How resilient are Polylepis

reticulata and Escallonia

myrtilloides to drought?

Klara Bouwen

Student number: 01403834

Promoter: Prof. dr. ir. Kathy Steppe

Tutor: ir. Fran Lauriks

A dissertation submitted to Ghent University in partial fulfilment of the requirements for

the degree of Master of Science in Bioscience Engineering: Forest and Nature

Management

Academic year: 2018-2019

“For the world is changing:

I feel it in the water,

I feel it in the earth,

And I smell it in the air.”

~ Treebeard,

J.R.R Tolkien, Many Partings,

The Return of the King

i

Declaration of Authorship

“De auteur en de promotor 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 de bron

te vermelden bij het aanhalen van resultaten uit deze scriptie”

“The author and the promoter 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 results from this thesis””

Ghent, August 2019

The promoter, The tutor, The author,

Prof.dr.ir. Kathy Steppe Ir. Fran Lauriks Klara Bouwen

ii

Thank you

Yes, it is cliché to say, but it is also very true: you do not write a thesis alone… and there are

so many people that contributed to this work in one way or another.

First of all, I want to thank Kathy for giving me the opportunity to collaborate in such an

unique project. Polylepis forests are truly special and without this thesis, I would never have

known they even existed. Thank you for believing in me and reviewing my work.

Next of course, I would like to thank my tutor: Fran, without your help, this research project

would maybe still be stored on my computer by the name of ‘thesis_draft.doc’, instead of

being the fancy booklet it is today. Thank you so much for all your time and patience, but

most of all, for supporting me along the entire way. You are the best! Also, thank you, Niels,

Linus, Roberto, Jonas and Olivier for helping me with my experiment. Your advices helped

me to come to these results

This thesis would of course not be same without all the new people I met and learnt from

during this last year. Thank you Heidi, for giving a Belgian girl the chance to work in such an

unique environment. I wish you all the luck with the further development of the project and I

really hope this thesis can contribute in some kind of way. Also, my special gratitude goes to

Ximena. Your kindness and hospitality are beyond this world. Thank you for all your help

during this amazing experience! Alberto, Aldemar, Franklin and Fausto, muchas gracias for

making me feel at home on the other side of the world. I really hope we could see each other

again someday/somewhere!

Liselot and Wouter, it would not have been such an wonderful experience without you two. I

really cannot believe it already has been a year ago. When do you want to leave again?

To all my friends and family, thank you for believing in me and making me the person I am

today.

.

iii

Summary

Fuelled by climate change, drought induced tree mortality increases in forest ecosystems

around the world. Especially montane ecosystems, in which plant species are already

surviving at the edge of their physical boundaries, are considered very vulnerable to

changes in temperature and precipitation regimes. Degradation of these unique ecosystems

can have disastrous consequences for local communities, depending directly on the high-

altitudinal regions in their water supply. Despite the importance of montane ecosystems and

the predicted increase of climate change induced drought stress, research assessing their

vulnerability to drought remains very limited.

Predicting drought responses at ecosystem level starts with a detailed and mechanistic

understanding of soil-plant-water interactions at plant level. In this research project, we

quantified drought vulnerability and hydraulic capacitance of two abundant páramo trees,

Polylepis reticulata and Escallonia myrtilloides. Water potentials were measured in the field

(Zhurucay Ecohydrological Observatory (Ecuador)) for twelve different days in September

2018. At the end of the measurement campaign, branches were collected and transported to

Ghent University (Belgium) to establish vulnerability and desorption curves using acoustic

emission sensors and continuously weighing of branches during dehydration. Plant anatomy

of two branches was analysed and structural xylem features including vessel diameter,

vessel area, and the degree of vessel connectivity were determined.

Our results show that both páramo species are highly resilient against drought, with P.

reticulata (P50 = -4.97 ± 1.39 MPa) being less vulnerable than E. myrtilloides (P50 = -9.11

± 0.25 MPa). The low hydraulic capacitances of both species suggest low reliance on

internal water reserves and high adaptation of the xylem tissue to avoid embolism formation.

This was also supported by the microscopic analysis indicating low vessel connectivity and

small vessel diameters. Overall, these results show that P. reticulata and E. myrtilloides are

highly resistant to drought and suspects that both páramo species are resisted against

increasing drought stress.

Key words: acoustic vulnerability curves – hydraulic capacitance – embolism formation–

Polylepis reticulata – Escallonia myrtilloides – páramo

iv

Samenvatting

Massale boomsterfte neemt onder invloed van klimaatsverandering steeds meer toe. Vooral

bergecosystemen, waarin planten omwille van het barre klimaat sterk aangepast zijn aan

specifieke standplaatsen, blijken erg kwetsbaar voor veranderingen van temperatuur- en

neerslagregimes. Degradatie van deze fragiele ecosystemen kan rampzalige gevolgen

hebben voor bevolkingsgroepen die rechtstreeks afhankelijk zijn van ecosysteemdiensten

die worden geleverd door de captatie van water op grote hoogte. Hoewel het essentieel is

om te kunnen inschatten hoe bergecosystemen zullen reageren op toenemende

droogtestress, is er tot vandaag weinig gekend over hun vatbaarheid op droogte.

Om te voorspellen welke invloed toenemende droogtestress zal hebben op

ecosysteemniveau, is een goede kennis van bodem-plant-water relaties op plantniveau

noodzakelijk. In dit onderzoek werd de droogteresistentie van twee veelvoorkomende

páramo-bomen, Polylepis reticulata en Escallonia myrtilloides, bepaald. Waterpotentialen

werden opgemeten in het veld (Zhurucay Ecohydrological Observatory (Ecuador))

gedurende 12 verschillende dagen in september 2018. Takken werden op het einde van de

meetcampagne verzameld en overgebracht naar de Universiteit van Gent (België) voor de

bepaling van vatbaarheids- en uitdrogingscurves met behulp van akoestische emissie

sensoren en door continue weging van takken tijdens dehydratatie. Plantanatomie van twee

takken werd in detail bekeken en structurele xyleemkenmerken waaronder vatdiameter,

vatoppervlakte en onderlinge vatconnecties werden bepaald.

De bekomen vatbaarheidscurves tonen de hoge droogteresistentie aan van beide páramo-

boomsoorten. Bovendien blijkt P. reticulata (P50 = - 4.97 ± 1.39 MPa) gevoeliger aan

droogte dan E. Myrtilloides (P50 = -9.11 ± 0.25 MPa). De lage hydraulische capaciteiten (<

100 kg m-3 MPa-1) voor beide soorten wijzen op de lage afhankelijkheid van interne

waterreserves en de hoge adaptatie van het xyleem om embolisatie te vermijden. Dit werd

ook bevestigd in de anatomische analyses door de beperkte connecties tussen vaten

onderling (Solitary vessel index = 0.72) en de lage vatdiameter (~ 20 µm). Deze resultaten

tonen de hoge droogteresistentie van P. Reticulata en E. Myrtilloides aan en doen

vermoeden dat beide páramo soorten bestand zijn tegen toenemende droogtestress.

Trefwoorden: Vatbaarheidscurves – Hydraulische capaciteit – Embolisatie – Polylepis

reticulata – Escallonia myrtilloides – Páramo

v

Table of contents

Declaration of Authorship i

Thank you ii

Summary iii

Samenvatting iv

Table of contents v

Abbreviations & Symbols vi

1 Introduction 1

2 Literature review 2

2.1 Movement of water in plants 2

2.2 Drought-induced forest mortality across the globe 14

2.3 Tropical montane ecosystems under threat 18

3 Materials and methods 24

3.1 Zhurucay Ecohydrological Observatory 24

3.2 In situ measurements 28

3.3 Branch sampling procedure 30

3.4 Measurements during dehydration 30

3.5 Microscopic analysis 34

4 Results 35

4.1 In situ measurements 35

4.2 Dehydration experiment 37

4.3 Desorption curves 44

4.4 Microscopic analysis 48

5 Discussion 50

5.1 Vulnerability of P. reticulata and E. myrtilloides to drought-induced embolism 50

5.2 Resilience of P. reticulata and E. myrtilloides to drought under a changing climate 55

6 Conclusion 57

7 Further research 58

8 Appendix 59

9 Bibliography 61

vi

Abbreviations & Symbols

Abbreviations

AE Acoustic Emission

a.s.l Above sea level

C-T Theory Cohesion-Tension Theory

DC Desorption Curve

ITCZ Inter Tropical Convergence Zone

IPCC Intergovernmental Panel on Climate Change

RH Relative Humidity (%)

PLC Percentage Loss of hydraulic Conductivity (%)

Px Water potential at x percentage loss of hydraulic conductivity (MPa)

SPAC Soil-Plant-Atmosphere Continuum

TMCF Tropical Montane Cloud Forest

VC Vulnerability Curve

VPD Vapour Pressure Deficit (kPa)

VWC Volumetric Water Content (kg m-3)

VSH Vulnerability Segmentation Hypothesis

ZHU Zhurucay river Ecohydrology Observatory

PLC Percentage Loss of hydraulic Conductivity (%)

Symbols

Cel Elastic hydraulic capacitance (kg m-3 MPa-1)

Cinel Inelastic hydraulic capacitance (kg m-3 MPa-1)

R Leaf to stem water potential ratio (-)

Vf Vessel frequency (mm-2)

VS Solitary vessel Index (-)

VG Vessel grouping index (-)

𝜌𝑏 Basis wood density (kg m-3)

𝜑𝑙𝑒𝑎𝑓 Leaf water potential (MPa)

𝜑𝑠𝑡𝑒𝑚 Stem water potential (MPa)

1

1 Introduction

Our climate is changing at a rapid pace. Due to increasing greenhouse gas emissions, the

earth’s surface temperature has already raised 0.5 °C since 1970 (IPCC, 2014). Changes in

temperature, shifts of precipitation patterns and melting snow and ice affect numerous

hydrological systems. One of the already observed consequences is the prolongation of

drought periods in many parts of the world (IPCC, 2014).

Ever since ‘climate change’ was mentioned for the first time (Revelle & Suess, 1957),

mounting evidence of climate change impacts have been observed in a wide range of

ecosystems. Climate change has affected terrestrial (Deutsch et al., 2008; Graham &

Grimm, 1990; Melillo et al., 1993), fresh water (Woodward et al., 2010; Xenopoulos et al.,

2005) and marine (Beardall et al., 1998; Occhipinti-Ambrogi, 2007) plant species around the

world. Many of these species have shifted their geographical range, migration patterns,

seasonal activities, abundance or overall distribution to changing climatic conditions.

Forest ecosystems are being rapidly transformed by ongoing climate change. The effects of

climate change on forests can be both positive (e.g. increasing growth because of CO2-

fertilization (Poulter et al., 2014) and negative (e.g. decreasing growth due to increasing

drought stress (Adams et al., 2009)). Forest mortality can have disastrous consequences for

local communities who are directly depending on ecosystems services as livelihood

maintenance (Anderegg et al., 2013). Especially, tropical montane ecosystems, which act as

water suppliers for many major cities around the world (Buytaert et al., 2011), have been

shown extremely vulnerable to temperature changes (Anderson et al., 2017). To secure

future existence of these ecosystem services, policy makers have to make decisions today

on how to manage and secure tropical montane forests. However, the development of

effective strategies is only possible when it is known how montane ecosystems will respond

to future climate changes. However, until today, research focussing on the climate change

effects on these precious ecosystems remains very limited (Aparecido et al., 2018).

To address this knowledge gap, this study focuses on the effect of drought of Polylepis

reticulata and Escallonia myrtilloides. Vulnerability and desorption curves are drafted to

quantify the drought resistance and water potential measurements in the field are conducted

to assess actual stress levels. This to make a substantiated statement on the drought

resistance of two abundant tropical montane forest trees.

2

2 Literature review

2.1 Movement of water in plants

2.1.1 The soil-plant-atmosphere continuum

Plants extract water from the soil by their root system and transport it all the way up to the

leaves. Here, water is released into the atmosphere in a process called transpiration. In

literature, this pathway is referred to as the soil-plant-atmosphere continuum or SPAC

(Gardner, 1960; Philip, 1957). For most plants, the majority of the transportation pathway

consist of tracheary elements in the xylem tissue. These non-living conducting cells are

anatomically adapted to form long hollow conduits through which water can flow with

minimal resistance. In order to provide a continuous flow, multiple vessel elements are

interconnected through microscopically small cell wall depressions or pits. At these points

formation of lignified secondary walls is minimized or absent (Esau, 1977). Primary cell walls

and middle lamella of two opposing pits from neighbouring vessels form a porous layer, the

pit membrane. The architecture of this membrane differs across species (Figure 2.1). In

most gymnosperms, the aperture of the membrane is sealed by a central thickening or

margo-torus pit structure acting as a valve to regulate the entering water flow. In

angiosperms, this feature is absent in most species (Choat et al., 2008). In this thesis, we

focus on Polylepis reticulata and Escallonia myrtilloides, both angiosperms. Further literature

review will therefore emphasize the research and behaviour of angiosperm species.

Figure 2.1 Schematic representation of the architecture of intervessel pits in angiosperms (left)

and gymnosperms (right) (adapted from Choat et al. (2008)).

3

Under steady-state conditions, i.e. when inflow equals outflow and total flow remains

unchanged over time, water transport can be represented as a catenary process

corresponding to the current flow in an electric circuit. By Ohm’s law analogue1, plant water

flow F [kg s-1] is proportional to the product of the total conductance 𝐾ℎ [kg s-1 MPa-1] (or

invers resistance R [MPa s kg-1]) and the difference in water potential ∆𝜑 [MPa] (Van den

Honert, 1948) (Eq. 1) .

𝐹 = ∆𝜑

𝑅 = 𝐾ℎ ∆𝜑 (Eq.1)

According to the tree hydraulic architecture, total conductance 𝐾ℎ is the resultant of the

conductance (k) of different conductance elements e.g. root, stem, leaf, … (Figure 2.2;

Tyree and Ewers 1991). The water potential difference, ∆𝜑, expresses the difference in

potential energy needed to transfer water molecules from reference level (by convention

pure water at 0 m height, 25 °C and 1 atm) to a plant tissue. Plant water potential has

become one of the most important measurements in plant physiology to express water

availability (Meron, 2018; Shackel et al., 1997). By definition, plant water potential consists

of the sum of four different components, the gravimetric potential (𝜑𝑔, [MPa]), the osmotic

potential ( 𝜑𝜋 , [MPa]), the pressure potential (𝜑𝑝, [MPa]) and the matrix potential (Eq.2).

𝜑 = 𝜑𝑔 + 𝜑𝜋 + 𝜑𝑝 + 𝜑𝑚 (Eq. 2)

The gravimetric potential expresses the effect of gravity on the free energy of water. Under

static conditions, the gravimetric water potential change is expected to equal 0.01 MPa m-1.

Therefore it can often be omitted in plants and small trees as it has a negligible influence on

the overall water potential. The osmotic potential expresses the effect of dissolved solutes

and is proportional to the solute concentration. Increasing concentrations decrease the free

energy of water, with pure water as reference state ( 𝜑𝑜,𝑟𝑒𝑓 = 0.0 MPa). The pressure

potential expresses the hydrostatic pressure of the solution within the cells and can either be

positive (turgor pressure) or negative (xylem pressure). The matrix potential expresses the

adhesion force between water molecules and structural elements or colloidal components

e.g. cell walls, membranes,… . Since adhesion forces lower the free energy of water, the

matrix potential is always negative.

Water flows in the direction of decreasing water potential. In plants this means from a less

negative water potential in the roots to a more negative water potential in the leaves. This

forms the basis of the cohesion-tension theory (C-T theory) introduced by Dixon and Joly

1 Although the Ohms’ law analogue is certainly useful to estimate water flow rates, it remains an oversimplification of the

system and may lose some of its credibility when the complexity of the pathway increases (Cowan, 1965).

