Photosynthetic Characteristics of Trees and Lianas Along an Altitudinal Gradient in Ecuador

PHOTOSYNTHETIC CHARACTERISTICS OF TREES AND LIANAS ALONG AN ALTITUDINAL GRADIENT IN ECUADOR

Jana Pauwels Student number: 01403120

Promotors: Prof. dr. Hans Verbeeck and dr. Marijn Bauters Tutors: ir. Miro Demol and Lore Verryckt

Master’s Dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master of Science in Bioscience Engineering: Environmental Technology

Academic year: 2018 - 2019

De auteurs en promotors geven de toelating deze scriptie voor consultatie beschikbaar te stellen en delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting uitdrukkelijk de bron te vermelden bij het aanhalen van resultaten uit deze scriptie.

The authors and supervisors give the permission to use this thesis for consultation and to copy parts of it for personal use. Every other use is subject to the copyright laws, more speciﬁcally the source must be extensively speciﬁed when using from this thesis.

Gent, juni 2019

De promotor, De auteurs, Prof. dr. Hans Verbeeck Jana Pauwels iv Acknowledgements

After two months of research in Ecuador and 8 months of analysis and writing, I can proudly say my thesis is finally finished. I can guarantee you it was not plain sailing. Without the help of a bunch of great people, it was never possible to write this thesis. In Ecuador, I was dealing with various technical problems. At the end, these difficulties improved my problem-solving thinking. Besides, I arrived in a team with Spanish speaking people. Luckily, they welcomed me with open arms and showed me a good time. Now, I can count them as my friends. They taught me the basics of Spanish by repeating words infinite times (cuchillo, cuchara, tenedor, cuchillo, cuchara, tenedor, ...). We stayed at places miles from everywhere without any connection with the outside world, where the mulas had to carry our stuff to. We pulled pranks, had discussions about salty or sweet popcorn or taught each other games. We did tree climbing and slid from mama piedra. In short, it was an instructive, but most of all an experience worth remembering. First of all, I want to express my gratitude to my promotor Prof. dr. ir. Hans Verbeeck for offering me this opportunity. I’m deeply indebted to my co-promotor dr. ir. Marijn Bauters and my supervisors ir. Miro De Mol and dr. Lore Verryckt (also known as the photosynthesis-guru). I am looking up to each one of you, guys! During these 10 months, you helped me with solving problems in the field, with the analysis of my data and with proofreading my text. Your critical eye helped me enormously. A special thanks to Michael and James from PP Systems to answer all my many questions about the CIRAS-3. You succored me with the solving of various technical problems with the available resources. Also, I would like to thank Thomas Sibret for demonstrating me the functioning of the CIRAS-3. I cannot leave Selene Baez and Debbie Eraly without mentioning, who assisted me with the practical arrangements. Thank youight Alberto for translating my abstract to Spanish. I’d like to acknowledge Mindo Cloudforest Foundation for letting us stay in one of the cabañas. It was stunning to eat with a view on many squirrels, guatusas, toucans, hummingbirds and other colourful birds. Germania and Karina cooked for us the most mouthwatering meals and made delicious jugos. We were really spoiled by you! I also want to thank German for being hospitable. Your lodge is without a doubt very pleasant to stay. On top of that, you helped me also to determinate my species. Gracias Jens, for jumping in this big adventure with me. Even if our paths during the thesis research in Ecuador were most of the time separated, you were a big support to me. It was always a pleasure to talk some Dutch again, it felt a bit like coming home. The three weeks of travelling together through the beautiful Ecuador was the best ending of this experience I could wish for. I can proudly call you one of my best friends. I had a great pleasure working with the entire team "casa estudio". Nico, my favourite metal friend, I admire your passions in life. I am truly amazed that you taught yourself recognizing botanical plants. I am so grateful that you searched and determined my species every single day. Paúl assisted with gathering my leaves, even if that meant hanging on the tree to be able to cut them with the pruner. Muy amable, amigo. I often think back to our stupid conversations and making jokes about each other. Also, Paula was unmissable these two months. When my Spanish vocabulary was still limited to a few words, you stood up for me. I really miss your weird Spanish accent when you were talking English. Unfortunately, Paola and Tito were vi only with us half of the time. You were both such kind people who I will never forget. Many thanks Paola for letting me use your cell phone when we were in the middle of nowhere and I had to contact someone if my máquina was stubborn again. Last but not least, I wouldn’t be writing this without the endless support of my family. Thank you mama, for making me who I am now and giving me all the chances in my life. Also, I want to give my appreciation to my friends and my boyfriend Jelle for working together and to take a break from time to time. Thank you all for being such amazing and supporting persons. Contents

Acknowledgements v

List of Abbreviations xi

Abstract xiii

Samenvatting xiv

Resumen xv

1 Introduction 1

2 Literature review 3 2.1 From forest to tropical montane cloud forest ...... 3 2.1.1 Forest ecosystems ...... 3 2.1.2 Tropical rainforest ...... 3 2.1.2.1 The role of lianas in tropical rainforests ...... 5 2.1.3 Tropical montane cloud forest ...... 5 2.1.3.1 Hydrology of TMCF ...... 6 2.1.3.2 Threats of TMCF ...... 6 2.1.3.3 TMCF in Ecuador ...... 8 2.2 Water Use Efficiency ...... 9 2.2.1 Definitions ...... 9 2.2.2 WUE and climate change ...... 9 2.2.3 13C isotope signature and discrimination as evaluation ...... 9 2.2.4 WUE differs between trees and lianas ...... 10 2.2.5 Case study: the isotope trends in Ecuador and Rwanda ...... 11 2.3 Leaf photosynthesis ...... 11 2.3.1 Photosynthetic light-response curves ...... 13 2.3.1.1 Theory ...... 13 CONTENTS viii

2.3.1.2 Fitting ...... 13 2.3.1.3 C3 and C4 pathway ...... 15 2.3.1.4 Sun and shade leaves ...... 15

2.3.2 gs-VPD curves ...... 17 2.3.2.1 Theory ...... 17 2.3.2.2 Fitting ...... 17 2.4 Altitudinal gradients ...... 19 2.4.1 Changing parameters with the altitude ...... 19 2.4.2 High altitudinal ecosystems are more vulnerable to climate change ...... 19

3 Material and methods 21 3.1 Site description ...... 21 3.2 Leaf inventory ...... 24 3.3 Leaf sample analysis ...... 25 3.3.1 Response curves ...... 25 3.3.1.1 Leaf net photosynthesis ...... 25 3.3.1.2 Light response curves ...... 26

3.3.1.3 gs-VPD response curves ...... 26 3.3.2 Speciﬁc leaf area ...... 26 3.4 Data processing and statistical analysis ...... 28 3.4.1 Data quality check ...... 28 3.4.2 Regression selection ...... 28 3.4.3 Statistical analysis ...... 29

4 Results 31 4.1 Regression selection ...... 31

4.2 gs-VPD measurements ...... 31 4.2.1 Comparison between growth forms and altitudes ...... 32 4.2.2 The eﬀect of the diﬀerent genera ...... 34 4.2.3 Comparison between growth forms and forest type ...... 35 4.3 SLA...... 36 4.4 Carbon assimilation ...... 39 4.5 Intrinsic and instantaneous WUE ...... 42 CONTENTS ix

5 Discussion 47 5.1 Water relations ...... 47 5.1.1 Goodness of fit ...... 47 5.1.2 The response of the stomata on atmospheric drought stress ...... 47 5.1.3 Altitudinal changes ...... 48 5.1.4 Differences between growth forms ...... 49 5.1.5 Genetic effects ...... 52 5.2 Carbon relations ...... 52 5.2.1 SLA ...... 52 5.2.2 Maximum carbon assimilation ...... 53 5.3 Carbon-water relations ...... 54 5.4 Impacts of climate change on TMCFs ...... 55 5.5 Pitfalls of the measurement method ...... 56

6 Conclusions 57

7 Bibliography 59

A Extra Tables and Figures 68 CONTENTS x List of Abbreviations

Φ0 Apparent quantum eﬃciency A Net photosynthetic rate

Amass Mass-based saturated net photosynthetic rate

Asat Area-based saturated net photosynthetic rate a.s.l. Above sea level C Carbon ca Atmospheric leaf CO2 concentration CAVElab Computational & Applied Vegetation Ecology ci Internal leaf CO2 concentration CIRAS Combined Infra-Red Analysis System

CO2 Carbon dioxide E Leaf level transpiration ET Evapotranspiration FAO Food and Agriculture Organization FWU Foliar water uptake GHG Greenhouse gases GPP Gross primary production gmax Maximum stomatal conductance gs Stomatal conductance

H2O Water

Icomp Light compensation point IRGA Infra-red gas analyser iWUE Intrinsic water use eﬃciency LA Leaf area LM Leaf mass LMA Leaf mass per area LMER Linear mixed-eﬀects regression MAE Mean absolute error xii

MAPE Mean absolute percentage error MCF Mindo Cloudforest Foundation MSE Mean squared error

Narea Area-based nitrogen content

Nmass Mass-based nitrogen content PAR Photosynthetically active radiation PPFD Photosynthetic photon ﬂux density PSP Permanent sampling plot RAINFOR Red Amazonica de Inventarios Forestales

Rd Dark respiration rate RH Relative humidity RMSE Root mean squared error SLA Speciﬁc leaf area

Tamb Ambient temperature

Tcuv Cuvette temperature

Tleaf Leaf temperature TMCF Tropical montane cloud forest VPD Vapour pressure deﬁcit WUE Instantaneous water use eﬃciency Abstract

Research questions: Is there a different response to atmospheric drought between lianas and trees as well as between the different types of forests? Similarly, do the saturated carbon assimilation (Asat), specific leaf area (SLA), intrinsic and instantaneous water use efficiency differ (iWUE and WUE respectively)? Is the carbon assimilation linked with SLA? Are the genetic effects playing an important role in the differences of aforementioned variables between plants? Location: Four strata at a 400-3000m a.s.l. elevational transect on the Western flank of the Ecuadorian Andes, comprising lowland rainforest, tropical montane cloud forest (TMCF) and high altitudinal primary forest. Methods: Sun leaves of lianas and trees were gathered at sixteen 40m x 40m permanent sampling plots at the four strata. Ex-situ leaf gas exchange measurements were performed with the portable photosynthesis system CIRAS-3. Per stratum, a few light response curves were performed. Especially, gs-VPD curves were executed, where vapour pressure deficit was increased during the measurement to simulate atmospheric drought and the stomatal response was measured. Besides, SLA, Asat, leaf level transpiration (E) as well as iWUE and WUE were measured. A linear, exponential and logarithmic regression were fitted on the gs-VPD curves and evaluated by four measures of accuracy (MAE, MAPE, MSE and RMSE). After a data quality check was performed, linear mixed model regressions were used in R-1.1.463 to evaluate possible trends.

Results: In total, 45 ecologically relevant gs-VPD curves were established (17 from liana leaves and 28 from tree leaves). A linear regression was the best fit of the curves. Stomatal response to increasing VPD was lower in TMCFs compared to lowland rainforest and the high altitudinal primary forest, while the zero drift was higher. The stomatal sensitivity to atmospheric drought and zero drift was not significantly different between lianas and trees. Also, the maximal stomatal conductance measured was lower at TMCFs. SLA decreased significantly with the altitude. Asat increased for lianas with the elevation, while Asat of trees decreased. Mass-based saturated assimilation (Amass) had a strong inverse relation with the leaf mass per area (LMA). iWUE and WUE were both increasing with the altitude for lianas and slightly decreasing for trees. WUE was inversely correlated with SLA. Conclusions: Our results indicate that plants acclimated to an environment with a dry atmosphere or with high seasonality will show a faster response to atmospheric drought stress, compared to plants used to sufficient moisture. The response did not significantly differ between lianas and trees. SLA decreased with the altitude probably by establishing thicker leaves for the associated declining temperature. Amass was lower in TMCFs due to the reduced radiation by the cloud cover. The inverse correlation between WUE (and iWUE) and SLA was presumably because the evaporative demand is greater for leaves with a high leaf area. Therefore, plants can assimilate more nitrogen and stack mesophyll cells more compact in order to increase the assimilation rate per unit leaf area. Samenvatting

Onderzoeksvragen: Is de snelheid van de stomatale respons op atmosferische droogte verschillend tussen bomen en lianen alsook tussen verschillende soorten bossen? Zijn de licht-verzadigde assimilatiesnelheid (Asat), het specifiek bladoppervlak (SLA), de intrinsic watergebruik-efficiëntie (iWUE) en instantaneous watergebruik-efficiëntie (WUE) verschillend tussen de twee groeivormen en tussen de verschillende soorten bossen? Kan Asat gelinkt worden met SLA? Spelen de genetische effecten een grote rol in de verschillen van de eerdergenoemde variabelen tussen planten? Locatie: Vier strata in natuurlijk bos op een transect van 400 tot 3000m boven zeeniveau op de westelijke flank van het Andesgebergte in Ecuador, welke een tropisch laaglandregenwoud, twee tropische nevelwouden (TMCFs) en een hoogland primair bos omvat. Werkwijze: In zestien 40m x 40m permanente proefvlakken (PSP) werden zonnebladeren van zowel lianen als bomen verzameld. Ex situ gasuitwisseling metingen werden uitgevoerd op de bladeren met het draagbare fotosynthesesysteem CIRAS-3. Op elk stratum werden enkele lichtresponsiecurves gemeten. Voornamelijk werden er gs-VPD curves uitgevoerd, waarbij de vapour pressure deficit werd verhoogd om atmospherische droogtestress te simuleren en de stomatale geleidbaarheid werd gemeten. Bovendien, werd ook SLA, Asat, transpiratie op bladniveau (E), iWUE en WUE geobserveerd. Een lineaire, exponentiële en logaritmische regressie werd op de gs-VPD curves gefit. De standaardafwijkingen van de regressies werden vergeleken met behulp van MAE, MAPE, MSE en RMSE. Na het controleren van de kwaliteit van de data, werden mogelijke verschillende tussen de groeivormen en tussen de soorten bossen geanalyseerd met lineaire gemende modellen in R-1.1.463.

Resultaten: 45 ecologisch relevante gs-VPD curves werden bekomen, waarvan 17 lianabladeren en 28 boombladeren. De lineaire regressie bleek de data het best te fitten. De stomatale respons op stijgende VPDs was lager in TMCFs, terwijl de zero drift hoger was. Tussen lianen en bomen werd geen significant verschil geobserveerd. De maximale gemeten stomatale geleidbaarheid was lager in TMCFs. SLA daalde met stijgende altitude. Asat van lianen steeg met de hoogte, terwijl deze van bomen daalde. Asat op massa basis (Amass) had een omgekeerd evenredige relatie met het bladgewicht per eenheid bladoppervlak (LMA). iWUE en WUE stegen beide met de hoogte voor lianen, terwijl deze van bomen daalden. WUE was daarbovenop negatief gecorreleerd met SLA. Conclusie: Onze resultaten toonden aan dat planten die geacclimatiseerd zijn aan een omgeving met een droge atmosfeer of die een grote seizoensgebondenheid heeft een snellere stomatale respons hebben op atmosperische droogtestress, in vergelijking met planten die gewoon zijn aan voldoende vocht. Deze respons was echter niet significant verschillend tussen bomen en lianen. SLA daalde met de altitude waarschijnlijk doordat de bladeren op hogere hoogtes boven zeeniveau dikkere bladeren vervaardigen tegen de geassocieerde lagere temperaturen. Amass in TMCFs hadden lagere waardes vergeleken met de twee andere soorten bos, doordat ze minder straling ontvangen door de bewolking. Het omgekeerd evenredig verband tussen WUE (en iWUE) en SLA is vermoedelijk doordat een lagere SLA voordelig is voor planten om hun watergebruik- efficiëntie te verbeteren. Een hoog bladoppervlak is namelijk nadelig door de grotere waterverlies dat optreedt. Bladeren kunnen hun assimilatiesnelheid per eenheid bladoppervlak verhogen door meer stikstof op te slaan en hun mesophylcellen compacter te organiseren in het blad. Resumen

Preguntas de investigación: ¿Existe una respuesta diferente a la sequía atmosférica entre lianas y árboles, así como entre los diferentes tipos de bosques? De manera similar, ¿difieren la asimilación de carbono saturado (Asat), el área específica de la hoja (SLA), la eficiencia del uso del agua instantánea e intrínseca (iWUE) y WUE respectivamente)? ¿Está la asimilación de carbono vinculada al SLA? ¿Están los efectos genéticos jugando un papel importante en las diferencias de las variables antes mencionadas entre las plantas? Ubicación: Cuatro estratos a 400-3000m sobre el nivel del mar en el flanco occidental de los Andes ecuatorianos, que comprenden bosque lluvioso de tierras bajas, bosque montañoso nuboso tropical (TMCF) y bosque primario de alta altitud. Métodos: Se recolectaron hojas solares de lianas y árboles en dieciséis parcelas de muestreo permanentes de 40 m x 40 m en los cuatro estratos. Las mediciones de intercambio de gas de la hoja ex situ se realizaron con el sistema de fotosíntesis portátil CIRAS-3. Por estrato, se realizaron algunas curvas de respuesta a la luz. Especialmente, se ejecutaron las curvas gs-VPD, donde se incrementó el déficit de presión de vapor durante la medición para simular la sequía atmosférica y se midió la respuesta estomática. Además, se midieron SLA, Asat, transpiración a nivel de la hoja (E), así como iWUE y WUE. Se ajustó una regresión lineal, exponencial y logarítmica en las curvas gs-VPD y se evaluó mediante cuatro medidas de precisión (MAE, MAPE, MSE y RMSE). Después de realizar un control de calidad de los datos, se utilizaron regresiones de modelos lineales mixtos en R-1.1.463 para evaluar posibles tendencias.

Resultados: En total, se establecieron 45 curvas gs-VPD ecológicamente relevantes (17 de hojas de liana y 28 de hojas de árboles). Una regresión lineal fue el mejor ajuste de las curvas. La respuesta estomática al aumento de la VPD fue menor en TMCFs en comparación con el bosque lluvioso de tierras bajas y el bosque primario de alta altitud, mientras que la deriva cero fue mayor. La sensibilidad estomática a la sequía atmosférica y la deriva cero no fue significativamente diferente entre las lianas y los árboles. Además, la conductancia estomática máxima medida fue menor en los TMCFs. El SLA disminuyó significativamente con la altitud. Asat aumentó para lianas con la elevación, mientras que Asat de árboles disminuyó. La asimilación saturada basada en masa (Amass) tuvo una fuerte relación de carácter inverso con la masa de la hoja por superficie (LMA). iWUE y WUE aumentaron con la altitud para las lianas y disminuyeron ligeramente para los árboles. WUE se correlacionó inversamente con el SLA. Conclusiones: Nuestros resultados indican que las plantas aclimatadas a un ambiente con una atmósfera seca o con una alta estacionalidad mostrarán una respuesta más rápida al estrés atmosférico por sequía, en comparación con las plantas acostumbradas a una humedad considerable. La respuesta no difirió significativamente entre lianas y árboles. El SLA disminuyó con la altitud probablemente al establecer hojas más gruesas para la temperatura de disminución asociada. La Amass en los TMCFs fue menor debido a la reducida radiación ocasionada por las nubes. La correlación inversa entre WUE (y iWUE) y SLA fue presumiblemente debida a que la demanda de evaporación es mayor para hojas con un área foliar alta. Por lo tanto, las plantas pueden asimilar más nitrógeno y apilar las células mesófilas más compactas para aumentar la tasa de asimilación por unidad de área foliar. xvi 1 Introduction

Forests, and more specifically tropical forests are among the most important ecosystems in the world. They play a key role in the hydrological as well in the carbon cycle (Wright, 2010) and have enormous biodiversity (Dellasala et al., 2018; Mana et al., 2015). Unfortunately, forests are subjected to hazards, like climate change and land use change (Wright, 2010). In particular, tropical montane cloud forests (TMCFs) are one of the most threatened ecosystems (Aldrich et al., 1997), notwithstanding they are highly important for the hydrological cycle and they are the residence for a high number of endemic species (Toledo-Aceves et al., 2011). To predict the effects of these hazards on forests and their magnitude, models are necessary. However, the current terrestrial ecosystem models do not include lianas, despite their importance in tropical forests. Lianas compete with trees for light, water and nutrients. They rely, for support, on the structural investment of trees and can thus put a bigger part of their resources in the development of their canopy. A crucial structural change happening nowadays in tropical forests is the increasing abundance and biomass of lianas (Phillips et al., 2002). As they reduce tree growth and increase tree mortality risk, it is absolutely essential to fully understand how lianas affect forests dynamics (van der Heijden et al., 2015). For example, the carbon stock declines with an enlarged amount of lianas, thus the carbon cycle will also be affected by a liana proliferation (van der Heijden et al., 2015; Verbeeck and Kearsley, 2016). Another illustration is the different water use strategy of lianas compared to trees, which is not fully understood yet (Restom and Nepstad, 2004; Schnitzer, 2005; Cai et al., 2009; Chen et al., 2015b; De Deurwaerder et al., 2018). The TREECLIMBERS project of CAVElab (Computational & Applied Vegetation Ecology) aims to com- prehend the impact of lianas on tropical forest dynamics. Furthermore, the project builds the first vegetation model including lianas to assess how lianas influence the carbon balance of tropical forests. My thesis will be an aid in understanding the physiology of lianas. Ex-situ gas exchange measurements were taken of liana and tree leaves on four different altitudes in the Andes mountains of northern Ecuador, which comprise three diverse forest systems: lowland tropical rainforest, tropical montane cloud forest and a primary forest at the highest altitude. The atmospheric humidity was modified during the measurements and the conductance of stomata was measured. In this way, differences in the response on atmospheric drought stress could be evaluated. Also, the specific leaf area was determined for all measured leaves. Furthermore, the conditions at maximal stomatal opening were analysed, like the leaf-level transpiration, carbon assimilation as well as intrinsic and instantaneous water use efficiency. Two main research questions are formulated to aim at. Firstly, are lianas responding differently than trees to atmospheric drought stress? Lianas establish most of their leaves at the top of the canopy, where the vapour pressure deficit is high (Chen et al., 2015b). Furthermore, in contrast to trees which have high internal water storage, lianas have to rely only on soil water for their water source (Chen et al., 2015b). Still, lianas are highly abundant in seasonal tropical forests with dry periods (Schnitzer and Bongers, 2002) and need 2 physiological adaptations to changing humidities. Therefore, we expect that lianas show a faster stomatal response to increasing vapour pressure deficit (Campanello et al., 2016). Secondly, is their a dissimilar response to atmospheric drought between the different types of forests? In particular, do trees and/or lianas in tropical montane cloud forests have a different response on increasing atmospheric water demand in comparison to lowland rainforest and a high altitudinal primary forest? TMCFs are distinct from other forests by their feature of receiving additional water via foliar water uptake (Aldrich et al., 1997). Plants of TMCFs are developed in a water-rich environment, wherefore a poor developed response to water deficits can be expected. The way plants cope with atmospheric drought was our biggest concern, although we analysed some other features of plants in order to answer following research questions:

• Does the saturated carbon assimilation of trees and lianas diﬀer with the altitude and/or between the diﬀerent forests?

