil; (5) Department of Plant

Biology, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo,

Brazil; (6) Plant Functional Biology and Climate Change Cluster, University of

Technology Sydney, Sydney, NSW, Australia; (7) Instituto Nacional de Pesquisas

Espaciais; (8) Embrapa Amazônia Oriental, 68035-110 Santarém, Pará, Brazil;

(9) School of Natural Resources and the Environment, University of Arizona, Tucson,

AZ, USA; (10) Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ,

USA; (11) Smithsonian Environmental Research Center, Edgewater, MD, USA;

(12) Smithsonian Institution’s Forest Global Earth Observatory; (13) Ecology and

Evolutionary Biology and Center for Environmental Biology, University of California,

Irvine, CA 92629, USA.

Keywords: tropical forests, climate change, drought, El Niño, forest structure, phenology, seasonality

Abstract

Predicting the future of Amazonian forests is of critical importance as changes in the dynamics of these forests can influence global climate and ecosystems. Monitoring forest responses to seasonal environmental variations (e.g. dry seasons) may provide insights into how they will respond to future climate (e.g. droughts). Many

Earth system models are not able to accurately simulate the observed seasonal pattern of evergreen tropical forest photosynthesis, probably because they do not adequately incorporate seasonal changes in the physiological function of the forest.

Recent studies have shown phenological changes in leaf quantity (leaf area index,

LAI) and quality (photosynthetic capacity) to be critical to explaining the seasonality of ecosystem productivity. While many studies have characterised the seasonal pattern of LAI, none have investigated vertical changes in canopy structure, although canopy environments (e.g. light and water status) and many important leaf physiological traits are dependent on canopy position.

We assessed seasonal patterns of LAI and vertical canopy structure across three years, including a severe El Niño-induced drought, in the Tapajós National

Forest, Brazil using a multi-temporal ground-based LiDAR dataset. While seasonal variations in total LAI were normally small, variations in the LAI of different canopy layers were more dynamic, especially during the dry season and early wet season.

Across years, the upper and lower canopy exhibited divergent responses. During the

70 dry season, leaf area increased in the upper canopy and decreased in the lower canopy, and these trends reversed at the onset of the wet season. Seasonal patterns of LAI and vertical canopy structure were similar during the El Niño year, but trends were amplified. Total forest LAI declined dramatically at the height of the 2015-

2016 El Niño in comparison to the non-El Niño years. The majority of leaf area lost

(85%) was from the low canopy, while there was a small increase in leaf area in the upper canopy.

Our results suggest that the lower canopy is most responsive to seasonal and interannual droughts, perhaps due to the shallower roots of understory trees.

Increases in upper canopy LAI during seasonal dry periods and drought, despite declines in total LAI, may be consistent with satellite “green-up” observations, if satellite metrics are predominantly capturing structural changes at the top of the canopy. Measuring vertical canopy structure can reveal important mechanisms of tropical forest response to climatic change mediated by contrasting plant traits and strategies at low, mid, and upper canopy levels.

Introduction

71 precipitation patterns (Gedney and Valdes 2000; Werth and Avissar 2002) and atmospheric carbon content. There is uncertainty about how tropical forests will respond to projected climate change (Sitch et al. 2008; Malhi et al. 2009), which includes increases in the severity and frequency of droughts (Chadwick et al. 2015;

Duffy et al. 2015). Understanding how tropical forests respond to seasonal environmental perturbations may provide important insights into how they will respond to future climate.

The phenology of temperate forests is well understood, defined by distinct growing and dormant seasons driven by temperature changes. But the seasonality of tropical forests is less obvious, especially in evergreen tropical rainforests that lack major seasonal climate variations. Many tropical forests experience annual dry periods, the length of which governs much of tropical forest phenology (Reich

1995). Much of Amazonia experiences a marked dry season (when precipitation is less than 100 mm per month), with lengths ranging from 0 to 7 months across the region (Sombroek 2001).

Many dynamic global vegetation models (DGVMs) show a seasonal pattern of vegetation photosynthesis divergent from the pattern observed from eddy flux towers (Restrepo-Coupe et al. 2016). While observations at central Amazon sites show increases in gross primary productivity (GPP) during the dry season (Saleska et al. 2003; Goulden et al. 2004; Hutyra et al. 2007; Restrepo-Coupe et al. 2013),

DGVMs simulate dry season declines (Restrepo-Coupe et al. 2016). This model-data mismatch appears to be in large part because modelled GPP seasonality is driven by the seasonal changes in environmental variables, and does not adequately

72 incorporate changes in canopy phenology. However, in equatorial Amazonian forests that are not water-limited, analysis of eddy flux data has found that GPP seasonality is not correlated directly to seasonal changes in light or water availability (Restrepo-Coupe et al. 2013). Instead, recent research highlights that phenological changes in leaf quantity (leaf area index, LAI) and leaf quality

(photosynthetic capacity) are critical to the seasonality of forest productivity

(Restrepo-Coupe et al., 2013; Wu et al., 2016).

Vertical canopy structure determines light transmission through the canopy

(e.g. Stark et al. 2012), which in turn determines many leaf functional traits and demographic rates, e.g. leaf dry mass per unit area, chlorophyll content, photosynthetic rate, abscission rate, and leaf life-span (Osada et al. 2001; Niinemets

2010; Kenzo et al. 2015). Different canopy heights probably experience contrasting patterns of seasonal resource availability (e.g. water and light availability). Hence, it is likely that different canopy strata have contrasting rates of leaf turnover and seasonal patterns of LAI that may ultimately help to explain the observed seasonality of GPP. While many ground-based studies have documented seasonal changes in total LAI in evergreen tropical forests (Carswell et al. 2002; Asner et al.

2004; Doughty and Goulden 2008; Juárez et al. 2009; Malhado et al. 2009; Brando et al. 2010; Girardin et al. 2016), none have yet investigated these potentially important seasonal changes in vertical canopy structure. In large part this is because until recently, we lacked methods capable of making high frequency, fine-scale measurements of forest structure through all canopy levels. LiDAR (light detection and ranging) provides unprecedented three-dimensional information on canopy

73 structure (Lefsky et al. 2002) and the development of a ground-based system

(Parker et al. 2004) provides opportunities to make multi-temporal measurements across the full vertical range of the forest canopy.

Seasonal changes in vertical canopy structure may identify important mechanisms by which forests respond to seasonal and anomalous climate variability. Here, we present what we believe to be the first analysis of a seasonal ground-based LiDAR dataset for a tropical forest. We collected monthly measurements of forest structure over three years, including an El Niño-induced drought event, in order to address the following questions:

 How do LAI and vertical canopy structure change seasonally? Specifically,

do different canopy levels (and hence, tree size classes) exhibit

contrasting seasonal patterns?

 Does the seasonal response of canopy structure predict its response to

anomalous drought?