4

(1894). This theory became widely accepted as the mechanism explaining upward sap flow

up to 10.3 m (equivalent to the atmospheric pressure). The C-T theory states that the driving

force for upward water transport is generated by transpiration water losses at the leaf

surface. As a consequence of the developed surface tension in porous cell walls, transpired

water is replenished by water from neighbouring xylem vessels. This places the plant water

mass under a negative pressure and creates a continuous water column running from

leaves to roots. The hydrogen bonds enable cohesion between the water molecules to

withstand high tension.

The C-T theory has remained largely unmodified, until controversy emanated recently with

the development of the xylem pressure probe (XPP) allowing direct pressure measurements

in intact plants (Balling & Zimmermann, 1990; Melcher et al., 1998; Zimmermann et al.,

2004). Although probe measurements confirm the existence of tension in plants, measured

tensions are too small to maintain an upward flow. This contradicts the hypothesis of an

exclusive tension-driven water flow. Furthermore, low probe tension measurements could

indicate that tensions obtained by pressure chamber measurements (Scholander et al.,

1965) are likely to be an overestimation of the actual plant pressure. Taking their findings

into consideration, Balling and Zimmermann (1990) suggest the ‘Multi-force’ or ‘Watergate’

theory as alternative to the C-T theory. This provoked a lively debate about the inadequacy

and validity of the XPP technique despite extensive and numerous prior tests (Angeles et al.,

2004).

Figure 2.2 Ohm's law analogue for plants. Total conductance is represented as the resultant of the

conductance of the root, stem, leaf, stomatal and boundary layer in series (Melvin T Tyree & Ewers,

1991).

5

2.1.2 Interruption of the water transport by embolism formation

Due to negative hydrostatic pressure in the water column, water transport in xylem vessels

is prone to bubble formation causing hydraulic failure. This poses a direct threat to upwards

water transport and hence plant growth and survival (Anderegg et al., 2012). In physics, this

phenomenon is defined as cavitation which is the formation of bubbles inside an initially

homogeneous liquid medium under very low pressure (Franc & Michel, 2006). However in

plants, observed tensions are not large enough to trigger random bubble formation and

cause spontaneous rupture of the water column (Pickard, 1981). Additionally, xylem sap

cannot be considered as a homogenous solution of pure water. Therefore, this means that

strictly speaking the term cavitation is unable to cover the full extent of the nucleation in

plants and embolism formation would be a better term to describe this phenomenon.

However, since cavitation and embolism formation are used interchangeably in literature, in

this thesis both terms will be treated as synonyms as well.

Under well-watered conditions, xylem conduits function at negative pressures of -1 to -2

MPa (Tyree & Sperry, 1989). Therefore, water must remain liquid in plants at pressures

below its vapour pressure. In this metastable state, xylem conduits are constantly

functioning at the edge of their physical boundaries, suitably nicknaming xylem as the

‘vulnerable pipeline’ (Zimmermann & Tyree, 2013).

When plants are exposed to drought, tension in the xylem conduits increases to such an

extent that gas dissolves from the liquid, possibly resulting in hydraulic failure. Research

focussing on the water-stress-induced cavitation mechanism has resulted in the air-seeding

hypothesis (Holbrook & Zwieniecki, 1999; Sperry & Tyree, 1988). This hypothesis states that

air-seeding, i.e. bubble formation, takes place at the porous pit membranes. When air-

seeding occurs, a concave meniscus forms at the boundary between the air-filled and water-

filled vessel (Figure 2.3). As tension progressively increases, the shape of the water-air

interface changes and the air bubble withdraws further into the pit. When the pressure

difference drops below a threshold value, dependent on the pore radius (Eq. 3), the air

bubble will move into the xylem conduit lumen and explode, causing embolism formation.

∆𝑃 = 2𝜎

𝑟 (Eq. 3)

with ∆𝑃 [MPa] the difference between the atmospheric and water pressure, 𝜎 the surface

tension of water (0.072 N m-1) and 𝑟 the pore radius [m].

Eq. 3 indicates the correlation of xylem vulnerability with the pore diameter and not with the

conduit length as suggested in early research (Ellmore & Ewers, 1985; Hargrave et al.,

1994). In general, the larger the pore diameter, the higher the vulnerability to drought-

induced-cavitation. Although this hypothesis is proven theoretically, it has been difficult to

provide experimental evidence of air-seeding pores at realistic pressures (Choat &

6

Pittermann, 2009; Shane et al., 2000; Wheeler, 1983). Since cavitation occurs at the largest

pore in the vessel network, only one large pore is required to initiate embolism formation.

This rare pit hypothesis suggests that because of the large quantities of pit connections, air-

seeding pores are extremely rare and therefore often undetected by electron microscopy or

particle-exclusion experiments (Jansen et al., 2009; Wheeler et al., 2005). The more

extensive the pitting, the greater the chance of pore failure (Christman et al., 2009;

Christman et al., 2012; Sperry et al., 2005). Besides, Choat et al. (2004) suggest that pit

membranes can temporarily deflect under increasing pressure differences, increasing pit

porosity and lowering the cavitation threshold (Eq. 3). Since pore sizes are experimentally

measured in relaxed states, this could provide another explanation for the difficulties to

observe air-seeding pores. In addition, the air-seeding hypothesis can only hold if some

vessels are already embolized. In nature, cavitation can also occur due to pathogens or

herbivores damaging the stem and branches (McElrone et al., 2008; Tyree & Sperry, 1989) .

The air-seeding theory states that entry of a gas bubble in a xylem conduit will immediately

lead to vessel cavitation. This is however in contradiction with the observations of Oertli

(1971), who denoted that gas bubbles can remain stable under a critical radius when

entering the liquid phase of a neighbouring xylem conduit and are thus unlikely to cause

cavitation. To explain this Schenk et al. (2015, 2017) suggests the occurrence of plant

nanobubbles. In analogy of air bubbles, nanobubbles are formed by the air-water menisci.

Xylem surfactants and gas supersaturation of xylem sap stabilize the bubbles and prevent

embolism formation. Vessel embolism can occur if the nanobubble size surpasses a critical

value, the Blake threshold.

Besides being induced by drought, embolism formation can also be the result of freeze-thaw

cycles of the xylem sap. When xylem sap freezes under tension, dissolved gasses become

insoluble and are trapped in the ice as air bubbles. During thawing tension develops, forcing

air bubbles out of the solution, until they expand and nucleate cavitation (Cox & Zhu, 2003;

Sperry et al., 1988). In the scope of this thesis, future literature review will focus on drought-

induced cavitation.

7

Figure 2.3: Schematic representation of a cavitated vessel and neighbouring functional vessel. (A)

Water flow in a functional xylem network, connected by bordered pit membranes; (B)-(C) Embolism

formation in the left xylem conduit. At the pit membranes, concave air-water menisci are formed; (D)

Detailed representation of air-water menisci. When the xylem pressure decreases, the air bubble

withdraws further into the pits (adapted from Venturas et al. (2017)).

2.1.3 How plants cope with embolism formation

As embolism formation poses a serious threat to the hydraulic transport by reducing the

hydraulic conductivity, it could eventually lead to plant mortality. Plants have therefore

developed different strategies to avoid, restrict and reverse cavitation. These physiological

traits are investigated with increasing interests, especially in the context of climate change,

as they are expected to be one of the major factors determining forests’ drought vulnerability

(Anderegg et al., 2018; Choat et al., 2012).

2.1.3.1 Protection against embolism formation

Plants are extensively subjected to large tensions (i.e. -1 MPa to -2 MPa). However, plants

prove to maintain a continuous water flow well below these pressures while simultaneously

minimizing embolism formation. Therefore, plants possess different anatomical features

ranging from pit to plant level, and physiological traits as protection against cavitation

Plants have adjusted their xylem architecture to prevent destructive hydraulic failure. Xylem

consists of a highly interconnected and compartmentalized network of tracheary elements.

This allows restriction of emboli dispersion while continuing upward sap flow by redirection

of water through neighbouring vessels (Choat et al., 2008). The overall performance of the

vessel network relies largely on the nanoscopic pores of the pit membrane located in the

lateral cell wall of neighbouring conduits. As mentioned before (see 2.1.1), these pores act

as safety valves through which water flows freely but emboli and pathogen spreading is

restricted (cf. air seeding theory). Increased cavitation resistance is associated with

8

structural features such as thicker and shallower pore membranes, smaller pit apertures or

reduced porosity (Lens et al., 2011, 2013). The pit structure also affects the hydraulic

resistance, with inter-vessel flow resistance estimated to account for more than 50 % of the

total vessel network resistance (Choat et al., 2008; Wheeler et al., 2005), suggesting a

trade-off between hydraulic conductivity and cavitation resistance (Li et al., 2016).

A second safety-efficiency trade-off applies to the geometry of the vessels themselves. More

cavitation resistant species tend to possess shorter vessel elements that are manifold

interconnected. This vessel connectivity or clustering (i.e. average number of vessels

contacting a vessel) (Loepfe et al., 2007) allows the circumvention of embolisms and thus

increases the embolism resistance, while at the same time hydraulic conductivity is

decreased due to increased pit passages (Carlquist, 1984; Lens et al., 2011). However,

theoretical models also suggest that high vessel connectivity may lower the embolism

resistance by increasing the probability for the spread of embolism through air-seeding

(Loepfe et al., 2007). Furthermore, wide conduits tend to have higher hydraulic conductivity

but their vessel walls need to be mechanically reinforced to reduce the odds of collapse

initiating hydraulic failure ( Hacke et al., 2001). Cell wall strength is positively correlated with

the thickness-to-span ratio (𝑇𝑤 𝐷𝑣−1) , or two times the vessel thickness (𝑇𝑤, µm) per conduit

diameter (𝐷𝑣, µm) (U. G. Hacke, Sperry, et al., 2001; Jacobsen et al., 2005; Lens et al.,

2011; Pratt & Jacobsen, 2017). This indicates that wide conduits must possess thicker cell

walls in comparison to small conduits to withstand similar negative pressures. The

thickness-to-span ratio of tracheary elements also strongly affects the wood density (WD);

higher 𝑇𝑤 𝐷𝑣−1, higher WD . For many species, higher cavitation resistance is associated with

a higher thickness-to-span ratio and thus higher wood density ( Hacke et al., 2001; Lens et

al., 2011; Martínez‐Cabrera et al., 2009). Also, fibres are believed to play a role in

implosion prevention, however evidence is still lacking (Jacobsen et al., 2005). Because

xylem vessels are dead at maturity, acclimation to environmental conditions is only possible

during growth and development. Therefore, xylem structure represents a critical feature in

the search for the embolism resistance limits in different ecosystems (Choat et al., 2012).

Yet, general observed correlations between xylem safety and hydraulic efficiency are weak,

with several species having both a low efficiency and low safety (Gleason et al., 2016).

Although this does not mean that safety-efficiency trade-off is absent in the xylem. Further

research is essential to understand the underlying hydraulic strategies.

At plant level, embolism formation can be restricted by the plant’s hydraulic architecture

(Zimmermann, 1978). This term describes the hydraulic conductivity of the xylem in various

parts of the plant, with increasing conductivity towards the leaves resulting in smaller safety

margins to embolism formation. According to Zimmermann’s vulnerability segmentation

hypothesis (VSH), cavitation will first take place in the leaves where the water potential is

lowest. Dehydration of these parts will decrease the transpiration water loss and thus

prevent further embolism formation in permanent, more important parts of the plant such as

the stem (Wason et al., 2018).

9

In drying soils, plants operate at the edge of their physical boundaries. Short drought periods

can be mitigated by water withdrawal from storage tissues to the transpiration stream,

relaxing the water tension in the woody tissue (Hölttä et al., 2009; Vergeynst et al., 2015).

The hydraulic capacitance, C [kg m-3 MPa-1], is the capacity of plant tissues to store and

release water and can be defined as the amount of released water per unit water potential

decrease (Eq. 4; Edwards and Jarvis 1982).

𝐶 = 𝑑𝑉𝑊𝐶

𝑑𝜑 (Eq. 3)

with 𝑑𝑉𝑊𝐶 the difference in volumetric water content [kg m-3] and 𝑑𝜑 [MPa] the

corresponding variation in water potential.

During prolonged drought stress, water from different plant organs and tissues, each with an

associated hydraulic capacitance, may be released sequentially to the transpiration stream.

With increasing drought, water will be depleted from capillaries (e.g. intercellular spaces),

from elastic storage tissue (e.g. parenchyma) and finally from vessel cavitation. Stating also

that the xylem tissue, after vessel cavitation, can attribute to the water release enabling to

maintain the transpiration stream and buffer a further water potential decline under drought

stress (Hölttä et al., 2009; Steppe, 2018; Tyree & Yang, 1990). This idea is supported by the

results of Vergeynst et al. (2015) who linked water release and the corresponding hydraulic

capacitance with diameter shrinkage under decreasing water potentials. This strategy

permits plants to maintain stomatal opening and carbon dioxide (CO2) uptake, and under

drought stress conditions.

However, when tensions become too large, even with all taken precautions and delays,

plants can still respond by (partially) close the stomata and thereby restricting water losses

(Klein, 2014). Since stomata also control the photosynthesis, this avoidance of hydraulic

failure could eventually lead to carbon starvation and mortality with persisting periods of

drought (McDowell et al., 2008).

2.1.3.2 Embolism reversal

Although embolism prevention is key for plant hydraulic functioning, loss of active xylem

vessels occurs on a daily basis (McCulloh & Meinzer, 2015; McCully et al., 1998). This

raises the question on how plants manage to maintain continuous water transport despite

daily loss of active vessels.

Until recently, embolism repair was thought to be solely feasible by spontaneous dissolution

of gas bubbles under a weak negative or slightly positive pressure (Brodersen & McElrone

2013). This would constrain embolism repair to periods with high soil water availability and

minimal transpiration, mainly during night time. Under these conditions xylem pressure

exceeds the critical capillary threshold value, compressing gas bubbles while dissolving the

10

air in the xylem sap (Eq. 3). Positive root pressures up to 0.15 MPa have been reported for

grapevine species (Knipfer et al., 2015; Sperry et al., 1987; Tibbetts & Ewers, 2000).

However, the needed xylem pressure depends on the amount of dissolved gas bubbles in

the sap. Also the required time allowing recovery at these pressures, depends on the initial

extent of the embolism and the xylem anatomy (Venturas et al., 2017; Yang & Tyree, 1992).

However, Ewers et al. (1997) indicated that several species do not develop positive root

pressure and this reversal mechanism can therefore not apply for all plants. In addition,

different studies observed fast refilling of embolized xylem vessels under moderate negative

pressures (< - 0.5 MPa) (Christman et al., 2012; Hacke & Sperry, 2003; Klein et al., 2018;

McCully et al., 1998; Venturas et al., 2017). First evidence of embolism repair under tension

provoked intense controversy because of the apparent violation against the laws of

thermodynamics (Zwieniecki & Holbrook, 2009). In addition, some measurement artefacts

were discovered resulting in embolism formation caused by the experiments (Venturas et al.,

2017) including induced air entry due to sampling under tension (cf. excision artefact;

Wheeler et al. 2013). Today, the occurrence of embolism reversal under negative pressures

has been demonstrated using both destructive (Canny, 1997; Hacke et al., 2001; McCully et

al., 1998) and in vivo techniques (Brodersen & McElrone, 2013; Brodersen et al., 2018;

Holbrook et al., 2001; Ryu et al., 2016). The increasing evidence exposed the knowledge

gap concerning vessel repair under tension and urged scientists to unravel the full extent of

embolism repair.

Canny (1995, 1998) introduced a compensating tissue-pressure theory, suggesting

embolism refilling to be entirely pressure driven. This theory states that living cells convert

starch to sugars during the day, generating an inward water flow. As a result, cells start

swelling causing neighbouring living cells to release water in the cavitated vessel. However,

this theory has been questioned because it remains difficult to attribute a simultaneous water

flow into swelling living tissues and embolized conduits (Meinzer et al., 2001; Tyree et al.,

1999).