• Are lianas establishing leaves with higher speciﬁc leaf area (SLA; leaf area:dry weight) than trees? Do the altitudes that experience the biggest atmospheric drought have lower SLA values?

• Is the carbon assimilation linked with SLA?

• Do lianas maintain a higher intrinsic and instantaneous water use eﬃciency? Sites that are used to atmospheric drought, do they have a higher water use eﬃciency?

• Are the genetic diﬀerences playing an extensive role in the way plant response to atmospheric drought and at the previous questions?

The literature review in chapter 2 provides background information to fully understand the importance of this thesis research. It frames the functions and hazards of tropical montane cloud forests and the role of lianas in tropical forests. In addition, the scientific knowledge about water use efficiency, leaf photosynthesis and altitudinal gradients is given. Materials and methods will be explained in the third chapter, which includes the fieldwork protocol in Ecuador, the data processing and statistical analysis. The results and discussion are given in chapters 4 and 5 respectively. Finally, a general conclusion of this master thesis and suggestions for further research can be found in chapter 6. 2 Literature review

2.1 From forest to tropical montane cloud forest

2.1.1 Forest ecosystems

The UN Food and Agriculture Organization (FAO, 2012) defines “forest” as a land area bigger than half a hectare with trees higher than 5 meters and a tree canopy cover of more than 10%, or with young trees that will be able to meet these thresholds. The FAO distinguishes primary and secondary forests. The former are forests of native tree species with no indications of human activities and the latter have been cleared by natural or man-made causes. They differ in structure and/or species composition (Chokkalingam and Jong, 2001). According to the World Bank (2015), 41,3 thousand sq. km or 30,8% of the global land area is covered by forests. In Ecuador, this percentage is 50,5%, while in 1998 it was still 56%. Houghton (2016) claimed that 40-50% of the original Earth’s forest area disappeared. A loss in forests will result in a loss of biodiversity and a reduction of the functions that forests are providing. Already since the establishment of human civilization, the most dominant function of forests is the supply of natural resource products (Führer, 2000). Fruit, bark and leaves are examples of these resources, next to timber and fuelwood (Piussi and Farrell, 2000). Besides, forests provide social functions like recreation, ecotourism, fishing or hunting and amenities like conservation of sites with cultural, spiritual or historical importance (Shvidenko et al., 2005). A protective forest prevents or reduces the impact of a natural hazard, whereby soil and water protection is one of the most important contributions. High-quality water is maintained by the reduction of erosion and air is purified from pollutants. Deforestation would therefore lead to an increased risk of flooding and drought (Adikari et al., 2015). Other protective functions are the provision of clean water, coastal stabilization and the control of desertification and avalanches. Last but not least, forests’ biomes sequester carbon (Miura et al., 2015). Earth’s forests contain 50% of the world’s carbon stock in their vegetation and soil (Shvidenko et al., 2005).

2.1.2 Tropical rainforest

The FAO (2010) reported that more than half of the world’s forests are in tropical or subtropical regions, respectively 45% and 8%. The deforestation rate nowadays in the tropics is still 9 million hectares a year, which is not that much lower than the peak rate of 12 million hectares in the 1990s (Houghton, 2016). Harris et al. (2012) calculated that gross annual carbon emissions within the tropics were around 0,8 Pg/yr from 2.1 FROM FOREST TO TROPICAL MONTANE CLOUD FOREST 4

2000 to 2005. This is due to the loss of 43 million hectares tropical forest. In Table 2.1 Pan et al. (2011) listed the global forest carbon budget between 1990 and 2007 for the diﬀerent types of forests, subdividing tropical forests into intact and regrowth forests. The total global terrestrial gross primary productivity (GPP) was estimated to be 122 Pg C yr-1, of which tropical forests contribute about 41 Pg C yr-1, 34% of the global total (Malhi, 2012). Besides, tropical forests account for a quarter of the global terrestrial carbon storage (Dellasala et al., 2018). Tropical forests have thus a critical role in the carbon cycle. Mitchard (2018) recently pointed out the potential contrasting eﬀects of climate change and land-use change on the intact forest carbon sink (Figure 2.1). Predicting the extent of these changes is very hard. This shows that it is still highly uncertain how forests will respond to climate change and a better understanding of tropical forests is required for accurate estimations of the global C budgets.

Table 2.1: Global forest carbon budget (Pg C yr-1) between 1990-2007 (Pan et al., 2011).

Carbon sink and source in biomes 1990-2007 Boreal forest 0,50 ± 0,08 Temperate forest 0,72 ± 0,08 Tropical intact forest 1,19 ± 0,41 Total sink in global established forests 2,41 ± 0,42

Tropical regrowth forest 1,64 ± 0,52 Tropical gross deforestation emission -2,94 ± 0,48 Tropical land-use change emission -1,30 ± 0,70

Global gross forest sink 4,05 ± 0,67

Global net forest sink 1,11 ± 0,82

Figure 2.1: The potential contrasting eﬀects of climate change and land-use changes on the magnitude of the intact forest carbon sink. Arrows pointing up show an increased size of the sink due to the associated eﬀect, whereas arrows pointing down show how it will be reduced (Mitchard, 2018). 2.1 FROM FOREST TO TROPICAL MONTANE CLOUD FOREST 5

2.1.2.1 The role of lianas in tropical rainforests

Lianas are woody climbing plants that are not self-supporting, but relying on trees for their support. They can thus invest a larger amount of their resources in reproduction, canopy development, and stem and root elongation. Therefore, the canopy:stem ratios of lianas are very high (ratio > 5) (Schnitzer and Bongers, 2002; Toledo-Aceves and Swaine, 2008; Tang et al., 2012; Pausenberger, 2015), reducing the regeneration of trees and increasing the mortality. Lianas also cause tree fall and branch breakage (Jerozolinski et al., 2001; Schnitzer and Bongers, 2002). A prominent structural change now occurring in tropical forests is the augmented amount of lianas (Schnitzer and Bongers, 2011). Laurance et al. (2014) concluded that lianas are increasing in abundance based on an experiment over 14 years in undisturbed central Amazonian forests. Phillips et al. (2002) proved that lianas increase in abundance with decreasing precipitation and increasing seasonality, which explains the high liana abundance in dry tropical forests. In a tropical forest in Mexico, liana abundance was significantly higher in a highly disturbed forest compared to a low disturbed forest, as lianas are able to capitalize on disturbance (Ibarra-Manríquez and Martínez-Ramos, 2002). Rogers (1996) formulated disturbance as "any relatively discrete event in time that disrupts ecosystems, community, or population structure and changes resources, substrate availability, or the physical environment." Malizia et al. (2010) also confirmed that the number of lianas increased with the density of treefall gaps. Schnitzer and Bongers (2011) mentioned that the elevated amount of lianas can be explained by an increasing forest disturbance and turnover, changes in land use and fragmentation, increasing evapotranspirative demand and rising atmospheric CO2 concentrations. Recent evidence indicates that lianas significantly reduce the tropical forest carbon stock (van der Heijden et al., 2015). In a three-year liana removal experiment in Panama, lianas were removed in 8 of the 16 forest plots of the forest. In the presence of lianas, only 24% of the forest plots’ carbon sink potential was achieved compared to the liana-free plots (van der Heijden et al., 2015). van der Heijden et al. (2014) alleged that the lower carbon stock in the presence of lianas is the result of three reasons. Firstly, lianas reduce the growth and longevity of trees. Secondly, they compete more with slow-growing trees. Consequently, a species shift to fast-growing trees will occur, which sequester less carbon. Thirdly, the increasing abundance leads to the replacing of trees by lianas. As lianas store less carbon due to their lower wood volume, the carbon stock will decline. Van Der Heijden and Phillips (2009) showed that the carbon gain in an Amazonian forest in Peru was reduced to 0,25 Mg C ha-1 yr-1 by lianas, mainly caused by competition between lianas and trees. Due to the increased abundance of lianas in tropical forests and their negative effect on carbon storage, we need to better understand the ecosystem functioning of lianas. Durán and Gianoli (2013) estimated a negative correlation between carbon stocks of large trees and liana abundance in tropical forests. These evidences show the importance of including liana abundance assessments in forest carbon cycle studies.

2.1.3 Tropical montane cloud forest

Tropical montane cloud forest (TMCF) is a type of rainforest existing at high altitudes in the tropics. It is mostly shrouded in mist and fog, which explains the name and can be deﬁned as: "Cloud forests include all forests in the humid tropics that are frequently covered in clouds or mist; thus receiving additional humidity, other than rainfall, through the capture and/or condensation of water droplets (horizontal precipitation), which inﬂuences the hydrological regime, radiation balance and several other climatic, edaphic and ecological parameters." (Stadtmüller, 1987) The level of endemism in TMCF is really high (Gentry, 1992). It has a unique plant community including abundant epiphytes (mosses, ferns and bromeliads), orchids and lichens (Stadtmüller, 1987). Hamilton et al. 2.1 FROM FOREST TO TROPICAL MONTANE CLOUD FOREST 6

(1995) stated that the number of these species increases with the altitude, which was not expected due to the decreasing number of tree species with the altitude. Bruijnzeel et al. (2011) claimed that TMCF comprises only 0,26% of the global land surface, but it is one of the most important biodiversity hotspots on earth. TMCF ecosystems occur in South America, Africa, South-east Asia and the Caribbean (Bubb et al., 2004). Hamilton et al. (1995) provided a global map showing the general concentrations of TMCF (Figure 2.2). The altitude at which the TMCF occurs depends on the latitude, but general they occur at an altitude of 1500 to 3300 meters. Although much lower and higher limits are possible due to local factors (Bruijnzeel and Proctor, 1995).

Figure 2.2: General concentrations of tropical montane cloud forest throughout the world (Bruijnzeel and Proctor, 1995).

2.1.3.1 Hydrology of TMCF

In most cases, plants move water from the soil and evaporate the water in the air. Because trees of TMCF are under constant fog or cloud cover, water can be absorbed by the leaves. Foliar water uptake (FWU) will occur when there is an insufficient water supply of the soil. This water will move downwards through the xylem vessels and will be released in the rhizosphere (Goldsmith et al., 2013). A recent study showed that this enhances the leaf water potential, photosynthesis, stomatal conductance (gs) and growth (Eller et al., 2013). Water uptake due to the condensation of clouds or fogs or by direct contact of the water droplets is named horizontal precipitation (Stadtmüller, 1987). These water droplets form a protective layer for the plant against radiation and large differences in temperature and relative humidity (Stadtmüller, 1987). Next to this watershed function, TMCF can regulate the seasonal amount of incoming water by the sponge-like effect of epiphytes. In the rain-season, floods and erosion can be controlled and water can be stored for the dry season (Still et al., 1999). A TMCF can be seen as a buffer: interception with precipitation in the rain season and source of water in the dry season.

2.1.3.2 Threats of TMCF

It is known that TMCF is disappearing quickly, even though it is hard to find accurate data. Doumenge et al. (1995) mentioned that the annual deforestation rate in tropical montane and submontane forests is 1,1%, 2.1 FROM FOREST TO TROPICAL MONTANE CLOUD FOREST 7 which is higher than in the entire tropics (0,8%). Mexico stands out with a rate of 3% (Leija-Loredo et al., 2018). This deforestation contributes to CO2 emissions. Harris et al. (2012) estimated that 7 to 14% of the total global anthropogenic CO2 emissions between 2000 and 2005 are due to deforestation, from which Latin-America accounted for 54%. Bruijnzeel and Hamilton (2000) claimed that the greatest loss worldwide of TMCF is caused by the conversion to grazing land for cattle, sheep and goats. As a consequence, erosion due to overgrazing and landsliding on unstable slopes are occurring because the area is free of vegetation. The vulnerability of TMCFs to land-use change is illustrated by the 40% loss of TMCF over 30 years due to deforestation (Leija-Loredo et al., 2018). In the northern Andes of Columbia 90% TMCF is lost, mostly by conversion to pastures and agricultural fields (Bruijnzeel and Hamilton, 2000). Also, climate change and air pollution have a adverse impact on TMCF. Gaseous and particulate pollutants carried in clouds have caused serious changes in soil chemistry and tree mortality when deposited in redundant amounts (Bruijnzeel and Hamilton, 2000). Also, lifting of the condensation level of clouds can be a result of atmospheric warming. This will induce a gradual shrinking of the cloud-affected zone (Alan Pounds et al., 1999). On multiple-peaked mountains habitat fragmentation will be increased as an additional adverse effect (Figure 2.3). Bubb et al. (2004) mentioned that especially epiphytes and amphibians are sensitive to both changes in humidity as to water stress.

Figure 2.3: Possible changes in spatial cloud forest distribution in response to a rise in the cloud condensation level on (A) a single peak and (B) a mountain with several peaks (Bruijnzeel and Hamilton, 2000).

Bubb et al. (2004) also listed other important examples of hazards of cloud forests: conversion to grazing land and to cultivated crops, fuelwood cutting, non-wood product harvesting and hunting, alien species, tourism, fires, telecommunication and media transmitting stations, mining, constructing of roads and deforestation for drug cultivation. All these menaces have consequences for cloud forests. Compaction by housing and by tramping of cattle cause a declining soil infiltration capacity with an increasing amount of surface runoff as a result. Likewise, the cleared area is no longer able to intercept the same amount of rainfall or fog and (barely) no more transpiration will happen, followed by an increasing volume of the streamflow. This also means higher water availability in the soil, which is expected to induce an increase in baseflow levels. However, in practice a decline of the soil’s infiltration capacity is noticed. So the result will be a peak runoff in the wet season and a reduced streamflow during the dry season, because no FWU can occur (Bruijnzeel and Hamilton, 2000). The awareness of the value of TMCFs needs to increase urgently. This will result in more support by local, national or international non-governmental organizations. Also, at the macro level (e.g. global climate 2.1 FROM FOREST TO TROPICAL MONTANE CLOUD FOREST 8 change, transport, etc.) there has to be more consciousness to avoid the extinction of TCMFs (Bruijnzeel and Hamilton, 2000).

2.1.3.3 TMCF in Ecuador

Bubb et al. (2004) stated that half of the species in Ecuador are situated in only 10% of the country, being the middle-elevation Andes (900-3000 meters). Besides, 39% of these species are Ecuadorian endemics. The data of the World Bank (2017) specifies the number of threatened species in Ecuador of fish, birds, mammals and higher plants to be respectively 61, 107, 45 and 1857 in 2017. Sarmiento (1995) divided the Ecuadorian Andes into two domains. On the one hand transandean forests on the outer slopes of the western and eastern mountain range and on the other hand interandean forests on the plateaus and valleys between the mountain peaks (Figure 2.4). The former encounter more precipitation compared to interandean forests and therefore the species composition will differ. This is caused by the Föhn effect: at the windward slope air is forced to rise and will cool down with precipitation as result, while the air will sink again on the leeward side where the climate will be much drier and warmer.

Figure 2.4: Transandean and interandean domains of the Ecuadorean Andes (Sarmiento, 1995). 2.2 WATER USE EFFICIENCY 9

2.2 Water Use Efﬁciency

2.2.1 Deﬁnitions

Three types of water use efficiency can be distinguished: intrinsic, instantaneous and integrated. Intrinsic WUE (iWUE) is the ratio of net CO2 assimilation rate to stomatal conductance (A/gs), while instantaneous WUE (WUE) is the ratio of net CO2 assimilation to transpiration (A/E), both on leaf level. Integrated WUE is the total carbon gain to total water loss on the level of the leaf, the plant or even the ecosystem (Seibt et al., 2008), connecting water, energy and carbon dynamics (Pan et al., 2017). iWUE can be derived from stable carbon isotope signatures and is a relevant parameter for long-term scales (see Subsection 2.2.3). WUE is useful for short-term interpretation to quantify the carbon-water balance in plants, whereas integrated WUE is more important on larger time scales since it accounts for non-photosynthetic water loss (Seibt et al., 2008). Instantaneous WUE is usually replaced with the ratio of gross primary productivity (GPP) over evapotranspiration (ET) when scaling up from leaf to ecosystem level. Zhou et al. (2017) proposed underlying WUE, given by uWUE = GPP ∗ VPD0.5/ET where VPD stands for the vapour pressure deficit. uWUE is firmly linked to plant physiological regulation of CO2 and to water exchanges.

2.2.2 WUE and climate change

Global atmospheric CO2 concentrations and other greenhouse gases (GHG) are rising at a high speed. In -1 the previous century, CO2 concentrations increased by 80 ppm, from approximately 280 to 360 µmol mol (Morison and Lawlor, 2002). With an expanding world population and increasing economic activity, the concentrations are estimated to be around 700 µmol mol-1 at the end of the current century. Nowadays, the terrestrial biosphere is a huge carbon sink, but it might become a source in the 21st century (Anderegg et al., 2015).

Stomata will tend to close more in an atmosphere with higher CO2 levels to keep the internal CO2 (ci) constant, with less water loss as a result. Thus, iWUE improves due to decreasing gs (Huang et al., 2017; Pan et al., 2017). Keenan et al. (2013) observed an increasing trend in WUE over a period of 20 years. This process is called CO2 fertilization (Keenan et al., 2013; Sun et al., 2017).

2.2.3 13C isotope signature and discrimination as evaluation

In nature two stable isotopes of carbon are found: 12C (98,9%) and 13C (1,1%). These two isotopes are used to calculate the isotope signature, which is the ratio of 13C:12C abundances of the sample material and the reference material minus one:

Rsample δ13C = ( − 1) ∗ 1000‰ (2.1) Rstandard

13C where Rsample and Rstandard are the ratio in respectively the sample and the standard (PDB, Pee Dee 12C Belemnite; equal to 0.01118). 2.2 WATER USE EFFICIENCY 10

Farquhar et al. (1982); Griﬃths (1993); Battipaglia et al. (2013) gave the equation to calculate the isotope discrimination, i.e. the isotope diﬀerence between source and product, in this case, atmosphere and plant:

δa − δp ∆13C = (2.2) 1 + δp/1000 with δp and δa the isotope signatures of plant and atmosphere respectively.

Two types of discrimination are distinguished: fractionation during CO2 diffusion through the stomata imposed by gs and discrimination associated with reactions by Rubisco and PEP carboxylase (Seibt et al., 2008). Griffiths (1993) discovered that the latter, the carboxylation resistance, is twice as limiting as the diffusion discrimination. Therefore, the probability of CO2 diffusing back out of the leaf is twice as high as its probability to be carboxylated. Farquhar et al. (1982) reported the biochemical basis of this carboxylation discrimination in C3 plants: the primary carboxylation enzyme ribulose-1,5-biphosphate (RuP2) carboxylase discriminates against the heavier 13C isotope because of his lower reactivity. Similar, molecules containing heavier isotopes will diffuse more slowly (Griffiths, 1993). δ13C and ∆13C can, similarly, be formulated in function of these two discrimination factors (Farquhar et al., 1982; Griffiths, 1993; Battipaglia et al., 2013):

13 ca − ci ci δ C = δs + a + b (2.3) ca ca

c ∆13C = a + (b − a) i (2.4) ca with a representing the diffusion limitation coefficient, equal to 4, 4‰ and b standing for the Rubisco limitation coefficient, which has a value of 27‰. ci and ca represent the internal and atmospheric leaf CO2 concentration.

If a decrease in gs and/or an increase in A occurs, ci will decrease. Reduced discrimination will be the c result of this decreasing i gradient (as can be derived from equation 2.4) (Battipaglia et al., 2013; Huang ca et al., 2017). The positively correlated relationship between maximum leaf conductance and carbon isotope discrimination is shown in Figure 2.5. Grossnickle et al. (2005) reported a stronger relationship between gs and carbon isotope discrimination than between A and ∆13C for western red cedar populations. As iWUE 13 is the ratio of A to gs, discrimination against C can be used as a proxy for the intrinsic WUE (Battipaglia et al., 2013). This relationship can, therefore, be related to changes in gs, A or both depending on the species (Grossnickle et al., 2005). A study with container-grown conifers under cyclic irrigation showed a negative correlation between iWUE and ∆13C (Taylor et al., 2013). Wright et al. (2006) and Wang et al. (2018) reported as well a negative correlation. Marshall and Zhang (1994) proved signiﬁcant decreasing discrimination along an increasing altitude (m a.s.l.) for both evergreen and deciduous species. Pellizzari et al. (2016) described the relationship between iWUE and ∆13C as followed:

c b − ∆13C iWUE = a . (2.5) 1.6 b − a

2.2.4 WUE differs between trees and lianas

Lianas generally feature a deep root system, enabling them to access deep soil water (Restom and Nepstad, 2004; Schnitzer, 2005; Cai et al., 2009; Chen et al., 2015b). In contrast, De Deurwaerder et al. (2018) showed that lianas maintain an active root system in shallow soil layers during the dry season. Lianas in the 2.3 LEAF PHOTOSYNTHESIS 11

Figure 2.5: Correlation between maximum leaf conductance to water vapor (gmax) and carbon isotope discrimination of whole leaf material of different cultivars of Phaseolus vulgaris. Black and white dots represent adaxial and abaxial surfaces respectively (Ehieringer, 1990). dry season established strong stomatal control to minimize water loss and maximize carbon fixation with significantly lower water consumption as a result. Because lianas adapt more easily to seasonality by higher photosynthetic rates in the wet season and better physiological adjustments in the dry season, they are highly abundant in seasonally dry forests (Chen et al., 2015b). Chen et al. (2017) emphasized that tropical lianas achieve their maximal E at a relatively lower VPD than co-occurring trees, indicating a higher photosynthetic rate before noon. They establish an earlier stomatal closure and an efficient water transport, which results in less water loss and therefore in higher water potential of the stem. Manzané-Pinzón et al. (2018) mentioned that the density of lianas is enhanced by less rainfall and a higher seasonality.

2.2.5 Case study: the isotope trends in Ecuador and Rwanda

In Rwanda and Ecuador the isotope signatures were measured for both lianas and trees along the altitude (pers. comm. Francis Mumbanza). The data is plotted in Figure 2.6. It points out positive correlations with the altitude for both lianas and trees, but the signatures of trees show clearly steeper slopes with the elevation for Rwanda as well for Ecuador than these of lianas. The carbon isotope composition of plant tissues can vary in function of several environmental factors such as air temperature and humidity, precipitation, barometric pressure and soil moisture. These factors act upon two main parameters of plants, namely gs and photosynthetic capacity, or both. Additional investigation is necessary to know if the response of the carbon isotope signature along the altitude is a result of gs and photosynthetic capacity, or the combination of both parameters. The fact that this could not be explained without any further research was the trigger to this thesis subject.