Materials and Methods

Study site

The Tapajós National Forest (TNF) near Santarém, Pará, Brazil (K67 site) is located to the east of the Tapajós River and to the west of the Santarém – Cuiabá highway

(BR-163). It is a terra firme (upland) Holdridge Life Zone tropical dry forest, receiving an average rainfall of 1920 mm per year, and average temperature and humidity of 25°C and 85%, respectively (Rice et al., 2004). The forest experiences a

5-month dry season between July and November (months when mean rainfall is less

74 than 100 mm per month) (Pyle et al. 2008), putting it within the 30th percentile of

Amazonian forests in terms of dry season length; this may mean that its response to climate change will foreshadow the responses of other, wetter forests (Saleska et al.

2003).

LiDAR data collections

Ground-based LiDAR surveys were made using a Riegl LD90-3100VHS-FLP (Riegl

Laser Measurement Systems, Horn, Austria). The instrument collects two- dimensional information comprising leaf area densities (LAD, m2m-3) in one vertical plane (Parker et al. 2004). Monthly surveys were conducted during three periods at

K67: January to December 2010, August 2012 to February 2013, and November

2015 to September 2016. LiDAR survey transects were positioned along the central axes of forest inventory plots where litterfall collection baskets are located. Surveys comprise three, or normally four 1 km long transects. Transects are measured by walking at a constant speed along cleared trails. LiDAR data was processed as per the methods developed by Stark et al. (2012).

Leaf litterfall and flush

Monthly litterfall collections concurrent with LiDAR surveys exist for two periods at

K67: May 2011 to September 2013 and February 2015 to July 2016. Non- overlapping litterfall data are also available every 2 weeks for the period July 2000 to June 2005 (Rice et al. 2004). Collections were made from 40 (2000-2005), 64

(2011-2013) or 78 baskets (2015-2016) located along each of the four 1 km long

75 transects at the site. Dried leaf litterfall weights were converted to an area (m2/m2) by multiplying by specific leaf area (SLA: 0.0032 m2 gC-1, Domingues et al. 2005).

In order to estimate leaf flush, we first calculated the area of leaf litterfall

(m2m-2) since the previous LiDAR survey date (by summing daily rates of litterfall between LiDAR surveys). Leaf flush was then estimated as the sum of dLAI (m2m-2) and leaf litterfall (m2m-2) (Fig. S2).

Enhanced vegetation index (EVI) values

Here, we present EVI data derived from the Moderate Resolution Imaging

Spectroradiometer (MODIS) produced by Davies et al. in prep. (Fig. 4A). They calculated the mean EVI seasonality over a ten year period (2003-2012) for evergreen upland forests in the eastern Amazon.

Environmental variables

Monthly precipitation data was obtained from the Tropical Rainfall Measuring

Mission (TRMM) data product. We used a bimonthly timeseries of the Multivariate

El Niño-Southern Oscillation (ENSO) Index (MEI) produced by the National Oceanic and Atmospheric Administration, calculated as the first PC of normalized and seasonalised sea-level pressure (P), zonal (U) and meridional (V) components of the surface wind, sea surface temperature (S), surface air temperature (A), and total cloudiness fraction of the sky (C) (NOAA 2016). MEI is a measure of ENSO intensity, with positive values indicating drier than conditions in Amazonia, and negative values indicating wetter than normal conditions.

Results

Seasonal patterns of LAI, litterfall, and leaf flush

LAI showed modest seasonal variation in baseline (non El Niño) years (2010 and

2012-2013) and much higher variation during the 2015-2016 El Niño year (Fig. 1).

In baseline years, LAI increased a small amount during the dry season, declined at the dry-wet season transition, increased in the mid wet season, and declined again through the rest of the wet season (Fig. 2). The El Niño year showed a similar pattern, but the decline in LAI at the end of the dry season was amplified, coinciding with the peak of the 2015-2016 El Niño (November to December 2015) (Fig. 1). At this time, LAI dropped by 0.34 m2m-2 (a 7% reduction in the LAI of the canopy), a change almost twice as large (89%) as the average annual amplitude of LAI (the maximum annual value minus the minimum annual value) during baseline years

(0.18 m2m-2 + 0.08). The reduction in LAI coincided with a comparable area of leaf litterfall (Figs. 2 and S2).

Litterfall peaked during the dry season and reached minimum values during the wet season, as has been shown in numerous other studies of this region

(Goulden et al. 2004; Rice et al. 2004; Bonal et al. 2008; Doughty & Goulden 2008;

Malhado et al. 2009; Brando et al. 2010). The maximum rate of litterfall in the 2015 dry season (0.46 m2m-2 month-2) was almost twice as high as the maximum rate in

2012 (0.24 m2m-2 month-2), and ~30% greater than the maximum rate of litterfall from 2000-2005 mean values (0.36 m2m-2 month-2).

There was insufficient overlap in the timing of LiDAR and litterfall surveys for us to estimate an entire seasonal cycle of leaf flush. However, in general our

77 estimates indicate that leaf flush followed a similar seasonal cycle to leaf litterfall, reaching a peak during the dry season, and lowest values during the wet season (Fig.

2). This is again in accordance with other studies that have estimated leaf flush from dLAI and litterfall (Doughty & Goulden 2008; Malhado et al. 2009; Brando et al.

2010).

Seasonal patterns of LAI in the low, mid, and upper canopy

In baseline years, lower canopy LAI (0-15 m) decreased towards the end of the dry season, while upper canopy LAI (> 31 m) increased, and mid canopy LAI (16-30 m) remained constant or increased slightly (Fig. 3). During the wet season, lower canopy LAI increased, while upper and mid canopy LAI decreased. Like the seasonal pattern of total LAI, the canopy layers displayed similar but much accentuated trends during the El Niño year. In particular, the reduction of upper canopy leaf area and recovery of understory leaf area were much more pronounced at the dry-wet season transition.

The majority of the LAI lost during the peak of the 2015-2016 El Niño

(November to December 2015) was from low (0.29 m2m-2 or 85%) and mid levels of the canopy (0.14 m2m-2 or 41%), while LAI of the upper canopy increased by 0.09 m2m-2 (Fig. 3). Furthermore, there appears to have been a time lag in the response of the upper canopy to the drought, since LAI was first lost from the lower canopy level in November-December 2015, and then from the upper canopy level in

February-May 2016.

There was an interannual trend in the LAI of different canopy layers from

2010 to 2016, namely, a consistent increase in LAI in the upper canopy and decrease in the lower canopy (Fig. 3). We attribute this to a decrease in annual precipitation from 2010 to 2016, coupled with the cumulative effects of two El Niño events (in

2010 and 2015). LAI of the mid canopy remained about the same across years.

Analysis of the vertical canopy profiles identified the end of the dry season and beginning of the wet season as the most dynamic periods; trends were consistent across years but more pronounced during the 2015-2016 El Niño year

(Fig. 4). Leaf area was lost from the lower canopy and gained in the upper canopy between the mid and end of the dry season. In the following period, from the end of the dry season to the mid wet season, leaf area increased in the lower canopy and decreased in the upper. From the mid to end of the wet season, the vertical profiles showed little change across years. Profiles were notably different in the El Niño year: from the mid to end of the dry season, understory leaves were lost from higher up in the canopy (up to ~25 m, rather than up to ~20 m in baseline years), and from the end of the dry season to mid wet season, LAD differences in the upper and lower canopy were much larger. The seasonality of the upper canopy measured from

LiDAR corresponded to satellite-derived canopy greenness (enhanced vegetation index, EVI, from Davies et al. in prep.; Fig. 4).