A second possible explanation states that xylem refilling is facilitated by the release of

osmotic active components (e.g. sugars and ions) from surrounding intact xylem, phloem

and parenchyma cells into the embolized vessel (De Baerdemaeker et al., 2017; C.

Brodersen & McElrone, 2013; Pagliarani et al., 2019; Romoleroux & Pitman, 2004; Secchi &

Zwieniecki, 2012). The generated osmotic gradient creates a water flow from living cells

towards the cavitated conduit (Figure 2.4, A). To support this theory, it was observed that

embolism refilling decreased with phloem girdling (i.e. process whereby a narrow strip of

bark and cambium is removed to inhibit upward phloem sugar transport) (Christman et al.,

2009; Salleo et al., 2004; Trifilò et al., 2019). However, involvement of these low-molecular

weight solutes requires hydraulically isolation between the embolized vessel and

neighbouring vessels until refilling. If not, the acquired water would directly drain into the

transpiration water flow (Figure 2.4, B). This condition might be fulfilled by air trapped within

11

the bordered pits, forming air-water menisci in the pit channel. If maintained, this can prevent

water drainage to neighbouring vessels (Zwieniecki & Holbrook, 2009). However, to succeed

this refilling mechanism needs available water, sufficient energy and active metabolic

solutes.

Furthermore, aquaporins are also thought to be involved in the refilling process. However

their role is still strongly debated because of their complex nature (Kaldenhoff et al., 2008).

Although these theories provide an idea of the refilling process, no existing theory can

provide a complete explanation. Further research will be needed to fully understand the

complete mechanism and all its involved actors (Klein et al., 2018).

Figure 2.4 Embolism repair model. The embolised vessel triggers the adjacent cells to release

solutes (S) into the vessel, generating a water flow (W) from the living fibres, parenchyma, xylem and

phloem into the embolized conduit by osmosis (A, 1–2). Osmotic water droplets are formed on

internal vessel walls and grow to fill the vessel (A, 3–6). If the embolised vessel is in direct contact

with a functional vessel, water may be drained directly into the continuous water flow, restraining

refilling of the xylem vessel (B, 1-6). Vessels that are hydraulic isolated receive enough water to

restore their functionality (C, 1-3), embolisms are then removed by forcing gas bubbles into the

solution (C, 4a) or by forcing them out into surrounding hydrophobic microchannels in the vessel wall

(C, 4b) (C. R. Brodersen et al., 2010).

The refilling process also knows some flaws. Hacke et al. (2001) observed in Populus

angustifolia, P. tremuloides, Helianthus annuus stems, and Aesculus hippocastanum

petioles, that embolism formation may affect the vulnerability to reoccurring cavitation events

due to damage of inter-vessel pit membranes. Repeated flexing of the pit membrane, may

12

cause loosening or rupturing of the membrane microfibres. This could lead to enlarging of

the pores resulting in faster cavitation (cf. cavitation fatigue). This phenomenon was support

by the study of Hillabrand et al. (2016).

2.1.4 How plants fail to cope with cavitation

Plants are only able to mitigate and reverse cavitation to a certain extent. Once the water

potential drops below a xylem pressure at which 50 % of hydraulic conductivity is lost (𝜑50),

plants are exposed to an imminent risk of runaway embolism (Jarbeau et al., 1995). The loss

of hydraulic conductivity leaves the remaining operating vessels under even larger tensions.

Consequently, this could result in additional embolism formation and thus increasing loss of

conductivity. This accelerated embolism formation affects the long-term productivity and

tissue heath, eventually leading to plant death. The hydraulic safety margin (i.e. the

difference between the minimum xylem pressure measured in plants under natural

conditions 2 , 3 ( 𝜑𝑚𝑖𝑛 ) and 𝜑50 ) indicates whether plants are closely functioning to their

hydraulic failure limit (Choat et al., 2012). Safety margins for different species are

represented in Figure 2.5, showing that angiosperms operate at a considerable smaller

safety margin in comparison to gymnosperms.

Figure 2.5 Minimum xylem pressure of plants under natural conditions as a function of the xylem

pressure at which 50 % hydraulic conductivity loss occurs for 191 angiosperm and 32 gymnosperm

species. The safety margin of all species is represented by the distance between the dots and the 1:1

line (dashed line) (Choat et al., 2012).

2 ‘The water potential under natural conditions’ refers to the tension plants are daily subjected to in their normal ecosystem

without the occurrence of unexpected stress events. 3 𝜑𝑚𝑖𝑛 is measured at solar noon when evaporation rates are assumed to be the highest.

13

This “risky” strategy suggests that angiosperm species might have a greater capacity to

reverse embolisms. This tactic allows flowering plants to maintain a higher carbon gain

under short periods of drought stress conditions. However, refilling mechanisms are tightly

linked to regained water availability to restore hydraulic functioning of xylem vessels. This

means that in the context of climate change, it remains questionable how plants will mitigate

and react on both regional and global scale to more extreme and prolonged drought

conditions (Anderegg et al., 2016; Choat et al., 2012).

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2.2 Drought-induced forest mortality across the globe

Drought4 induced tree mortality is an emerging global phenomenon that has been reported

for various forest ecosystems across the world (Allen et al., 2015). Increasing temperatures

and reducing soil moisture content are considered the main drivers for increasing forest

dieback (McDowell et al., 2008). Due to raising greenhouse gas emissions, global

temperature has approximately risen 1 °C since pre-industrial times. This resulted in a shift

of rain patterns and changes in drought frequency and intensity (Special Report IPCC; Allen

et al. 2018). Since 1950, most land areas have warmed up, with the largest temperature

increases detected in North America (+ 2 – 2.5 °C) and northern Asia (+ 2 – 3 °C) (Figure

2.6, A). Simultaneously, precipitation regimes have changed in most parts of the world.

Precipitation decreased in most parts of the African continent, southern Europe, South and

East Asia, eastern and western Australia, Central America and some parts of South America

(Figure 2.6, B). In these areas, also the runoff has decreased (Figure 2.6, C) (Dai, 2011).

Mounting evidence predicts an increasing forest mortality within the next century in various

parts of the world, due to more severe and widespread droughts (Trenberth et al., 2014).

Models show a serious decrease in soil moisture content in most of the Americas, Europe,

southern Africa, Southeast Asia and Australia under future climates (Dai, 2013). This may

have a devastating impact on forest distribution across large geographical areas, affecting

both natural and human systems. However, the accelerating effects of droughts on forest

ecosystems remain difficult to quantify. Current spatial variation between observations and

model-simulated tree mortality, both locally and globally, hampers the ability to accurately

identify vulnerable forests and to predict their responses under changing climate conditions

(O’Brien et al., 2017).

To date, numerous studies have reported substantial drought-induced tree mortality by

elevated temperature and climatic water stress (updated sequentially by Allen et al. 2010,

2015; Hartmann et al. 2018) (Figure 2.7). The most distinguished drought effects are

manifesting at regional scales, irreversibly damaging vast forest areas within a relatively

short time span. For example, in Texas and California (USA) the unexpected die-off of

approximately 300 million and 102 million trees respectively was attributed to extreme

drought events in 2012 – 2015 (Asner et al., 2016; Moore et al., 2016). Drought-induced

mortality is not only restricted to (semi-) arid areas but also occurs in various forest types

which are normally not considered at drought risk e.g. temperate forests and tropical forests

(Figure 2.7) (Barros et al., 2019; Lewis et al., 2011; Peng et al., 2011; Stephenson et al.,

2018; Venturas et al., 2016).

4 “Drought is a recurring extreme climate event over land characterized by below-normal precipitation over a period of months

to years. Drought is a temporary dry period, in contrast to the permanent aridity in arid areas. Drought occurs over most parts

of the world, even in wet and humid regions” (Dai, 2011).

15

Figure 2.6 Global trends in observed annual surface temperature [K 50 yrs-1], precipitation [mm d-1 50

yrs-1] and runoff [0.1 mm d-1 50 yrs-1] for a period between 1948/1950 – 2004/2008 (Adapted from Dai

(2011)).

16

Also physiologically, forests seem to be equally vulnerable to drought stress regardless of

their precipitation conditions (Choat et al., 2012). However, some studies only report

increased manifestation of drought-induced forest mortality in arid regions, yet these trends

remain weak as spatial variability is high (Steinkamp & Hickler, 2015) or mortality is only

locally investigated (Young et al., 2017). This variability emphasises the urge to further

unravel the mechanisms and processes that underly drought-induced forest mortality and

improve current dynamic global vegetation models.

Figure 2.7 Documented locations of substantial drought-induced tree mortality around the world by

Allen et al. (2010) (red dots). Later studies were sequentially added by Allen et al. (2015) (black dots)

and by Hartmann et al. (2018) (blue dots). Grey ovals represent post-2009 studies over larger areas.

Vegetation classification is based on FAO (2005); with forests (dark green) and other wooded areas

(light green) (adapted from Hartmann et al. (2018)).

Predicting drought-induced forest mortality remains challenging as it requires integration of

processes occurring on different temporal and spatial scales (Hartmann et al., 2018). In

addition, a detailed understanding of physiological processes is needed to determine tree

vulnerability to drought. However, the underlying key mechanisms: (1) hydraulic failure of the

xylem network (see 2.1.2), (2) carbon starvation during prolonged drought stress (see

2.1.3.1) and (3) increased sensitivity to biotic agents (e.g. bark beetles and fungi), are highly

interdependent (McDowell et al., 2008). This renders drought-induced predictions to be more

complex. However, all three key mechanisms are affected by stomatal closure or increasing

xylem tension during drought stress (Choat et al., 2018).

Prediction models often rely on easy to measure key functional plant traits to assess these

key mechanisms and thus tree mortality (O’Brien et al., 2017). In addition, these traits must

define plant response to environmental changes and should be easy to extrapolated to other

17

species and ecosystems (O’Brien et al., 2017), allowing upscaling to ecosystem, region and

world level (Hartmann et al., 2018). Functional plant traits include wood density, specific leaf

area, hydraulic safety margin, 𝜑50, 𝜑88, root depth, height, etc. A non-exhaustive summary

of functional plant traits can be found in the review paper of O’Brien et al. (2017).

A growing number of empirical studies have investigated correlations between various

functional traits and tree mortality. However, to date, data analysis has not made enough

progress across different species and biomes to assess drought-induced tree mortality at

global scale (Adams et al., 2017). Yet, hydraulic traits associated with hydraulic failure (i.e.

hydraulic safety margins, P50, P88 and 𝜑𝑚𝑖𝑛) are suggested to be very promising indicators

for drought response prediction in most climate zones (Adams et al., 2017; Anderegg et al.,

2016, 2018). The hydraulic failure process is well-understood and vulnerability threshold

values can be easily established for a given species or population (Choat et al., 2018).

Nonetheless, meaningful mortality predictions can only be achieved if failure thresholds are

accurate and precise. However, challenges still exist since hydraulic failure measurements

can be subjected to measurement induced artefacts (Paragraph 2.1.3.2). In addition, most

failure thresholds are derived from experimental manipulation of small trees while recent

studies suggest large trees might be more vulnerable (Bennett et al., 2015).

Despite recent interest and research in climate change induced global forest mortality, the

widespread impact of drought on forest ecosystems is still poorly understood. Currently, no

general consensus exists on the degree to which forest ecosystems are vulnerable to the

expected increasing drought stress. Some forests are expected to be resilient and even

benefit from climate change (Poulter et al., 2014), while increasing mortality rates are

predicted in others (Allen et al., 2015). However, consensus does exist on the severe

ecological and societal consequences if widespread tree mortality would occur. Depending

on the ecosystem and the occurring drought impact, severe biodiversity losses and changes

in species distribution, decreasing water availability, increasing flood risks, reducing carbon

sequestration and diminishing wood production are expected (Anderegg et al., 2013).

18

2.3 Tropical montane ecosystems under threat

Montane ecosystems are considered very vulnerable to climate change conditions and

increasing droughts (Diaz et al., 2003; Gottfried et al., 2012). As most species are adapted

and restricted to specific altitudinal zones within the mountain range, slight changes in

temperature and precipitation can cause serious loss of biodiversity. In turn, this could have

disastrous consequences for communities around the world that are dependent on montane

ecosystems for water-related services (e.g. water supply, flow regulation and energy)

(Anderson et al., 2017). Montane ecosystems are likely to become one of the climate

change “early indicators”. However, research on how these ecosystems will be affected by

climate change is limited and often merely focussed on temperate and boreal regions. This

results in a knowledge gap on the behaviour of other montane ecosystems, including tropical

montane ecosystems, in climate change conditions.

2.3.1 Main characteristics of tropical montane ecosystems

Tropical montane ecosystems, found at altitudes between 1200 m and 4500 m above sea

level (a.s.l.), are geographically distributed in tropical mountain regions of the Andes (South

America), the Afro-alpine belt, insular Southeast Asia and New Guinea (Figure 2.8) (FAO,

2012). The largest extension of tropical montane ecosystems is found in the eastern Andean

mountains in Venezuela, Columbia, Ecuador and northern Peru, with patches in Brazil,

Costa Rica and Panama. These neotropical ecosystems are considered among the most

biodiverse ecosystems, and are one of the twenty-five biodiversity hotspots on earth (cf.

Tropical Andes) (Myers et al., 2000).

Figure 2.8 Map of the geographic distribution of different (sub-)tropical ecological zones classified by

FAO. Tropical montane ecosystems are shown in dark orange (adapted from FAO 2012).

Although climatic conditions and associated vegetation types vary widely with altitude level,

neotropical montane ecosystems can be categorized in two major groups. This includes (1)

forest ecosystems consisting of tropical montane rainforests (TMRFs) and tropical montane

cloud forests (TMCFs) and (2) the grasslands and scrublands, locally known as páramos

and punas in the Andean mountains and campos de altitude and campos rupestres in Brazil

19

(Aparecido et al., 2018). The underlying and distinguished factors leading to one of these

vegetation types are poorly defined and altitudinal and climatic determinants often overlap

strongly (Table 2.1). In literature, terms such as ‘Brazilian páramos’ also occur (Campos et

al., 2018; Coelho et al., 2017), emphasizing the limited knowledge concerning categorisation

of the neotropical alpine vegetation types. For simplicity, the term páramos5 will be used in

this thesis to describe all neotropical montane grasslands and scrublands. Since both

research species Polylepis reticulata and Escallonia myrtilloides are representative for the

páramos, this research report will focus on the páramo ecosystem.

Table 2.1 General vegetation and climatic characteristics of mountainous ecosystems in the

neotropics (Aparecido et al., 2018). Forest vegetation is divided in tropical montane rainforest (TMRF)

and tropical montane cloud forests (TMCF).

Vegetation type Altitude

(m a.s.l)

Precipitation

(mm yr-1)

Seasonality

(n° dry months)

Forest TMRF 700- 2500 3000-8000 5-6

TMCF 800-3500 2000-3500 0-3

Grassland/

Scrubland

Campo rupestre 900-2100 1100-1800 5-6

Campo de altitude 1800-2900 1500-3000 1-3

Páramos 3000-4500 700-3000 2-5

Punas 3200-5000 1000-2000 6-8

Páramos extend between the upper limit of the montane cloud forest and the snow line,

covering approximately an area of 35 000 km2 (Madriñán et al., 2013) to 70 000 km²

(Dinerstein et al., 1995) between 11 °N and 8 °S latitude. Due to the high altitude, páramo

ecosystems are cold with frequent freezing night temperatures, have a high relative

humidity, strong winds and a high solar energy input and UV radiation. Páramos differ from

temperate alpine environments by strong diurnal temperature range (i.e. 20 °C) but low

annual temperature variations (i.e. 8 °C) (Buytaert, Célleri, et al., 2006). Hedberg (1964)

described this daily pattern as ‘summer every day and winter every night’. By contrast,

precipitation in the páramo is highly variable, ranging from 700 mm yr-1 to 3000 mm yr-1

(Luteyn, 1999) with outliners up to 6000 mm yr-1 (Buytaert et al., 2006). Depending on the

geographic distribution, climate and precipitation are strongly influenced by the Intertropical

Convergence Zone position (ITCZ) (Vuille et al., 2000), regional circulation patterns (e.g. El

Niño) (Martínez et al., 2011), north-easterly Caribbean trade winds (Lauer, 1981) and the

Humbolt current (Aparecido et al., 2018; Jørgensen et al., 2011). This variability effects the

occurrence and duration of dry and wet seasons (Table 2.1). At local level, strong winds and

irregular topography of the area (i.e. orientation of slopes, steepness etc.) contribute to a

5Páramo originates from the ancient Spanish word for ‘an elevated, barren, treeless plateau’. The term was used for the first

time by the conquistadores and colonialists to describe the exposed grasslands from the Andean mountains (Ramsay, 1992).