2.3 Leaf photosynthesis

Photosynthesis is a very complex process with numerous smaller processes. It is essential for understanding how terrestrial ecosystems work. Models are indispensable to describe photosynthesis mathematically. Leaf 2.3 LEAF PHOTOSYNTHESIS 12

Trees − Ecuador ● ● −28 ● Trees − Rwanda ● Lianas − Ecuador ● ● ● Lianas − Rwanda ● ● −29

● ● ●

● ●

C ●

● −30 13 δ ● ●● ● ● ● ● ● ● ● Leaf ● ● ● −31 ● ● ● ● ● ●● ● ● ● (community weighted mean, ‰) weighted (community

−32 ● −33

500 1000 1500 2000 2500 3000

Altitude (m asl.)

Figure 2.6: Relationship between the carbon isotope signatures δ13C and the altitude (meters above sea level, m a.s.l.) for lianas in Ecuador (2016) and Rwanda (2017) and for trees in Ecuador (2015) and in Rwanda (2015). The correlation for each is visualized by regression lines (pers. comm. Francis Mumbanza). photosynthetic models are coupling leaf biochemistry and gas exchange kinetics. They can be scaled to larger models of the global C cycle or to feedback models of the land surface response to climate. Also, they can be scaled to models of a longer time scale that take into account environmental eﬀects (Farquhar et al., 2001).

Leaf photosynthesis measurements like CO2 uptake, gs, E and ci are carried out with an infrared gas analyser (IRGA). The main advantages of the IRGAs are that they are portable and provide real-time measurements. Additionally, it is possible to control the leaf environment by defining CO2, humidity, temperature and light. These measurements can be used to validate and improve photosynthesis models. An example is the CIRAS-3 (PP Systems, Amesbury USA), which uses open differential gas exchange systems with four non-dispersive gas analysers (Field et al., 1982). A differential gas exchange system measures the leaf gas exchange of a leaf sample using IRGAs. IRGAs measure the concentration of carbon dioxide and water molecules. An infrared source emits mid-infra-red wavelengths, which are pulsed through a gold-plated cell with the target gas. CO2 and H2O absorb IR, but they have a dissimilar absorption spectrum. Both CO2 and H2O cells employ a unique optical filter that narrows the bandwidth of the IR source received by the detector to the specific wavelength absorbed by the target molecule. The higher the target gas concentration, the lower the infra-red signal received by the detector (known as the law Lambert- Beer). In this way, the concentration of both CO2 and H2O can be determined. The principle of an IRGA is visualised in Figure 2.7. Two IRGAs will be necessary to measure one target molecule: one for measuring the concentration in the airflow going through the chamber containing the leaf sample and the second one to determine the concentration in the controlled airflow (PP Systems, 2017). 2.3 LEAF PHOTOSYNTHESIS 13

Figure 2.7: Schematic visualised principle of an infrared gas analyser with indication of the diﬀerent parts (PP Systems, 2017).

2.3.1 Photosynthetic light-response curves

2.3.1.1 Theory

The photosynthetic response of individual leaves to light levels is typically described with a light response curve. The curve mostly has a hyperbolic shape (Dangan-Galon et al., 2013). Under low photosynthetic photon ﬂux density (PPFD), leaf-level net photosynthesis increases quasi-linearly with increasing PPFD. Under higher PPFD levels the light saturation point is reached, beyond which a further increase in the PPFD does not result in an increasing photosynthetic rate. At this plateau, the capacity rate of steady-state electron transport from water to CO2 is slower than the rate of photon absorption (Sakshaug et al., 1997). However, sometimes photoinhibition occurs which causes a decreasing net photosynthetic rate after a critical irradiance level is exceeded. This happens when the incoming light energy in photosynthetic pigments exceeds the energy consumption in chloroplasts (Takahashi and Murata, 2008). A light-response curve can provide parameters describing the response of leaf photosynthesis to light (Figure 2.8). The dark respiration rate (Rd) is the rate of leaf CO2 production in the dark. The light intensity at which the CO2 uptake equals the production is called the light compensation point (Icomp). The initial slope of the curve in Icomp is known as the apparent quantum eﬃciency (Φ0) which is a measure of CO2 taken up per unit PPFD. The higher this number and thus the steeper the curve, the more the plant uses the available light. Asat is the saturated net photosynthetic rate (Lobo et al., 2013).

2.3.1.2 Fitting

Drake and Read (1981) described the light-response curve as follows:

a(I − Icomp) A = (2.6) 1 + b(I − Icomp) where a and b are two coeﬃcients. A linear regression can be made of the ﬁrst four points of the data and the intersect of the resulting straight line with the x-axis is the light compensation point. To obtain a en b, (2.6) is transformed to the following equation:

A = a − bA (2.7) I − Icomp 2.3 LEAF PHOTOSYNTHESIS 14

Figure 2.8: Light-response curve with the indication of the parameters describing the response of leaf photosynthesis to light intensity. The intercept with x-axis and y-axis are respectively the light compensation point (Icomp) and the dark respiration (Rd), the initial slope of the line is the quantum eﬃciency (Φ0) and the saturated photosynthetic rate is Asat. Photoinhibition is represented by the bending dashed line (Sibret, 2017) .

A is plotted in function of A, after which a and b are derived from the linear regression. The derivable I − Icomp parameters can be found with the next formulas:

a Asat = lim A = (2.8) I→∞ b

The dark respiration rate is the photosynthetic rate when the light intensity is zero:

−aIcomp Rd = (2.9) 1 − bIcomp

As the quantum eﬃciency is the slope of the curve in the point where the irradiance is equal to Icomp, Φ0 is equal to a:

dA a(1 + b(I − I ) − a(I − I )b) Φ = = comp comp = a 0 2 (2.10) dI 1 + b(I − Icomp)

The derivable parameters can be inserted in (2.6):

Φ0(I − Icomp)Asat A = (2.11) Asat + Φ0(I − Icomp) 2.3 LEAF PHOTOSYNTHESIS 15

2.3.1.3 C3 and C4 pathway

The photosynthetic efficiency of C3 plants is highly dependent on the mechanism of the ribulose-1,5- biphosphate carboxylase/oxygenase or in short Rubisco. In the preferred scenario, it acts as carboxylase and attaches CO2 to ribulose-biphosphate to eventually form carbohydrates. In the C3 pathway, two 3- phosphoglycerate molecules are formed. As they consist of three carbon atoms, it is named the C3 photosynthetic pathway. Energy from the sun is necessary to realize the global reaction (Ghannoum et al., 1999). However, Rubisco can also perform as oxygenase which causes photorespiration. CO2 is formed after a series of metabolic reactions. It is undesired because energy is dissipated and previously fixed CO2 releases again (Kozaki and Takeba, 1996). Photorespiration is stimulated by high oxygen and low carbon dioxide concentrations and at high temperatures (Zelitch, 1971). C4 plants have little photorespiration due to their CO2 concentration mechanism. CO2 is fixed in acids containing four carbon atoms, which diffuse from the mesophyll cells to the bundle sheath cells, where the acids decarboxylate again. In the bundle sheath cells, the carbon dioxide enters the C3 pathway (Dai et al., 1993; Yin and Struik, 2009). It pumps the CO2 in the vascular bundle sheath cells with the need of extra energy and maintains consequently high CO2 pressures, which promotes the efficiency of Rubisco (Pearcy and Ehleringer, 1984). Ghannoum et al. (1999) mentioned that above 25°C the energy costs of photorespiration exceeds that of the CO2 concentration mechanism and therefore C4 plants have an advantage at high temperatures. Because the pathway of C4 plants is spatially separated, it is essential to maintain close contact between the mesophyll and bundle sheath cells. C4 plants therefore have distinctive leaf anatomy (Gowik and Westhoff, 2011) (Figure 2.9).

Figure 2.9: Comparison of the leaf anatomy of C4 (left) and C3 (right) plants (Szarka, 2014).

Due to these different pathways of C3 and C4 plants, their light response curves will have a different course (Figure 2.10). At lower temperatures, light-response curves of C3 plants show a higher initial slope or quantum efficiency. With increasing temperatures, the maximum photosynthetic rate will achieve a higher value, but a reduction in quantum efficiency will occur. For C4 plants, the plateau level will be much higher with rising temperatures. However, the initial slope will be equal for all temperatures. So in colder climates, C3 plants have an advantage, whereas in hot weather, C4 plants will show much higher photosynthetic rates (Ghannoum et al., 1999).

2.3.1.4 Sun and shade leaves

Lambers et al. (2008) stated that Asat values for sun leaves are higher than for shade leaves. However, shade leaves show lower light compensation points and higher photosynthetic rates at low light intensities, because 2.3 LEAF PHOTOSYNTHESIS 16

Figure 2.10: Comparison of light response curves of C4 (left) and C3 (right) plants (Ghannoum et al., 1999). they respire less per unit leaf area. Leaves adapt to the conditions they grow in. That is why plants produce thicker leaves with more chloroplasts per unit leaf area when they catch a lot of sunlight to obtain these higher maximum photosynthetic rates. Leaves that receive less sunlight may contain specialized anatomical structures to enhance light absorption. Pyttel et al. (2013) conﬁrmed these diﬀerences with the Sorbus torminalis, a C3 tree (Figure 2.11). The maximum photosynthesis rates were much higher for sun leaves -2 -1 compared to shade leaves (mean Amax of 8,53 and 5,96 µmol CO2 m s respectively). On the other hand, the light compensation point was lower for shade leaves (7,2 compared to 25,4 µmol PAR m-2 s-1) and the -2 -1 dark respiration rate was less negative (-0,73 compared to -1,57 µmol CO2 m s ).

sun

shade

Figure 2.11: Comparison of light response curves of sun (solid line) and shade (dashed line) leaves of Sorbus torminalis (Pyttel et al., 2013). 2.3 LEAF PHOTOSYNTHESIS 17

2.3.2g s-VPD curves

2.3.2.1 Theory

Plants are able to control E as well as the CO2 uptake by pores in their leaves named stomata. By changing the degree of stomatal aperture, plants can adapt to a changing environment and stress. The measure to denote this extent of opening is the gs, which represents the rate of gas exchange (Damour et al., 2010). It depends on the aperture, size and density of the stomata. The way stomata respond to these environmental conditions is very complex and the physiological functioning is not yet entirely understood. For this matter, semi-empirical models are often employed to describe this response. These models can be used to predict the behaviour of photosynthesis and water use under certain circumstances (Leuning, 1995). To model the response of plants to atmospheric drought, an appropriate parameter should be considered to define this drought stress, namely vapour pressure deficit (VPD). It is important to note that the dryness or wetness of a climate has nothing to do with the amount of water vapour in the atmosphere. VPD is the difference between the amount of moisture in the air and the amount the air can hold when it is saturated at a particular temperature. It is preferred above RH as it is a better indication of the driving force of water loss from a leaf. Equal RH will only represent similar moisture conditions when the temperature is also equal (Anderson, 1936). Lange et al. (1971) concluded that stomata close when plants are treated with dry air and open with humid air. The peristomatal transpiration, the direct cuticular water loss of the guard cell and the subsidiary cells, serves as a "humidity sensor". The turgidity of these guard cells increases (to open) or decreases (to close) depending on the relation between water loss and gain. This infers that the atmospheric evaporation conditions influence the plant’s E. Cunningham (2004) claimed that plants exposed to atmospheric drought show a lower maximal gs and lower relative stomatal sensitivity to rising VPD to reduce water loss via E. Cunningham (2004) observed higher sensitivities to VPD for tropical species compared to temperate species. So, a small change in VPD will have a greater effect on tropical species. A plausible explanation is that tropical plants experience less frequent atmospheric drought during their growing season. A decreasing trend of gs with VPD for Carica papaya is shown in Figure 2.12.

2.3.2.2 Fitting

Various attempts have been made to model gs-VPD curves (Oren et al., 1999; Patanè, 2011; Kröber et al., 2015; Emberson et al., 2000; Verryckt et al., 2017).

Oren et al. (1999) described a logarithmic relationship between the gs and the VPD (Figure 2.13; left):

gs = −m.ln(VPD) + b (2.12)

While Patanè (2011) and Kröber et al. (2015) ﬁtted their data with a 3-parameter exponential function (Figure 2.13; middle) in the form of:

2 ln(gs) = a.VPD + b.VPD + c (2.13)

Verryckt et al. (2017) modiﬁed the stomatal multiplicative model (Figure 2.13; right) of Emberson et al. (2000) to:

gs = gmax.( f min + (1 − f min). f VPD) (2.14) 2.3 LEAF PHOTOSYNTHESIS 18

Figure 2.12: A gs-VPD curve of Carica papaya (Vincent et al., 2015).

VPDmin − VPD f = min(1; max(0; )) (2.15) VPD VPDmin − VPDmax

gmin with gmax the maximal stomatal conductance, fVPD the modiﬁcation of gmax by VPD. fmin is the ratio , gmax where gmin the minimal stomatal conductance is. VPDmin and VPDmax are the thresholds for minimal and maximal stomatal opening respectively.

Figure 2.13: Three gs-VPD curves ﬁtted by a logarithmic regression (left) (Oren et al., 1999), by an exponential regression (middle) (Patanè, 2011) and by the multiplicative model (right) (Verryckt et al., 2017). 2.4 ALTITUDINAL GRADIENTS 19

2.4 Altitudinal gradients

2.4.1 Changing parameters with the altitude

Mountains cover only 20% of the land surface of the earth, but are highly important. They provide water, forests, agriculture, energy, mineral wealth, recreation, etc. Moreover, they are a shelter for many endemic and endangered species (Högger et al., 1992). Mountain Agenda (1998), an NGO, claimed that more than half of the world’s population rely on the freshwater of mountains because they receive a lot of precipitation. Mountains additionally store and distribute water to the lowlands. They provide extra water in dry seasons due to the accessory supply of meltwater. Alexander von Humboldt was the first who formulated the altitudinal zonation in 1807 (Kruckeberg, 2002). He noticed dissimilar vegetations on different elevations as he travelled in the Andean mountains (Kruckeberg, 2002). Körner (2007) listed environmental changes with elevation. Temperature drops by 5,5 °C per kilometer gain of altitude. This will subsequently lower the saturation vapour pressure, so evaporation will also decline. Also, solar radiation rises with the elevation due to lower atmospheric turbidity. Atmospheric pressure and partial pressure change as well with the altitude, according to an inverse relationship. A decrease of 11% per increased kilometer of elevation is observed. Besides, the UV-B fraction increases when ascending a mountain. Also, precipitation, season length, geology, hours of sunshine and wind vary with the altitude, but the trends depend on many other factors too (Körner, 2007; Abbott and Brennan, 2014). Chen et al. (2015a) demonstrated how morpho-anatomical characteristics change over a wide range of altitude levels for Tibetan Quercus aquifolioides. The leaf mass per unit of area (LMA), which is the inverse of SLA, exhibits a negative correlation with altitude, due to both a decreasing leaf area and an increasing leaf mass with rising elevation. Also, leaf thickness was observed to increase with the altitude. All of these trends are significant. Akinlabi et al. (2014) showed a reduced mean leaf area and mean petiole length of Chromolaena odorata for altitudes increasing from 280 to 360 m a.s.l..

2.4.2 High altitudinal ecosystems are more vulnerable to climate change

Mountains are the most susceptible areas to all climate changes in the atmosphere (UNCED, 1992) and they are a powerful tool to study the effects of climate change (Fischer et al., 2011). On altitudinal gradients, different ecosystems exist quite close to each other. Transects in altitude represent climate gradients in time and they can possibly help to identify certain responses of species to changing climate. Separating confounding effects is a challenge that has to be faced with transect analyses (Fischer et al., 2011). Another issue is that precipitation is, next to temperature, one of the most important climatic parameters, but it is highly variable and it is hard to predict certain changes and their directions. Also, long-term meteorological stations are very sparse in the mountains (Pepin et al., 2015). According to Ohmura (2012), the Andean region is unique for research of temperature change trends and accompanying effects. First of all, the Andes is the sole region in the Southern Hemisphere having climatic observations at high altitudes. Secondly, due to the north-south orientation of the mountain range, data can be sampled from high latitudes to the equator. Finally, both the continental (east) and the maritime (west) area can be investigated for the effect of elevation. Ohmura (2012) reported that the highest variability of mean annual temperature was at the highest elevations. Vuille et al. (2003) observed that the temperature rise is significantly higher for the western Andean slope compared to the eastern slope. Their model suggests that this is, on the one hand, a result of the simultaneous warming of the global sea surface and, on the other hand, of different trends in cloud cover. 2.4 ALTITUDINAL GRADIENTS 20

Ponce-Reyes et al. (2013) pointed out that cloud forests are highly fragmented ecosystems, sensitive to climate change. They observed that the extent of areas in Mexico that will be suited for cloud forests in the next 70 years, will sharply decline and the degree of fragmentation will increase. This will result in rising extinction risk. The remaining cloud forests require protection to avoid habitat loss. Hermes et al. (2018) investigated the impact of climate change on the availability of forest habitat of the endemic El Oro parakeet. The climate niche was modelled and projected to four diﬀerent climate change scenarios. They concluded that the range of the parakeet will be between 500m and 1700m uphill in the year 2100. The habitat of this species is located in cloud forests, which sharply decreased in extent with increasing elevation. Thus, the shift uphill is accompanied by a reduction of the forest habitat to 10% of the original size. Additionally, the connectivity between populations in diﬀerent areas declines at higher altitudes. 3 Material and methods

3.1 Site description

The data for this thesis was gathered along an altitudinal gradient on the west Andes cordillera of the provinces Pichincha and Imbabura in northern Ecuador. On four strata, each on a diﬀerent altitude, sixteen permanent sampling plots (PSP) were established in natural forests (Figure 3.1).

Figure 3.1: Fake relief map of a part of Ecuador that contains the four strata. Altitude is increasing from left (West) to right (East). Map data is obtained from Google Maps.

Rio Silanche Bird Sanctuary is situated at the lowest altitude where measurements were done for this ﬁeld campaign, to be exact at 400m a.s.l. It consists of a 100 hectares Bird Sanctuary in the Chocó-lowland 3.1 SITE DESCRIPTION 22 rainforest with a 15-meter high canopy observation tower. The reserve was bought in 2005 by Mindo Cloudforest Foundation (MCF) (Mindo Cloudforest Foundation, a). Milpe Bird Sanctuary is also a 100-hectares protected reserve owned by MCF. The area was purchased in 2004 and is a part of the Chocó-Andean foothills rainforest. The leaves were collected at an altitude of 1100m a.s.l. (Mindo Cloudforest Foundation, b). El Cedral is a biologic station at an elevation of 2200m a.s.l.. In 1989 the forest was declared as a protective area. The 71-hectare territory including the house is owned by biologist Germán Toasa. El Cedral is the residence of bears, panthers, tigrillos, armadillos and diﬀerent bird species (Planeta, 2014). Peribuela Protected Forest belongs to a community with 240 indigenous people of pre-Inca Caranqui. The inhabitants of Peribuela were poor and to improve their life quality they developed an ecotourism project. The establishment of a 1040 hectares native Andean forest was the result. It is located between the montane forest ecosystem and the high altitude páramo grasslands.

Figure 3.2: Pictures of the vegetation at the four diﬀerent strata. Top left: Rio Silanche (400m a.s.l.); top right: Milpe (1100m a.s.l.); bottom left: El Cedral (2200m a.s.l.) and bottom right: Peribuela (3000m a.s.l.).

Table 3.1 gives the amount of PSPs per stratum, which are situated on a similar elevation. The plots are square-shaped with a size of 40 m x 40 m. PSPs are located at random within the stratum, however, they have to fulﬁl some criteria listed in the RAINFOR protocol (Phillips et al., 2016):

• having adequate access

• absence of human disturbance 3.1 SITE DESCRIPTION 23

• receiving suﬃcient long-term institutional support

• location on a tolerably homogenous soil parent material and soil type

• exclusion of areas inﬂuenced by anthropogenic disturbance

Table 3.1: The names and elevation of the reserves where data is gathered for the ﬁeldwork of this thesis. The number of PSPs for each stratum is represented in the fourth column.

Name of reserve Elevation No. of PSPs Stratum 1 Rio Silanche 400m a.s.l. 5 Stratum 2 Milpe 1100m a.s.l. 4 Stratum 3 El Cedral 2200m a.s.l. 3 Stratum 4 Peribuela 3000m a.s.l. 4

Seasonality is weak in Ecuador with 12 hours long days throughout the year and a quite homogenous minimum and maximum temperature. Yet, there is a dry and rainy season of which the timing depends on the geography. The temperature and precipitation of the four strata are given in Figure 3.3. The data was obtained from WorldClim. Temperature, precipitation and seasonality are higher for lower altitudes during all year. August and September - when the ﬁeld work was accomplished - are clearly in the dry season with lower precipitation levels compared to December-April.

Figure 3.3: Precipitation (blue bars) levels and mean day temperature (red line) during the whole year for Rio Silanche, Milpe, El Cedral and Peribuela. 3.2 LEAF INVENTORY 24

3.2 Leaf inventory

During two months (August and September 2018), we collected field data in the west-northern Andes range for this thesis. Normally all strata would be measured one week in August and one week in September. Each month the order was according to increasing altitude. This sequence was to reduce influence by weather conditions. Due to multiple technical issues, the third and fourth strata were only measured one week in the second month. The tree and liana species were chosen based on their abundance in the strata. Determination of the species was done by Nicanor Mejía, a forest engineer of the Mindo Cloudforest Foundation, specialised in Neotropical botany (Figure 3.4; top left). To make a comparison between lianas and trees, an approximately equal amount of tree and liana leaves were gathered. The alternation between lianas and trees was chosen to avoid data variability by weather conditions. The necessary sun leaves are often difficult to reach, especially at lower altitudes like Rio Silanche with tree heights up to 25 m, therefore ex-situ measurements were selected over in-situ measurements. In the case of in-situ measurements, the leaves are not excised in order that no cavitation could take place. The branches were collected with a telescoping tree pruner (Figure 3.4; right). As soon as possible after cutting, the branches were put in a bucket with water. While in water, 10 cm of the branch was cut again, to prevent air to enter the vessels or briefly: to avoid cavitation. The bucket with the leaves to be measured was carried to the place where the analysis took place (Figure 3.4; bottom left).

Figure 3.4: Pictures illustrating leaf inventory during ﬁeldwork. First the leaves were determinated with the help of Nicanor Mejía (top left), afterwards the leaves were collected with a pruner (right), after which the leaves were put in a bucket and brought to the CIRAS-3 (bottom left). 3.3 LEAF SAMPLE ANALYSIS 25

3.3 Leaf sample analysis

3.3.1 Response curves

3.3.1.1 Leaf net photosynthesis

Leaf net photosynthesis of the cut branches was measured ex-situ with a portable photosynthesis system (CIRAS-3; PP Systems, Amesbury USA). CIRAS stands for Combined Infra-Red Analysis System. The four Infra-red Gas Analysers (IRGAs) allow to measure CO2 and H2O concentrations. A reference and an -2 -1 analysis IRGA is needed to measure each target molecule. In this way, A (µmol CO2 m s ), E (mmol H2O -2 -1 -1 m s ) and ci (µmol CO2 mol ) of the leaf sample can be calculated as well as the temperatures obtained -2 -1 from energy balance equations. gs (mmol H2O m s ) is the inverse of stomatal resistance, which can also be calculated from the gas exchange measurements. The equations can be found in PP Systems (2017). The CIRAS was equipped with a PLC3 Universal Leaf Cuvette 25 mm x 18 mm.