Discussion

In baseline (non El Niño) years (2010 and 2012-2013), LAI exhibited modest seasonal variation (average annual amplitude was 0.18 m2m-2 + 0.08), most notably,

79 increasing during the dry season and decreasing at the onset of the wet season (Fig.

2). Strata within the canopy showed greater seasonal dynamics than total LAI (Fig.

3). Specifically, from the mid to end of the dry season, leaf area was lost from the lower canopy and gained in the upper canopy, while from the end of the dry season to mid wet season, the lower and upper canopy showed offsetting trends, and from the mid to the end of the wet season, there was little change in the canopy profile

(Fig. 4). Seasonal leaf area changes in the upper canopy showed good agreement with satellite-derived EVI seasonality, suggesting that satellite metrics of canopy greenness are capturing the leaf phenology of the upper canopy. The 2015-2016 El

Niño year showed similar—but amplified—seasonal patterns of LAI, leaf area profiles, and leaf litterfall to baseline years. The decline in LAI towards the end of the 2015-2016 dry season (corresponding to the peak of the El Niño) was about twice as large as the average annual amplitude during baseline years, as was the concurrent increase in leaf litterfall rate (Figs. 1 and 2). During this time, total forest

LAI declined dramatically, largely due to a loss in lower canopy LAI, while upper canopy LAI increased slightly (Fig. 3). The differential response of the lower and upper canopy was more pronounced during the El Niño year, especially from the end of the dry season to the mid wet season.

Many ground-based studies have documented the seasonal pattern of total

LAI in evergreen tropical forests (Carswell et al. 2002; Asner et al. 2004; Doughty and Goulden 2008; Juárez et al. 2009; Malhado et al. 2009; Brando et al. 2010;

Girardin et al. 2016). Across these studies, total LAI shows low seasonal variation, with an average seasonal amplitude (the difference between the maximum dry

80 season value and the minimum wet season value) of 0.7 m2m-2, corresponding to a

13% annual change in LAI (Albert et al. in prep.). However, as we show here, there is a strong seasonality within individual vertical strata, particularly during the dry season and early wet season (Fig. 4). This is a compelling example of “cryptic phenology”, a term coined by Albert et al. (in prep.) to describe “the cyclic or recurrent changes in plant structures and plant processes that are hidden, deceptive, or functionally dynamic, and as a consequence, have rarely been measured at the temporal or spatial scale necessary to document and understand.”

In this case, previous studies have used LAI estimation methods (LAI-2000, PAR sensors, and hemispherical camera photos) incapable of detecting changes in the vertical canopy and as such, important within-canopy dynamics have remained invisible. To our knowledge, we present the first ground-based study to document the dynamic seasonality of vertical canopy layers, which indicates alternative strategies of the upper and lower canopy that may have important implications for seasonal changes in GEP and photosynthetic capacity.

Across years, the upper and lower canopy exhibited divergent responses, which were more pronounced during seasonally dry periods and the 2015-2016 El

Niño-induced drought. Further work is needed to determine what controls these contrasting responses, but we expect that the principle factors are height-structured seasonal changes in water and light availability, and that the limiting resource depends upon the canopy layer, and duration and severity of the dry period. The smaller trees that make up the majority of leaf area in the lower canopy are probably more readily water-limited, while the upper canopy (i.e. larger trees) may

81 tend to be more light-limited (at least during non-drought years). A key hypothesis to explain this is “root niche separation” (Ivanov et al. 2012). This is the idea that the roots of large canopy trees extend to deep soil layers, while the roots of small understory trees are predominantly contained within shallower soil layers. Ivanov et al. (2012) found support for root niche separation using a mechanistic model forced with meteorological data from the TNF, indicating that large trees avoid seasonal drought stress in the dry season by accessing deep water resources. Under this hypothesis, the increase in upper canopy LAI observed during the dry season and drought is likely driven by increases in radiation (since water is not limiting, at least for dry periods that are short or not severe), while the concurrent decrease in lower canopy LAI may result from depleted soil moisture layers utilised by smaller tree size classes. Indeed, a 22-week experimental drought at a tropical forest in

Panama found that soil water content was maintained at deeper soil depths (15-40 cm) but decreased sharply at the surface layers (< 15 cm), reaching a minimum soil water potential of -6 MPa (Engelbrecht and Kursar 2003). The length of this experimental drought approximately corresponds to the mean dry season length at the K67 site (5 months), and the 2015-2016 El Niño (7 months).

The decrease in upper canopy LAI at the onset of the wet season in baseline years may be due to declining radiation levels (again, assuming that large trees normally have access to deep water sources), while the increase in lower canopy

LAI may be in response to increased soil moisture as rainfall recharges the upper soil layers. However, at the onset of the 2016 wet season, upper canopy LAI declined much more rapidly than at any other period in the timeseries, possibly indicating a

82 time lag in the response of larger trees to the El Niño event. This could again be explained by root niche separation, whereby the deep water reserves of large canopy trees had not yet been sufficiently recharged after the severe and prolonged drought (Ivanov et al. 2012).

Alternatively, the seasonality of lower canopy leaf area may be controlled by changes in understory light penetration caused by seasonal fluctuations in upper canopy leaf area. In this case, increases in lower canopy LAI at the onset of the wet season would indicate a release from light competition as leaf area is simultaneously lost from the upper canopy. If light is the main limiting resource for understory trees, it makes sense that seasonal patterns of LAI in the lower canopy do not coincide with the seasonality of radiation because light levels are always suboptimal in the understory; rather, understory trees may flush new leaves during periods when understory light levels increase, which is determined by the quantity of leaves in the canopy above them. It is obviously challenging to decouple the independent effects of light and water availability because the seasonality of precipitation and radiation covary, in addition to which, studying the root distributions of different tree size classes is logistically difficult. However, Brenes-Arguedas et al. (2011) decoupled these environmental covariates by conducting common garden experiments in forests spanning a rainfall gradient in Panama. They found that under higher light levels, seedling performance was improved at sites that were not water limited, but reduced at water-limited sites, illustrating that water availability strongly determines the influence of understory light on species performance.

While there was a large reduction in total LAI at the peak of the El Niño, there was a concurrent increase in upper canopy LAI. Other studies have also documented reductions in LAI and increases in litterfall following drought in tropical forests. In a throughfall exclusion experiment (TFE) in the TNF, LAI was reduced by 7% in the drought plot relative to the control at the beginning of the dry season and by ~20% at the end of the dry season (Asner et al. 2004). The initial dry season value is comparable to the decline in LAI that we document here at the peak of the 2015-

2016 El Niño. According to satellite-based studies of Amazonia, canopy “greenness”

(measured by MODIS-derived EVI), an indicator of photosynthetic activity, increases during drought (Brando et al., 2010; Saleska et al. 2007). Although we observed a decline in total forest LAI, photosynthetic rates could still have increased due to leaf flush in the upper canopy. Upper canopy leaves have higher photosynthetic rates and may dominate forest GEP. Another possibility is that satellite metrics predominantly capture structural changes at the top of the canopy (Bradley et al.