20

high spatial variability of rainfall patterns. Additionally, unpredictable fog and dew

precipitation also influence the overall water availability (Figure 2.9, C). However, the relative

importance of fog to the total water balance remains difficult to quantify (Buytaert, Iñiguez, et

al., 2006).

Páramo soils are generally slightly developed and are formed by accumulation of organic

matter and ash from past volcanic activity. Main soil types are Andisols and Histosols,

although also Entisols and Inceptisols can occur (Buytaert et al., 2006). Most soils are dark,

humic, acidic (pH between 3.7 - 5.5 (Osha, 2000)) and a location dependent soil thickness

(Buytaert et al., 2006). Because of their high organic content and porous structure, Páramo

soils possess an infiltration and water storage capacity up to 90 % of their total volume

(Buytaert et al., 2006). These remarkable soil properties contribute to the high water

regulation capacity of the Páramo ecosystem, which is considered as the primarily water

source for the inter-Andean valley (Buytaert et al., 2011).

The páramo vegetation is well-adapted to the extreme climate and physiochemical

conditions, with a high speciation and diversification of plants both at species and genus

level. The páramo ecosystems host over 3500 native vascular species of which up to 60 %

is endemic (Luteyn, 1999; Sklenář et al., 2014). Páramos are dominated by evergreen

microphyllous scrublands or grasslands composed of tussock grasses (>70 %; i.e.

Calamagrotis, Festuca and Stipa) and cushion plants (<25 %; i.e. Plantago rigida,

Xenophyllum, humile, Azorella spp.). Small and isolated dwarf forests islands (5 m – 10 m in

height) are scattered along the entire páramo range (<5 %) (Aparecido et al., 2018; G. M.

Mosquera et al., 2015) (Figure 2.9 A-C). Most of these dwarf forests are oligarchic and

dominated by endemic tree species in the genus Polylepis (Rosaceae) in combination with

individual tree species including Escallonia, Weinmannia, Clethra, Vallea stipularis,

Citharexylem, Clusia (Kessler, 2006). In Ecuador alone, seven out of approximately twenty-

eight different species of Polylepis are found (Segovia-Salcedo et al., 2018). Polylepis

woodlands are often present at special microsites such as ravines, boulder slopes, rock

faces or near human settlements (Figure 2.9 B; Kessler 2002), suggesting the preferences

of Polylepis species to specific microclimatic conditions. Ellenberg (1958) was the first to

question this, stating that the patchy distribution could be strongly attributed to intensive

human activities (i.e. burning, livestock grazing and timber extraction). Yet, the suggestion

that Polylepis forests once formed a continuous woodland belt, remains largely speculative

(Gosling et al., 2009). Presumably, the patchy distribution of the Polylepis woodlands is the

result of the complex interaction of environmental and anthropogenic factors affecting the

vegetation (Kessler, 2002; Toivonen et al., 2017). This hypothesis is also supported by

recent palaeo-ecological reconstructions of potential past habitats (Gosling et al., 2009;

Valencia et al., 2018). Still, many question remain concerning the natural constraints of

Polylepis species and the underlying physiological mechanisms.

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Figure 2.9: Páramo grasslands: (A) Example of common giant rosette plants (Espeletia sp.); (B)

Wind-sheltered Polylepis reticulata forest; (C) Fog event in the Páramo ecosystem (Zhurucay River

Ecohydrological Observatory at 3800 m altitude, San Fernando, Ecuador).

2.3.2 Possible effects of increasing droughts

Climate simulations predict a warming of 3 ± 1.5 °C, depending on the considered location

and scenario, in the Andean mountains towards the end of the 21th century. Future

projections of the precipitation regimes are highly variable. However, all predict the

occurrence of longer and/or more intense dry seasons (Buytaert et al., 2011; Urrutia &

Vuille, 2009). In addition, temperature increases are expected to decrease cloud covering,

reduce fog frequency and influence precipitation patterns (Pepin et al., 2015).

Since montane vegetation is well-adapted to survive in a stress-limiting environment (i.e.

large diurnal temperature variations, high solar radiation levels, strong winds, night frost,

etc.), plant species are strongly restricted to specific climatic conditions and habitat

characteristics. Consequently, climatic changes will pose serious threats to future

ecosystems viability. In theory, an increase of 3 °C corresponds to a 600 m upslope shift of

altitudinal ecotone succession (Anderson et al., 2017). However, this would imply changes in

22

habitat conditions to occur gradually, enabling species to adjust at the same pace, whereas

in reality this is often not the case (Rehm & Feeley, 2013). Also, the upwards movement

cannot continue endlessly as montane species are often located at the top of mountain

ranges, constraining the upper boundary of migration (Bubb et al., 2004). Hence, these

expected climate changes could lead to serious biodiversity loss and increased spatial

isolation (Anderson et al., 2017; Helmer et al., 2019). Cuesta Camacho (2007) estimates

that approximately 60 % of northern Andean species will become extinct or endangered by

2080. Also, Cuyckens et al. (2016) suggests that by the end of the 21th century, there will be

a significant reduction of approximately 56 % in potential habitats for Polylepis tarapacana

due to increasing droughts.

Aparecido et al. (2018) synthesizes that the vulnerability of montane ecosystems under

future climate change will mainly be determined by the adaptation capacity of plant species.

Higher temperatures and decreased cloud cover will lower soil water availability and

increase the atmospheric vapour pressure deficit. Plant species that are less acclimated to

drought conditions will therefore be subjected to an increasing risk of embolism formation.

Particularly, ecosystems associated with wetter sites can often be considered more

vulnerable to drought-induced mortality (O’Brien et al., 2017). In addition, the degree to

which plant species rely on fog events as water source will most likely also affect the

resilience of the ecosystem to drought. Given the predictions of reduction of fog frequency

and intensity, fog-dependable plant species will be exposed to higher levels of drought-

stress. Also, already limited nutrient availability might be exacerbated due to expected run-

off increases and reductions in microbial activity.

Changes in tropical montane vegetation may also affect local and regional climate patterns

as vegetation and hydrology are strongly coupled through ecohydrological processes

(Aparecido et al., 2018). Plant mortality (i.e. less vegetation cover) could lead to

enhancement of the temperature increases due to higher radiation incidence and higher

vapour pressure deficits, again causing plant mortality to aggravate (Foster, 2001).

Degradation of montane ecosystems indirectly effects hydrologic cycles, soil water infiltration

and storage capacity, evapotranspiration rates, vegetation water storage and fog

interception. Consequently, this may affect ecosystem functioning of lowland forests and

jeopardize ecosystem services to the surrounding cities. Small changes in precipitation and

ecosystem distribution would have major economic impacts on cities such as Quito relying

almost completely (> 85 %) on the water supply from the head catchments of páramos

(Buytaert et al., 2011).

However, our understanding of how neotropical montane ecosystems will respond to these

unprecedented climatic changes, and their associated feedbacks on ecohydrological

processes, remain highly uncertain (Buytaert et al., 2011). One of the greatest limitations is

the lacking research on how these well-adapted plant species respond to drought, and how

23

these vegetation-climate interactions scale up to influence watershed processes of

streamflow dynamics and groundwater recharge (Aparecido et al., 2018).

2.3.3 State of art: drought vulnerability research

Predicting drought responses at ecosystem level starts with a detailed and mechanistic

understanding of soil-plant-water interactions at plant level. Ecophysiological traits related to

different plant hydraulic functions are able to provide valuable insights in plant drought

vulnerability (see 2.1.3). Xylem drought vulnerability seems to be a promising tool to assess

plant resilience against increasing droughts.

However, while ecophysiological plants traits and mechanisms have been investigated in

numerous studies world-wide, plant species in tropical montane ecosystems remain poorly

investigated (Aparecido et al., 2018). Although some studies show that water stress is an

important limiting factor in the distribution of dominant páramo trees (Gosling et al., 2009;

Valencia et al., 2018), to date, the degree of drought vulnerability has never been quantified

and hydraulic safety margins for páramo ecosystems remain unknown (Macek et al., 2009;

Morales et al., 2004; Valencia et al., 2018).

In this research project, we are the first to quantify drought vulnerability of two abundant

páramo trees: Polylepis reticulata and Escallonia myrtilloides, using acoustic emission

technology. Branches were collected at the Zhurucay Ecohydrological Observatory

(Ecuador) and transported to Ghent University (Belgium) to establish vulnerability and

desorption curves, enabling us to gain more insight in the hydraulic functioning of these

unique trees and to predict their behaviour under changing climate conditions.

24

3 Materials and methods

3.1 Zhurucay Ecohydrological Observatory

3.1.1 Study site description

Measurements for this study were conducted in two Polylepis forests of the Zhurucay river

Ecohydrology Observatory (ZHU), located in the headwaters of a 7.36 km² catchment in the

Andean cordilleras about 30 km from the city of Cuenca, Ecuador (3 °03’46” S 79°14’17” W)

(Carrillo-Rojas et al., 2019) (Figure 3.1). Situated at 3800 m a.s.l in the northern Andean

páramo ecoregion, the site has a cold and humid climate influenced by both Pacific and

Atlantic weather regimes (Crespo et al., 2011). The average annual temperature equals

6.1°C (ranging from 0.4 °C to 14.2° C), with little variation over the year. Also, relative

humidity (RH) remains uniform, with an average annual value of 93.6 %. Solar radiation

levels are high, with daily averages of 3.73 MJ m−2 d-1 and annual averages of 4942 MJ m−2

yr-1. Increased solar radiation is registered from October to December (up to 500 MJ m−2

month-1). The mean wind velocity in the area equals 3.6 m s-1 in mainly north-eastern

direction, with monthly averages ranging from 3.21 m s-1 (October – March) to 4.77 m s-1

(June-September) (Carrillo-Rojas et al., 2019)6.

Figure 3.1 Geographic location of the field site in the Zhurucay ecohydrology observatory, Ecuador.

(A) Satellite image of the Polylepis forests research sites (white contoured, dark green patches) and

the meteorological station (red diamond) (Carrillo-Rojas et al., 2019).

6 The climatologic data is derived from climate monitoring over a six-year period from January 2012 to December 2017 at the

field site in the Zhurucay river observatory (Carrillo-Rojas et al., 2019). Detailed monthly climographs and wind charts can be

found in the Appendix.

25

Mean annual precipitation equals 1300 mm yr-1 (Carrillo-Rojas et al., 2019)7 and shows low

seasonality with a rainy season (January-June; 62% of annual rainfall) and a dry season

(July-December; 38% of annual rainfall) as result of the ITCZ regime (Figure 3.2) (Correa et

al., 2016). Typically, precipitation occurs in the afternoon and is of low intensity (cf. drizzle; <

2 mm hr-1) and high frequency (Ochoa‐Sánchez et al., 2018). Drizzle comprises 80 % of all

rain and accounts for 30 % of the total annual precipitation. Only 20 % of the days are

completely dry and of these days, only a few are successive (Padrón et al., 2015)8.

Figure 3.2: Average monthly precipitation (mm month-1) for the ZHU field site calculated over the

January 2012 to December 2017 period (adapted from Carrillo-Rojas et al. (2019)).

All Polylepis sites are sheltered by rock faces, generating a favourable microclimate to

maintain the forest ecosystem on Andosol soils. Most forests are dominated by Polylepis

reticulata and Escallonia myrtilloides. However, much of the forest inventory data remains

unknown. Forest boundaries are sharp with abrupt transition to Páramo grasslands. On drier

areas, these grasslands are almost entirely covered by tussock grasses (Calamagrostis

instermedia or pajonal), on wetter areas cushion plants also occur (Plantago rigida,

Xenophyllum humile, Azorella spp.) (Ochoa‐Sánchez et al., 2018). On the forest research

site the University of New Hampshire, in collaboration with the University of Cuenca,

established in the March 2018, five monitoring stations are present along a gradient from

forest edge to forest interior (Detailed map, see further). In each of these plots, three

monitored trees i.e. P. reticulata or E. myrtilloides, are equipped with plant-based sensors.

Furthermore, in between the two forests sites, a microclimatological station is installed but

not yet operational.

7 For comparison: annual precipitation in Belgium equals 925 mm yr-1 (Brouwers, 2018). 8 Climatologic data was derived from the meteorological station located ca. 130 m from the closest Polylepis forest.

26

3.1.2 Polylepis reticulata

Polylepis reticulata is an endemic species of the Ecuadorian Andes within the genus

Polylepis spp. (Rosaceae) and currently classified as vulnerable to extinction (Romoleroux &

Pitman, 2004). Trees are commonly 4 – 8 m tall (Simpson, 1979) but can reach up to 15 m

(Pinos, 2015) in favourable microhabitats. P. reticulata trees can become over 450 years old

and grow very slowly (Saravia & Vintimilla, 2016). Variating annual growth rates are reported

by Mosquera et al. (2016) and appears dependent on tree sizes. Trees with small diameters

(4 – 10 cm) grew 0.45 mm yr-1, whereas larger trees (30 – 40 cm) had lower growth rates of

0.04 mm yr-1. Growth also decelerates with decreasing temperature.

P. reticulata stems and branches are twisted and crooked. The bark is up to 2.5 cm thick

and consists out of numerous layers of thin, dark red to orange exfoliating sheets (Simpson,

1979) (Figure 3.3 A). Presumably, this thickened bark serves as isolation from frost and high

irradiation levels. Leaves are clustered at the branch tips and are compound and

imparipinnated (Figure 3.3 B) (1.2 – 5 cm wide and 1 – 4 cm long). Each leaf consists out of

two to four pairs of oblanceolate leaflets which are characterized by thick cuticles for the

reticulata group

P. reticulata wood is diffuse - porous and has no distinct growth rings. Stem wood density

equals 503 ± 10 kg m-3 (Montalvo et al., 2018). Basic wood anatomy remains unreported for

P. reticulata, anatomical characteristics of closely related Polylepis species can be found in

Table 3.1.

3.1.3 Escallonia myrtilloides

Escallonia myrtilloides is an evergreen plant species in the Saxifragaceae family, thriving in

the tropical Andes above 2600 m a.s.l. (Zapata & Villarroel, 2019). The trees or shrubs are

between 2 m to 10 m tall (Gargiullo et al., 2008; Romoleroux et al., 2019). The stem is

rounded and characterized by a white bark (Figure 3.3 D). The crown is irregular to conical

shaped and branches grow almost horizontally. Leaves are alternated and usually serrated,

dark green coloured, glabrous and leathery (Figure 3.3 C). They are and obovate to

obovate-oblong shaped, with dimensions from 0.5 cm - 1.5 cm in width and 3.0 cm - 3.1 cm

in length (Sede & Denham, 2018).

E. myrtilloides wood is diffuse-porous and growth rings are entirely absent (Stern, 1974).

Basic anatomical characteristics can be found in Table 3.1.

27

Figure 3.3 Polylepis reticulata (A-B) and Escallonia myrtilloides (C-D) at the ZHU field site (photo credits: Franklin Marín Molina).

Table 3.1 Non-exhaustive list with anatomical characteristics of Polylepis species (P. incana, P. australis, P. pallidistigma) (S.-Y. Zhang, 1992) and E. myrtilloides (Stern, 1974; E. A. Wheeler, 2004).