To avoid the influence of changing ambient CO2 concentrations on the opening degree of the stomata, the concentration can be kept constant via a CO2 cartridge. Although most of the time ambient CO2 was used due to technical issues. A long tube was therefore employed to take air from around 5m away to minimize fluctuations. The humidity can also be adjusted to the desired water concentration, but the CIRAS-3 does not allow to achieve a higher humidity than the actual humidity in the surrounding atmosphere. Part of the gas with the selected CO2 and H2O concentration goes through the leaf while another part does not, in order to observe the difference in uptake and release. Besides that, a gas stream with a zero CO2 and H2O concentration is used for calibration. Also, the temperature can be modified to the preferred leaf temperature. It is important to state that the fixed settings were always adjusted if necessary. This was done to make chamber conditions representative for the actual external microclimate and sometimes to address technical issues. The cuvette temperature (Tcuv) was always set a bit higher than the ambient temperature to avoid condensation problems. Light distribution was fixed at 21/39/40% (red/green/blue). The value of the cuvette flow was kept 200 cc min-1 and of the analyser flow 100 cc min-1. The leaf was always placed with as low as possible veins in the chamber, to minimize leaks. In Figure 3.5 a picture of the use of the CIRAS-3 in the field is shown.

Figure 3.5: Picture illustrating ex-situ leaf gas exchange measurements with the CIRAS-3. The photograph was taken in Peribuela. 3.3 LEAF SAMPLE ANALYSIS 26

3.3.1.2 Light response curves

At every stratum, a couple of light response curves were measured, whereafter photosynthetically active radiation in the cuvette (PARi) was set at light saturation. This average PARi is used for the VPD-gs curves at that stratum. In total 18 light response curves were established (Table 3.2). For the light-response curves, a declining sequence of irradiance values were set as stomata respond faster to decreasing light levels. The settings were set in the following order: 2000, 1800, 1600, 1400, 1200, 1000, 800, 600, 400, 200, 100, 50 and 25 µmol m-2 s-1. Several measurements were taken at each step, with suﬃcient acclimatisation time between the steps. As the leaf chamber’s RH with CIRAS-3 has to be below 70%, the H2O reference had to be adjusted to external conditions to keep the humidity below this value.

3.3.1.3g s-VPD response curves

A VPD-gs response curve was established from three leaves per individual growth form. The leaf gas- exchange measurements were taken during the day when the stomata were already open. In total 143 gs-VPD curves were measured (Table 3.2). To establish the VPD-gs curves, the vapour pressure deﬁcit had to be varied. Because VPD could not be set directly in decimal numbers, ﬁxed % of H2O reference was chosen as H2O level. Due to the divers ambient humidities along the altitudes and even during the day, the starting point of the VPD sequence had to be set with caution. The starting value of the leaf chamber humidity was set as close as possible to 70% to start with the lowest feasible value of VPD. Afterwards, the settings were adjusted to decreasing humidities as stomata respond faster to increasing drought.

3.3.2 Speciﬁc leaf area

The SLA is the ratio of the leaf surface area (LA) and the leaf mass in dry weight (LM). After leaf gas exchange measurements were performed, the leaves were collected to obtain the SLAs. On average, three leaves per individual was gathered to obtain the SLA. In total, the SLA of 150 leaves was measured (Table 3.2). In the field, these leaves were pressed to remove the relief and placed on a white background with a ruler next to it as scale to take a picture perpendicular to the leaf. After fieldwork, the photos were processed with the open source image processing program ImageJ to calculate the area in sq. cm. An example is shown in Figure 3.6. The leaves were compressed in newsprint and dried for a night in a drying oven with a temperature of 30°C at the office of Milpe Bird Sanctuary. In Belgium, the leaves were again dried at 50°C for a day. The dried leaves were weighed on a balance with an accuracy of 0,001 grams.

The complete inventory of all the sampled genera with the number of gs-VPD curves, light response curves and SLA measured per genus are shown in Table 3.2. The altitude and growth form of each genus are appended as well in the table.

Table 3.2: The amount of measured gs-VPD curves, light response curves and SLA for the diﬀerent genera (or species if the plant was determined on species level). The associated altitude and growth form are displayed as well.

Genus or species if known Growth form Altitude #gs-VPD #A-PAR #SLA Asteraceae Mikania Liana 400 14 1 13 Cucurbitaceae Cayaponia Liana 400 2 0 2 Hydrangeaceae Hydrangea Liana 400 3 1 5 Clusiaceae Clusia Liana 1100 9 0 9 3.3 LEAF SAMPLE ANALYSIS 27

Ericaceae Psammisia Liana 1100 3 2 5 Melastomataceae Blakea Liana 1100 2 0 2 Sapindaceae Serjania Liana 1100 5 0 5 Aristolochiaceae Aristolochia Liana 2200 3 0 3 Asteraceae Mikania Liana 2200 8 0 8 Cucurbitaceae Gurania Liana 2200 3 0 3 Rhamnaceae Gouania Liana 2200 2 0 2 Vitaceae Cissu Liana 2200 3 1 4 Asteraceae Mikania Liana 3000 2 0 2 Smilacaceae Smilax Liana 3000 3 0 3 Annonaceae Rollinia mucosa Tree 400 0 1 2 Burseraceae Protium Tree 400 2 0 2 Lecythidaceae Grias neuberthii Tree 400 2 0 2 Malvaceae Theobroma gileri Tree 400 2 0 2 Meliaceae Guarea Tree 400 5 0 5 Phyllanthaceae Hieronyma alchorneoides Tree 400 0 1 2 Urticaceae Cecropia hastae Tree 400 6 0 6 Urticaceae Pourouma Tree 400 5 1 0 Clusiaceae Tovomita weddelliana Tree 1100 6 1 6 Dichapetalaceae Dichapetalum Tree 1100 3 1 4 Dichapetalaceae Stephanopodium angulatum Tree 1100 3 0 3 Lauraceae Caryodaphnopsis theobromifolia Tree 1100 0 1 1 Lauraceae Ocotea ﬂoribunda Tree 1100 3 0 3 Lauraceae Ocotea fraxifera Tree 1100 2 0 2 Leguminosae Dussia Tree 1100 3 0 3 Myristicaceae Otoba gordonifolia Tree 1100 3 2 5 Actinidiaceae Saurauia Tree 2200 3 0 3 Lauraceae Nectandra subbullata Tree 2200 2 0 2 Moraceae Morus insignis Tree 2200 3 0 3 Phyllanthaceae Hieronyma asperifolia Tree 2200 3 1 4 Sapindaceae Billia rosea Tree 2200 5 1 5 Theaceae Gordonia fruticosa Tree 2200 1 0 0 Adoxaceae Viburnum hallii Tree 3000 2 0 2 Araliaceae Oreopanax Tree 3000 2 0 2 Betulaceae Alnus acuminata Tree 3000 4 0 4 Ericaceae Cavendishia bracteata Tree 3000 3 0 3 Rosaceae Rubus Tree 3000 3 0 3 Rubiaceae Palicourea Tree 3000 2 0 2 Siparunaceae Siparuna Tree 3000 3 0 3 Unknown 1 Unknown 3000 0 1 0 Unknown 2 Unknown 3000 0 1 0 Unknown 3 Unknown 3000 0 1 0 Total 143 18 150 3.4 DATA PROCESSING AND STATISTICAL ANALYSIS 28

Figure 3.6: Determination of the speciﬁc leaf area with the open source processing program ImageJ with an example. Left picture: the original photo of a tree leaf collected in Rio Silanche with a ruler next to it as a reference. Middle picture: The ImageJ threshold module splits background from foreground. Right picture: After noise removal (’Despeckle’) and a median ﬁlter (pixel size 4), the leaf was selected with the wand tracing tool and subsequently the area of the selected leaf was measured.

3.4 Data processing and statistical analysis

3.4.1 Data quality check

The ecological relevance of the data was evaluated ﬁrst in both Excel-16.23 (Microsoft, 2019) and R-1.1.463 -2 -1 (The R Foundation, 2019). Leaves where A was around zero or where gs was under 30 mmol m s were discarded. Because the measurements were always established during the day and on sun leaves, these values are not representative for the aim of this study. The appropriate leaves were then averaged over a time series in R-1.1.463 (The R Foundation, 2019). When ﬁve data points were measured within a short time interval, only one data point was obtained by taking the mean of all the other parameters.

3.4.2 Regression selection

Subsequently, various regressions were fitted on the data with R-1.1.463 (The R Foundation, 2019): a linear (gs ∼ VPD), an exponential (ln(gs) ∼ VPD) and a logarithmic regression (gs ∼ ln(VPD)). As there are many discussions about the "best" measure of accuracy (Botchkarev, 2018), four performance metrics are chosen: MAE, MAPE, MSE, and RMSE. A big disadvantage of MAPE is that if the measurements are close to zero, MAPE will be undefined or infinite (Kim and Kim, 2016). For this thesis, gs is always higher than zero. Besides, there is a lot of criticism on RMSE. Willmott and Matsuura (2005) claimed that RMSE is ambiguous and cannot be interpreted easily as it not only describes the average error. They also said that it is a misleading indicator of the average error. Brassington (2017) showed that MAE is an unbiased estimator in contrast to RMSE, which is biased. When the model errors follow a normal distribution, Chai and Draxler (2014) assert that RMSE is a better indicator than MAE. Moreover, RMSE avoids the use of an absolute value, which is often undesirable. MAE, MSE, and RMSE are scale-dependent. This means that they are not useful when comparing data across series with a different scale (Hyndman, 2014). This problem doesn’t emerge when using MAPE. Here, the metrics are used to compare the different regressions, so the scale is, fortunately, the same. To avoid reliance on one measure of goodness of fit, all four were considered. The terms are mathematically explained in the equations below:

n 1 Õ MAE = |y − y | (3.1) n i ei i=1 3.4 DATA PROCESSING AND STATISTICAL ANALYSIS 29

n 1 Õ yi − yi MAPE = | e | (3.2) n yi i=1

n 1 Õ MSE = (y − y )2 (3.3) n i ei i=1

tv n 1 Õ RMSE = (y − y )2 (3.4) n i ei i=1 with yi the observed true values, eyi the predicted values of the model and n the number of data points.

3.4.3 Statistical analysis

The statistics for this thesis research were performed in R-1.1.463 (The R Foundation, 2019). The relationship between the observed variables and on the one hand the altitude and on the other hand the two growth forms were visualised in the boxplots and ggplots. Statistical relationships were found significant if the probability value (p-value) was 0,05 or smaller. Linear mixed-effects regressions (LMERs) were used to examine the data, which incorporate random effects next to fixed effects. Altitude and growth form are set as fixed variables. To investigate how the genus specificity influences the variability in the data, the genera were established as random effect. The analysed data were the parameters of the chosen regression, gmax, E, A at gmax, SLA, LM and LA. Additionally, iWUE and WUE were also studied using a LMER with SLA as an additional fixed effect. The intrinsic WUE was calculated by dividing A at gmax by that gs. The instantaneous WUE was obtained by dividing A at gmax by E. The function lmer of the R package lmerTest was utilized to perform the LMERs. The function r.squaredGLMM of the R package MuMIn was used to describe the predictive capacity of the models. Furthermore, the correlations between the fixed effects were investigated by LMERs and ignored if not significant. If significant, an interaction graph was plotted. The q-q plot of the model and the histogram of the residuals of the model were verified and extreme outliers were removed. After the data quality check and the removal of the outlines, 45 gs-VPD curves remained (Table 3.3). To summarize, Table 3.4 renders the total number of workable gs-VPD curves at each measured altitude for lianas and trees. 3.4 DATA PROCESSING AND STATISTICAL ANALYSIS 30

Table 3.3: The total number of ecological relevant gs-VPD curves for the diﬀerent genera. The associated altitude and growth form are displayed as well.

Genus Growth form Altitude (m) #gs-VPD curves Asteraceae Mikania Liana 400 2 Cucurbitaceae Cayaponia Liana 400 2 Clusiaceae Clusia Liana 1100 3 Ericaceae Psammisia Liana 1100 1 Asteraceae Mikania Liana 2200 5 Cucurbitaceae Gurania Liana 2200 1 Rhamnaceae Gouania Liana 2200 2 Vitaceae Cissus Liana 2200 1 Burseraceae Protium Tree 400 1 Urticaceae Cecropia Tree 400 3 Urticaceae Pourouma Tree 400 2 Clusiaceae Tovomita Tree 1100 5 Dichapetalaceae Stephanopodium Tree 1100 1 Lauraceae Ocotea Tree 1100 2 Leguminosae Dussia Tree 1100 1 Myristicaceae Otoba Tree 1100 1 Actinidiaceae Saurauia Tree 2200 1 Sapindaceae Billia Tree 2200 3 Araliaceae Oreopanax Tree 3000 2 Ericaceae Cavendishia Tree 3000 1 Rosaceae Rubus Tree 3000 2 Rubiaceae Palicourea Tree 3000 2 Siparunaceae Siparuna Tree 3000 1 Total 45

Table 3.4: The total quantity of ecologically relevant gs-VPD curves for each growth form on each altitude. The number of genera are also exhibited.

Growth form Altitude #Genera #gs-VPD curves Liana 400 2 4 Liana 1100 2 4 Liana 2200 4 9 Liana 3000 0 0 Tree 400 3 6 Tree 1100 5 10 Tree 2200 2 4 Tree 3000 5 8 Total 23 45 4 Results

4.1 Regression selection

A linear, exponential and logarithmic regression were proposed to fit the gs-VPD graphs. In Figs. A.1–A.8, the three potential regressions are fitted on the data of each leaf after the data quality check. Each datapoint is an average of multiple sequential measurements taken under similar conditions. Four measures of goodness of fit (MAE, MAPE, MSE and RMSE) were calculated for the three regressions for all the usable leaves. Table 4.1 shows the sum of each performance metric for every regression and in Table A.1 the metrics for all the leaves separately can be found. In general, all the values for each measure of accuracy are close to each other. For MAE, MSE and RMSE, the linear regression has the lowest value of the three evaluated regressions. Only for MAPE, the exponential regression has a slighter lower value than the linear and logarithmic regression. Overall, the linear regression appears to be the best measure of accuracy and was chosen to answer the research questions.

Table 4.1: The sum of each measure of accuracy (MAE, MAPE, MSE and RMSE) for all the leaves of the three applied regressions on the data. For each metric, the smallest value of that metric out of the three regression types is displayed in bold.

Exp Log Lin MAE 305,98 307,19 301,53 MAPE 3,33 3,41 3,41 MSE 4796,07 4662,45 4472,75 RMSE 372,59 368,49 361,73

4.2g s-VPD measurements

gs-VPD curves were generated for 45 leaves, whereof 17 liana leaves and 28 tree eaves. As the few lianas measured at 3000m were not ecologically relevant, there is no useful data of lianas at 3000m. To ﬁt the data, a linear regression was used. The parameters are derived from the linear equation: gs = a*VPD+b and are given in Table A.2 for every leaf. The slope (a), which reﬂects the sensitivity to VPD, ranges from -89,80 -2 -1 -1 to -0,79 mmol H2O m s kPa and the intercept (b), representing the zero drift, varies between 35,27 and -2 -1 446,91 mmol H2O m s . 4.2 GS-VPD MEASUREMENTS 32

4.2.1 Comparison between growth forms and altitudes

The two parameters (a and b) are used to investigate if gs responds differently to changes in VPD between lianas and trees as well between varying altitudes. Figs. 4.1 and 4.2 show the boxplots to compare parameter a en b respectively between growth form and altitude. The medians of the boxplots of parameter a are -2 -1 -1 -38,58, -12,63, -38,72 H2O m s kPa for lianas at an altitude of 400, 1100 and 2200m a.s.l. respectively -2 -1 -1 and -37,40, -5,62, -6,40 and -36,99 mmol H2O m s kPa for trees at 400, 1100, 2200 and 3000m a.s.l. respectively. The medians are relatively similar for trees and lianas, except for lianas at 2200m. It also seems like a increases with altitude for both lianas and trees and decreases again beyond a certain elevation. For -2 -1 parameter b, the medians were 196,44, 80,11, 204,81, 273,3, 57,56, 52,03 and 191,97 mmol H2O m s respectively for lianas at 400, 1100 and 2200m a.s.l. and trees at 400, 1100, 2200 and 3000m a.s.l. The boxplots also show signs of a parabolic behaviour: a decline with altitude, and again the opposite trend as of a certain elevation. The elevation at which this breaking point occurs seems similar for parameter a and b, but at a higher altitude for trees than for lianas for both parameters. The data with the estimated regression for both trees and lianas and the associated 95% confidence interval are plotted in Figs. 4.3 and 4.4. The confidence intervals for lianas and trees are highly overlapping for each parameter. This indicates that there won’t be significant different values of both parameters nor significant divergent effect of the altitude between both growth forms. This can also be interpreted from the boxplots.

Parameter a 40 Lianas at 400m Lianas at 1100m Lianas at 2200m Lianas at 3000m

20 Trees at 400m Trees at 1100m Trees at 2200m Trees at 3000m 0 −20 parameter a parameter −40 −60 −80

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.1: Comparison of parameter a between trees and lianas in combination with the four altitudes where measurements took place.

To describe potentially different responses numerically, LMERs were applied. Firstly, the interaction between altitude and growth form was examined. They are not significant, so the interaction effects of both parameters were left out. The intercept of the LMERs of the two parameters (lianas at sea level) are significant (p < -2 -1 -1 0,01 and p < 0,001 respectively). Intercept of parameter a is -43,83 mmol H2O m s kPa and of b 213,68 -2 -1 mmol H2O m s . The effect of the altitude is only significant for parameter b (p < 0,05). b is decreasing -2 -1 with 41,6 mmol H2O m s per kilometer increase in altitude. The growth form is not significant for both a and b. Furthermore, the percentage of the variance explained by the models was evaluated with the R function r.squaredGLMM. The LMER of parameter a declares 5,9% and the model of b 10,9%. 4.2 GS-VPD MEASUREMENTS 33

Parameter b

Lianas at 400m Lianas at 1100m Lianas at 2200m Lianas at 3000m Trees at 400m Trees at 1100m

400 Trees at 2200m Trees at 3000m 300 parameter b parameter 200 100

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.2: Comparison of parameter b between trees and lianas in combination with the four diﬀerent altitudes.

−25

Growth_form

Liana Tree −50 parameter a parameter

−75

1000 2000 3000 Altitude (m a.s.l.)

Figure 4.3: The plotted data of parameter a in function of the altitude with the regression line and 95% conﬁdence interval for both growth forms.

gmax and E were also considered. The boxplots are shown in Figs. 4.5 and 4.6. The medians for gmax -2 -1 were 118,5, 51,8, 133,8, 181,6, 50,80, 42,2 and 141,79 mmol H2O m s respectively from the left to the -2 -1 right boxplot. For E, the medians were found to be 2,8, 1,5, 3,5, 4,4, 1,5, 1,4 and 3,6 mmol H2O m s respectively. The performed LMERs show no significant effect of both growth form and altitude. Only the intercept of the models is significant (p < 0,001 and p < 0,01 for max gs and E respectively) and have a value of 128,1 -2 -1 -2 -1 mmol H2O m s and 2,5 mmol H2O m s respectively. The models explain respectively 8,3% and 6,2% of the variability. 4.2 GS-VPD MEASUREMENTS 34

400

300

Growth_form

Liana Tree parameter b parameter 200

100

1000 2000 3000 Altitude (m a.s.l.)

Figure 4.4: The plotted data of parameter b in function of the altitude with the regression line and 95% conﬁdence interval for both trees and lianas.

Maximum stomatal conductance

Lianas at 400m Lianas at 1100m

300 Lianas at 2200m Lianas at 3000m Trees at 400m Trees at 1100m Trees at 2200m 250 ] Trees at 3000m 1 − s 2 − m O 2 H 200 150 maximum gs [mmol maximum 100 50

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.5: Comparison of gmax between trees and lianas in combination with the four altitudes where measurements took place.

4.2.2 The effect of the different genera

As not all the measured leaves are determined on a species level, the genera of the leaves are considered as comparison. To forecast what the influence is of the genus of the variability of parameters a and b as well on A at gmax, LMERs are again considered. They show which part of the variability that is not explained by the model, is a result of the differences in genera. The genetic effects between genera are regarded as random effect of the model. The specificity of the genera for the parameters of the linear regression explains a large extent of the remaining variability, e.g. 85,9% for a and 84,54% for b. For the LMERs of max gs and E, this 4.2 GS-VPD MEASUREMENTS 35

Transpiration

7 Lianas at 400m Lianas at 1100m Lianas at 2200m Lianas at 3000m

6 Trees at 400m Trees at 1100m Trees at 2200m Trees at 3000m ] 1 5 − s 2 − m O 4 2 H 3 E [mmol 2 1

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.6: Comparison of leaf level transpiration between trees and lianas in combination with the four altitudes where measurements took place. is 70,9 and 69,1% respectively.

4.2.3 Comparison between growth forms and forest type

The boxplots show a rather parabolic change of the parameters, within the middle elevations a less negative slope and a lower intercept. At 400m a.s.l. elevation, the ecosystem was a lowland rainforest, while Peribuela at the highest altitude is a high altitudinal primary forest. The actual montane cloud forest was at 1100 and 2200m a.s.l. To evaluate the influence of the forest system, LMERs were performed not with altitude as fixed effect, but the forest type as fixed effect.

-2 -1 The sensitivity of gs to VPD (parameter a) of lianas in the lowland rainforest is -48,8 mmol H2O m s kPa-1 (p < 0,001). The difference with the primary forest in Peribuela is not significant, but with TMCF it is. -2 -1 -1 Parameter a is estimated to be 21,0 mmol H2O m s kPa higher (p < 0,05). Trees and lianas do not have a significantly different sensitivity. The zero drift (parameter b) of lianas in the rainforest is 234,9 mmol H2O m-2 s-1 (p < 0,001). Again, the difference with TMCF is the only effect that is significant. TMCF establishes -2 -1 a lower zero drift, to be exact 105,5 mmol H2O m s lower (p < 0,01). Both LMERs explain more of the variability than the models that compare the altitude instead of the forest type. The model of parameter a declares 9,1% (in comparison with 5,9 %) and of parameter b 13,3% (compared to 10,9% when considering the altitude). The 85,6% of the additional variability is explained by the genera genetics for parameter a and 83,6% for parameter b. This is in tune with the models including the altitude.