2011), as is suggested by Fig. 4. If this is the case, then an increase in EVI may reflect an increase in LAI in the upper canopy, as we document here, while failing to capture the large loss of LAI from the lower canopy. The potential for structural changes in lower strata to go undetected by satellite measures in dense tropical forests is an important consideration to add to the debate about the ability of

MODIS-derived LAI/EVI to align with ground-based measures of LAI (e.g., Doughty

& Goulden, 2008).

Our results indicate that the lower canopy (< 15 m) was initially most strongly affected at the peak of the 2015-2016 El Niño, while the upper canopy (>

31 m) showed a lagged response. According to estimates of leaf area profiles for specific size groups (Fig. 2 of Stark et al., 2015), the lower canopy most likely corresponds to small trees (<18 cm in diameter), but could also include understory branches of larger trees (~20-88 cm in diameter). The upper canopy comprises the upper crowns of large canopy trees (~60-140 cm in diameter) and the entire crowns of the largest trees in the forest (>140 cm in diameter). Since the ground- based LiDAR actually measures plant area index (PAI), loss of LAI from either canopy level could result from leaf abscission, branch fall (from the diameter size classes as listed above), or mortality. Hence, it is not possible to determine whether the initial declines in the lower canopy, and later, upper canopy, were due to leaf abscission as part of a drought-avoidance strategy, or mortality. On the one hand, these within-canopy dynamics seem fairly consistent between years, suggesting a plastic response via leaf area changes. However, the consistent decline in LAI of the lower canopy from 2010 to 2016 would seem to indicate a reduction in the abundance of smaller size classes (via increased mortality or reduced recruitment), rather than a short-term loss of leaf area. Size-specific responses of trees to drought are well documented. Although the majority of studies from tropical forest plots and

TFE have identified large trees as being more vulnerable to drought-induced mortality (Nepstad et al. 2007; da Costa et al. 2010; Phillips et al. 2010), trees at both ends of the size spectrum seem to be most susceptible (reviewed in McDowell et al. 2008).

The majority of studies show evidence for LAI seasonality in Amazonian evergreen forests, and in particular, document a dry season increase (Table S1).

Despite this, many ecosystem models hold tropical forest LAI constant, in part because of the paucity of seasonal data (Caldararu et al. 2012; Powell et al. 2013), and those that do allow LAI to vary are unable to reproduce the dry season increase in LAI (Restrepo-Coupe et al. 2016). Some modelling studies have incorporated leaf phenology; however, this is normally achieved by prescribing seasonality. For example, Poulter et al. (2009) parameterised the LPJmL Dynamic Global Vegetation

Model (DVGM) with satellite LAI values. On the other hand, Kim et al. (2012) incorporated a phenology submodel into the Ecosystem Demography model version

2 (ED2). The model comprised a function of increasing leaf turnover with increasing radiation that improved model predictions of seasonal ecosystem dynamics (water, carbon, and litter fluxes). We encourage future model developments to explore the impact of seasonal changes in vertical canopy structure on ecosystem fluxes.

Conclusions

All levels of the forest canopy likely respond to water and light, but on time scales that depend on tree size. Seasonal changes in total LAI mask more dynamic phenologies of individual canopy strata. In particular, our results suggest that the seasonality of forest greenness derived from satellites is largely capturing the response of the upper canopy to seasonal and anomalous drought. Drought drives structural responses that are similar to seasonally dry periods, but amplified. Lower canopy responses are best explained by water limitation of small trees and upper canopy responses by changes in light availability. However, significant deep soil water depletion appears to lead to a delayed negative response of the upper canopy

86 to drought. Smaller trees appear to be more responsive to shorter, less severe dry periods, while larger trees are more susceptible to lengthier or more extreme droughts. The dynamic nature of within-canopy structural changes have not previously been documented but may be important in determining the seasonal pattern of productivity observed at evergreen tropical forest sites.

Acknowledgements

Special thanks to Darlisson Bentes, Veronika Leitold, Tara Woodcock, Cleuton

Pereira, and Chico for data collection; GoAmazon co-PI Marciel Ferreira for collaboration; and the LBA project for field infrastructure and support. Project funding was provided by NSF PIRE, and the GoAmazon project, funded jointly by U.S.

Department of Energy (DOE) and the Brazilian state science foundations in São

Paulo state (FAPESP), and Amazônas state (FAPEAM). MNS was supported by a

NASA Earth and Space Science Fellowship.

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Figures

Figure 1. Seasonal patterns of leaf area index (LAI) for baseline years (blue and green) and the 2015-2016 El Niño year (red), plotted as the percentage deviation from the maximum dry season LAI for each respective year. Error bars indicate standard error of transect LAI values for each survey.

Figure 2. Seasonal values of leaf area index (LAI, red lines), leaf litterfall (green lines), leaf flush (blue lines), monthly precipitation (blue bars, from TRMM), and MEI index (red bars) for baseline years (2010 and 2012-2013) and an El Niño year

(2015-2016). Shaded regions depict dry season months for each year (months when total precipitation was < 100 mm). Leaf flush was calculated as the sum of dLAI and leaf litterfall; litterfall rates were interpolating according to LiDAR survey dates

(interpolated version of leaf litterfall is shown in Fig. S2). Please note: no litterfall collections were available in 2010, so the long-term mean monthly leaf litterfall rates from 2000-2005 are shown, from which the leaf flush rates have also been estimated.

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Figure 3. Seasonal values of leaf area index for the low (0-15 m, blue lines), mid (16-30 m, green lines), and upper canopy

(> 31 m, red lines) levels for baseline years (2010 and 2012-2013) and an El Niño year (2015-2016).

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Figure 4. (A) Seasonality of canopy greenness from satellite-derived enhanced vegetation index (EVI) for evergreen forests in the eastern Amazon (from Davies et al. in prep.). (B) Average leaf area profile for the mid dry season across all years

(2010, 2012-2013, and 2015-2016), and average profile differences for baseline years (2010 and 2012-2013) for the periods (C) from the mid dry season to end of the dry season (red), (D) end of the dry season to the mid wet season (green), and

(E) mid wet season to end of the wet season (blue); thick red lines on plots C, D, and

E show profile changes for the El Niño year (2015-2016).