Variable Definition Polylepis spp. Escallonia spp. Unit

V A -1

Vessel frequency = number of vessels per woody area

93 - 159 > 100 mm-2

Vs Vessel solitary fraction = ratio of solitary vessels to all vessels

53 - 77 > 90 %

Dv,tang Tangential vessel diameter 25 - 60 (14 - 80) 50 - 100 μm

Dv,rad Radial vessel diameter 36 - 75 (20 - 90) 22 - 59 μm

Tw Vessel wall thickness 2 - 3 0.66 - 4.62 μm

Le Vessel element length 350 (250 - 550) 482 (350 - 683) μm

- Intervessel pit arrangement Non-vestured, alternated

Scalariform, opposite

-

Dp Pit membrane pore diameter 4 - 8 2.50 - 8.58 μm

Lf Fibre/tracheid length 730 (550 - 880) 805 (555 - 1182) μm

Tw,f Fibre/tracheid wall thickness “Very thin - to medium thick”

1.98 - 6.60 μm

28

3.2 In situ measurements

3.2.1 Data collection

Eighteen trees, i.e. nine P. reticulata and nine E. myrtilloides, were selected out of four of the

five monitoring stations. Tree selection criteria were (1) plot location, (2) tree species and (3)

branch reachability. Both sampling plots situated at the edge and in the interior of the forests

were selected to capture the most extreme microclimate situations (Figure 3.5).

Stem and leaf water potential were measured in situ, using a pump-up pressure chamber

(PMS Instrument Company, Albany, OR, USA) (Figure 3.4 A-B). Leaf water potential

measurements (𝜑𝑙𝑒𝑎𝑓 ) were conducted on mature, sun-exposed leaves, preferably from

twigs older than one year. For all sampled trees (n = 18), 𝜑𝑙𝑒𝑎𝑓 was measured once per day

around solar noon (i.e. between 11h00 and 14h00; on 12 days between 10 and 26

September 2018). The order in which the plots were sampled, was conducted randomly to

obtain tree specific data over the complete timeframe. Leaves were excised using a pruner

and collected at similar heights (~ 3 - 4 m) corresponding with the location of most sun-

exposed and easily accessible leaves.

Stem (𝜑𝑠𝑡𝑒𝑚) and additional leaf water potentials were measured throughout the day (7

days between 10 and 26 September 2018) when midday leaf water potentials were not

measured (i.e. 09h30 - 11h00 and 14h30 - 17h00). One hour prior to measuring, leaves

were wrapped in small bags of aluminium foil and opaque polyethene to initiate stomatal

closure and inhibit evaporation (Figure 3.4).

Temperature and relative humidity data were used to calculate the vapour pressure deficit

(VPD) at sampling time of each leaf.

Figure 3.4 In situ water potential measurements at the ZHU site. (A) Pump-up pressure chamber; (B) Close up of the sealing lid with P. reticulata leaf cluster; (C) Wrapped leaves for 𝜑𝑠𝑡𝑒𝑚 measurements.

29

Figure 3.5 Detailed map of the Zhurucay river Ecohydrology Observatory (3 °03’46” S 79°14’17” W) with indication of the Polylepis forests and sampled trees (P. reticulata, pink dots; E. myrtilloides orange dots). Location of the sampled trees was defined by GPS system. The five permanent monitoring plots are not drawn at scale (darker green squares) and the indicated meteorological station is not yet operational (red square). (Made in the open software program QGIS, adapted from Mosquera et al. (2016).)

3.2.2 Statistical analysis

Statistic relationships were analysed using the R software ( R version 3.5.2; R Core Team

2018) and considered at the 5 % significance level (p < 0.05). Differences in average

𝜑𝑠𝑡𝑒𝑚 𝜑𝑠𝑡𝑒𝑚⁄ - ratios between P. reticulata and E. myrtilloides were analysed using a Welch

two sample t-test. Effects of the location and tree species on the measured midday leaf

water potential versus the effect of ambient vapour pressure deficit were examined by an

analyse of covariance (ancova). Data was tested for normality and homogeneity via quantile-

quantile plots, Shapiro-Wilk tests and Levene tests. Similarity in variances for average ratios

was determined by a F-test.

30

3.3 Branch sampling procedure

Fifty-two branches, i.e. twenty-six P. reticulata and twenty-six E. myrtilloides, of the sampled

trees were collected on 27 September or 28 September 2018. To compare hydraulic

properties between forest edges and interior, branch sampling was equally distributed over

the four measuring sites. At least two branches per tree were collected (cf. one branch pair).

Branch selection was executed one day prior to the harvesting. Selection was based on

branch length (~ 70 cm), diameter (~ 7 mm – 10 mm), structure (straight part of at least 8

cm), the amount of leaves (at least 50 – 60) and health appearance. Branches were cut

using a pruner and directly immersed in water to relax the xylem pressure. To avoid excision

artefacts due to possible air entry, two additional cuts were made under water (Cochard et

al., 2013; Torres-Ruiz et al., 2015; Venturas et al., 2017). The cut ends were put in small

glass vials filled with water which were sealed watertight with Teflon and duct tape. The

excised branches were enclosed in opaque polyethylene bags with humid cotton preventing

transpiration and dehydration during storage and transport. Leaf water potentials were also

measured just before sampling to register possible water potential changes due to sampling

and transportation. Branches were cleaned to meet Ecuadorian export standards and stored

in a fridge (4 °C) to lower plant metabolism until transportation to Belgium (3 October 2018).

3.4 Measurements during dehydration

3.4.1 Experimental set-up

Three bench-top dehydration experiments were conducted on 5 October, 9 October and 11

October 2018 at the Laboratory of Plant Ecology, Ghent University, Belgium (51°03'12.6" N

3°42'31.3" E). In each experiment, eight branch pairs (i.e. total of sixteen branches; eight P.

reticulata and eight E. myrtilloides; eight edge trees and eight interior trees) were selected.

Per branch pair, one branch was used for was used for determination of drought-induced

cavitation and the other one for registration of hydraulic capacitance (detailed information;

Figure 3.6). Prior to the first experiment (5 October 2018), branches were removed from

their vials and stored over-night outside at 11 °C in water-filled buckets to ensure full

hydration (4 October 2018). During the remaining duration of the experiments, branches

were stored in the fridge (4 °C) in water-filled buckets.

Sampled branches were recut under water (i.e. twice, about 3 cm each time) to avoid

potential air-entry artefacts (Cochard et al., 2013; Torres-Ruiz et al., 2015; Venturas et al.,

2017). To prevent preliminary dehydration during the experimental set-up, cut ends of the

branches were covered with wet paper cloths. To inhibit photosynthesis and water

transportation, the experiment was set-up in a darkened room using solely artificial, green

light.

For characterisation of drought-induced cavitation vulnerability, leaves were wrapped with

aluminium foil. This to ensure equilibrium between stem and leaf water potential and equal

dehydration rates between branches (i.e. transpiration shut down, making dehydration only

possible at cut ends) (Figure 3.7 A). Per experiment, eight branches were equipped with a

31

broadband point-contact acoustic emission (AE) sensor (KRNBB-PC, KRN services,

Grandville, MI, USA) and a dendrometer (DD-S, Ecomatik, Dachau, Germany) (Figure 3.7

B). The AE sensor was fixed in a custom-made PVC holder and pressed against the xylem

with a compression spring (Figure 3.7 (2)) (D22050, Tevema, Amsterdam, The Netherlands)

in the middle of a leaf-free section to reduce noise interference from leaves and cut ends

(De Baerdemaeker et al., 2017). Both sensors were spaced in custom-built holders at 10 cm

distance of each other to ensure an unbiased linkage between AE and diameter shrinkage

(Epila et al., 2017). Sensors were directly installed on exposed branch xylem (1.5 cm²; bark

removed with scalpel). Water loss from the exposed xylem tissue at the place of

dendrometer installation was prevented by applying petroleum grease commercially known

as Vaseline. Likewise, a droplet vacuum grease (High-Vacuum Grease, Dow Corning,

Seneffe, Belgium) was applied at the tip of the AE sensor, also ensuring good transmission

of acoustic signals.

Figure 3.6 Schematic representation of used branches in one dehydration experiment. In total, eight

branch pairs or sixteen branches were selected per experiment and equally divided for determination

of vulnerability and desorption curves (VC and DC). Letters represent the branch pairs of which one

branch is used for VC determination and the other for DC determination.

The performance and installation of the AE sensor was validated by the pencil lead break

test (Vergeynst et al., 2015). AE signals were amplified by 35.6 dB with an amplifier (AMP-

1BB-J, KRN Services, Richland, WA, USA) and waveforms of 7168 sample length were

acquired at a 10 MHz sample rate. Signals were compiled by 2-channel PCI boards and

directly uploaded to the software program AEwin (PCI-2, AEwin E4.70, Mistras Group BV,

Schiedam, The Netherlands). A 20-1000 kHz electronic band pass filter was applied and

only waveforms with noise levels higher than 28 dB were retained, capturing only signals

originating from nearby the location of the sensor (Vergeynst et al., 2016).

16 Branches

8 VCs

4 P. reticulata

2 Edgea

2 Interiorb

4 E. myrtilloides

2 Edgec

2 Interiord

8 DCs

4 P. reticulata

2 Edgea

2 Interiorb

4 E. myrtilloides

2 Edgec

2 Interiord

32

For the determination of the desorption curves and the hydraulic capacitance, branches were

stripped from all their branches. Therefore, it could be assumed that dehydration conditions

were similar to the branches used for vulnerability characterisation. Petroleum jelly was

applied to the petal wounds to prevent evaporation. Branches were placed in custom-made

holders on weight balances (DK 6200 with 0.01 g Accuracy, Henk Maas, Veen, The

Netherlands) and continuously monitored during dehydration (Figure 3.7 C).

Once sensors were installed, wet paper cloths were removed from the cut ends and normal

light was switch on. Dendrometer and weight balances read outs were registered every

minute. Stem water potential of wrapped excised leaves was measured using a pressure

bomb (PMS Instrument Company, Albany, OR, USA), enabling establishment of the relation

between xylem diameter shrinkage and stem water potential. Just before leaf excision, the

AE sensor was temporarily deactivated (about 5 seconds) to avoid interference with

embolism formation induced AE signal registration.

Figure 3.7 Experimental set-up of the dehydration experiment. (A) Vulnerability characterisation, leaves are wrapped in aluminium foil and subsequently excised during the experiment to measure 𝜑𝑠𝑡𝑒𝑚 ; (B) Detailed representation of the vulnerability set-up with (1) dendrometer and (2) AE sensor. Both sensors were placed directly on the branch xylem; (C) Hydraulic capacitance characterisation, branches are stripped of their leaves and placed in custom-built holders on weight balances.

Immediately before and after the dehydration experiment, a wood sample of ~3 cm was

taken from the cut ends of all branches. From these samples, weight (Sartorius precision

balance with 0.001 g accuracy, Sartorius Weighing Technology GmBH, Goettingen,

Germany) and diameter and length were measured. Samples were oven dried at 70 °C for

two weeks, after which weight, diameter and length were remeasured.

3.4.2 Data analysis

For each branch, cumulative acoustic emission signals (cumAE) of the entire dehydration

period were plotted in function of the stem water potential. Continuous water potential data

was obtained by fitting a linear regression line between the point measurements of 𝜑𝑠𝑡𝑒𝑚

and their corresponding relative radial xylem shrinkage to produce a pooled stress-strain

curve.

∆𝑑

𝑑 = (

𝑑𝑡 − 𝑑

𝑑) (Eq.5)

33

With ∆𝑑 the difference between the diameter at five minute timestep t (𝑑𝑡) and the initial

diameter (𝑑).

However, not all dendrometer sensors registered radial xylem shrinkage properly. When

data was lacking, pooled stress-strain curves with error margins were calculated by plotting

the branch specific point measurements of 𝜑𝑠𝑡𝑒𝑚 in function of the average xylem shrinkage

data from other the branches.

Cumulated AE signals were converted to percentage loss of hydraulic conductivity (PLC, %),

expressed as the ratio between the cumulative AE at timestep t and the cumulative AE at

complete loss of hydraulic conductivity (100 % PLC). Following the procedure of Vergeynst

et al. (2015), this endpoint was defined at the end of the AE activity peak (first derivative of

the cumulative AE over time) and mathematically corresponded with the local maximum of

the third derivative of the curve of the cumulative AE over time. Water potentials

corresponding with the onset of cavitation (i.e. at 12 % PLC; 𝜑12), 50 % loss of hydraulic

conductivity (i.e. at 50 % PLC; 𝜑50) and complete embolization (i.e. at 88 % PLC; ; 𝜑88 )

were also calculated. If the experiment was terminated before complete branch dehydration,

the endpoint could not be defined according to the above method. Using the definition of

Nolf et al. (2015), the 𝜑50 value could be obtained as it corresponds with the water potential

at maximum AE activity. When possible, both methods were applied to compare derived 𝜑50

values.

Desorption curves (DC) were established by plotting the volumetric water content (VWC, kg

m-3) versus the water potential. Assuming cylindrical shaped branch samples, initial and final

VWC values were calculated by multiplying mass fractions (i.e. oven dry mass - green mass)

with wood density (i.e. oven dry mass/ green volume; 𝜌𝑏, kg m-3), obtained by the drying

experiment. The change in VWC was then calculated by rescaling the continuous weight

data in between the initial and final VWC of the samples.

From the DCs, three breakpoints were derived using the segmented R package (Muggeo,

2008, 2017).The first and second breakpoint corresponded with respectively the beginning

and the end of the elastic shrinkage phase or the first phase (Phase I). The third breakpoint

defines the end point of the second phase (Phase II) or the inelastic shrinkage phase and

corresponded with 𝜑100 . In both phases the hydraulic capacitance was calculated using

Equation 6.

𝐶 = 𝑑𝑉𝑊𝐶

𝑑𝜑 =

𝑑𝑉𝑊𝐶

𝑑(∆𝑑𝑑⁄ )

(𝑑𝜑

𝑑(∆𝑑𝑑⁄ )

)−1

(Eq.6)

34

3.5 Microscopic analysis

To gain further insight in the hydraulic capacitance and vulnerability to cavitation, wood

anatomy of four branches (i.e. two P. reticulata and two E. myrtilloides) was analysed.

Sampling was conducted directly after termination of the first dehydration experiment (8

October 2018). Branch samples (~ 4 cm) were excised in between the AE sensor and

dendrometer so wood anatomy could be assumed similar as underneath the sensors. The

samples were preserved in a mixture of 60% v/v ethanol, 20% v/v distilled water and 20%

v/v glycerol and stored at ambient temperature. On 19 July 2019, samples were transferred

to the microscopy lab of the Department of Biology for wood analysis. Branch samples were

positioned in the sample holder of a sliding microtome (Reichert-Jung Hn-40 Heidelberg,

Germany) and a droplet of glycerine was added to the cut surface to permit smooth slicing.

Samples were microscopic sectioned at a thickness of 40 μm using a sharp knife. The

obtained transvers wood sections were stained for three minutes with a 0.5% w/v astra blue,

0.5% w/v chrysoidine and 0.5% w/v acridine red solution. Colouring enabled differentiation of

cell types as parenchyma and tension cell walls were coloured blue and lignin-enriched cell

walls (i.e. fibres, vessels) were coloured red. After rinsing the excessive colorant with

distilled water, the wood sections were re-dehydrated with isopropyl alcohol. Once fully

dehydrated, wood sections were placed on microscope slides and mounted with Euparal

(Carl Roth GmbH + Co. KG, Karlsruhe, Germany). The samples were covered with

microscope cover glasses, labelled and placed in a fume hood to dry. When sufficiently dried

(i.e. one hour at least), the permanent wood sections were microscopically observed using

an bright field microscope (Nikon Ni-U, Nikon Instruments Inc., Melville, NY, USA) with 4x

and 10x objective magnifications and photographed using a digital microscope camera

(Nikon DS-Fi1c, Nikon Instruments Inc., Melville, NY, USA) for further anatomical analysis.