-2 In case of gmax, TMCF is also significantly different (p < 0,05) and was assessed to be 60,6 mmol H2O m s-1 lower than in the other forest types. The LMER of E didn’t find any significant difference between the different kinds of forest. The models explained 12,6 and 13,7% respectively, which is again more than the models, considering altitude. 4.3 SLA 36

4.3 SLA

In the same way the parameters were evaluated, the SLA was assessed. The SLA values of all the leaves of which SLA was measured are listed in Table A.4. The comparison of SLA between growth form and altitude is visualised in the boxplot in Figure 4.7. The SLA has a broad range from 45,5 to 145,7 cm2 g-1. The medians are 289,6, 145,6, 142,8, 134,7, 179,7, 127,6, 128,3 and 98,6 cm2 g-1 respectively from the most left to the most right boxplot. A slight decrease with increasing elevation can be expected for both growth forms. Moreover, lianas seem to establish leaves with a higher SLA than trees. This assumption is also visualised in Figure 4.8.

Specific Leaf Area

600 Lianas at 400m Lianas at 1100m Lianas at 2200m Lianas at 3000m Trees at 400m 500 Trees at 1100m Trees at 2200m Trees at 3000m 400 /g) 2 300 SLA (cm 200 100 0

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.7: Comparison of SLA between trees and lianas as well between the four diﬀerent altitudes.

Again, a LMER was conducted in RStudio. The interaction between growth form and altitude was not found to be significant. The SLA of lianas at sea level was estimated to be 266,9 cm2 g-1 (p < 0,001). The effect of the altitude on SLA was assessed to decrease 0,042 cm2 g-1 per meter (p < 0,001). The effect of the growth form was not found to be significant. The total model describes 19,8% of the variability. 60,7% of the remaining variability is the result of the differences between genera. The SLA of three leaves (Hydrangeaceae Hydrangea 3.1, Phyllanthaceae Hieronyma alchorneoides 1.1 and Phyllanthaceae Hieronyma alchorneoides 1.2) are outliners and were not included in the LMER and boxplots. Furthermore, the components of SLA were scrutinized (Figs. 4.9 and 4.10). The leaf area did not significantly differ with the altitude or between the growth forms, according to the applied LMER. For the LM, the interaction effect was the only one that was significant (p < 0,05), which pointed out a higher decrease with the altitude for trees (1,13g greater decrease per kilometer uphill). Both models declare 2,9% and 11,1% of the variability respectively. The specific differences between genera explain 64,5% and 70,54% of the surplus variability respectively. Additionally, the boxplots of the SLA, considering only the further studied leaves, are visualised in Figure 4.11. The executed LMER resulted in no significant effect of both growth form and altitude. 4.3 SLA 37

500

400

/g) Growth_form 2 300 Liana Tree SLA (cm

200

100

1000 2000 3000 Altitude (m a.s.l.)

Figure 4.8: The plotted data of SLA in function of the altitude with the regression line and 95% conﬁdence interval for both growth forms.

Leaf Area

Lianas at 400m Lianas at 1100m Lianas at 2200m Lianas at 3000m 1200 Trees at 400m Trees at 1100m Trees at 2200m Trees at 3000m 1000 ] 2 800 600 Leaf Area [cm 400 200 0

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.9: Comparison of leaf area between trees and lianas as well as between the four diﬀerent altitudes. 4.3 SLA 38

Leaf Mass

7 Lianas at 400m Lianas at 1100m Lianas at 2200m Lianas at 3000m Trees at 400m 6 Trees at 1100m Trees at 2200m Trees at 3000m 5 4 3 leaf mass [g] 2 1 0

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.10: Comparison of leaf mass between trees and lianas as well as between the four diﬀerent altitudes.

Specific Leaf Area 400 300 /g] 2 SLA [cm 200 100

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.11: Comparison of SLA of the investigated leaves between trees and lianas as well as between the four diﬀerent altitudes. 4.4 CARBON ASSIMILATION 39

4.4 Carbon assimilation

Additionally, A at gmax was analysed to approach Asat (Table A.3). The lowest value of all leaves is only 0,5 -2 -1 -2 -1 -2 -1 µmol CO2 m s for a gmax of 37 mmol H2O m s . The leaf with a gmax of 204,4 mmol H2O m s -2 -1 featured an A of 12,14 µmol CO2 m s .

Asat was compared between both growth forms and between the altitudes with a LMER. The boxplot is visualised in Figure 4.12. Asat looks like increasing with the altitude for lianas, but decreasing for trees till a certain elevation and increasing beyond that elevation. An interaction effect between altitude and growth form can therefore be expected. The medians of the boxplots from left to right are respectively 2,37, 3,64, -2 -1 6,76, 8,58, 2,35, 3,62 and 4,06 µmol CO2 m s . The totally different trends between lianas and trees in Figure 4.13 confirms the expectation of an interaction effect.

Assimilation at the maximum stomatal conductance

Lianas at 400m

12 Lianas at 1100m Lianas at 2200m Lianas at 3000m Trees at 400m Trees at 1100m

10 Trees at 2200m Trees at 3000m 8 ] at maximum gs ] at maximum 1 − s

2 6 − m

2 O C

4 mol

µ A [ 2

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.12: Comparison of A at the gmax between trees and lianas as well as between the four diﬀerent altitudes.

When conducting a LMER, the interaction effect is indeed significant (p < 0,01) and cannot be left out in the model. This means that the effect of the altitude is not the same for both growth forms and vice versa. To illustrate the different trends of Asat with the altitude between lianas and trees, an interaction plot is shown in Figure 4.14. The means are plotted at each altitude for both growth forms. Both altitude and growth form have a significant effect on the assimilation (p < 0,01 and 0,05 respectively). At sea level, trees establish a -2 -1 -2 -1 5,53 µmol CO2 m s higher Asat. It increases by 2,65 µmol CO2 m s per kilometer rise in altitude for -2 -1 lianas. Due to the interaction effect, trees have a different slope with the altitude: -1,12 µmol CO2 m s per kilometer. The starting value for lianas at sea level is not significant (p > 0,35). The LMER described 20,3% of the variance. 55,4% of the variability that is not declared by the LMER of Asat, is explained by the genetic effects by genera.

When conducting the LMERs evaluating Asat with forest type as fixed effect, TMCF is again significantly -2 -1 different. It is estimated that the lianas of TMCF establish a 3,9 µmol CO2 m s higher A (p < 0,05). A -2 -1 of trees in TMCF is 3,8 µmol CO2 m s lower than A in the rainforest and primary forest (p < 0,01). The value of A for lianas in the rainforest is not significant, but for trees the value is significantly 5,8 µmol CO2 m-2 s-1 higher (p < 0,05). 4.4 CARBON ASSIMILATION 40

12.5

10.0

7.5 Growth_form ] at maximum gs ] at maximum 1 −

s Liana

2 − Tree m

2 O C 5.0 mol

µ A [

2.5

0.0 1000 2000 3000 Altitude (m a.s.l.)

Figure 4.13: The plotted data of A at the gmax in function of the altitude with the regression line and 95% conﬁdence interval for both growth forms.

Interaction plot

8 Growth_form

Tree Liana 7 6 ] at maximum gs ] at maximum 1 − s

2 − m

2 O C

5 mol

µ A [ 4

400 1100 2200 3000

Altitude (m a.s.l.)

Figure 4.14: Interaction plot of A at gmax with the altitude between lianas (green) and trees (blue). The means of each growth form on every measured elevation is plotted.

Additionally, A at gmax was regarded on mass base or in short Amass (Figure 4.15). The trend for lianas with the altitude looks diﬀerent compared to the trend of A based on area, where the trend of trees is quite -1 similar. The medians of the boxplots are 0,074, 0,033, 0,128, 0,091, 0,0278, 0,033 and 0,088 µmol CO2 g -1 -1 -1 s . For lianas an increase of Amass of 0,035 µmol CO2 g s was determined by LMER (p < 0,05). For -1 -1 trees, a decrease of 0,007 µmol CO2 g s was assessed (p < 0,05). 4.4 CARBON ASSIMILATION 41

Assimilation at the maximum stomatal conductance (mass based)

Lianas at 400m Lianas at 1100m Lianas at 2200m 0.20 Lianas at 3000m Trees at 400m Trees at 1100m Trees at 2200m Trees at 3000m 0.15 ] at maximum gs ] at maximum 1 − s

1 − g 0.10

2 O C

mol

µ A [ 0.05 0.00

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 4.15: Comparison of A at the gmax on mass base between trees and lianas as well as between the four diﬀerent altitudes.

A strong relationship was found between Amass and LMA (Figure 4.16). Amass decreased 6,8 µmol CO2 -1 -1 -2 g s for each g cm LMA increased (p < 0,001). Amass of lianas increased with the altitude (0,04 µmol -1 -1 -1 -1 CO2 g s ) per kilometer rise), while Amass of trees increased only 0,001 µmol CO2 g s per 1000m. The LMER of Amass in function of LMA, altitude and growth forms explains 52,2% of the variance. Only 15% of the residual variability is due to diﬀerent genetic eﬀects between genera.

0.2

0.1

Growth_form ] at maximum gs ] at maximum 1 − Liana s

1 − Tree g

2 O C

0.0 mol

µ A [

−0.1

0.005 0.010 0.015 0.020 LMA [g/cm2]

Figure 4.16: The plotted data of Amass in function of the LMA with the regression line and 95% conﬁdence interval for both growth forms. 4.5 INTRINSIC AND INSTANTANEOUS WUE 42

4.5 Intrinsic and instantaneous WUE

The iWUE and instantaneous WUE values are itemized in Table A.5 together with the corresponding altitude and growth form of each measured leaf. iWUE ranges from a minimum value of 6,06 to a maximum of 109,0 µmol CO2 / mol H2O. The boxplots which provide a comparison of iWUE between altitude and growth form are shown in Figure 4.17. The medians are 31,7, 67,5 and 62,5 µmol CO2 / mol H2O for lianas at 400, 1100 and 2200m a.s.l. respectively and 45,1, 43,0, 73,0 and 32,6 µmol CO2 / mol H2O for trees at 400, 1100, 2200 and 3000m a.s.l. respectively. The iWUE for liana and tree leaves appears to increase to a fixed altitude and decrease again beyond. For trees, this elevation seems to be higher. The regression lines of lianas and trees are fitted on the data in Figure 4.18. Based on the latter figure, an interaction between the growth form and altitude can be expected.

Intrinsic water Use Efficiency

Lianas at 400m

120 Lianas at 1100m Lianas at 2200m Lianas at 3000m Trees at 400m Trees at 1100m 100 Trees at 2200m Trees at 3000m O] 2 80 /mol H 2 60 mol CO µ 40 iWUE [ 20 0

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m), Type

Figure 4.17: Comparison of iWUE between trees and lianas as well as between the four diﬀerent altitudes.

A LMER was established to study what the influences of the altitude, growth form and SLA are on iWUE. The interaction between altitude and growth form is the only interaction effect that is significant (p < 0,05) and has a value of -0,017 µmol CO2 / mol H2O. An interaction plot is shown in Figure 4.19. The iWUE at sea level of lianas with an SLA of zero is 52,62 µmol CO2 / mol H2O and significant (p < 0,01). The possibly different intercept of trees compared to lianas, as well the effect of SLA is not significant (p > 0,05). Furthermore, the altitude is again the only variable that has a significant effect (p < 0,05). The iWUE of lianas gained 0,016 µmol CO2 / mol H2O for each meter rise in altitude. For trees this effect is 0,016-0,017 = -0,001 µmol CO2 / mol H2O / m. So for trees, the altitude has only a limited decreasing effect on iWUE. This model accounts for 36,3% of the variability. 53,0% of the other part is explained by the variability between the genera. When performing a LMER regarding the sort of forest, it appears that iWUE of TMCFs is 20,1 µmol CO2 / mol H2O higher. It should be noticed that the iWUE was observed at the best conditions for each leaf and the best conditions varied between the leaves (See Discussion). Therefore, the iWUE was considered at a VPD of 2 kPa, by estimating gs from the linear regression between gs and VPD and A from the linear regression between the estimated gs and A. No significant trend with the elevation was perceived. However, iWUE of lianas is 4.5 INTRINSIC AND INSTANTANEOUS WUE 43

90 O] 2

/mol H Growth_form 2 60 Liana Tree mol CO µ iWUE [

1000 2000 3000 Altitude (m a.s.l.)

Figure 4.18: The plotted data of iWUE in function of the altitude with the regression line and 95% conﬁdence interval for both growth forms.

around 78,7 µmol CO2 / mmol H2O (p < 0,001) and the iWUE of trees is 28,6 µmol CO2 / mmol H2O lower (p < 0,01). Furthermore, iWUE and SLA are inversely correlated. For each cm2 g-1 SLA increased, iWUE declined with 0,11 µmol CO2 / mmol H2O (p < 0,01). The total model described 39,4% and the specificity of the genera explained 41,9% of the additional variability. Considering the different types of forests, no statistical differences are found between the kinds of forests. Although, TMCFs seems to establish a slightly higher iWUE (p = 0,06). The LMER assessed TMCF to have a 17,5 µmol CO2 / mmol H2O higher iWUE.

Interaction plot

Growth_form 65

Tree Liana 60 O] 2 55 /mol H 2 50 mol CO µ 45 iWUE [ 40 35

400 1100 2200 3000

Altitude (m a.s.l.)

Figure 4.19: Interaction plot of iWUE with the altitude between lianas (green) and trees (blue). The means of each growth form on every measured elevation is plotted. 4.5 INTRINSIC AND INSTANTANEOUS WUE 44

Additionally, the instantaneous WUE was considered as well. WUE ranges from 0,34 to 4,41 mmol CO2 / mol H2O. The boxplots and estimated regressions are plotted in Figs. 4.20 and 4.21. The medians of the boxplots from left to right are 1,35, 2,38, 2,69, 1,93, 1,61 and 2,33 mmol CO2 / mol H2O respectively. The trends with the altitude look very different between trees and lianas, which indicates a significant interaction effect. For lianas, WUE seems to increase sharply with the elevation, while WUE of trees is declining.

Instantaneous water Use Efficiency

Lianas at 400m Lianas at 1100m Lianas at 2200m

4 Lianas at 3000m Trees at 400m Trees at 1100m Trees at 2200m Trees at 3000m O] 2 3 /mol H 2 2 WUE [mmol CO 1

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m), Type

Figure 4.20: Comparison of WUE between trees and lianas as well as between the four diﬀerent altitudes.

3 O] 2 /mol H

2 Growth_form

Liana Tree

2 iWUE [mmol CO

1000 2000 3000 Altitude (m a.s.l.)

Figure 4.21: The plotted data of WUE in function of the altitude with the regression line and 95% conﬁdence interval for both growth forms. 4.5 INTRINSIC AND INSTANTANEOUS WUE 45

Interaction plot

Growth_form

Tree Liana 2.5 O] 2 /mol H 2 2.0 WUE [mmol CO 1.5

400 1100 2200 3000

Altitude (m a.s.l.)

Figure 4.22: Interaction plot of WUE with the altitude between lianas (green) and trees (blue). The means of each growth form on every measured elevation is plotted.

Indeed, a significant interaction effect can be deduced from the performed LMER (p < 0,01). An interaction plot is visualised in Figure 4.22. The WUE of lianas with a zero SLA at sea level was significantly estimated to be 2,01 mmol CO2 / mol H2O. WUE increases 0,64 mmol CO2 / mol H2O for each kilometer rise in altitude (p < 0,01). For trees, a decline of 0,18 mmol CO2 / mol H2O was assessed. In contrast to iWUE, WUE was significantly dependent on the SLA. WUE declined 0,0028 mmol CO2 / mol H2O per unit rise in SLA. Almost half of the variability is explained by the model (45,3%). 22,1% of the residual variability is declared by the specificity of the genera. WUE of TMCFs was in this case not significantly different. 4.5 INTRINSIC AND INSTANTANEOUS WUE 46 5 Discussion

5.1 Water relations

Water is an essential resource for the plant. It serves as a transport mean, as a solvent, to preserve the turgor, to cool the plant, etc. (Araya and Garcia-Baquero, 2014). Plants, therefore, need to conserve enough water to fulﬁl all these functions. When drought occurs, plants need to adapt. A quick response to retain water is to close the stomata, which is the biggest route through which is water released. Stomata are deemed as highly important for a plant as they are the key elements in photosynthesis, cooling and nutrient uptake. Closing stomata to minimize water loss also means less cooling, less uptake and transportation of nutrients, and less CO2 uptake. Therefore, it is highly important for the plant to maintain the best balance. Optimal stomatal sensitivity to environmental conditions is very important for the survival and success of a plant (Aasamaa and Sõber, 2011). Leaf gas exchange was measured under varying VPD to simulate the atmospheric drought eﬀect. The response of the plant by closing their stomata and reducing the transpiration was analysed. For the response of the stomata, stomatal conductance was considered. Stomatal conductance depends on stomatal density, stomatal aperture, and stomatal size.

5.1.1 Goodness of ﬁt

MAE is regarded as the most interpretable measure of accuracy. The error between the data points and the 301,5 -2 -1 predicted linear regression is on average 45 = 6,7 mmol H2O m s . For the logarithmic and exponential -2 -1 regression, the data points are on average respectively 6,83 and 6,8 mmol H2O m s away from the regression line. The errors are really close to each other, because the regression lines are also near to one another (Figs. A.1–A.8). In general, the linear regression came out best.

5.1.2 The response of the stomata on atmospheric drought stress

The slope of the linear regression between gs and VPD is always negative, which means that gs is inversely proportional to VPD. The higher VPD, the bigger the diﬀerence between the amount of moisture the air can hold and the actual amount of moisture. As mentioned in 2.3.2.1, VPD is a far better indicator of the driving force of water compared to RH. When VPD is high, the air demands more water and the leaf will transpire more. High amounts of water loss will reduce growth or in the worst case kill the plant (Shanker and Venkateswarlu, 2011). To quickly minimize this water loss by transpiration, the stomata of the plant 5.1 WATER RELATIONS 48 will close more, which explains the negative slope. High maximal stomatal conductance reﬂects a rapid closure under stress (Jane and Green, 1985). Indeed, the sites with the highest gmax showed also the highest sensitivity. Cunningham (2004) also found a decreasing correlation between stomatal sensitivity and gmax under ambient VPD.

5.1.3 Altitudinal changes

Only the intercept of the estimated regressions changes significantly with the altitude. It is assessed to -2 -1 decrease 41,6 mmol H2O m s per kilometer rise in altitude. So, the response on the altitude of the -2 stomatal aperture is not significantly different, but the conductance will be around 41,6 mmol H2O m -1 s lower for all VPDs if the elevation is a kilometer higher. For both parameters, the sensitivity of gs to VPD and the zero drift, are significantly different between TMCF and both lowland rainforest as well as the -2 -1 -1 primary forest in Peribuela. The slope of the gs-VPD data is assessed to be 21,0 H2O m s kPa higher -2 -1 and the intercept 105,5 H2O m s lower. As mentioned, the response of plants in TMCF will be slower, but the stomata will be more closed. The gmax was also considered. These gs values were significantly -2 -1 lower in TMCFs (p < 0,05). A 60,6 H2O m s lower value was found compared to the lowland rainforest. Gotsch et al. (2016) reported 40% lower average gs of various TMCF sites than of a lowland forest in Panama. Bruijnzeel and Veneklaas (1998) also stated that lower gs are observed in TMCF compared to lowland rainforest, due to different stomatal behaviour and not because of lower stomatal densities and sizes. Transpiration was not found significantly different with the altitude or between different forest types. From the boxplots, E seems to decline until a certain elevation (third site for lianas and fourth for trees), after which E rises. So, E showed similar trends as gmax, from which can be concluded that gs fully controls E. Plants which are used to sufficient moisture show less stomatal control under drought stress (Jane and Green, 1985). A unique feature of cloud forests is the ability to intercept fog and cloud droplets, which provides supplementary moisture for plant growth. The clouds will reduce solar radiation. Cooler temperatures, lower VPD, and more precipitation are expected in TMCFs (Rada et al., 2009; Oliveira et al., 2014; Gotsch et al., 2016). Bruijnzeel and Proctor (1995) mentioned that there is a very low atmospheric demand. Sometimes VPD can be so low that water and nutrient uptake are suppressed. Goldsmith et al. (2013) mentioned that the clouds can add water to the soil by dripping from the canopy. Besides, FWU provides an extra source of water for the plants (Bruijnzeel and Proctor, 1995; Gotsch et al., 2016). Goldsmith et al. (2013); Gotsch et al. (2016); Alvarado-Barrientos et al. (2014) reported low E in TMCFs, due to lower atmospheric demand. These facts all demonstrate that TMCFs are not used to high atmospheric demands, which could explain the lower stomatal sensitivities to VPD in the TMCF sites. However, Cunningham (2004) observed the reversed: seedlings developed in low VPD environments showed a higher stomatal sensitivity. It is important to note that Cunningham (2004) measured the stomatal response till a VPD of 1,9 kPa, while our measurements only start on average at a VPD of 2 kPa. According to the statement of Jane and Green (1985), it could also be expected that at the altitudes/forest types that encounter more drought have a faster response to increasing atmospheric drought. The highest altitude, Peribuela, experiences the driest atmosphere (See Figure 3.3), does not take up water from surrounding clouds and receives higher shortwave radiation. Gale (2004) mentioned that shortwave solar radiation increases with altitude. This causes an increase of the potential evaporation, when the temperature lapse rate is less than 0,6°C per 100m increase in elevation. The mean average temperature decreased at a lower lapse rate (See Figure 3.3). When working in the field, the high radiation was remarkably perceptible. The ambient temperature (Tamb) was highly variable even during one measurement. For example, leaf Oreopanax sp. 64.2 changed from 27 to 32 °C. However, it is important to highlight that the plants were collected from the forest and the leaf gas exchange measurements were performed in open space. As sun 5.1 WATER RELATIONS 49 leaves were analysed, one can still assume that they normally sense the intense radiation. Next to the higher total radiation absorbed by leaves, the diffusion coefficient of water vapour increases in the air with increasing elevation due to the reduced barometric pressure, and a higher density gradient of water vapour from the leaf to the air is expected (Smith and Geller, 1979; Gale, 2004). Leuschner (2000) also expected higher potential evaporation in tropical mountains with high radiation, low rainfall, and low cloudiness compared to middle latitudes. The lower air pressure partially counterbalances the reducing effect on transpiration by low temperatures (Gale, 1972b). As ambient VPD only decreases a little with the elevation at equatorial latitudes, potential E will decline less with the elevation compared to middle latitudes (Leuschner, 2000). The potential evaporation is considered instead of the actual, because morphological changes of the leaf and changes in stomatal aperture are not considered (Gale, 1972a). The frequent atmospheric drought Peribuela experiences explains the higher sensitivity to increasing VPD. Cunningham (2004) claimed that plants that experienced high precipitation seasonality during their growth, had a higher sensitivity to VPD. Rio Silanche is subjected to the highest differences in precipitation between rainy and dry season, which makes it necessary to adapt to changing environmental conditions.