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Supporting Information

Table S1: Summary of studies that document seasonal LAI values in Amazonian evergreen tropical forests Study Location Mean LAI estimation Was strong LAI Duration and number of LAI pattern Annual annual method seasonality measurements LAI rainfall observed? amplitude (mm) In situ measurements

Carswell et Floresta Nacional 2,500 Spherical Yes, but only Monthly measurements LAI increased during the dry 1.7 (dry al. (2002) de Caxiuana, Pará, densiometer measured during from April to October 1999 season season Brazil dry season (late wet to late dry amplitude) season) Asner et al. K67, Tapajós 2,000 LAI-2000 No Monthly measurements LAI increased during the first 0.5 (2004) National Forest, from January 2001 to part of the dry season, decreased Pará, Brazil January 2002 mid-dry season, and continued to (control plot of slowly decrease through the wet rainfall exclusion season. experiment) Bonal et al. Guyaflux 3,041 LAI-2000 No, but only two Two measurements during There was no significant 0.1 (2008) experimental site, measurements 2005 difference between LAI values French Guiana measured during wet and dry periods (7.0 + 0.2 and 6.9 + 0.3m2 m-2, respectively) Doughty & K83, Tapajós 1,920 PAR sensors Yes Calculated monthly LAI increased during the dry ~1.0 Goulden National Forest, averages from continuous season, reaching a peak in (2008) Pará, Brazil measurements from Aug November-January (end of the 2001 to March 2004 dry season), remained high during most of the wet season, and declined at the end of the wet season reaching the lowest value in May

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Juárez et K83, Tapajós 1,911 Hemispherical Yes, but Infrequent measurements LAI was highest at the end of the ~1.0 al. (2009) National Forest, photography measurements from July 2000 to July 2003 wet season and early dry season Pará, Brazil were infrequent (May-July) and lowest during the and likely to have mid wet season been affected by logging activities during 2001 Malhado et K67 Tapajós 1,911 LAI-2000 No 1 year of monthly 0.4 al. (2009) National Forest, measurements (December Pará, Brazil (50 2003 to November 2004) plots around the tower site) Brando et K67, Tapajós 1,700- LAI-2000 Yes Monthly measurements LAI increased at the start of the ~1.0 al. (2010) National Forest, 3,000 from January 2000 to dry season, remained high Pará, Brazil December 2005 during most of the wet season (control plot of and decreased towards the end rainfall exclusion of the wet season experiment) Satellite measurements

Myneni et Amazon basin LAI data from Yes 5 years of monthly MODIS Leaf area was higher in the dry ~1.0 for al. (2007) (~7.2 X 106 km2) the MODIS LAI data season than the wet season in the most sensor onboard majority of the forest in the forests NASA’s Terra Amazon basin (58%). Amplitude satellite was 25% of the average annual LAI (4.7) Doughty & K83, Tapajós 1,920 MOD15 LAI Yes Calculated monthly MODIS LAI increased during the ~1.0 Goulden National Forest, product (for averages from MODIS LAI dry season, peaked in July (mid (2008) Pará, Brazil km83 site) values from 2000 to 2006 dry season), and decreased at the onset of the wet season (November / December). This pattern was out of phase with in situ measurements by ~4 months Caldararu Amazon basin Various C5 MODIS Yes 2001-2005 LAI increased during the dry ~1.5 et al. (10◦ N–10◦ S, Terra LAI data season and decreased during the (2012) 80◦W–50◦ W) wet season

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Samanta et Amazon region Various C5 MODIS Yes Monthly LAI values from LAI increased during the dry 0.93 al. (2012) 0°–20°S and 80°– Terra LAI data February 2000 to season and decreased during the 40°W December 2009 wet season. LAI increased by 18% (0.93 units) in 33% of Amazonian forests Bi et al. 1200 x 1200 km2 Various Terra MODIS Yes Calculated 16-day average Leaf area increased during the ~1.1 (2015) area of moist Collection 5 LAI LAI values for the period dry season (June to October), Amazonian 2000-2008 stayed high during the first part rainforests of the wet season (November to February) and decreased towards the end of the wet season (March to May). Leaf area varied by 20% seasonally in 70% of forests included in the study

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Figure S1. Long-term records of LiDAR-derived leaf area index (LAI, black lines) and leaf litterfall (green lines) at K67, aligned with monthly precipitation from the Tropical Rainfall Measuring Mission (TRMM, blue lines) and multivariate ENSO index

(MEI) values (red lines). Red shading shows the two El Niño events experienced at the site in 2009-2010 and 2015-2016. Blue shaded areas show the three periods of seasonal LiDAR measurements included in the analyses presented in this paper

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Figure S2. Seasonal values of LAI (red) and change in leaf area (m2m-2) from the previous time point in terms of dLAI (green), leaf litterfall (blue), and leaf flush

(purple). Change in litterfall area from the previous time point was calculating by summing the daily rates of litterfall between the LiDAR survey dates. Leaf flush was calculated as the sum of dLAI and leaf litterfall.

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APPENDIX C: Interannual changes in canopy structure with climate in an

evergreen Amazonian forest

Authors: Marielle N. Smith1, Scott C. Stark2, Joost van Haren3, Veronika Leitold4,5,

Tara Woodcock1, Luciana F. Alves6, Natalia Restrepo-Coupe1,7, Plinio B. de Camargo8,

Raimundo C. de Oliveira9, Sean McMahon10,11, Donald A. Falk12,13, Travis Huxman14,

Scott R. Saleska1

Author affiliations: (1) Ecology & Evolutionary Biology, University of Arizona,

Tucson, AZ, USA; (2) Department of Forestry, Michigan State University, East

Lansing, MI 48824, USA; (3) Biosphere 2, University of Arizona, 32540 S. Biosphere

Road, Oracle, AZ 85623, USA; (4) Remote Sensing Division, National Institute for

Space Research (INPE), São José dos Campos, Brazil; (5) Biospheric Sciences

Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA; (6) Department of Plant Biology, Universidade Estadual de Campinas (UNICAMP), Campinas, São

Paulo, Brazil; (7) Plant Functional Biology and Climate Change Cluster, University of

Technology Sydney, Sydney, NSW, Australia; (8) CENA, University of São Paulo,

Brazil; (9) Embrapa Amazônia Oriental, 68035-110 Santarém, Pará, Brazil;

(10) Smithsonian Environmental Research Center, Edgewater, MD, USA;

(11) Smithsonian Institution’s Forest Global Earth Observatory; (12) School of

Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA;

(13) Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, USA;

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(14) Ecology and Evolutionary Biology and Center for Environmental Biology,

University of California, Irvine, CA 92629, USA.

Keywords: tropical forests, Amazon, climate change, drought, El Niño, forest structure, LiDAR, LAI, leaf area profiles

Abstract

A key priority of global change science is to predict the future of Amazon forests.

However, considerable knowledge gaps remain concerning the temporal dynamics of forest structure under steady-state conditions and directional climate forcings, reducing our ability to make accurate projections for the future. Until recently, we lacked the methods and long-term datasets necessary to quantitatively assess the structural dynamics of complex tropical forest canopies at scales relevant to ecosystem-scale carbon dynamics. LiDAR provides unprecedented three- dimensional information on forest canopy structure, a fundamental property of the forest that has been mechanistically linked to carbon dynamics and forest demography.

We analysed a unique 11-year time-series of ground-based LiDAR measurements made at the Tapajós National Forest, Brazil, in order to quantify how canopy structure responds to interannual climate variability at this old-growth tropical forest site. The dataset encompassed a wetter period (2005-2010), followed by a drier period (2010-2016) that included two El Niño-induced drought events in

2009-2010 and 2015-2016.

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LiDAR-derived metrics of forest structure showed contrasting trends during the two periods, consistent with a recovery phase and a disturbance phase. During the first (wetter) period, leaf area index (LAI) of the lower canopy increased, while there was a small decrease in upper canopy LAI. Larger structural changes occurred in the second (drier) period, especially in years following El Niño-induced droughts, during which considerable LAI was lost from the lower canopy and there were smaller increases in upper canopy LAI.