Anatomical features were analysed with the open source image analysis software Fiji. This

included the average vessel area (VA, µm²), vessel frequency (Vf, mm-2) and vessel

connectivity (VS, VG, -). Using those variables, average diameter and the percentage of total

wood area occupied by vessel was then determined.

35

4 Results

4.1 In situ measurements

4.1.1 Water potential measurements

The interaction between tree species (i.e. P. reticulata and E. myrtilloides), tree location (i.e.

edge 1, edge 2 and interior) and measured midday leaf water potential (𝜑𝑙𝑒𝑎𝑓,𝑚𝑖𝑑𝑑𝑎𝑦) was

examined by relating vapour pressure deficits (VPD) with 𝜑𝑙𝑒𝑎𝑓,𝑚𝑖𝑑𝑑𝑎𝑦 (Figure 4.1). Data was

screened for outliers and one multivariate outliner (using Mahalanobis distance) was

detected and excluded from the original dataset. Normality, linearity, homogeneity and

homoscedasticity (Levene’s F(5,180) = 1.99, p = 0.08 > 0.05) assumptions were all met.

Figure 4.1 Bar plot of the average measured midday water potential (MPa) with standard errors margins in function of location and tree species.

The effect of location on the midday leaf water potential, depending on tree species and

after adjustments for the ambient VPD, was tested using a two-way ANCOVA. VPD had a

significant negative effect (r = -0.72) on the measured midday leaf water potential (F(1,179)

= 350.69, p = < 2e-16, np² = 0.66), indicating a less negative midday water potential with

decreasing VPD. Tree species had a significant effect at the 5 % significance-level (F(1,179)

= 122.86, p = < 2e-16, np² = 0.41), indicating that lower leaf midday water potentials were

measured for P. reticulata in comparison to E. myrtilloides. Location also showed to have a

significant effect (F(2,179) = 17.76, p = 9.18e10-8, np² = 0.17) with decreasing water

potentials from interior to edge 1 to edge 2. The interaction between tree species and

location was not significant (F(2,179) = 2.43, p = 0.09, np² = 0.03). Independent t-tests with a

Tukey correction were used to examine the difference between the tree species and location

effect (Table 4.1). The mean midday leaf water potential of P. reticulata was 0.21 MPa lower

than the mean water potential of E. myrtilloides. Water potentials measured at the forest

36

edges were significantly lower than the water potentials in the interior. The difference

between the average leaf water potential at edge 2 and edge 1 was significant, with a 0.06

MPa water potential reduction from edge 1 to edge 2.

Table 4.1 Tukey correction post-hoc analysis of the significant main effects on midday leaf water potential. Significance levels: p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Difference Standard error (σ) P-value

Edge 2 - Edge 1 -0.06 0.02 <2e-16***

Interior - Edge1 0.09 0.03 0.03*

Interior - Edge 2 0.15 0.03 0.002**

P. reticulata - E. myrtilloides -0.21 0.02 <1e-14***

4.1.2 Stem to leaf water potential ratio

Figure 4.2 shows the distribution of the stem to leaf water potential ratio (R = 𝜑𝑠𝑡𝑒𝑚 𝜑𝑙𝑒𝑎𝑓⁄ )

for both tree species. Normality assumptions were met for both groups (Shapiro-Wilk test; p

= 0.73 and p = 0.99 for P. reticulata and E. myrtilloides, respectively), homogeneity of

variance could not be assumed (F(1,15) = 4.63; p = 0.002), forcing the use of a Welch two

sample t-test for analysis of the average ratios. No significant difference, on a 5 %

significance level, was found between the average ratio of P. reticulata and E. myrtilloides (p

= 0.09). Therefore, measured ratios of both species were pooled and the mean ratio (R =

0.64) was used to convert leaf water potential to stem water potential for both tree species.

Figure 4.2 Boxplot of the 𝜑𝑠𝑡𝑒𝑚 𝜑𝑙𝑒𝑎𝑓⁄ ratios for E. myrtilloides (n = 17) and P. reticulata (n = 21).

37

4.2 Dehydration experiment

From all measured branch pairs (n = 12) only the ones reaching a 50 % dehydration level at

the end of the experiment (n = 6) were analysed. Each branch pair consist out of one branch

used for determination of a vulnerability curve (VC) and one for drafting up a desorption

curve (DC). Only branches from the first dehydration experiment, 5 October 2018, met this

criteria. Table 4.2 summarizes the branch specific information on the collected data of the

branches used for VC determination. In total, three P. reticulata and three E. myrtilloides

branches were selected. For most branches both AE signals and radial xylem shrinkage

data could successfully be obtained. For P. reticulata Branch 3 and E. myrtilloides Branch 6

only AE signals were available. All P. reticulata branches reached full dehydration before the

end of the experiment, E. myrtilloides Branches 4 and 5 were not completely dehydrated

when terminating the experiment.

Table 2.2 Summary of the selected P. reticulata and E. myrtilloides branches for VC analysis from the first dehydration experiment with detailed information on the location of the trees from which the branches were harvested (Map; see Figure 3.5), the final dehydration percentage when concluding the experiment and availability of AE signals and radial xylem shrinkage data during dehydration.

Location Dehydration AE sensor Dendrometer

P.

reti

cu

lata

Branch 1 Edge 2 100 % Yes Yes

Branch 2 Interior 100 % Yes Yes

Branch 3 Edge 2 100 % Yes No

E.

myrt

illo

ide

s

Branch 4 Edge 1 50 - 100 % Yes Yes

Branch 5 Edge 1 50 - 100 % Yes Yes

Branch 6 Interior 100 % Yes No

4.2.1 Stress-strain relations

Point measurements of the xylem water potential and continuous radial xylem shrinkage

data were used to establish a branch specific stress-strain relation allowing estimation of

continuous xylem water potential data over the entire coarse of the dehydration period

(Figue 4.3). For P. reticulata, obtained relationships were linear (Figure 4.3, A) whereas for

E. myrtilloides the relationships were segmented linear (Figure 4.3, B). For all branches, the

obtained correlation was good with minimal R² values of 0.85.

Since there was no data available for the radial xylem shrinkage of P. reticulata Branch 3

and E. myrtilloides Branch 6, the stress-strain relationship was estimated based on

dendrometer data of the other two branches. P. reticulata xylem water potential of Branch 3

was plotted against the relative radial xylem shrinkage data (i.e. strain) of Branch 1, Branch

2 and their average (Figure 4.4, A). E. myrtilloides xylem water potential measurements of

38

Branch 6 were plotted against the strain of Branch 4, Branch 5 and their average (Figure

4.4, B).

Figure 4.3 Branch specific stress-strain relationships. Point measurements of 𝜑𝑠𝑡𝑒𝑚 were plotted against their corresponding relative radial xylem shrinkage. (A) Stress-strain curves for P. reticulata with Branch 1 (total WP, n = 6) in blue and Branch 2 (total WP, n = 7) in green; (B) Stress-strain curves for E. myrtilloides with Branch 4 (total WP, n = 21) in blue and Branch 5 (total WP, n = 23) in green. Regression equations and corresponding R squared value are added in the right corner of the

graphs. 𝜑𝑏𝑒𝑓𝑜𝑟𝑒 , regression equation before the breakpoint; 𝜑𝑎𝑓𝑡𝑒𝑟 , regression equation after the

breakpoint. Additional water potential of other branches are represented in different shades of grey.

Figure 4.4 Pooled stress-strain curves of P. reticulata Branch 3 (total WP, n = 6) (A) and E. myrtilloides Branch 6 (total WP, n = 24) (B). The green regression line represents the relationship of the branch specific water potentials with pooled radial xylem shrinkage of the other branches. Under and Upper limits represent stress-strain relations of available radial xylem shrinkage with branch specific water potentials measurements.Regression equations and corresponding R2 values are added in the right corner of the graphs. 𝜑𝑏𝑒𝑓𝑜𝑟𝑒, regression equation before the breakpoint; 𝜑𝑎𝑓𝑡𝑒𝑟,

regression equation after the breakpoint.

39

4.2.2 Vulnerability curves

Vulnerability curves of all branches are established by determination of the endpoint,

corresponding with 100 % percentage loss of hydraulic conductivity (PLC), using the

protocol of Vergeynst et al. (2015). 100 % PLC is defined as complete loss of hydraulic

conductivity in the xylem vessels and can mathematicly be indiced as the moment when the

the third derivative of cumulative AE over time peaks after the highest peak of the first

derivative (AE activity) took place. The moment of 50 % PLC or point of embolisation of half

of the xylem vessels, was calculated following the protocol of Nolf et al. (2015), who states

this moment corresponds with the highest peak of the first derivative of cumulative AE over

time. Cumulative AE signals of the P. reticulata and E. myrtilloides branches were plotted

over time (days) in Figure 4.5 – 4.6. All P. reticulata branches were fully dehydrated after

2.25 days. E. myrtilloides branches, in exeption of Branch 6, only reached 50 % hydraulic

conductivity loss over this period (Figure 4.6, C). A summary of the moments of 50 % PLC

and 100 % PLC are represented in Table 4.3.

Table 4.3 Summary of the time (days) after which 50 % and 100 % hydraulic conductivity loss occurred based on the protocols of N Nolf et al. (2015) and V Vergeynst et al. (2015). The letters A - C correspond with the graphs of Figure 21 - 22. Location of the trees used for branch harvesting is indicated (Map; see Figure 3.5) a Interior, b edge 1, c edge 2.

50 % PLC N (days)

100 % PLC V (days)

P.

reti

cu

lata

Branch 1c A 1.52 1.78

Branch 2a B 1.80 2.25

Branch 3c C 1.11 1.35

E.

myrt

illo

ide

s

Branch 4b A 2.38 -

Branch 5b B 2.67 -

Branch 6a C 1.90 2.92

40

Figure 4.5 The cumulative AE (-) signals over time (days) for three P. reticulata branches in blue; (A) Branch 1, (B) Branch 2, (C) Branch 3. The green and orange lines represent the first and third derivative. The dashed lines represent the moments at 50 % PLC and 100 % PLC based on the protocols of Nolf et al. (2015) and Vergeynst et al. (2015), respectively.

41

Figure 4.6 The cumulative AE signals (-) over time (days) for three E. myrtilloides branches in blue: (A) Branch 4, (B) Branch 5, (C) Branch 6. The green and orange lines represent the first and third derivative. The dashed lines represent the moments at 50% PLC and 100% PLC based on the protocols of Nolf et al. (2015) and Vergeynst et al. (2015), respectively.

42

Figure 4.7 shows an overview of all established vulnerability. Water potential values

corresponding with 12, 50, 88 and 100 percentage loss of hydraulic conductivity (i.e. P12,

P50, P88, P100) are summarized in Table 4.4.

For P. reticulata, obtained water potential values for a specific percentage of hydraulic

conductivity loss were highly variable. The maximal water potential difference equalled -

8.85 MPa between Branch 2 and 3 for P100 and the P100 value of Branch 1 approximates

the P12 value of Branch 2. Less negative water potential values were measured for all

hydraulic characteristics for both branches harvested at edge 2, i.e. Branch 1 and Branch 3,

in comparison to Branch 2 originating for the forest interior. However, precaution is needed

when reading these results since hydraulic characteristics from Branch 3 are calculated

based on an averaged stress-strain relation. The limited water potential evolution of Branch

3 between P12, P50 and P88 reinforce the idea that the VC and corresponding hydraulic

characteristics VC of Branch 3 need to be evaluated critically.

The P12 and P50 values of E. myrtilloides vary little between branches and no differences

between edge 1 (i.e. Branch 4 and Branch 6) and the interior (Branch 5) can be observed.

Water potential extrapolation of Branch 4 and Branch 5 to the P88 and P100 level was not

possible, as this would assume a perfect sigmoidal S-shape of the vulnerability curve.

Looking at the already established VCs curves, this will most likely not be the case.

Table 4.4 Overview of P12, P50, P88 and P100 values (MPa) of the VCs from all branches. P50

values are calculated according to the protocol of N Nolf et al. (2015) and V Vergeynst et al. (2015).

Standard error margins are given for Branch 3 and Branch 6. Location of the trees used for branch

harvesting is indicated (Map; see Figure 3.5) a Interior, b edge 1, c edge 2.

P12 P50 N P50 V P88 P100

P.

reti

cu

lata

Branch 1c A -4.42 -6.01 -5.97 -6.41 -6.64

Branch 2a B -6.01 -9.66 -9.21 -11.30 -12.48

Branch 3c C -2.88 ± 0.14 -3.19 ± 0.11 -3.19 ± 0.11 -3.56 ± 0.11 -3.75 ± 0.13

E.

myrt

illo

ide

s

Branch 4b A -6.07 -9.35 - - -

Branch 5b B -5.89 -9.70 - - -

Branch 6a C -5.08 ± 0.01 -9.39 ± 0.5 -8.84 ± 0.32 -10.53 ± 0.78 -11.41 ± 0.99

43

Figure 4.7 Vulnerability curves of (1; A-C) P. reticulata and (2; A-C) E. myrtilloides with vulnerability values related to 12 % (▼), 50 % (♦) and 88 % (●) PLC.

Values related to 100 % PLC are represented by the dashed line. (1;A-C) Branch 1, Branch 2; Branch 3 and (2; A-C) Branch 4, Branch 5, Branch 6. VCs calculated using the protocol of Vergeynst et al. (2015). Standard error margins are given for Branch 3 and Branch 6 (light blue), although standard error margin of Branch 3 was too small to be visible on the graph.

44

When comparing P. reticulata and E. myrtilloides, all P50 values of Polylepis branches were

less negative than those of all the Escallonia branches, except for Branch 2 which had a P50

value in the same range as the Escallonia branches.

P50 values were determined based on the protocol of Nolf et al. (2015) and Vergeynst et al.

(2015). Although both protocols resulted in similar P50-values, values based on the Nolf et

al. (2015) protocol were always slightly lower (max. difference of 0.55 MPa) than those

obtained by the Vergeynst et al. (2015) method.

4.2.3 Safety margins

Based on the derived P50 values and the leaf water potentials measured in the field

(𝜑𝑙𝑒𝑎𝑓,𝑓𝑖𝑒𝑙𝑑), the minimum safety margin was calculated for all branches (Table 4.5). Using

the minimal measured 𝜑𝑙𝑒𝑎𝑓,𝑓𝑖𝑒𝑙𝑑 and the calculated 𝜑𝑠𝑡𝑒𝑚 𝜑𝑙𝑒𝑎𝑓⁄ - ratio (see paragraph 1.2),

the minimal 𝜑𝑠𝑡𝑒𝑚, 𝑓𝑖𝑒𝑙𝑑 and corresponding safety margin was calculated. All safety margins

were positive, meaning minimal daily water potentials were less negative than the water

potential at which 50 % loss of hydraulic conductivity occurred. Safety margins ranged from

8.05 MPa to 9.27 MPa for E. myrtilloides. Except from Branch 2 (i.e. 8.51 MPa), P. reticulata

safety margins were lower and varied between 2.54 to 5.25 MPa.

Table 4.5 Minimal safety margins (𝜑𝑚𝑖𝑛,𝑠𝑡𝑒𝑚 - P50; MPa) calculated for all P. reticulata and E.

myrtilloides branches. Minimum leaf water potentials (𝜑𝑚𝑖𝑛, 𝑙𝑒𝑎𝑓; MPa) were converted to stem water

potentials (𝜑𝑚𝑖𝑛,𝑠𝑡𝑒𝑚; MPa) with use of the multifaction factor 0.64 (𝜑𝑠𝑡𝑒𝑚 𝜑𝑙𝑒𝑎𝑓⁄ 𝑟𝑎𝑡𝑖𝑜; see paragraph

1.2). * Water potentials measured around midday. Location of the trees used for branch harvesting is indicated (Map; see Figure 3.5) a Interior, b edge 1, c edge 2.