To approach the optimal VPD at each site, the VPD at gmax was considered. The conducted LMER pointed out a significant drawdown of this VPD of 0,13 kPa per kilometer rise in elevation. Wang et al. (2017) reported a decreasing VPD of 0,41 kPa per kilometer if the prerequisite of declining leaf temperature (Tleaf) with the altitude was met. Another LMER was established to evaluate Tleaf. Indeed, Tleaf declined significantly: -0,97°C for each kilometer for trees and -2,53°C for lianas. An issue arising with the assessments of the optimal conditions is that the range of VPD measured for the leaves alternates depending on the site specific conditions. VPD is a function of temperature and RH. Due to the fact that the CIRAS-3 couldn’t measure above a RH of 70% and the RH differs between sites and from day to day, the starting value of VPD was not easy to fix. For lower elevations, the range of VPD where gs was measured began at a higher VPD, because of the higher humidity. Owing to this technical issue, the range of VPD where gas exchange was performed varies with altitude. This leads to more difficulties to compare the data. In Figure 5.1 all the linear regression lines are plotted together in their corresponding VPD range. Unfortunately, there is not a single VPD that is in the range of all 45 analysed leaves. The boxplots of the minimum and maximum VPDs for each site are shown in Figure 5.2.

5.1.4 Differences between growth forms

Campanello et al. (2016) stated the importance of stomatal regulation for lianas, because of two reasons: they have a lower internal stem water storage and they have a higher proportion of leaves that are exposed on the top of the canopy, where evaporative demand is high. They also mentioned that lianas have higher sensitivities to increasing VPD and stronger partial closures of the stomata. According to Campanello et al. (2016), the slopes of the linear regressions are more negative and parameter b will be lower in general. In our results, no signiﬁcant diﬀerences are remarked between lianas and trees for the stomatal response to VPD. However, the boxplots show a peculiar range of both parameters for lianas in El Cedral. From the statements above, it could be expected that the lianas in the cloud forest of both Milpe (1100m a.s.l.) and El Cedral (2200m a.s.l.) showed similar results. However, lianas at 2200m a.s.l. have a far quicker average response to VPD and a higher mean zero drift. Besides, both parameters are more divergent at the site in comparison with lianas in Milpe. In the literature, no explanations for this behaviour are found. A possible reason could be that the range of VPD was much lower for the lianas there (Figure 5.2). A general lower drought stress was exercised on these lianas. Therefore, they may had higher gs values, because the atmospheric water demand at the beginning of the measurement was not as strong in comparison with the atmospheric drought that the lianas in Milpe experienced. With increasing demand, the stomata closed quickly. At higher 5.1 WATER RELATIONS 50

Lianas at 400m 300 Lianas at 1100m Lianas at 2200m Lianas at 3000m Trees at 400m Trees at 1100m 250 Trees at 2200m Trees at 3000m 200 ] 1 − s 2 − m O 2 H 150 gs [mmol 100 50 0

1.5 2.0 2.5 3.0 3.5 4.0 4.5

VPD [kPa]

Figure 5.1: All the linear regression lines plotted together on the data. The VPD range of the regression lines corresponds to the VPDs they are actually measured. 5.1 WATER RELATIONS 51

minimum VPD

Lianas at 400m Lianas at 1100m Lianas at 2200m Lianas at 3000m Trees at 400m Trees at 1100m Trees at 2200m Trees at 3000m 2.5 VPD [kPa] 2.0 1.5

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

maximum VPD

Lianas at 400m Lianas at 1100m Lianas at 2200m Lianas at 3000m Trees at 400m 4.5 Trees at 1100m Trees at 2200m Trees at 3000m 4.0 VPD [kPa] 3.5 3.0 2.5

400.LIANA 1100.LIANA 2200.LIANA 3000.LIANA 400.TREE 1100.TREE 2200.TREE 3000.TREE

Altitude (m) and Type

Figure 5.2: Comparison of minimum (left) and maximum (right) VPD between trees and lianas in combination with the four altitudes where measurements took place. 5.2 CARBON RELATIONS 52

VPDs the response may slow down. This suggests that if the ranges of measured VPDs were broader, an exponential regression could have been a better fit. For example, if the leaf Mikania sp. 48.1 is compared with Mikania sp. 48.2, Mikania sp. 48.1 performed a lower sensitivity and lower gs values. The range of VPD measured was higher, therefore leaf 48.1 had to endure higher evaporative demand. Idem for Mikania sp. 51, 51.1 experienced higher VPDs and had, therefore, a lower sensitivity, but also lower conductance. This explains the differences in response and indicated an actual exponential stomatal response. Especially for lianas, highly different measured VPD ranges exist. The second site experienced the highest atmospheric demand with lower gs values and VPDs as consequence. Campanello et al. (2016) mentioned that lianas reach their maximum conductance already at VPDs lower than 1 kPa. This could be the reason that the gs values and stomatal response are already very low at high VPDs. Additionally, the starting VPDs for the lianas in Milpe were very high in contrast to the lianas in El Cedral. This could lead to an underestimation of the assessed stomatal sensitivity to VPD and an overestimation of the zero drift.

5.1.5 Genetic effects

Around 85% of the variability that is not declared by the models, is explained by the specific different responses between genera. Aasamaa and Sõber (2011) reported also a high difference of the sensitivity between species. A higher sensitivity was observed for slow growing species. Jane and Green (1985) also noticed a high variability of gs between plants. Stomatal conductance is an indicator of species adaptation, next to site stresses. On the same site, plants originating from wet habitats will have higher gs than plants from dry sites (Jane and Green, 1985). Bunce (1981) perceived a significant inverse correlation between root length per unit leaf area and the sensitivity of gs to VPD. This relationship was found for the same species grown in different environments and for different species grown in the same environment.

5.2 Carbon relations

To understand the productivity of an ecosystem, carbon relations at leaf level need to be understood. The most important aspects to provide insight in productivity and biomass accumulation are leaf carbon assimilation and respiration (Gotsch et al., 2016). Only carbon assimilation was considered by approaching it by A at gmax. Furthermore, SLA was regarded as it is the trade-oﬀ between resource assimilation and preservation (Long et al., 2011).

5.2.1 SLA

SLA is reputed as one of the most meaningful functional trait as it is an indicator of relative growth rate, stress tolerance and leaf longevity of plants (Weiher et al., 1999; Wright and Westoby, 2002; Scheepens et al., 2010; de la Riva et al., 2016). An increased SLA signifies an increased leaf thickness or/and increased leaf density (Scheepens et al., 2010). SLA was changing significantly with the altitude. A decline of the SLA of 42 cm2 g-1 per kilometer rise in altitude was assessed. Meeussen (2016) demonstrated also an inverse relationship of SLA and altitude for both lianas and trees. Wittich et al. (2012) observed increasing LMA (the inverse of SLA) with the altitude for trees in Ecuador. Rosado et al. (2016) reported also lower SLA values in a montane site than in a lowland site in the Atlantic Rain Forest, Brazil. More studies can be found that reported lower SLA values at higher altitudes (Cordell et al., 1997; Hultine and Marshall, 2000; Duursma et al., 2005; Pla et al., 2012; Chen et al., 2015a). SLA is strongly dependent of the temperature, 5.2 CARBON RELATIONS 53 the irradiance and the water availability and also greatly variable due to species differences (Scheepens et al., 2010). Decreasing SLA with rising heights is assumedly due to decreasing temperatures by establishing thicker leaves (Körner, 2003) or sometimes it is an adjustment to moisture availability (Scheepens et al., 2010). Due to the high evaporative demand at drier places, high SLA can there be a drawback (Kazda, 2015). Although the mean of the SLA at each site was higher for lianas, the LMER did not result in a significant difference. Various authors obtained statistical higher SLAs for lianas in comparison with trees (Zhu and Cao, 2010; Gallagher and Leishman, 2012; Santiago et al., 2014; Kazda, 2015). This is characteristic for fast-growing plants (Körner, 2003; Lambers and Poorter, 2004; Poorter and Bongers, 2006; Zhu and Cao, 2010; Kazda, 2015). According to Kazda (2015), high SLAs have three advantages: lower carbon requirements for leaf assembly, a lower weight on the trees where lianas support on, and a lower resistance for CO2 diffusion, which increases the photosynthetic potential. Cordell et al. (1997) also claimed that thicker leaves have more difficulties with CO2 uptake. Wright and Westoby (2002) showed that plants with low SLA have a longer leaf lifespan. Lambers and Poorter (1992) claimed that low SLA is an indication for efficient nutrients conservation, whereas high SLA features rapid biomass production. Although the model of SLA in function of growth form and altitude described almost 20% of the variability, a large part is due to genetic effects, namely 60,7% of the additional variability. Poorter and Bongers (2006); Poorter et al. (2009); Long et al. (2011) reported also large variability among species, because there are even within functional groups different strategies to capture carbon and conserve it (Poorter et al., 2009).

5.2.2 Maximum carbon assimilation

To understand the productivity of lianas and trees, A was considered at gmax to approach Asat. Photosynthetic capacity is depending on stomatal conductance and on the carboxylation capacity (Wright et al., 2004). The -2 -1 intercept of trees at sea level was significantly 5,5 µmol CO2 m s higher than for lianas. However, with -2 -1 rising altitude, photosynthesis of trees decreased (1,12 µmol CO2 m s per 1000m), while Asat of lianas -2 -1 got higher (2,65 µmol CO2 m s ). Instead of considering A at gmax to approach Asat, A was regarded at VPD equal to 2 kPa. This was done to avoid large biases on the results due to the different VPD ranges at which the measurements were executed. Similar trends were observed with the altitude. The intercept -2 -1 of trees was still assessed to be 5,5 µmol CO2 m s higher. The decrease of photosynthesis for trees is -2 -1 -2 -1 now 1,3 µmol CO2 m s per kilometer rise in altitude and the increase for lianas is 2,0 µmol CO2 m s -2 -1 per kilometer. The LMER considering forest type as a fixed effect, estimated a 3,8 µmol CO2 m s lower -2 -1 photosynthesis in TMCFs for trees and a 3,9 µmol CO2 m s higher photosynthesis for lianas. Instead of the water relations, photosynthesis showed clearly different correlations with the altitude between the growth forms. Wittich et al. (2012) remarked a similar correlation of trees’ Asat with the altitude: a -2 -1 significant decrease of 1,3 µmol CO2 m s for each 1000m rise in elevation. For trees, the lowest Asat was measured in the TMCF sites. Gotsch et al. (2016) and Bruijnzeel and Veneklaas (1998) pointed out that lower Asat values in TMCF are rather a result of limiting environmental factors (T, PAR, partial pressure of CO2 and nutrient availability). For the measurements, PARi was kept constant, but Tleaf was variable. The trees of Milpe experienced the lowest Tleaf and have the lowest average Asat. As trees in TMCFs are used to lower irradiance due to the clouds, their Asat will be lower, analogous to shade leaves (Figure 2.11). Gotsch et al. (2016) reported that the Asat in TMCFs are 25-30% lower, compared to lowland rainforests. We found a 35% lower Asat for TMCFs. Wittich et al. (2012) claimed that a general trend of Asat with the altitude is impeded by the diversity of the species. Indeed, 55,4% of the LMER is explained by genetic effects.

Various studies exist that compare Asat between trees and lianas, but the strongly increasing photosynthesis with the elevation for lianas cannot be related to other literature studies. Regarding studies that investigated 5.3 CARBON-WATER RELATIONS 54

one site, no consistent picture is shown related to the difference of Asat between lianas and trees. Zhu and Cao (2009, 2010); Chen et al. (2015b); Pasquini et al. (2015) observed higher Asat values for lianas than trees, while the study of Santiago and Wright (2007); Han et al. (2010) pointed out higher estimations for trees. Santiago et al. (2014); van der Sande et al. (2013) observed no significant difference between the growth forms. Kazda (2015) asserted that due to higher potassium content in liana leaves, Ribulose activity is enhanced and consequently the photosynthetic rate. Therefore, it would be useful to measure Asat of the growth forms at different elevations and the corresponding nutrient content in the leaves. Previous mentioned studies are all site-based, and therefore fully depending on the local conditions (Santiago et al., 2014). Unfortunately, no articles were detected that studied the effect of the altitude on the carbon fluxes of lianas.

-1 -1 A strong relationship was found between Amass and LMA. Amass decreased 6,8 µmol CO2 g s for each g -2 cm LMA increased. The LMER of Amass in function of LMA, altitude and growth forms explains 52,2% of the variance. Only 15% of the residual variability is due to different genetic effects between genera. The model only considering LMA declares 34,4%. This reflects a very strong interaction between Amass and LMA, with not much variance within genera. As it is not greatly dependent on the genera, LMA could be a very useful plant trait to estimate Amass. Santiago and Wright (2007) found a significant increasing Amass with rising SLA values for lianas and trees as well. Another study observed strong declining Amass with rising LMA for both lianas and trees in the rainy season, but solely for lianas in the dry season in a tropical seasonal rainforest in China (Cai et al., 2009). Cai et al. (2009) deduced therefrom that lianas also in the dry season could establish high carbon assimilations at low leaf construction costs. Quero et al. (2006) noticed rising Amass for greater SLAs.

Lianas at 1100m showed a high diﬀerence between mass-based and area-based Asat, due to their low SLA values. When considering Amass, A of the lianas at 1100m altitude is lower than the lianas at 400m, in contrast to area-based Asat. The boxplots of Amass show similar trend with the altitude and growth form compared to the boxplots of SLA. The strongly increasing trend of Asat is a result of the higher SLA values of lianas in Milpe, compared to the lianas at the other sites. Although, we recommend more research to altitudinal gradients in carbon assimilation of lianas. Another leaf trait that is useful for photosynthesis is the area-based or mass-based nitrogen content in a leaf (Narea and Nmass respectively) (Cordell et al., 1997; Reich et al., 1997; Clearwater and Meinzer, 2001; Hultine and Marshall, 2000; Kazda, 2015). The correlation between Asat and Narea or between Amass and Nmass is because that Rubisco and other components of the photosynthetic apparatus contain nitrogen (Field and Mooney, 1986; Evans, 1989; Wong et al., 1979; Garnier et al., 1999; Cordell et al., 1997).

5.3 Carbon-water relations

The water and carbon processes in a plant are inextricably coupled. The stomata open to assimilate CO2 and water will escape through the same stomata. Consequently, plants continuously need to make the equipoise to maximize CO2 assimilation, while maintaining their water balance. Two ways to define this coupling have been considered: intrinsic and instantaneous WUE. iWUE is a measure that defines the amount of CO2 assimilation per unit of stomatal conductance, while WUE gives the CO2 assimilation per unit of transpiration. The latter is maybe even more important, because it actually considers to effective water loss by transpiration in contrast to the stomatal conductivity. The interpretation of both terms is different (See Subsection 2.2.1). Mujawamariya et al. (2018) reported a negative correlated trend between iWUE and elevation during the dry season for tropical tree species along a 1700–2700m altitude gradient in Rwanda. In the same study, iWUE 5.4 IMPACTS OF CLIMATE CHANGE ON TMCFS 55 did not vary during the wet season. Another study in California found an increasing iWUE with the elevation within and across species (Maxwell et al., 2018). iWUE was obtained from ∆13C. From the measured ∆18O one can derive that the differences in iWUE are attributed to closing stomata with the elevation. Wu et al. (2015) instead observed lower iWUEs at higher sites. So, there is no general trend with the altitude, which is consistent with our findings. Due to the lower partial pressure of CO2, the iWUE can be expected to decrease with the altitude. However, the increased rate of diffusion of CO2 in air with altitude offset the lower partial pressure (Gale, 1972b). The increasing WUE for lianas and decreasing WUE of trees seem to be a result of the similar trends of Asat. The inverse relation between SLA and iWUE at a VPD of 2 kPa is consistent with findings of Medrano et al. (2009); De Miguel et al. (2011). Zhang et al. (2009, 2015) perceived declining WUE with increasing SLA, like our observations. Xu et al. (2009) alleged that leaves with larger leaf area must deal with greater evaporative demand, because of the thicker boundary layer. Plants that encounter frequents droughts have leaves with lower SLA due to a thicker cuticle and epidermis and densely packed mesophyll cells with also thicker cell walls (Galmés et al., 2011). Liu and Stützel (2004) explained as well that thicker leaves have more nitrogen and mesophyll cells per unit area with higher Asat and biomass production as a consequence. Zhang et al. (2015) believed that drought inhibited leaf expansion and the translocation of assimilates, causing thicker leaves. Unluckily, the amount of studies comparing the two growth forms is scarce. However, Cernusak et al. (2008) descried no significant distinct ∆13C between the growth forms. van der Sande et al. (2013) observed no significant different iWUE. Cai et al. (2009); Pausenberger (2015) discerned higher WUE for lianas than for trees. The differences in genera explained 22,1 and 39,4% of the variability that is not described by the models of iWUE and WUE respectively. Species differences have a great impact on the WUE values, due to varying physiological strategies to trade-off water deficits with carbon gain (Rada et al., 2009). Moreover, the microclimate conditions play a significant role in the water-carbon balance. Factors such as fog events and VPD changes, nutrient and soil moisture availability, exposure to wind and solar radiation will influence WUE (Gotsch et al., 2016).

5.4 Impacts of climate change on TMCFs

TMCFs are distinguished by their one-oﬀ biological and hydroclimatic characteristics: the presence of a frequent cloud cover, cool temperatures, low solar radiation, low VPD, high precipitation and high biodiversity and endemism (Rada et al., 2009; Oliveira et al., 2014; Gotsch et al., 2016). Their unique microclimate makes these type of forests one of the most vulnerable ecosystems to climate change (Oliveira et al., 2014). As climate is changing at high rates, it is of great importance to understand them before they are becoming history. Jane and Green (1985) reported two reasons why the fog in TMCFs makes plants more vulnerable to drought. Firstly, the soil is usually saturated with water. Plants have a poorly developed root system in these waterlogged conditions. Secondly, the fog reduces the plant growth potential by lower light irradiance and temperature. Furthermore, the species that establish higher FWU are expected to lose quicker turgor during droughts. Our results show that plants in the TMCFs have distinctive attributes compared to lowland rainforest and the highest altitude. They are characterized by a lower gmax and by a greater stomatal response to higher evaporative demand. Moreover, Asat of lianas was the highest in TMCFs, while Asat of trees was the lowest. Their speciﬁc adaptations to their unique environment make them highly vulnerable to climate change. One unique feature that distinguished TMCFs is cloud immersion. With higher temperatures the sea will evaporate more, from which more clouds in the future could be expected. On the other hand, a 5.5 PITFALLS OF THE MEASUREMENT METHOD 56 warmer atmosphere needs more water vapour to become saturated and form clouds (NASA, 2000). Helmer et al. (2019) assessed that a decline in cloud immersion will have an enormous impact on cloud forests.

5.5 Pitfalls of the measurement method

The CIRAS-3 gives an error message when the RH is above 70% due to possible instrumental problems. This was a big disadvantage for the gs-VPD measurements, as this limited the range of VPD. VPD is a function of temperature and RH. The VPD at RH of 70% will therefore be dependent on the ambient temperature. Consequently, the temperature determines the lowest VPD where gs can be measured. This caused biased comparisons due to unequal conditions as discussed before. Furthermore, the CIRAS-3 responded highly unstable to changes in humidity. It was necessary to wait adequate time after a modification of the VPD before measuring gs. The VPD values in the beginning of the measurements were due to this technical issue already high. Because of this, the responses from no drought to drought could not be analysed, which is more valuable than investigating the response from drought to more severe drought. Linear regressions are the best fit to these responses for our results, but in case of a broader range of measured VPDs, an exponential regression could possible turn out to be more appropriate. Measurements starting at lower VPDs could reveal if the unexpected results of lianas at 2200m a.s.l. are a result of the lower range of VPD or are owing to other factors. Long and Bernacchi (2003) report some general drawbacks of portable open gas-exchange systems. Leaks of carbon dioxide can happen through the gasket, especially in case of leaves with big nerves. Another issue, occurring at lower sun angles, is that the part of the leaf outside the chamber can respire due to the shadow, while the leaf in the chamber will photosynthesise because of the artificial light. Because in-situ measurements are time-consuming and more expensive, ex-situ measurements in the field were performed. Nevertheless, ex-situ measurements have their drawbacks too. In particular, due to excising the branches, air can enter the vessels. Wheeler et al. (2013) allege that severing branches underwater still can induce embolism in vessels. The larger xylem tension, the deeper an air bubble can penetrate in the conduit. Lianas have longer and wider vessels than trees with higher vulnerability for cavitation as consequence (Zhu and Cao, 2009). Up to now, no single study tested the impact of branch excision of lianas and the possible worse implications for the gas exchange measurements compared to trees. The ex-situ measurements could be the cause of our results with A values around zero, even at low VPDs. We left out 98 measured leaves from the 143 leaves evaluated in total. Almost 70% of the results were rejected, which maybe is the result of the ex-situ measurements. On the other hand, in-situ measurements could easily be such time-consuming that, after the same duration of fieldwork, the amount of useful leaves would be still less.

In addition, a specific problem we needed to deal with was that the CO2 capsules did not fit in the CIRAS-3 due to a redesign of the cartridges. Therefore, ambient CO2 was used most of the time, which also can influence the measurements. Cernusak et al. (2013) observed increasing Asat with elevated CO2 concentrations for Ficus insipida. Un under- or overestimation of Asat could consequently be made. 6 Conclusions

Although this thesis consists of various limitations, we can make some conclusions. The biggest objective of this thesis was to investigate whether the response to increasing atmospheric drought was different between lianas and trees as well as between the studied forest types. In contrast to our expectations, no significantly different responses are found between the two growth forms. However, lianas have no internal water storage and assemble their leaves at the canopy with high VPD. We may therefore conclude that the mechanisms whereof lianas distinct from trees to cope with atmospheric drought are not concerning stomatal response. In opposition to the growth forms, the response of plants in the TMCFs did significantly differ from trees and lianas in the lowland rainforest and the primary forest. Both lianas and trees, except for the lianas at 2200m a.s.l., had a lower response to atmospheric drought stress. It is also estimated that they established lower gs in a humid environment, and consequently had lower transpiration rates. Our results indicate that forest systems acclimated to humid conditions have a lower sensitivity to increasing VPD, while environments that experience high seasonality or long-lasting droughts have a fast response. The clouds in TMCFs provide the trees and lianas an additional water supply by dripping down the vegetation to the soil and by foliar water uptake. The lowland rainforest was exposed to the highest seasonality and therefore showed higher responses to atmospheric drought stress. The primary forest at the highest altitude was subjected to the driest atmosphere due to the low rainfall, the high radiation and the low cloud cover. The fast response to increasing VPD therefore also confirms the hypothesis that acclimatisation to dry atmospheric conditions is the cause for the fast response. Low gs at optimal conditions occur along with a low response. The unpredicted faster response and higher gs of lianas in the cloud forest at 2200m a.s.l. may be due to the less atmospheric drought conditions they are analysed at. In that scenario, an exponential response to atmospheric drought can be expected. Furthermore, SLA decreased significantly with the elevation in consistence with many other studies. Pre- sumably, this is due to the corresponding decreasing temperatures which causes the establishment of thicker leaves. The strongly increasing approached Asat with the elevation for lianas is a result of the high LMA of the lianas at 1100m a.s.l. LMA and Amass were strongly correlated, which is in tune with observations of multiple other studies. Amass was the lowest in TMCFs, again with the exception of lianas at 2200m a.s.l. This may be the result of the reduced radiation reaching the plants by the cloud cover. Finally, we observed iWUE and WUE, which interlink previous water and carbon relations. The increasing iWUE and WUE for lianas and declining iWUE and WUE for trees with the altitudes is because of the similar trends in Asat. Furthermore, WUE was negatively correlated with SLA. Leaves that experience long lasting drought will establish leaves with lower SLA to reduce the evaporative demand. Due to the storage of more nitrogen and to the more compact stacking of mesophyll cells, Asat can be increased. A big part of the variability of the analysed variables is due the different genetics between genera. The correlation between Amass and LMA 58

was the less dependent on the genetic effects. LMA would consequently be a good indicator to assess Amass and thus Asat. We acknowledge that this thesis comprises several limitations. The biggest disadvantage was that the range of measured VPDs differed between the growth forms as well as between the sites. Consequently, the numerical values of our conclusions will be biased. Moreover, the measurements started at already a quite high atmospheric drought and the VPD ranges were small. It would be more interesting to evaluate the stomatal response on atmospheric drought stress from the point where plants are not experiencing drought yet. A broader range could maybe reveal an actually exponential response on increasing VPD, in contrast to the linear response we assessed. Besides, the estimated optimal conditions are not a good approximation due to the presence of the atmospheric drought. Furthermore, ambient CO2 was used, which also can influence the stomatal response. Also, a disadvantage of the performed ex-situ measurements is that air can enter the vessels.