To identify the processes responsible for structural changes, we compared

LiDAR-derived leaf area profiles to profiles estimated from tree size distributions.

LiDAR- and tree inventory-estimated profiles both showed gains in lower canopy

LAI during the first period and losses in lower canopy LAI during the second period, indicating that structural differences were driven by changes in woody biomass

(growth and mortality). However, estimated profiles showed less congruence in the upper canopy, suggesting that these structural changes were predominantly due to more plastic leaf area changes. If small trees are susceptible to drought-induced mortality, as our results indicate suggest, and the incidence of droughts increases, this could prevent the recovery of the forest from drought-induced disturbances.

Introduction

Tropical forests store about half of global forest carbon, of which over a third is contained within Amazon forests (Malhi et al. 2008; Pan et al. 2011). Release of these globally important carbon stocks to the atmosphere could exacerbate global warming (Cox et al. 2000). Therefore, a key priority of global change science is to

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predict the future structure and functioning of Amazon forests. The severity and frequency of droughts in Amazonia is projected to increase (Duffy et al. 2015), which may alter the dynamics of disturbance and recovery cycles (Chambers et al.

2013). However, Amazon forest response to drought remains highly uncertain

(Corlett 2016) as are the consequences for demographic trajectories, forest structure, and carbon.

Forest inventory plots, drought experiments, and satellite-based studies have provided insights into the response of Amazon forests to droughts. Large biomass losses were identified across a network of forest plots (the RAINFOR network) following severe drought events in 2005 and 2010 that transformed this long-term carbon sink into a source (Lewis et al., 2011; Phillips et al., 2009). Biomass losses were predominantly due to an increase in mortality, but reductions in growth also occurred. Tree responses to drought appear to be size-specific and temporally dynamic. Satellite-based studies have demonstrated a counterintuitive ‘greening up’ of forest canopies (increase in Enhanced Vegetation Index, EVI) during drought, suggesting a positive response of photosynthetic capacity (Saleska et al. 2007;

Brando et al. 2010). However, plot-based observations of natural and experimental droughts have shown that the increase in mortality rates following drought is higher for large trees (Nepstad et al. 2007; da Costa et al. 2010; Phillips et al. 2010). In contrast, a recent study of water stress-induced tree mortality in North American trees found that smaller and larger trees are both more susceptible to drought than intermediate size classes (Hember et al. 2016).

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Size-specific responses of trees to drought suggests an alteration of overall canopy structure, including the distribution of leaf area and, as a consequence, light.

Vertical canopy structure is a fundamental property of the forest that has been linked mechanistically to carbon stocks (Chambers et al. 2007; Asner et al. 2010) and dynamics (Stark et al. 2012). Until recently, we lacked the methods and long- term datasets required to assess the dynamics of complex canopy structures of tropical forests quantitatively at scales relevant to ecosystem-scale carbon dynamics. LiDAR (Light Detection and Ranging) provides unprecedented three- dimensional information on canopy structure (Lefsky et al. 2002). The growing availability of multi-temporal LiDAR data affords unique opportunities to understand how old-growth forest structure is influenced by climatic variation and recovery from disturbance. However, studies that have addressed this question have utilised airborne LiDAR data from just two time-steps (Dubayah et al., 2010;

Kellner et al. 2009) or from multiple sites with contrasting legacies of natural disturbances (Kellner and Asner 2009), rather than repeat measurements at the same site.

Silva et al. (2013) analysed 12 years of annual canopy height measurements, made manually at a tropical forest site in Costa Rica. The high temporal resolution of the dataset enabled the authors to assess the impact of an El Niño-induced disturbance, consisting of high temperatures and drought, on the dynamics of canopy strata and gap formation, and to determine how long canopy height distributions remained out of equilibrium. However, they able to measure only

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outer canopy height (i.e. no sub-canopy structural attributes), only made measurements up to 15 m, and at a fairly coarse resolution (5 x 5 m).

In this study we analysed a rare 11-year dataset of ground-based LiDAR measurements made at the Tapajós National Forest (K67 site), near Santarém,

Brazil. The instrument collects two-dimensional information comprising leaf area densities (m2 m-3) through the whole vertical canopy and in a single horizontal direction (Parker et al. 2004). This dataset allowed us to assess detailed temporal dynamics of forest structure at this old-growth site, specifically addressing the following questions:

 How does forest structure respond to interannual climate variability in

old-growth tropical forests?

 What are the long-term structural dynamics of disturbance and recovery?

Previous work suggests that the K67 site experienced a large drought- induced disturbance, attributed to an El Niño event in 1997-98 (Pyle et al., 2008;

Rice et al., 2004; Hayek et al. in prep.). In addition, the site experienced two different precipitation regimes—a wetter followed by a drier period. Annual precipitation increased during period 1 (2003-2009) and decreased during period 2 (2009-2016)

(Fig. 1). Two El Niño events also occurred in period 2 (in 2009-2010 and 2015-

2016). Given this legacy of disturbance and climatic variation, we made the following two hypotheses. Firstly (H1), changes in forest structure in the first part of the timeseries will indicate a recovery from disturbance following the large El Niño event in 1997-98. Specifically, we predicted that we would observe a filling out of the lower canopy, consistent with high recruitment and growth rates of small tree

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size classes (Rice et al. 2004; Pyle et al. 2008). Secondly (H2), the trajectory of forest recovery will be reduced or reversed in the second period, characterised by a drier precipitation regime and two El Niño-induced drought events. We predicted that larger trees would be most negatively affected (i.e., the upper canopy) (Nepstad et al. 2007; da Costa et al. 2010; Phillips et al. 2010).

Materials and Methods

LiDAR surveys and tree inventories took place at the K67 site located in the Tapajós

National Forest, near Santarém, Brazil. It is a terra firme forest that experiences a 5 month dry season (where monthly rainfall is < 100 mm). As such, it is within the

30th percentile of Amazonian forests in terms of dry season length and its response to climate change may foreshadow the responses of other, wetter forests (Saleska et al. 2003).

Ground-based LiDAR surveys were conducted annually or biannually during dry season months at K67 using a Riegl LD90-3100VHS-FLP (Riegl Laser

Measurement Systems, Horn, Austria). The instrument collects two-dimensional information consisting of canopy heights in one vertical plane (Parker et al. 2004).

The first survey was conducted in 2005 and the last in 2016, such that a total of ten surveys are included in this analysis. LiDAR survey transects (4 km) were conducted along the central axes of forest inventory plots. The LiDAR data was processed code using developed by Stark et al. (2012) in the R statistical package (R Core Team

2016).

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Tree inventory transects were established at the site in 1999, comprising four permanent 50 m x 1000 m tree inventory transects (totalling an area of 19.75 hectares) (Rice et al. 2004; Pyle et al. 2008). Tree inventories have been conducted annually or biannually, during which all trees > 35 cm dbh (diameter at breast height) are tagged, identified, and measured, and trees > 10 cm dbh are subsampled in four transects of 10 x 1000 m (3.99 ha) positioned in the centre of the larger transects (Rice et al. 2004; Pyle et al. 2008). With each resurvey, trees that have grown into the smallest size class (10 cm) are tagged and measured, and considered as recruitment; trees that have died since the last survey are recorded. Here, we include data from inventories conducted between 1999 and 2011, processed and quality controlled as per the methods described in Longo (2014).