*𝝋𝒎𝒊𝒏,𝒍𝒆𝒂𝒇

(MPa)

*𝝋𝒎𝒊𝒏,𝒔𝒕𝒆𝒎

(MPa)

P50 (MPa)

Safety margin (MPa)

P.

reti

cu

lata

Branch 1c A -1.13 -0.72 -5.97 5.25

Branch 2a B -1.09 -0.70 -9.21 8.51

Branch 3c C -0.85 -0.54 -3.19 ± 0.11 2.65 ± 0.11

E.

myrt

illo

ide

s

Branch 4b A

-0.68 -0.44 -9.35 8.91

Branch 5b B -0.67 -0.43 -9.70 9.27

Branch 6a C -0.74 -0.47 -8.84 ± 0.32 8.37 ± 0.32

4.3 Desorption curves

Desorption curves (DC) were established by plotting the evolution of the volumetric water

content (VWC; kg m-3) of the second branch out of branch pair (cf. first branch used for VCs,

n = 6) against the corresponding water potential during dehydration. Since dehydration of

branches within the same branch pair are presumed to be equal, only one of the E.

45

myrtilloides branches used for DC determination was entirely dehydrated at the end of the

experiment. This allowed only the establishment of the desorption curve for Branch 6. Figure

4.8 shows four vulnerability and desorption curves. Using the averaged stress - strain

relation for Branch 3 and Branch 6, the standard error margins were calculated for both.

From the DCs, three breakpoints delineating two phases, could be derived. The first phase

(Phase I, i.e. bordered by breakpoint 1 and 2) is associated with few embolism formation.

During this phase, water is mainly withdrawn from living cells to feed the transpiration stream

leading to elastic shrinkage. During the second phase (Phase II; i.e. bordered by breakpoint

2 and 3) an exponential increase in AE signals is noticed due to water releases from vessel

embolization.

Table 4.6 summarizes the xylem water potential values, volumetric water content and

percentage loss of hydraulic conductivity at the DC breakpoints. Breakpoint 1 and

Breakpoint 3 were determined by respectively the 0 % and 100 % PLC values from the VC

curves. Following Vergeynst et al. (2015), the water potential of Breakpoint 2 should

correspond with the P12 of the VC curves. However in all but one DC, hydraulic conductivity

loss was higher than 12 % ranging from 14.16 to 26.04 %. For Branch 6, the PLC at

Breakpoint 2 equalled 5.48 ± 0.29, possibly indicating that the first phase in P. reticulata

proceeds longer than the first phase of E. myrtilloides. However, because of the limited

dataset, these results need to be considered with care. Volumetric water content at the

breakpoints were similar for all P. reticulata branches, but lower than the corresponding

value for E. myrtilloides.

Table 4.6 Values of the xylem water potential (𝜑; MPa), volumetric water content (VWC; kg m-3) and percentage loss of hydraulic conductivity (PLC; %) at the breakpoints derived from Figure 4.8. (P. reticulata; A-C) Branch 1, Branch 2; Branch 3 and (E. myrtilloides; A-C) Branch 4, Branch 5, Branch 6. For Branch 3 (P. reticulata, C) and Branch 6 (E. myrtilloides, C) mean values and standard deviation are given.

Breakpoint 1 Breakpoint 2 Breakpoint 3

𝝋 VWC PLC 𝝋 VWC PLC 𝝋 VWC %PLC

P.

reti

cu

lata

A -0.22 627.49 0.00 -4.64 303.62 14.16 -6.64 188.54 100.00

B -0.04 706.41 0.00 -6.65 305.40 19.94 -12.48 201.14 100.00

C -0.24 ±

0.12

672.11 ±

18.40

0.00 ± 0.00

-2.91 ± 0.21

327.72 ± 13.41

18.40 ± 7.64

-3.66 ± 0.41

197.25 ± 28.68

100.00 ± 0.00

E.

my

rtillo

ide

s

A - - - - - - - - -

B - - - - - - - - -

C -0.21 ±

0.07

725.69 ± 8.13

0.015 ± 0.02

-3.42 ± 0.04

488.00 ± 1.70

5.48 ± 0.29

-11.37 ± 0.57

251.13 ± 12.25

100.00 ± 0.00

46

The desorption curves breakpoints define the borders of Phase I and Phase II of the branch

hydraulic capacitance. The slopes of the corresponding phase before and after Breakpoint 2,

agree with the elastic and inelastic hydraulic capacitance (Cel, Cinel ; kg m-3 MPa-1). For all

branches, in exception of Branch 3 (i.e. P. reticulata, C), all calculated capacitances were

smaller than 100 kg m-3 and Cel was distinctly higher than Cinel (Table 4.7). Again, one should

keep in mind that continuous water potential measurements of Branch 3 were approximated

using the averaged stress-strain relation. Excluding Branch 3, Cel varied between 58.93 and

77.18 kg m-3 MPa-1. Cinel values were more variable and varied between 17.02 and 54.78 kg

m-3 MPa-1. No distinct hydraulic capacitances differences were noticed between P. reticulata

and E. myrtilloides.

The average wood density of P. reticulata and E. myrtilloides equalled 471.2 ± 25.2 kg m-3

(n = 22) and 382.7 ± 29.9 kg m-3 (n = 20) respectively.

Table 4.7 Elastic (Phase I) and inelastic (Phase II) hydraulic capacitances (C; kg m-3 MPa-1) calculated as the slope of the corresponding phase of the DC curves in Figure 4.8. Wood density (𝜌𝑏; kg m-3), initial stem diameter (dinit; mm) and volumetric water content (VWCinit) are also shown. (P. reticulata; A-C) Branch 1, Branch 2, Branch 3; and (E. myrtilloides; A-C) Branch 4, Branch 5, Branch 6. Location of the trees used for branch harvesting is indicated (Map; see Figure 3.5) a Interior, b edge 1, c edge 2.

C (kg m-3 MPa-1) Branch properties

Phase I Phase II 𝝆𝒃 (kg m-3) dinit (mm) VWCinit (kg m-3)

P.

reti

cu

lata

Branch 1c A 72.86 54.78 384.59 11.18 653.09

Branch 2a B 58.93 17.02 393.46 8.57 724.94

Branch 3c C 127.48 ± 7.22

125.40 ± 5.73

406.26 7.43 708.53

E.

myrt

illo

ide

s

Branch 4b

A - - 401.48 7.70 -

Branch 5b B - - 272.27 10.28 -

Branch 6a C 75.84 ± 1.34

30.19 ± 4.01

320.14 9.70 733.82

47

Figure 4.8 (Top) Vulnerability curves of (1) P. reticulata and (2) E. myrtilloides with vulnerability values related to 12 % (▼), 50 % (♦) and 88 % (●) PLC. Values

related to 100 % PLC are represented by the dashed line. (Bottom) Desorption curves with the volumetric water content (VWC; kg m-3) plotted against the xylem water potential (𝜑; MPa) for (1) P. reticulata and (2) E. myrtilloides branches. Phase I and II are delimited by vertical dashed green lines and corresponding slopes are defined as the elastic (Cel; kg m-3 MPa-1 ) and the inelastic (Cinel; kg m-3 MPa-1) hydraulic capacitance. (1; A-C) Branch 1, Branch 2 and Branch 3, (2; C) Branch 6. Standard error margins for Branch 3 and Branch 6 are shown (light blue), the standard error margin of Branch 1 was too small to be visible on the graph.

(1)

(2)

48

4.4 Microscopic analysis

Anatomical analysis was carried out on P. reticulata and E. myrtilloides branches with both

AE measurements and xylem shrinkage data during dehydration (i.e. Branch 1, 2, 4 and 5).

Examples of branch micrographs are given in Figure 4.9, branch specific results of the

anatomical analysis can be found in Table 4.9.

There was little variability in average vessel diameter (Dv,rad) and vessel area (VA) values,

ranging between respectively 17.84 ± 0.47 to 19.95 ± 0.37 µm and 278.33 to 345 µm²

between branches of both species. The percentage wood area occupied by vessels varied

between species and was slightly higher for P. reticulata branches in comparison to E.

myrtilloides branches. The vessel frequency (V A-1, mm-2) was higher in E. myrtilloides .

Vessel grouping index (VG) and vessel solitary index (Vs) were calculated using the 10X

magnified micrograph and ranged respectively between 1.24 to 1.64 and 0.58 and 0.80 for

P. reticulata and between 1.22 to 1.32, and 0.70 and 0.80 for E. myrtilloides, indicating a

high degree of solitary vessels in both species.

Table 4.9 Main anatomical features of P. reticulata and E. myrtilloides branches. The average vessel

area (VA) is calculated as 𝜋𝑟2; the vessel frequency (Vf) is given as the number of vessels per mm²; the vessel grouping index (VG) as the ratio of the total number of vessels to the total number of vessel groups; and the solitary vessel index (VS) as the ratio of solitary vessels to the total amount of vessel groups.

Variables P. reticulata E. myrtilloides

Branch 1 Branch 2 Branch 4 Branch 5

Average vessel area (VA, µm²)

289.02 ± 9.73 332.94 ± 12.17 264.52 ± 13.81 301.20 ± 5.60

Average vessel diameter (Dv,rad, µm)

18.63 ± 0.31 19.95 ± 0.37 17.84 ± 0.47 19.81 ± 0.17

Percentage total wood area (-, %)

18.04 13.00 9.84 6.28

Vessel frequency (Vf, mm-2)

207 181 371 293

Vessel grouping index (VG, -)

1.64 1.24 1.22 1.32

Solitary vessel index (Vs, -)

0.58 0.80 0.80 0.70

49

Figure 4.9 (Right) Micrographs of the transverse section of a complete diffuse-porous P. reticulata (A) and E. myrtilloides (B) branch at 4x magnification. Lignin-enriched cell walls, fibres and vessels are coloured red, parenchyma and tension cell walls are associated with a blue colour. (Left) High solitary vessel grouping can be noticed at a the 10x magnification level. LF, flattened and thickened libriform fibres; SV, solitary vessel; MV multiple vessel; RP, radial parenchyma; AP, axial parenchyma.

(B)

(A)

50

5 Discussion

5.1 Vulnerability of P. reticulata and E. myrtilloides to drought-

induced embolism

The main aim of this study was to determine the vulnerability of P. reticulata and E.

myrtilloides trees to drought-induced embolism formation. Therefore, the P50 (i.e. water

potential at which 50 % loss of hydraulic conductivity occurs) was calculated, as this is one

of the most common metrics to assess and compare drought vulnerability between species

and across ecosystems (Choat et al., 2012).

Comparison of average P50-values of P. reticulata (i.e. - 4.97 ± 1.39 MPa) and E.

myrtilloides (i.e. - 9.11 ± 0.25 MPa) with the species enlisted by Choat et al. (2012), suggest

that both páramo species are very drought resistant. More than eighty percent of all studied

species by Choat et al. (2012) have a less negative P50-value (see further, Figure 5.3).

However, this can be misleading since only 17 (i.e. angiosperms, n = 10; gymnosperms, n =

7) out of the 480 studied trees of Choat et al. (2012) grew at an altitude above 2000 m. a.s.l

and none of them grew in climatic conditions similar to the páramo ecosystem. Although

Choat et al. only included a limited amount of high altitude trees, their average P50-value

(i.e. -4.79 ± 2.20 MPa) indicates that a high drought resistance may be common at high

altitudes. The measured P50-values for P. reticulata agree with this hypothesis. The P50-

values of E. myrtilloides also indicate a strong resistance to drought, however the measured

values are much more negative than the range indicated by Choat et al. (2012).

Since this studied pioneered in establishing vulnerability curves (VCs) of trees of the páramo

climate, comparison of our data with previous studies proves difficult. However, few studies

have assessed drought vulnerability of tree species in montane cloud forest ecosystems. In

the tropics, cloud forests often form the lower limit of páramos ecosystem, enabling

comparison with results from this study. Previous studies reported P50-values range from -

1.56 to -6.0 MPa (Barros, 2017; Berry et al., 2015; Blackman et al., 2012; Oliveira et al.,

2014). Despite the large range and high species dependency, the lower bound of this P50-

interval is consistent with the measured P50-values of P. reticulata. Due to the negative

P50-values of E. myrtilloides, they are more excluded from this earlier determined range.

These results suggest that despite a humid environment in which water stress is lacking,

high drought resistance can occur. The advantage of possessing high resistance

mechanisms against drought in humid environments seems somewhat inefficient at first. So

perhaps it is not drought-induced but freezing-induced embolism formation páramo trees are

protecting themselves in the first place from. This would make more sense since high-

altitude species are consequently subjected to freezing temperatures. Different hydraulic

traits (e.g. diameter size, cell wall elasticity, …) have been attributed to both increased

resistance against drought as ice nucleation (Davis et al., 1999; Zhang et al., 2016), which

51

may suggest that increased drought resistance in humid alpine ecosystems may just be a

handy surplus in the protection against freezing cavitation.

5.1.1 Vulnerability based on structural xylem traits

Comparing pooled vulnerability curves, E. myrtilloides shows a higher drought resilience in

comparison to P. reticulata (Figure 5.1). In addition, the observed embolization rates (i.e.

slope of vulnerability curve between P12 and P88) suggest that P. reticulata is subjected to

a faster development of consecutive cavitation events. Therefore, embolism formation

occurs suddenly over a smaller water potential range, resulting in a smaller safety margin

when embolization is inevitable (Kavanagh et al., 1999). The smaller resistance and more

limited water potential range in which P. reticulata is operating, can also be portrayed by the

dehydration time span. Observed dehydration regimes indicate that all P. reticulata branches

reach complete dehydration in ~2.25 days, whereas most E. myrtilloides only reached 50 %

hydraulic conductivity loss over this period. Moreover, most E. myrtillodes branches did not

achieve full dehydration at all by the end of the experiment (~3 days). As the dehydration

endpoint (100 % loss of hydraulic conductivity) is essential to establish VCs, only one VC for

E. myrtilloides could be construced after the establishment of a pooled stress-strain curve

(Figure 5.1).This complicates the comparison between the drought vulnerability of both

species.

Figure 5.1 Average vulnerability curves of P. reticulata (light grey; n = 3) and E. myrtilloides (dark grey; n = 1; calculated using pooled stress-strain curve) with vulnerability values related to 12 % (▼),

50 % (♦) and 88 % (●) PLC. Vulnerability values corresponding with 100 % PLC are represented by the dashed line. For both species, standard error margins are given.

52

Although branches were not completely dehydrated at the end of the experiment, P50-

values could be calculated using the protocol of Nolf et al. (2015). This enabled estimation of

the P50-values of all branches. In addition, calculated P50-results following the method of

Vergeynst et al. (2015) and Nolf et al. (2015) could be compared for four branches. Overall,

these results show a good similarly between both procedures. Although consistently, small

overestimations (i.e. max. difference of 0.55 MPa) of the P50-values were observed when

using the method described by Nolf et al. (2015). This can be explained by the assumption

of a perfectly sigmoidal S-shaped VC in Nolf et al. (2015) protocol. Any deviation from this

curve changes the steepest part and slightly alters the targeted P50 value (Vergeynst et al.,

2016).