Suggestions for further research

During this research, some topics emerged wherefore further research could be useful. First of all, due to the small VPD ranges starting at already a quite high VPD, our results do not include the optimum conditions of the plants. We recommend therefore to investigate how the two growth forms respond from very low VPD to increasing drought stress along an altitudinal gradient. Broader VPD ranges could reveal a more realistic idea how plants respond. Additionally, more similar studies would be valuable at different locations with their specific climatic conditions. Furthermore, our study was very broad to evaluate general trends, considering two growth forms at different altitudes as well as in different forest systems. The underlying causes were not evaluated. Studies concentrating on one aspect at a time, also considering the leaf traits that may explain the know and how of certain trends, would also be very useful. Scarce studies considering lianas at different elevations were found. More studies inquiring into leaf traits of lianas along the altitude are recommended. Finally, we suggest to research the possibly worse effect of cavitation on ex-situ measurements for lianas compared to trees. Anderegg W. R. L, Ballantyne A. P, Smith W. K, Ma- jkut J, Rabin S, Beaulieu C, Birdsey R, Dunne J. P, Houghton R. A, Myneni R. B, Pan Y, Sarmiento J. L, 7 Serota N, Shevliakova E, Tans P, and Pacala S. W. Trop- ical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proceedings of the Na- tional Academy of Sciences, 112(51):201521479, dec Bibliography 2015.

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Table A.2: The parameters of the linear regression for every measured leaf with the growth form and altitude of each leaf given as well. The parameters correspond to the linear equation: gs = a*VPD+b.

Measured leaf Growth form Altitude a b Asteraceae Mikania 27.1 Liana 400 -56,19 270,43 Asteraceae Mikania 7.3 Liana 400 -58,86 334,11 Cucurbitaceae Cayaponia 30.1 Liana 400 -20,96 122,45 Cucurbitaceae Cayaponia 30.2 Liana 400 -5,86 57,15 Clusiaceae Clusia 18.1 Liana 1100 -11,29 73,49 Clusiaceae Clusia 18.2 Liana 1100 -14,45 86,74 Clusiaceae Clusia 40.1 Liana 1100 -13,97 105,43 Ericaceae Psammisia 17.3 Liana 1100 -4,71 43,48 Asteraceae Mikania 48.1 Liana 2000 -14,75 122,77 Asteraceae Mikania 48.2 Liana 2000 -40,31 233,18 Asteraceae Mikania 51.1 Liana 2000 -27,48 188,41 Asteraceae Mikania 51.2 Liana 2000 -38,72 212,76 Asteraceae Mikania 55.1 Liana 2000 -38,66 148,83 Cucurbitaceae Gurania 56.1 Liana 2000 -82,69 311,36 Gobiesocidae Gouania 59.1 Liana 2000 -61,10 260,13 Gobiesocidae Gouania 59.2 Liana 2000 -58,48 204,81 Vitaceae Cissus 57.3 Liana 2000 -9,76 59,45 Burseraceae Protium 31.2 Tree 400 -39,47 207,01 Urticaceae Cecropia hastae 33.3 Tree 400 -31,47 304,35 Urticaceae Cecropia hertha 8.2 Tree 400 -35,33 246,94 Urticaceae Cecropia hertha 8.3 Tree 400 -27,14 164,17 Urticaceae Pourouma 24.3 Tree 400 -47,25 299,59 Urticaceae Pourouma 4.2 Tree 400 -70,35 361,50 Clusiaceae Tovomita weddelliana 19.2 Tree 1100 -4,85 43,41 Clusiaceae Tovomita weddelliana 19.3 Tree 1100 -0,79 35,27 Clusiaceae Tovomita weddelliana 36.2 Tree 1100 -1,90 41,67 Clusiaceae Tovomita weddelliana 36.3 Tree 1100 -6,39 52,92 Clusiaceae Tovomita weddelliana 36.4 Tree 1100 -2,49 41,13 Dichapetalaceae Stephanopodium angulatum 39.3 Tree 1100 -3,92 62,20 69

Lauraceae Ocotea ﬂoribunda 20.2 Tree 1100 -89,80 446,91 Lauraceae Ocotea fraxifera 43.2 Tree 1100 -20,90 177,13 Leguminosae Dussia 15.1 Tree 1100 -32,50 151,54 Myristicaceae Otoba gordonifolia 23.1 Tree 1100 -42,65 241,90 Actinidiaceae Saurauia 58.1 Tree 2000 -5,53 45,57 Sapindaceae Billia rosea 44.2 Tree 2000 -5,58 55,05 Sapindaceae Billia rosea 44.3 Tree 2000 -29,50 143,24 Sapindaceae Billia rosea 52.1 Tree 2000 -7,21 49,01 Araliaceae Oreopanax 64.1 Tree 3000 -33,82 160,71 Araliaceae Oreopanax 64.2 Tree 3000 -5,58 58,42 Ericaceae Cavendishia bracteata 65.1 Tree 3000 -9,07 71,33 Rosaceae Rubus 69.1 Tree 3000 -64,90 304,19 Rosaceae Rubus 69.3 Tree 3000 -30,33 159,49 Rubiaceae Palicourea 71.1 Tree 3000 -66,35 346,68 Rubiaceae Palicourea 71.2 Tree 3000 -74,05 353,38 Siparunaceae Siparuna 70.3 Tree 3000 -40,17 223,23

Table A.3: The maximum gs and the corresponding A for every measured leaf with the growth form and altitude of each leaf given as well.

Measured leaf Growth form Altitude maximum gs A at maximum gs Measured leaf Growth form Altitude maximum gs A at maximum gs Asteraceae Mikania 27.1 Liana 400 167,80 7,24 Asteraceae Mikania 7.3 Liana 400 258,67 1,57 Cucurbitaceae Cayaponia 30.1 Liana 400 69,20 1,40 Cucurbitaceae Cayaponia 30.2 Liana 400 47,40 3,18 Clusiaceae Clusia 18.1 Liana 1100 45,60 3,18 Clusiaceae Clusia 18.2 Liana 1100 58,00 4,10 Clusiaceae Clusia 40.1 Liana 1100 75,20 4,38 Ericaceae Psammisia 17.3 Liana 1100 34,60 2,26 Asteraceae Mikania 48.1 Liana 2000 100,00 6,76 Asteraceae Mikania 48.2 Liana 2000 195,60 10,56 Asteraceae Mikania 51.1 Liana 2000 133,80 9,04 Asteraceae Mikania 51.2 Liana 2000 167,00 10,44 Asteraceae Mikania 55.1 Liana 2000 76,60 6,40 Cucurbitaceae Gurania 56.1 Liana 2000 193,80 10,86 Gobiesocidae Gouania 59.1 Liana 2000 159,80 6,60 Gobiesocidae Gouania 59.2 Liana 2000 99,60 5,88 Vitaceae Cissus 57.3 Liana 2000 44,60 4,86 Burseraceae Protium 31.2 Tree 400 117,00 5,00 Urticaceae Cecropia hastae 33.3 Tree 400 242,00 11,48 Urticaceae Cecropia hertha 8.2 Tree 400 187,13 5,67 Urticaceae Cecropia hertha 8.3 Tree 400 108,45 2,99 Urticaceae Pourouma 24.3 Tree 400 176,00 11,98 Urticaceae Pourouma 4.2 Tree 400 204,40 12,14 70

Table A.3: The maximum gs and the corresponding A for every measured leaf with the growth form and altitude of each leaf given as well.

Measured leaf Growth form Altitude maximum gs A at maximum gs Clusiaceae Tovomita weddelliana 19.2 Tree 1100 34,40 2,30 Clusiaceae Tovomita weddelliana 19.3 Tree 1100 38,00 2,10 Clusiaceae Tovomita weddelliana 36.2 Tree 1100 40,40 1,06 Clusiaceae Tovomita weddelliana 36.3 Tree 1100 41,60 2,60 Clusiaceae Tovomita weddelliana 36.4 Tree 1100 40,40 1,48 Dichapetalaceae Stephanopodium angulatum 39.3 Tree 1100 60,00 1,90 Lauraceae Ocotea ﬂoribunda 20.2 Tree 1100 292,40 8,46 Lauraceae Ocotea fraxifera 43.2 Tree 1100 154,60 9,98 Leguminosae Dussia 15.1 Tree 1100 102,67 2,40 Myristicaceae Otoba gordonifolia 23.1 Tree 1100 129,82 6,41 Actinidiaceae Saurauia 58.1 Tree 2000 37,00 0,50 Sapindaceae Billia rosea 44.2 Tree 2000 47,40 4,48 Sapindaceae Billia rosea 44.3 Tree 2000 82,00 5,58 Sapindaceae Billia rosea 52.1 Tree 2000 35,40 2,76 Araliaceae Oreopanax 64.1 Tree 3000 111,33 5,63 Araliaceae Oreopanax 64.2 Tree 3000 55,00 1,82 Ericaceae Cavendishia bracteata 65.1 Tree 3000 55,00 3,32 Rosaceae Rubus 69.1 Tree 3000 227,80 4,16 Rosaceae Rubus 69.3 Tree 3000 118,60 3,96 Rubiaceae Palicourea 71.1 Tree 3000 214,20 6,82 Rubiaceae Palicourea 71.2 Tree 3000 223,20 7,16 Siparunaceae Siparuna 70.3 Tree 3000 129,20 3,80

Table A.4: The SLA [cm2 g-1] for every measured leaf with the growth form and altitude of each leaf given as well.

Measured leaf Growth form Altitude [m a.s.l.] SLA [cm2/g] Asteraceae Mikania 27.1 Liana 400 120,35 Asteraceae Mikania 27.2 Liana 400 105,74 Asteraceae Mikania 28.1 Liana 400 167,99 Asteraceae Mikania 28.2 Liana 400 172,66 Asteraceae Mikania 28.3 Liana 400 185,23 Asteraceae Mikania 35.1 Liana 400 258,69 Asteraceae Mikania 35.2 Liana 400 289,62 Asteraceae Mikania 6.1 Liana 400 430,01 Asteraceae Mikania 6.2 Liana 400 441,69 Asteraceae Mikania 6.3 Liana 400 434,26 Asteraceae Mikania 7.1 Liana 400 378,36 Asteraceae Mikania 7.2 Liana 400 378,63 Asteraceae Mikania 7.3 Liana 400 395,30 Cucurbitaceae Cayaponia 30.1 Liana 400 430,75 Cucurbitaceae Cayaponia 30.2 Liana 400 375,20 Hydrangeaceae Hydrangea 3.1 Liana 400 717,16 71

Table A.4: The SLA [cm2 g-1] for every measured leaf with the growth form and altitude of each leaf given as well.

Measured leaf Growth form Altitude [m a.s.l.] SLA [cm2/g] Hydrangeaceae Hydrangea 3.2 Liana 400 545,67 Hydrangeaceae Hydrangea 32.1 Liana 400 264,68 Hydrangeaceae Hydrangea 32.2 Liana 400 264,06 Hydrangeaceae Hydrangea 32.3 Liana 400 240,81 Clusiaceae Clusia 18.1 Liana 1100 74,76 Clusiaceae Clusia 18.2 Liana 1100 78,58 Clusiaceae Clusia 18.3 Liana 1100 78,50 Clusiaceae Clusia 22.1 Liana 1100 72,54 Clusiaceae Clusia 22.2 Liana 1100 73,83 Clusiaceae Clusia 22.3 Liana 1100 51,74 Clusiaceae Clusia 40.1 Liana 1100 140,26 Clusiaceae Clusia 40.2 Liana 1100 130,90 Clusiaceae Clusia 40.3 Liana 1100 129,30 Ericaceae Psammisia 10 Liana 1100 138,43 Ericaceae Psammisia 37 Liana 1100 149,42 Ericaceae Psammisia 17.1 Liana 1100 168,67 Ericaceae Psammisia 17.2 Liana 1100 171,18 Ericaceae Psammisia 17.3 Liana 1100 145,57 Melastomataceae Blakea 42.1 Liana 1100 154,62 Melastomataceae Blakea 42.2 Liana 1100 151,28 Sapindaceae Serjania 41.1 Liana 1100 275,87 Sapindaceae Serjania 41.2 Liana 1100 274,40 Sapindaceae Serjania 16.1 Liana 1100 291,79 Sapindaceae Serjania 16.2 Liana 1100 261,41 Sapindaceae Serjania 16.3 Liana 1100 279,72 Aristolochiaceae Aristolochia 53.1 Liana 2000 86,84 Aristolochiaceae Aristolochia 53.2 Liana 2000 116,19 Aristolochiaceae Aristolochia 53.3 Liana 2000 93,10 Asteraceae Mikania 48.1 Liana 2000 160,06 Asteraceae Mikania 48.2 Liana 2000 119,18 Asteraceae Mikania 48.3 Liana 2000 154,70 Asteraceae Mikania 51.1 Liana 2000 135,65 Asteraceae Mikania 51.2 Liana 2000 125,29 Asteraceae Mikania 55.1 Liana 2000 267,11 Asteraceae Mikania 55.2 Liana 2000 129,16 Asteraceae Mikania 55.3 Liana 2000 290,53 Cucurbitaceae Gurania 56.1 Liana 2000 117,70 Cucurbitaceae Gurania 56.2 Liana 2000 132,07 Cucurbitaceae Gurania 56.3 Liana 2000 142,79 Gobiesocidae Gouania 59.1 Liana 2000 301,16 Gobiesocidae Gouania 59.2 Liana 2000 239,55 Vitaceae Cissu 57.1 Liana 2000 172,14 Vitaceae Cissu 57.2 Liana 2000 186,25 Vitaceae Cissu 57.3 Liana 2000 187,97 72

Table A.4: The SLA [cm2 g-1] for every measured leaf with the growth form and altitude of each leaf given as well.

Measured leaf Growth form Altitude [m a.s.l.] SLA [cm2/g] Asteraceae Mikania 68.1 Liana 3000 232,92 Asteraceae Mikania 68.2 Liana 3000 203,00 Smilacaceae Smilax 72.1 Liana 3000 121,92 Smilacaceae Smilax 72.2 Liana 3000 113,82 Smilacaceae Smilax 72.3 Liana 3000 134,71 Annonaceae Rollinia mucosa 2.1 Tree 400 176,56 Annonaceae Rollinia mucosa 2.2 Tree 400 162,56 Burseraceae Protium 31.1 Tree 400 148,49 Burseraceae Protium 31.2 Tree 400 157,94 Lecythidaceae Grias neuberthii 34.1 Tree 400 101,79 Lecythidaceae Grias neuberthii 34.2 Tree 400 84,35 Malvaceae Theobroma gileri 5.1 Tree 400 340,81 Malvaceae Theobroma gileri 5.2 Tree 400 374,58 Meliaceae Guarea 29.1 Tree 400 141,18 Meliaceae Guarea 29.2 Tree 400 364,28 Meliaceae Guarea 9.1 Tree 400 252,51 Meliaceae Guarea 9.2 Tree 400 207,76 Meliaceae Guarea 9.3 Tree 400 217,31 Phyllanthaceae Hieronyma alchorneoides 1.1 Tree 400 2945,91 Phyllanthaceae Hieronyma alchorneoides 1.2 Tree 400 2774,50 Urticaceae Cecropia hastae 33.1 Tree 400 201,52 Urticaceae Cecropia hastae 33.2 Tree 400 225,96 Urticaceae Cecropia hastae 33.3 Tree 400 185,32 Urticaceae Cecropia hertha 8.1 Tree 400 170,04 Urticaceae Cecropia hertha 8.2 Tree 400 179,66 Urticaceae Cecropia hertha 8.3 Tree 400 178,90 Clusiaceae Tovomita weddelliana 19.1 Tree 1100 82,31 Clusiaceae Tovomita weddelliana 19.2 Tree 1100 79,27 Clusiaceae Tovomita weddelliana 19.3 Tree 1100 81,34 Clusiaceae Tovomita weddelliana 36.1 Tree 1100 74,72 Clusiaceae Tovomita weddelliana 36.3 Tree 1100 61,60 Clusiaceae Tovomita weddelliana 36.4 Tree 1100 87,49 Dichapetalaceae Dichapetalum 13.1 Tree 1100 237,31 Dichapetalaceae Dichapetalum 13.2 Tree 1100 189,14 Dichapetalaceae Dichapetalum 13.3 Tree 1100 188,92 Dichapetalaceae Dichapetalum 13.4 Tree 1100 196,39 Dichapetalaceae Stephanopodium angulatum 39.1 Tree 1100 152,72 Dichapetalaceae Stephanopodium angulatum 39.2 Tree 1100 162,89 Dichapetalaceae Stephanopodium angulatum 39.3 Tree 1100 147,03 Lauraceae Caryodaphnopsis theobromifolia 12 Tree 1100 204,70 Lauraceae Ocotea floribunda 20.1 Tree 1100 127,43 Lauraceae Ocotea floribunda 20.2 Tree 1100 125,45 Lauraceae Ocotea floribunda 20.3 Tree 1100 121,30 Lauraceae Ocotea fraxifera 43.1 Tree 1100 133,18 73

Table A.4: The SLA [cm2 g-1] for every measured leaf with the growth form and altitude of each leaf given as well.

Measured leaf Growth form Altitude [m a.s.l.] SLA [cm2/g] Lauraceae Ocotea fraxifera 43.2 Tree 1100 127,60 Leguminosae Dussia 15.1 Tree 1100 264,52 Leguminosae Dussia 15.2 Tree 1100 278,62 Leguminosae Dussia 15.3 Tree 1100 293,34 Myristicaceae Otoba gordonifolia 11 Tree 1100 128,03 Myristicaceae Otoba gordonifolia 23.1 Tree 1100 99,47 Myristicaceae Otoba gordonifolia 23.2 Tree 1100 104,42 Myristicaceae Otoba gordonifolia 23.3 Tree 1100 105,01 Myristicaceae Otoba gordonifolia 38 Tree 1100 103,81 Actinidiaceae Saurauia 58.1 Tree 2000 69,84 Actinidiaceae Saurauia 58.2 Tree 2000 77,41 Actinidiaceae Saurauia 58.3 Tree 2000 72,36 Lauraceae Nectandra subbullata 50.1 Tree 2000 128,72 Lauraceae Nectandra subbullata 50.2 Tree 2000 128,71 Moraceae Morus insignis 54.1 Tree 2000 206,54 Moraceae Morus insignis 54.2 Tree 2000 243,21 Moraceae Morus insignis 54.3 Tree 2000 249,04 Phyllanthaceae Hieronyma asperifolia 45 Tree 2000 70,91 Phyllanthaceae Hieronyma asperifolia 47.1 Tree 2000 70,64 Phyllanthaceae Hieronyma asperifolia 47.2 Tree 2000 74,56 Phyllanthaceae Hieronyma asperifolia 47.3 Tree 2000 76,92 Sapindaceae Billia rosea 44.1 Tree 2000 138,74 Sapindaceae Billia rosea 44.2 Tree 2000 131,15 Sapindaceae Billia rosea 52.1 Tree 2000 119,30 Sapindaceae Billia rosea 52.2 Tree 2000 127,78 Sapindaceae Billia rosea 52.3 Tree 2000 143,99 Vitaceae Cissus 46 Tree 2000 165,25 Adoxaceae Viburnum hallii 67.1 Tree 3000 101,62 Adoxaceae Viburnum hallii 67.2 Tree 3000 93,96 Araliaceae Oreopanax 64.1 Tree 3000 51,11 Araliaceae Oreopanax 64.2 Tree 3000 45,52 Betulaceae Alnus acuminata 66.1 Tree 3000 87,00 Betulaceae Alnus acuminata 66.2 Tree 3000 92,81 Betulaceae Alnus acuminata 66.3 Tree 3000 98,61 Betulaceae Alnus acuminata 66.4 Tree 3000 82,66 Ericaceae Cavendishia bracteata 65.1 Tree 3000 78,85 Ericaceae Cavendishia bracteata 65.2 Tree 3000 88,87 Ericaceae Cavendishia bracteata 65.3 Tree 3000 77,17 Rosaceae Rubus 69.1 Tree 3000 250,12 Rosaceae Rubus 69.2 Tree 3000 354,22 Rosaceae Rubus 69.3 Tree 3000 362,59 Rubiaceae Palicourea 71.1 Tree 3000 142,59 Rubiaceae Palicourea 71.2 Tree 3000 142,30 Siparunaceae Siparuna 70.1 Tree 3000 148,85 74

Table A.4: The SLA [cm2 g-1] for every measured leaf with the growth form and altitude of each leaf given as well.

Measured leaf Growth form Altitude [m a.s.l.] SLA [cm2/g] Siparunaceae Siparuna 70.2 Tree 3000 216,86 Siparunaceae Siparuna 70.3 Tree 3000 211,98

Table A.5: The iWUE [µmol CO2 / mmol H2O] and WUE [mmol CO2 / mol H2O] for every measured leaf with the growth form and altitude [m a.s.l].