To identify mechanisms of structural change, we compared leaf area profiles estimated from ground-based LiDAR surveys against profiles estimated from tree size distributions. To do this, we selected tree inventories and LiDAR surveys for all available overlapping years: 2005, 2008, 2010, and 2011, and compared the profile changes via the two estimate types between each successive survey. Profiles were estimated from tree size distributions using a forest structure model (Model II) developed by Stark et al. (2015).

In addition to LAI and leaf area profiles, we calculated a number of other

LiDAR-derived metrics of forest structure, in order to explore which metrics are most sensitive to recovery and disturbance processes (Fig. S1). We calculated gap fraction by considering gaps as portions of the LiDAR transects where maximum canopy height was < 10 m. Rugosity is the standard deviation (SD) of the outer

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canopy height (Parker and Russ 2004). Canopy complexity was calculated as the SD of vertical LAI, followed by the SD of the horizontal SD values (Hardiman et al.

2011). Elevation-relief ratio (E) was calculated as follows:

푚푒푎푛 ℎ푒𝑖𝑔ℎ푡−푚𝑖푛𝑖푚푢푚 ℎ푒𝑖𝑔ℎ푡

푚푎푥𝑖푚푢푚 ℎ푒𝑖𝑔ℎ푡−푚𝑖푛𝑖푚푢푚 ℎ푒𝑖𝑔ℎ푡 and reflects the degree to which the outer canopy is located within the upper (E >

0.5) or lower (E < 0.5) portions of the vertical canopy (Parker and Russ 2004).

Monthly precipitation data was obtained from the Tropical Rainfall

Measuring Mission (TRMM) data product. We used a bimonthly timeseries of the

Multivariate El Niño-Southern Oscillation (ENSO) Index (MEI) produced by the

National Oceanic and Atmospheric Administration, calculated as the first PC of normalized and seasonalised sea-level pressure (P), zonal (U) and meridional (V) components of the surface wind, sea surface temperature (S), surface air temperature (A), and total cloudiness fraction of the sky (C) (NOAA 2016). MEI is a measure of ENSO intensity, with positive values indicating drier than conditions in

Amazonia, and negative values indicating wetter than normal conditions.

Results

Most LiDAR-derived metrics of forest canopy structure showed two distinct trends during the 11-year the timeseries, one from 2005 to 2010 (“period 1”), and another from 2010 to 2016 (“period 2”). These periods roughly coincided with a period during which annual precipitation increased and MEI decreased (2003-2009), and a period during which annual precipitation declined and MEI increased (2009-2016)

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(Fig. 1). Strong El Niño-induced drought events during period 2, in 2009-2010 and

2015-2016.

During the first period, structural changes to the vertical canopy were relatively modest, comprising an increase in low to mid canopy LAI and a smaller decrease in upper canopy LAI (Figs. 2 and 3). In the second period, the structural changes were larger and in the opposite direction, whereby there was a 29% reduction in lower canopy LAI and a 44% increase in LAI in the upper canopy, while the mid canopy LAI remained about the same. The greatest structural changes occurred after the 2009-2010 and 2015-2016 El Niño events (i.e., between surveys

2010 and 2011, and 2015 and 2016), consisting of considerable reductions in LAI from lower canopy levels, and smaller increases in LAI in the upper canopy (Fig. S3).

Across the whole timeseries, the lower canopy (<20 m) experienced much larger changes that the upper canopy (Figs 3 and S3).

We compared structural changes from LiDAR-estimated profiles and profiles estimated from tree size distributions for period 1 (2005-2008 and 2008-2010) and the first year of period 2 (2010-2011). During the first period, both estimates showed a gain in the lower canopy, but only the LiDAR-estimates captured a loss from the upper canopy (Fig. 4). This suggests that the increase in lower canopy LAI in period 1 was driven by woody biomass growth, whereas the loss of upper canopy

LAI was due predominantly to reductions in leaf area. In the period after the 2009-

2010 El Niño (2010-2011), LiDAR- and inventory-estimated profiles showed good agreement in low and upper canopy levels, whereby there was a loss in the lower levels and an increase in the upper levels. This suggests that changes in woody

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biomass were responsible for structural changes in both canopy levels, although the

LiDAR-estimated profile showed a greater magnitude of change than the inventory- estimate in the upper canopy, indicating that leaf area changes also occurred.

Changes in maximum canopy height were distinctly different between the two periods, with the first period being dominated by reductions in the height of the outer canopy in the largest tree size classes and the second dominated by increases in outer canopy height in smaller tree size classes (Figs. 5 and S5).

Upper and lower canopy LAI values were correlated significantly with the annual precipitation anomaly (p < 0.05, Fig. 6), but showed opposite trends. While lower canopy LAI was positively correlated to precipitation anomaly, such that higher LAI was observed during anomalously wet times, upper canopy LAI was negatively correlated, exhibiting higher LAI values during anomalously dry periods.

Total and mid canopy LAI were not significantly correlated, and neither were any of the regressions between LAI values and mean annual MEI.

Rugosity and canopy complexity increased during the first half of the timeseries and declined in the second half (Fig. S1). Mean canopy height and elevation-relief ratio showed the opposite pattern, decreasing during period 1 and increasing during period 2; these trends indicate that there was an increase in leaf area in lower canopy levels during the first period, followed by a decrease in the second period (as shown in Figs. 2 and 3). Seasonal changes in the profile were smaller than interannual changes seen across 11 years of measurements, especially in the lower canopy (<20 m) (Fig. S4).

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Discussion

This old-growth tropical forest at the Tapajós National Forest experienced dramatic changes in canopy structure across the 11-year ground-based LiDAR timeseries.

During the first (wetter) period (2005-2010), LAI increased in the lower canopy levels and decreased in the upper canopy (Figs. 2 and 3). We compared LiDAR- and tree inventory-estimated leaf area profiles in order to identify the source of profile changes, which can occur via changes in leaf area (through the processes of leaf flush and leaf abscission) and/or woody biomass (growth, recruitment, and mortality). The congruence among both estimates in the lower levels of the profile suggests that structural changes in the low canopy were dominated by woody biomass growth, while those at upper canopy levels were driven by leaf loss (Fig. 4).

The structural changes that occurred during this first period—specifically, biomass growth of smaller size classes—are consistent with recovery from disturbance likely caused by an El Niño event in 1997-1998 (i.e. in support of H1). Rice et al. (2004) and Pyle et al. (2008) hypothesised that a period of high mortality occurred prior to measurements began at K67 (i.e. pre-1999), leading to large coarse woody debris pools, high recruitment rates, and net accumulation of biomass in small size classes.

Alternatively, or in addition to a recovery mechanism, the structural changes could have resulted from increasing annual precipitation (Fig. 1) and associated decreases in radiation which suppressed growth of the upper canopy, allowing light to penetrate deeper within the canopy.