The high drought resistance of both species is confirmed with observation of very small

vessel diameters (~20 µm) (Table 4.9). As hypothesized by the rare pit hypothesis, small

vessel diameters decrease the total pit area and thus the probability of pore failure (Jansen

et al., 2009; Wheeler et al., 2005). Observations of decreasing drought vulnerability with

decreasing vessel diameter are demonstrated in various experimental and comparative

studies (Cai & Tyree, 2010; Christman et al., 2009; Hargrave et al., 1994; Wheeler et al.,

2005). However, the distinct difference in vulnerability between P. reticulata and E.

myrtilloides cannot be explained by this anatomical feature. Furthermore, the low degree of

vessel grouping and connectivity in both species signifies high resistance to cavitation by

decreasing possible embolism spreading while simultaneously lowering the hydraulic

efficiency. The solitary vessel index (Vs) ranged between 0.70 and 0.80 for E. myrtilloides

and from 0.58 to 0.80 for P. reticulata. Again, since both species show a similar solitary

vessel ratio (mostly 0.70 < Vs < 0.80), it is difficult to the observed drought resistance

difference to anatomical characteristics. However, previous studies do report a difference in

vessel grouping between both species. Lower vessel connectivity was reported for E.

myrtilloides (0.53 < Vs < 0.77) in comparison to P. reticulata (Vs > 0.90 ) (Stern 1974;

Wheeler 2004; Zhang 1992). It is possible that due the limited amount of anatomy samples

(per species n = 2) this difference was not observed in this study and supplementary

microscopical analysis of P. reticulata branches would lead to Vs - values closer to 0.58 (i.e.

lower boundary of our obtained Vs – values). Although vessel grouping effects drought

vulnerability (Loepfe et al., 2007), it is difficult to believe that the observed difference in P50-

value between both species can be solely attributed to vessel grouping.

In many species, differences in wood density are associated with differences in drought

vulnerability. Higher wood density is often linked with vessel and fibres traits strengthening

the vessel wall, therefore protecting the vessel against implosion and reducing the odds of

collapse initiating hydraulic failure ( Hacke et al., 2001; Lens et al., 2011). In this study E.

myrtilloides has a lower wood density (i.e. 𝜌𝑏 = 382.7 ± 29.9 kg m-3) but higher cavitation

resistance than P. reticulata (i.e. 𝜌𝑏 = 471.2 ± 25.2 kg m-3 ). Similar contrasting results were

reported by Cochard et al. (2008) among different Prunus species. However, they did find

better correlation between embolism resistance and the structural anatomy of fibres and cell

53

walls between adjacent vessels. They further concluded that wood density may not be a

good indicator to explain the difference in drought vulnerability between species species.

In this study cell wall reinforcement features were not investigated and previous studies

regarding anatomical characteristics remain limited and reported highly variable results.

Studies have described fibre wall thickness vaguely as “ranging from very thin to medium

thick” for closely related Polylepis species (i.e. P. incana, P. austraulis, P. pallidistigma) (S.-

Y. Zhang, 1992) and more specific from 1.98 to 6.60 µm for Escallonia species (Stern,

1974). Yet, these descriptions are so vague that no conclusions can be drawn. Furthermore,

cavitation resistance cannot solely be determined by anatomical tissue characteristics but

comprises complex interactions between traits from pit to tissue level (Lens et al., 2011).

Especially, structural pit characteristics including thicker and shallower pore membranes,

smaller pit apertures and reduced porosity prove promising in the study of species’ drought

vulnerability (Lens et al., 2011, 2013). In our study however, no intervessel pit traits were

investigated and previous documented values appear highly variable and unspecified (Table

3.1). Therefore, future research of structural xylem features is essential to provide possible

explanations for the distinct difference in vulnerability between P. reticulata and E.

myrtilloides.

5.1.2 Vulnerability based on hydraulic capacitance values

Despite its universal use as an index of resistance to xylem hydraulic failure, sole reliance

on P50-values may be misleading as it only accounts for xylem structural features . Recent

studies on hydraulic architecture have highlighted the importance of hydraulic capacitance in

tempering water potential decreases during drought by the release of internal stored water to

the transpiration stream (Epila et al., 2017; Meinzer et al., 2009; Vergeynst et al., 2015).

This study shows that both P. reticulata and E. myrtilloides rely only limitedly on the

hydraulic capacitance of plant tissues during increased drought stress as most hydraulic

capacitance values were relatively small (< 100 kg m-3 MPa-1) (Figure 5.2). Vergeynst et al.

(2015) reported elastic and inelastic hydraulic capacitances greater than 100 kg m-3 MPa-1 in

grapevine (Vitis vinifera L. ‘Johanniter’) and Epilea et al. (2017) reported hydraulic

capacitances above 200 kg m-3 MPa-1 for the tropical African tree species Maesopsis eminii

Engl. However, these tree species grew in different climatic conditions than the páramo

ecosystem. Yet both studies provide valuable information as they indicate that the use of

internally stored water can be essential for plant survival during drought periods. By drawing

water from plant water reservoirs (e.g. xylem parenchyma, embolised vessels, … ), the

water potential drop can be delayed leading to an increased drought resistance that is not

reflected in the P50-value. Other studies have reported of the trade-off between the

dependency on either hydraulic capacitance or xylem embolism resistance across a range of

wood species (Domec & Gartner, 2001; Guet et al., 2015; Meinzer et al., 2008, 2009). The

low hydraulic capacitance values for P. reticulata and E. myrtilloides indicate a strong

dependency on the resistance of xylem to hydraulic failure. However, it should be noted that

none of the previous studies reported such negative P50-values as reported in this study.

54

In the desorption curves, two distinct phases (i.e. elastic and inelastic shrinkage phase) can

be distinguish during dehydration. The first phase portrays water release from living cells by

elastic shrinkage. As indicated by Vergeynst et al. (2015), the end of this phase corresponds

with a steep hydraulic conductivity loss visualized in the vulnerability curves (Figure 5.2). For

P. reticulata, the obtained elastic hydraulic capacitances (Cel) were slightly variable.

Remarkably, one P. reticulata branch showed an relatively high Cel (i.e. 127.48 ± 7.22 kg m-3

MPa-1) in comparison with the other two (i.e. 58.93 kg m-3 MPa-1, 72.86 kg m-3 MPa-1 ). Often,

increased hydraulic capacitances are attributed to low wood density (Meinzer et al., 2009;

Scholz et al., 2013). However, in this case the opposite could be observed. Since the

corresponding DC was established using a pooled stress-strain relation, the reliability of this

increased Cel-value can be questioned (Figure 5.2).

Figure 5.2 Average desorption curves of P. reticulata (light grey; n = 3) and E. myrtilloides (dark grey; n = 1; calculated using pooled stress-strain curve). Phase I and Phase II are delimited by vertical dashed lines and corresponding slopes are defined as the elastic (Cel; kg m-3 MPa-1) and the inelastic (Cinel; kg m-3 MPa-1) hydraulic capacitance. For both species, standard error margins are given. However, keep in mind that the average P. reticulata DC may difference from the reality as very high values were reported for one of the branches.

The obtained average Cel-value for E. myrtilloides equalled 75.84 ± 1.34 kg m-3 MPa-1 and

was slightly higher than P. reticulata. In support of this, all (partial) VCs of E. myrtilloides

(Figure 4.7 - 5.1) show a stronger increase in the percentage cumulative AE at low drought

stress levels. This can partially be attributed to an increased elastic shrinkage, further

confirming the observed higher hydraulic capacitance of E. myrtilloides (De Baerdemaeker

et al., 2018; Kikuta, 2003; Vergeynst et al., 2016). The percentage parenchyma was

estimated to provide an explanation for the observed differences in elastic hydraulic

capacitance (Meinzer et al., 2009; Secchi et al., 2017). Unfortunately, the amount of ray and

vessel-associated parenchyma could not be quantified in this study. Yet, previous studies

55

pointed out that the amount of parenchyma strongly correlates with wood density

(Martínez‐Cabrera et al., 2009; Zieminska et al., 2015). Lower wood densities were found

for E. myrtilloides in comparison to P. reticulata (i.e. 382.7 ± 29.9 kg m-3, 471.2 ± 25.2 kg

m-3, respectively), possibly indicating a higher parenchyma percentage and explaining the

hydraulic capacitance difference. Interestingly, P. reticulata lost almost half of its total

volumetric water at the end of the elastic shrinkage phase while for E. myrtilloides only a

thirty percent loss was observed. This suggest that despite the smaller overall hydraulic

capacitance of P. reticulata, the relative importance of the elastic hydraulic capacitance is

higher for P. reticulata trees.

The second phase (i.e. inelastic shrinkage) of the desorption curves describes the water

release from xylem vessels after embolization to the transpiration stream. Overall, observed

inelastic hydraulic capacitances (Cinel) for E. myrtilloides and P. reticulata were smaller than

the elastic hydraulic capacitances. This results suggest that embolization events only

contribute limitedly to the survival of both species during drought stress.

5.2 Resilience of P. reticulata and E. myrtilloides to drought under a

changing climate

In a climate change context, sole reliance on the plant hydraulic resistance (P. reticulata, -

4.97 ± 1.39 MPa; E. myrtilloides, - 9.11 ± 0.25 MPa) to predict the responses of P. reticulata

and E. myrtilloides to increasing temperatures, would lead misleading results. Apart from

vulnerability to embolism formation, it is also important to assess the extent to which both

species are already subjected to drought stress under normal field conditions.

As expected, the measured field leaf water potentials show that both páramo species were

minimally exposed to high xylem tensions throughout the day (i.e. 𝜑𝑙𝑒𝑎𝑓,𝑚𝑖𝑛 > - 0.92 ± 0.02

MPa). Interestingly, previous studies showed that minimal leaf water potentials of Polylepis

species varied distinctly between the seasons (Table 5.1). In this study, water potentials

were measured during the dry season, suggesting that the obtained water potentials

represent the lowest values over the entire year.

Table 5.1 Comparison of minimum leaf water potentials (𝜑𝑙𝑒𝑎𝑓) measured in closely related Polylepis

species in the páramo ecosystem.

Species Season 𝝋𝒍𝒆𝒂𝒇 (MPa) Study

P. sericea Wet - 0.94 ± 0.18 Rada et al. (1996)

P. sericea Dry - 1.72 ± 0.14 Rada et al. (1996)

P. tarapacana Wet - 1.02 García-Núñez et al. (2004)

P. tarapacana Dry - 1.67 García-Núñez et al. (2004)

56

Considering the large difference between the minimal measured daily water potentials and

the calculated P50-values, P. reticulata and E. myrtilloides seem to be only limitedly

subjected to hydraulic failure in their natural ecosystem. It can therefore be assumed that

both páramo species are only little exposed to native embolism events on daily basis.

Species that operate well above their P50-values are hypothesized to rely mostly on xylem

structural features instead of embolism refilling mechanisms to survive periods of drought

(Choat et al., 2012). This embolism-avoidance hypothesis is in agreement with our previous

conclusions.

Comparison of the hydraulic safety margins (i.e. 𝜑𝑠𝑡𝑒𝑚,𝑚𝑖𝑛 - P50) obtained in this study with

the dataset of Choat et al. (2012), suggests that P. reticulata and E. myrtilloides are very

resilient to increasing drought (Figure 5.3). Only 6 gymnosperm species out of 223 studied

trees, had a safety margin lower than 5.47 MPa, the average safety margin of P. reticulata.

However, they all grew in ecosystems characterized by relatively high temperature and low

precipitation rates (i.e. 9 - 21°C yr-1 and 230 - 670 mm yr-1) that differ from the páramo

ecosystem. Similar safety intervals are reported by Berry et al. (2015) for the cloud forest

species Abies fraseri (i.e. 4.69 MPa). E. myrtilloides, on the other hand, seems to possess

safety margins that have rarely been reported before (i.e. 8.85 ± 0.26 MPa). Because of the

limited conducted studies in high altitude humid ecosystems, it is very difficult to define the

potential advantages of such large hydraulic safety margins for both páramo species.

Further research is therefore essential to gain more insight in these hydraulic safety

strategies.

Figure 5.3 Minimum stem water potential (𝜑 𝑠𝑡𝑒𝑚,𝑚𝑖𝑛, MPa) of plants under natural conditions as a

function of the xylem pressure at which 50 % hydraulic conductivity loss occurs (P50, MPa) for 191 angiosperm and 32 gymnosperm species. P. reticulata and E. myrtilloides values are represented by yellow and green dots, respectively. The safety margin of all species is represented by the distance between the dots and the 1:1 line (dashed line) (adapted from Choat et al., 2012).

57

6 Conclusion

Due to global warming, the páramo ecosystem is expected to be subjected to serious

changes of their climatic conditions within this next century. Future projections predict strong

temperature increases and a shift of the seasonal rain pattern resulting in longer and/or

more profound dry seasons (Buytaert et al., 2011; Urrutia & Vuille, 2009). In addition,

temperature increases are expected to decrease cloud covering, further reducing fog

frequency resulting in an alteration of precipitation patterns. Consequently, these intensified

droughts will pose an imminent threat to the viability of the páramo ecosystem.

To asses drought resistance, vulnerability curves of both species were established and P50-

values were determined using the protocol of Vergeynst et al. (2015) and Nolf et al. (2015).

Except for one P. reticulata branch, P50-values of both species ranged between - 6.01 and -

9.70 MPa, demonstrating the high resistance of P. reticulata and E. myrtilloides to drought-

induced embolism formation. In support of this findings, microscopic analysis showed both

species possessed small vessels (~ 20 µm) and a low vessel grouping capacity (Vs = 0.72).

Desorption curves indicated a low reliance of both species on the application of internal

stored water to maintain the transpiration stream (Cel and Cinel < 100 kg m-3 MPa-1).

Furthermore, all inelastic hydraulic capacitances were smaller than the elastic hydraulic

capacitances (i.e. max. difference 45.65 kg m-3 MPa-1) . These observations strongly

suggest both species try to avoid embolism formation and do not rely on refilling

mechanisms as protection against drought.

E. myrtilloides was distinctively more resistant to drought-induced embolism formation than

P. reticulata. However, no anatomical characteristics were observed to explain this

observation. Investigated anatomical characteristics (e.g. vessel size and vessel

connectivity) were similar for both species. Difference in wood density was observed

between the two species (P. reticulata, 382.7 ± 29.9 kg m-3 ; E. myrtilloides, 471.2 ± 25.2

kg m-3) implying that, in contrast with the other results, P. reticulata was more resistant to

drought than E. myrtilloides. This suggests that wood density may not be a good indicator for

vulnerability determination and indicates the need for further investigation of the xylem

structural features of both species.

To assess the amount of cavitation on a daily basis in natural growing conditions, hydraulic

safety margins were determined for both paramo species. The observed safety margins (i.e.

𝜑𝑠𝑡𝑒𝑚,𝑚𝑖𝑛 - P50) ranged between 5.25 MPa and 9.27 MPa for most branches. This strongly

indicates the high drought resistance of P. reticulata and E. myrtilloides .

58

7 Further research

Our study indicated some first insights in the possible effects of climate change on the

viability of the páramo ecosystem. However, as we were the first to develop vulnerability and

desorption curves for P. reticulata and E. myrtilloides, it remains difficult to compare and

verify our data with previous hydraulic safety studies. Therefore, repetition of these

measurements is of great importance to provide further support of our findings.

Since all experiments of this study were conducted during the dry season, we were able to

assess drought vulnerability under the most extreme climatic conditions. Nevertheless, this

does not necessarily imply that both species are subjected to less severe drought stress

during the wet season since resistance against embolism formation could change depending

on the season (Jacobsen et al., 2007). Especially when addressing climate change

responses, additional assessment of drought vulnerability is essential to gain additional

insights into the responses of both páramo species over the entire year.

Although this study provided some evidence to explain the high drought resistance of P.

reticulata and E. myrtilloides, the full extent of the hydraulic safety strategies is not yet

understood. No investigated hydraulic traits were able to capture the distinct difference in

drought vulnerability between both species. Therefore, future research should include

analysis of anatomical xylem structural features (e.g. cell wall reinforcement, percentage

parenchyma, pore membrane thickness, pit porosity,…) to explain the observed differences

between P. reticulata and E. myrtilloides to drought.

59

8 Appendix

Figure 8.110: Monthly climograph of a six-year period (January 2012 - December 2017) at the Zhurucay river ecohydrology observatory field site (Carrillo-Rojas et al., 2019).

60

Figure 8.2: Annual and monthly wind charts of a six-year period (January 2012 - December 2017) at the Zhurucay river ecohydrology observatory field site (Carrillo-Rojas et al., 2019).

61

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