Measured leaf Growth form Altitude WUEi WUE Asteraceae Mikania 27.1 Liana 400 43,15 1,95 Asteraceae Mikania 7.3 Liana 400 6,06 0,34 Cucurbitaceae Cayaponia 30.1 Liana 400 20,23 0,75 Cucurbitaceae Cayaponia 30.2 Liana 400 67,09 2,34 Clusiaceae Clusia 18.1 Liana 1100 69,74 2,37 Clusiaceae Clusia 18.2 Liana 1100 70,69 2,88 Clusiaceae Clusia 40.1 Liana 1100 58,24 1,84 Ericaceae Psammisia 17.3 Liana 1100 65,32 2,40 Asteraceae Mikania 48.1 Liana 2200 67,60 2,69 Asteraceae Mikania 48.2 Liana 2200 53,99 3,05 Asteraceae Mikania 51.1 Liana 2200 67,56 2,16 Asteraceae Mikania 51.2 Liana 2200 62,51 2,61 Asteraceae Mikania 55.1 Liana 2200 83,55 3,25 Cucurbitaceae Gurania 56.1 Liana 2200 56,04 2,70 Gobiesocidae Gouania 59.1 Liana 2200 41,30 1,76 Gobiesocidae Gouania 59.2 Liana 2200 59,04 2,53 Vitaceae Cissus 57.3 Liana 2200 108,97 4,41 Burseraceae Protium 31.2 Tree 400 42,74 1,85 Urticaceae Cecropia hastae 33.3 Tree 400 47,44 2,00 Urticaceae Cecropia hertha 8.2 Tree 400 30,32 1,34 Urticaceae Cecropia hertha 8.3 Tree 400 27,58 1,36 Urticaceae Pourouma 24.3 Tree 400 68,07 2,61 Urticaceae Pourouma 4.2 Tree 400 59,39 2,75 Clusiaceae Tovomita weddelliana 19.2 Tree 1100 66,86 2,34 Clusiaceae Tovomita weddelliana 19.3 Tree 1100 55,26 2,00 Clusiaceae Tovomita weddelliana 36.2 Tree 1100 26,24 0,64 Clusiaceae Tovomita weddelliana 36.3 Tree 1100 62,50 2,66 Clusiaceae Tovomita weddelliana 36.4 Tree 1100 36,63 1,22 Dichapetalaceae Stephanopodium angulatum 39.3 Tree 1100 31,67 1,33 Lauraceae Ocotea ﬂoribunda 20.2 Tree 1100 28,93 1,54 Lauraceae Ocotea fraxifera 43.2 Tree 1100 64,55 3,08 Leguminosae Dussia 15.1 Tree 1100 23,38 1,10 Myristicaceae Otoba gordonifolia 23.1 Tree 1100 49,39 1,69 Actinidiaceae Saurauia 58.1 Tree 2200 13,51 0,44 Sapindaceae Billia rosea 44.2 Tree 2200 94,51 2,83 Sapindaceae Billia rosea 44.3 Tree 2200 68,05 2,16 75

Table A.5: The iWUE [µmol CO2 / mmol H2O] and WUE [mmol CO2 / mol H2O] for every measured leaf with the growth form and altitude [m a.s.l].

Measured leaf Growth form Altitude WUEi WUE Sapindaceae Billia rosea 52.1 Tree 2200 77,97 2,28 Araliaceae Oreopanax 64.1 Tree 3000 50,60 2,05 Araliaceae Oreopanax 64.2 Tree 3000 33,09 0,88 Ericaceae Cavendishia bracteata 65.1 Tree 3000 60,36 2,12 Rosaceae Rubus 69.1 Tree 3000 18,26 0,86 Rosaceae Rubus 69.3 Tree 3000 33,39 1,41 Rubiaceae Palicourea 71.1 Tree 3000 31,84 0,99 Rubiaceae Palicourea 71.2 Tree 3000 32,08 1,30 Siparunaceae Siparuna 70.3 Tree 3000 29,41 0,87 76 u 0,8371 0,333 ,134 760 624 427 7,9384 361,73 368,49 372,59 4472,75 4662,45 4796,07 3,41 3,41 3,33 301,53 307,19 305,98 Sum 70.3 Siparuna Siparunaceae 71.2 Palicourea Rubiaceae 71.1 Palicourea Rubiaceae 69.3 Rubus Rosaceae 69.1 Rubus Rosaceae 65.1 bracteata Cavendishia Ericaceae 64.2 Oreopanax Araliaceae 64.1 Oreopanax Araliaceae 52.1 rosea Billia Sapindaceae 44.3 rosea Billia Sapindaceae 44.2 rosea Billia Sapindaceae 58.1 Saurauia Actinidiaceae 23.1 gordonifolia Otoba Myristicaceae 15.1 Dussia Leguminosae 43.2 fraxifera Ocotea Lauraceae 20.2 floribunda Ocotea Lauraceae angulatum Stephanopodium Dichapetalaceae 36.4 weddelliana Tovomita Clusiaceae 36.3 weddelliana Tovomita Clusiaceae 36.2 weddelliana Tovomita Clusiaceae 19.3 weddelliana Tovomita Clusiaceae 19.2 RMSE_lin weddelliana Tovomita Clusiaceae RMSE_log 4.2 Pourouma Urticaceae 24.3 RMSE_exp Pourouma Urticaceae 8.3 hertha Cecropia MSE_lin Urticaceae 8.2 hertha Cecropia Urticaceae MSE_log 33.3 hastae Cecropia Urticaceae MSE_exp 31.2 Protium Burseraceae MAPE_lin 57.3 Cissus Vitaceae MAPE_log 59.2 Gouania Rhamnaceae 59.1 MAPE_exp Gouania Rhamnaceae 56.1 Gurania MAE_lin Cucurbitaceae 55.1 Mikania MAE_log Asteraceae 51.2 Mikania Asteraceae MAE_exp 51.1 Mikania Asteraceae 48.2 Mikania Asteraceae 48.1 Mikania Asteraceae 17.3 Psammisia Ericaceae 40.1 Clusia Clusiaceae 18.2 Clusia Clusiaceae 18.1 Clusia Clusiaceae 30.2 Cayaponia Cucurbitaceae 30.1 Cayaponia Cucurbitaceae 7.3 Mikania Asteraceae 27.1 Mikania Asteraceae leaf Measured al A.1: Table vriwo ormaue facrc ME AE S n ME o h xoeta,lgrtmcadlna ersino h g the of regression linear and logarithmic exponential, the for RMSE) and MSE MAPE, (MAE, accuracy of measures four of Overview esrdla.Tesmo ahmti o l pcfi ersin sas hw nbl tteedo h columns. the of end the at bold in shown also is regressions specific all for metric each of sum The leaf. measured 9329 ,729 ,600 ,61,91,31,134 ,83,44 3,48 3,45 11,81 12,13 11,89 0,06 0,06 0,06 2,96 2,97 2,95 39.3 ,348 ,900 ,600 53 58 06 ,459 6,38 10,74 5,99 10,82 3,62 19,65 10,41 5,50 5,94 2,13 9,71 19,28 11,11 19,87 3,72 10,41 40,66 7,11 5,60 2,76 20,01 4,62 2,19 9,50 115,29 11,03 19,92 35,88 3,70 8,18 7,05 108,38 117,16 5,55 386,19 19,78 7,41 2,69 4,68 2,17 9,53 35,33 13,12 123,41 108,40 394,77 371,74 30,28 9,15 7,65 7,08 94,23 11,37 4,53 13,82 6,75 2,73 4,65 121,71 400,34 0,07 396,98 1,89 3,36 31,33 0,06 50,60 2,58 90,19 9,63 7,53 13,67 391,10 4,80 11,33 21,37 3,10 7,61 6,81 30,75 0,10 1,47 0,09 0,06 1,77 49,70 3,44 90,85 66,84 0,05 2,59 21,89 9,53 4,70 0,06 11,37 7,26 54,91 0,07 3,13 50,20 0,10 4,37 0,10 1,49 58,58 0,09 0,06 1,83 8,50 129,37 3,38 21,58 0,11 83,79 5,39 45,61 0,05 2,58 7,46 0,05 0,06 0,08 3,11 128,28 56,75 0,09 11,28 0,81 3,59 0,10 5,49 92,70 3,86 10,27 0,10 1,47 0,08 46,32 0,11 2,98 6,64 0,10 9,91 5,78 129,33 0,08 9,61 0,05 12,83 11,80 0,06 0,07 90,75 0,07 3,14 15,26 3,89 4,80 2,15 0,09 15,54 1,17 9,51 11,06 0,10 3,89 0,10 6,68 9,37 0,11 3,04 0,10 5,71 11,42 0,07 9,79 14,16 13,13 0,08 2,97 15,59 0,05 72,22 3,36 0,03 2,21 19,08 15,74 4,73 6,60 9,88 0,07 4,46 3,91 0,09 1,18 6,66 29,09 9,44 0,09 14,29 8,77 14,74 0,11 3,01 105,45 9,65 0,08 13,01 15,17 2,93 0,07 17,27 1,49 14,92 0,08 2,16 0,05 15,62 9,72 4,54 0,66 0,03 5,55 33,39 7,00 0,07 5,29 122,36 164,51 0,05 1,24 10,29 14,28 14,02 9,43 8,46 8,88 20,75 4,30 0,09 15,12 241,55 0,08 19,80 2,88 1,49 0,09 2,41 0,07 32,58 172,38 0,04 1,37 15,10 4,48 5,15 5,51 0,04 11,92 6,63 0,03 7,70 247,81 0,05 9,25 10,87 1,90 14,33 21,88 8,38 4,44 4,33 0,05 1,21 6,39 9,75 0,03 169,18 23,58 1,51 2,31 0,08 9,83 15,30 0,08 1,40 204,08 0,04 244,08 43,51 13,02 5,52 4,20 6,05 0,04 7,59 5,24 0,05 9,08 298,30 10,69 21,66 4,34 3,50 6,07 0,06 1,73 5,01 0,05 1,22 9,61 27,94 204,03 0,07 2,36 9,81 0,02 2,93 48,97 0,08 105,96 392,01 430,53 0,01 0,06 12,46 6,05 8,19 0,05 1,69 4,29 0,04 5,21 0,06 205,22 88,92 5,93 1,53 1,87 3,37 0,06 1,75 118,25 5,04 59,23 478,67 142,08 0,09 555,91 9,85 2,99 22,59 2,70 0,07 0,02 26,51 8,05 1,52 0,01 19,60 1,21 95,09 0,06 0,05 4,24 114,23 169,52 469,23 1,46 19,68 0,10 1,87 3,61 0,06 6,86 3,36 0,09 0,15 22,70 2,87 2,72 0,09 3,91 27,41 0,06 92,32 155,18 17,67 17,36 1,62 1,18 4,00 25,08 0,01 0,06 1,49 0,08 3,00 12,24 0,13 1,89 8,21 0,11 0,06 27,11 22,70 10,31 2,68 3,38 0,10 18,44 0,13 0,11 25,39 0,10 0,07 16,75 12,39 1,18 4,52 0,70 11,39 3,06 510,20 0,03 9,27 0,08 2,38 0,15 10,39 0,11 17,96 384,09 0,06 3,51 3,61 12,89 0,10 0,13 11,29 515,11 0,08 4,39 1,10 0,10 0,07 301,47 0,05 12,93 10,21 2,38 0,03 0,08 0,13 0,10 515,10 14,46 12,72 3,37 0,06 5,28 0,06 280,60 1,10 0,09 13,09 0,10 0,07 16,91 2,33 0,04 8,94 15,19 0,03 0,09 4,60 5,81 0,10 0,06 0,21 10,49 13,28 0,06 7,99 18,44 0,08 17,05 9,38 0,88 0,04 5,93 3,71 0,09 11,55 0,10 17,47 8,25 3,52 0,17 0,06 9,25 0,88 1,75 11,03 3,81 3,72 0,09 8,15 4,11 3,16 0,14 0,87 1,54 18,80 3,70 3,83 16,75 3,86 3,13 1,57 19,21 3,74 15,39 3,09 19,28 13,98 s VDdt o every for data -VPD 77

Asteraceae Mikania 27.1 Asteraceae Mikania 7.3

data 260 data linear fit linear fit logaritmic fit logaritmic fit 160 exponential fit exponential fit 240 140 ] ] 1 1 − − s s 120 2 2 − − m m 220 O O 2 2 H H 100 gs [mmol gs [mmol 200 80 60 180 40 2.0 2.5 3.0 3.5 4.0 1.8 2.0 2.2 2.4 2.6

VPD [kPa] VPD [kPa]

Cucurbitaceae Cayaponia 30.1 Cucurbitaceae Cayaponia 30.2 70 data data linear fit linear fit logaritmic fit logaritmic fit exponential fit exponential fit 45 65 ] ] 1 1 − − 60 s s 2 2 − − m m O O 40 2 2 H H 55 gs [mmol gs [mmol 50 35 45

2.6 2.8 3.0 3.2 3.4 3.6 2.5 3.0 3.5 4.0

VPD [kPa] VPD [kPa]

Clusiaceae Clusia 18.1 Clusiaceae Clusia 18.2

data data linear fit linear fit 45 logaritmic fit logaritmic fit exponential fit exponential fit 55 50 ] ] 1 1 40 − − s s 2 2 − − m m 45 O O 2 2 H H 40 gs [mmol gs [mmol 35 35 30 30 2.6 2.8 3.0 3.2 3.4 3.6 3.8 2.0 2.5 3.0 3.5

VPD [kPa] VPD [kPa]

Figure A.1: Fitting of the gs-VPD data of liana leaves with a linear (yellow), logarithmic (blue) and exponential (green) regression. 78

Clusiaceae Clusia 40.1 Ericaceae Psammisia 17.3

data data

75 linear fit linear fit logaritmic fit logaritmic fit 34 exponential fit exponential fit 70 32 ] ] 1 1 − − s s 2 2 − − m m O O 2 2 H H 30 65 gs [mmol gs [mmol 28 60 26

2.6 2.8 3.0 3.2 3.4 2.5 3.0 3.5

VPD [kPa] VPD [kPa]

Asteraceae Mikania 48.1 Asteraceae Mikania 48.2

data data 100 linear fit linear fit logaritmic fit logaritmic fit exponential fit exponential fit 180 90 ] ] 1 1 − − s s 2 2 − − 160 m m O O 2 2 H H 80 gs [mmol gs [mmol 140 70 120

2.0 2.5 3.0 1.5 2.0 2.5

VPD [kPa] VPD [kPa]

Asteraceae Mikania 51.1 Asteraceae Mikania 51.2

data data linear fit linear fit logaritmic fit logaritmic fit

130 exponential fit exponential fit 160 120 ] ] 140 1 1 − − s s 2 2 − − m m O O 2 2 H H 110 120 gs [mmol gs [mmol 100 100 90

2.0 2.5 3.0 3.5 2.0 2.5 3.0

VPD [kPa] VPD [kPa]

Figure A.2: Fitting of the gs-VPD data of liana leaves with a linear (yellow), logarithmic (blue) and exponential (green) regression. 79

Asteraceae Mikania 55.1 Cucurbitaceae Gurania 56.1

data data linear fit linear fit logaritmic fit logaritmic fit exponential fit exponential fit 70 150 60 ] ] 1 1 − − s s 2 2 − − m m O O 2 2 H H 50 100 gs [mmol gs [mmol 40 30 50

2.0 2.2 2.4 2.6 2.8 3.0 3.2 1.5 2.0 2.5 3.0

VPD [kPa] VPD [kPa]

Gobiesocidae Gouania 59.1 Gobiesocidae Gouania 59.2

data data 100 160 linear fit linear fit logaritmic fit logaritmic fit exponential fit exponential fit 80 140 ] ] 1 1 − − s s 2 2 − − m m O O 2 2 H H 60 120 gs [mmol gs [mmol 40 100

2.0 2.2 2.4 2.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0

VPD [kPa] VPD [kPa]

Vitaceae Cissus 57.3

45 data linear fit logaritmic fit exponential fit 40 ] 1 − s 2 − m O 2 H gs [mmol 35 30

1.8 2.0 2.2 2.4 2.6 2.8 3.0

VPD [kPa]

Figure A.3: Fitting of the gs-VPD data of liana leaves with a linear (yellow), logarithmic (blue) and exponential (green) regression. 80

Burseraceae Protium 31.2 Urticaceae Cecropia hastae 33.3

data data linear fit linear fit logaritmic fit 240 logaritmic fit 115 exponential fit exponential fit 230 110 ] ] 1 1 220 − − s s 2 2 − − 105 m m O O 2 2 H H 210 100 gs [mmol gs [mmol 200 95 190 90 180

2.4 2.6 2.8 3.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4

VPD [kPa] VPD [kPa]

Urticaceae Cecropia hertha 8.2 Urticaceae Cecropia hertha 8.3

data data linear fit linear fit logaritmic fit logaritmic fit exponential fit exponential fit 105 180 100 170 ] ] 1 1 − − 95 s s 2 2 − − m m O O 2 2 160 H H 90 gs [mmol gs [mmol 150 85 80 140 75 130

2.2 2.4 2.6 2.8 3.0 2.0 2.2 2.4 2.6 2.8 3.0 3.2

VPD [kPa] VPD [kPa]

Urticaceae Pourouma 24.3 Urticaceae Pourouma 4.2

data data linear fit linear fit

logaritmic fit 200 logaritmic fit exponential fit exponential fit 170 180 ] ] 1 1 − − s s 2 2 160 − − m m 160 O O 2 2 H H gs [mmol gs [mmol 140 150 120 140

2.8 3.0 3.2 3.4 3.6 2.2 2.4 2.6 2.8 3.0 3.2 3.4

VPD [kPa] VPD [kPa]

Figure A.4: Fitting of the gs-VPD data of tree leaves with a linear (yellow), logarithmic (blue) and exponential (green) regression. 81

Clusiaceae Tovomita weddelliana 19.2 Clusiaceae Tovomita weddelliana 19.3

data data 38 linear fit linear fit

34 logaritmic fit logaritmic fit exponential fit exponential fit 36 32 ] ] 1 1 − − s s 2 2 − − m m 34 O O 2 2 H H 30 gs [mmol gs [mmol 32 28 30 26

2.0 2.5 3.0 3.5 2.0 2.5 3.0 3.5

VPD [kPa] VPD [kPa]

Clusiaceae Tovomita weddelliana 36.2 Clusiaceae Tovomita weddelliana 36.3 42 data data linear fit linear fit 40 logaritmic fit logaritmic fit exponential fit exponential fit 40 38 38 ] ] 1 1 − − s s 2 2 − − m m O O 2 2 36 36 H H gs [mmol gs [mmol 34 34 32 32 30

3.0 3.5 4.0 2.0 2.5 3.0 3.5

VPD [kPa] VPD [kPa]

Clusiaceae Tovomita weddelliana 36.4 Dichapetalaceae Stephanopodium angulatum 39.3

data data 60 linear fit linear fit 40 logaritmic fit logaritmic fit exponential fit exponential fit 38 55 ] ] 1 1 − − 36 s s 2 2 − − m m O O 2 2 H H 34 gs [mmol gs [mmol 50 32 30 45

2.0 2.5 3.0 3.5 2.0 2.5 3.0 3.5

VPD [kPa] VPD [kPa]

Figure A.5: Fitting of the gs-VPD data of tree leaves with a linear (yellow), logarithmic (blue) and exponential (green) regression. 82

...Lauraceae Ocotea floribunda 20.2 Lauraceae Ocotea fraxifera 43.2

data data linear fit linear fit logaritmic fit logaritmic fit exponential fit exponential fit 150 280 ] ] 1 1 − − s s 140 260 2 2 − − m m O O 2 2 H H gs [mmol gs [mmol 240 130 220 120

1.8 2.0 2.2 2.4 2.6 1.6 1.8 2.0 2.2 2.4 2.6 2.8

VPD [kPa] VPD [kPa]

Leguminosae Dussia 15.1 Myristicaceae Otoba gordonifolia 23.1

data data linear fit 130 linear fit logaritmic fit logaritmic fit 100 exponential fit exponential fit 120 90 ] ] 1 1 − − s s 2 2 − − 110 m m O O 80 2 2 H H 100 gs [mmol gs [mmol 70 90 60 80

2.0 2.2 2.4 2.6 2.8 3.0 3.2 2.8 3.0 3.2 3.4 3.6 3.8

VPD [kPa] VPD [kPa]

Actinidiaceae Saurauia 58.1 Sapindaceae Billia rosea 44.2

data data linear fit linear fit logaritmic fit logaritmic fit

36 exponential fit exponential fit 45 34 ] ] 1 1 − − s s 2 2 − − m m 32 O O 2 2 40 H H gs [mmol gs [mmol 30 28 35 26

2.0 2.2 2.4 2.6 2.8 3.0 3.2 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4

VPD [kPa] VPD [kPa]

Figure A.6: Fitting of the gs-VPD data of tree leaves with a linear (yellow), logarithmic (blue) and exponential (green) regression. 83

Sapindaceae Billia rosea 44.3 Sapindaceae Billia rosea 52.1

data data linear fit linear fit logaritmic fit logaritmic fit 80 exponential fit exponential fit 34 75 32 ] ] 1 1 − − s s 2 2 − − m m 70 O O 2 2 H H 30 gs [mmol gs [mmol 65 28 60 26 55 2.5 2.6 2.7 2.8 2.9 2.2 2.4 2.6 2.8 3.0 3.2

VPD [kPa] VPD [kPa]

Araliaceae Oreopanax 64.1 Araliaceae Oreopanax 64.2

data data 55 linear fit linear fit 110 logaritmic fit logaritmic fit exponential fit exponential fit 50 100 ] ] 1 1 − − s s 2 2 − − 90 m m O O 45 2 2 H H gs [mmol gs [mmol 80 40 70 35

1.8 2.0 2.2 2.4 2.6 2.8 3.0 2.0 2.5 3.0 3.5

VPD [kPa] VPD [kPa]

Ericaceae Cavendishia bracteata 65.1 Rosaceae Rubus 69.1

data data 55 linear fit linear fit logaritmic fit logaritmic fit exponential fit exponential fit 220 50 200 ] ] 1 1 − − s s 2 2 − − m m O O 2 2 H H 45 180 gs [mmol gs [mmol 40 160 140

2.0 2.5 3.0 3.5 1.6 1.8 2.0 2.2 2.4

VPD [kPa] VPD [kPa]

Figure A.7: Fitting of the gs-VPD data of tree leaves with a linear (yellow), logarithmic (blue) and exponential (green) regression. 84

Rosaceae Rubus 69.3 Rubiaceae Palicourea 71.1

120 data data linear fit linear fit logaritmic fit logaritmic fit 210 exponential fit exponential fit 200 110 ] ] 190 1 1 − − s s 2 2 − − m m 100 O O 2 2 180 H H gs [mmol gs [mmol 170 90 160 150 80

1.8 2.0 2.2 2.4 2.6 2.2 2.4 2.6 2.8 3.0

VPD [kPa] VPD [kPa]

Rubiaceae Palicourea 71.2 Siparunaceae Siparuna 70.3

data data 130 linear fit linear fit

220 logaritmic fit logaritmic fit exponential fit exponential fit 120 110 200 ] ] 1 1 − − s s 2 2 − − m m 100 O O 2 2 H H 180 90 gs [mmol gs [mmol 80 160 70

2.0 2.2 2.4 2.6 2.8 2.5 3.0 3.5 4.0

VPD [kPa] VPD [kPa]

Figure A.8: Fitting of the gs-VPD data of tree leaves with a linear (yellow), logarithmic (blue) and exponential (green) regression.