Structural changes were more pronounced in the second half of the timeseries (2010-2016) and coincided with a period of declining annual

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precipitation and two strong El Niño-induced droughts (2009-2010 and 2015-

2016). Structural trends were opposite to those of the first period, with LAI decreasing in the lower canopy and increasing in the upper canopy (Figs. 2 and 3).

We attribute the decline in lower canopy LAI to a loss of woody biomass (i.e. mortality) in smaller tree size classes, rather than a reduction in leaf area (Fig. 4). In contrast, changes in the upper canopy were smaller and comprised increases in woody biomass and leaf area. These results suggest that precipitation reductions and severe droughts can cause significant changes to canopy structure, with smaller trees being particularly sensitive, while larger trees with access to deeper water resources may be more resilient. Size-specific responses of trees to drought are well documented. However, our results contradict the findings of throughfall exclusion experiments (TFE) in the Amazon, in which large trees (> 30 or > 40 cm dbh) showed the greatest increases in mortality rates following experimental droughts

(da Costa et al., 2010; Nepstad et al. 2007). While the majority (23 out of 33) of studies from tropical forest plots assessed by Phillips et al., (2010) also showed a greater relative increase in mortality rates of large trees following drought, six studies did not detect a size-related difference in mortality rates, and four identified greater post-drought mortality rates in small trees (< 30 or < 40 cm dbh). These results suggest that a greater diversity in size-specific mortality rates exists in natural forests following droughts as compared to drought experiments, and as such, K67 may reflect this diversity of responses.

There are reasons to expect that the results of experimental droughts may differ from natural droughts. For example, the Amazon TFE have taken place over

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much longer time scales (3-7 years) than the El Niño-induced droughts that we report on here (7 months for the 2015-2016 El Niño). Hence, perhaps smaller trees are more vulnerable in the short-term, but given a long enough drought, large trees will also succumb. Other practical problems concerning the design of TFE may prevent them from accurately representing natural droughts. For example, they have difficulty excluding water from upper soil layers and as such, may provide an unrealistic simulation of drought for smaller trees which still have access to water during the experiment. A further difference is that drought experiments dry the soil, not the air, and as a result, are not able to produce an environment with elevated

VPD, a characteristic of natural droughts (Corlett 2016) and one that smaller trees may be more susceptible to.

The more gradual change in forest structure during the first, recovery period, contrasts with the sudden changes observed during the second period, characterised by El Niño-induced drought disturbance events. The largest changes in the structure of the vertical profile occurred after the 2009-2010 and 2015-2016

El Niño events (Fig S3), predominantly resulting from biomass losses in the lower canopy (Fig. 4). These structural dynamics may be an example of the “slow in, rapid out” concept, whereby forest carbon is slowly accrued via tree growth, but is lost quickly due to gap dynamics or disturbances (Körner 2003). This suggests that the detection of mortality events can be improved not only by establishing larger plot sizes (as suggested by Chambers et al., 2013), but also by long-term, high frequency monitoring approaches like ours.

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During both periods, upper and lower canopy levels exhibited opposing structural trends (Fig. 3). Leaf area increased in the lower canopy during the wetter

(“recovery”) period, and decreased during the drier (“disturbance”) period, while the upper canopy responded in the opposite way in each period. We identified a similar phenomenon in the seasonal responses of upper and lower canopy strata at

K67 (Smith et al. in prep.). Specifically, we found that LAI of the lower canopy decreases during the dry season and increases at the onset of the wet season, while upper canopy LAI increases during the dry season and decreases at the beginning of the wet season. We were not able to identify whether these seasonal within-canopy dynamics were due to changes in leaf area (leaf flush and abscission) or woody biomass (growth and mortality). However, given that the dynamics are similar across years, structural changes on seasonal timescales must be due to plastic leaf area changes, either in response to environmental variations, or a programmed phenological response.

Changes in maximum canopy height did not reflect the observed changes in leaf area density during the two periods, and seemed somewhat contradictory to our hypotheses of a period of recovery followed by a period of disturbance. Period 1 was characterised by outer canopy height reductions of the largest tree size classes

(Fig. 5); this does not seem to be consistent with forest recovery, but may have caused an increase in light transmission to lower canopy levels, thus explaining the increase in lower canopy LAI. In contrast, period 2 was dominated by an increase in maximum canopy height of smaller tree size classes, which is unexpected given the concurrent decrease in lower canopy LAI during this period. These findings may

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reflect a decoupling of the dynamics of the outer and inner canopy with interesting implications; however, require further investigations are needed.

Changes in interannual vertical canopy structure could be driven by water availability (H1), light availability (H2), and/or legacy effects (e.g. disturbance history, H3). The fact that the upper canopy was correlated negatively to annual precipitation anomaly while the lower canopy was correlated positively (Fig. 6) indicates that these canopy strata have opposite responses to water stress (or light availability, which covaries with precipitation). It seems likely that the increase in woody biomass of the lower canopy during the first period was due to light gaps

(H2) created by the drought-induced death of large trees (H3), while the concurrent decrease in upper canopy leaf area may have resulted from lower radiation levels at a time when annual precipitation was rising. During period 2, the loss of leaf area and woody biomass from the lower canopy was probably due to critically low water availability for smaller size classes, while the increase in upper canopy leaf area and biomass growth was in response to increased light levels during this dry period. As we suggest in Smith et al. (in prep.), an increase in upper canopy LAI during drought is consistent with “green-up” reported from satellite-based studies (Saleska et al.

2007; Brando et al. 2010), given that downward-facing satellite observations are likely predominantly capturing upper level structural changes (Bradley et al. 2011).

However, we expect that a prolonged or severe enough drought would also result in biomass losses among larger tree size classes.

The recent drought-disturbance events appear to have “reset” the structure of the K67 forest to a post-disturbance state. We might predict that, in the coming

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years, the forest will undergo a similar structural recovery to the one we document here during period 1. However, if the region continues to experience a drying trend and severe droughts, biomass growth of small trees may be prevented. If, as our results suggest, small trees are more sensitive to drought, this has implications for future forest dynamics, since some proportion of them would ultimately have become canopy trees (Hember et al. 2016). Our results indicate that large trees respond more plastically to droughts via leaf area changes. However, they may also experience biomass losses if the duration or severity of drought is large enough to prevent recharge of deep soil layers (Ivanov et al. 2012).

Conclusions

We observed considerable changes in forest structure in a long-term dataset of ground-based LiDAR measurements at this old-growth tropical forest site, including a shift from a recovery to a disturbance phase. The filling out of the understory during the first half of the time series was consistent with recovery from a prior disturbance. Disturbance by drying and drought during the second half of the timeseries reversed the trends in the upper and lower canopy, consistent with a reset to the previous post-disturbance state. Changes in vertical canopy structure were gradual during the first, recovery phase and much larger during the second, disturbance phase, particularly after the 2009-2010 and 2015-2016 El Niño events.

Our results indicate that the upper canopy is plastically responsive to light, while the lower canopy is susceptible to water stress by mortality. We predict that the forest will return to a recovery phase, following the recent El Niño-induced drought

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disturbances. However, this may not be possible if drought frequency and severity increases, preventing the regeneration of small trees.

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