Legacies of Amazonian Dark Earths on Forest Composition, Structure and Dynamics

Estela María Quintero Vallejo

Legacies of Amazonian Dark Earths

on Forest Composition, Structure and Dynamics

Thesis committee

Promotors Prof. Dr F.J.J.M. Bongers Estela María Quintero Vallejo Personal chair at the Forest Ecology and Forest Management Group Wageningen University

Prof. Dr L. Poorter Personal chair at the Forest Ecology and Forest Management Group Wageningen University

Co-promotors Dr M. Peña Claros Associate professor, Forest Ecology and Management Group Wageningen University

Dr M. Toledo

Research Coordinator Instituto Boliviano de Investigación Forestal (IBIF), Santa Cruz de la Sierra, Bolivia Thesis Other members submitted in fulfillment of the requirements for the degree of doctor Prof. Dr E. Hoffland, Wageningen University at Wageningen University Prof. Dr R.G.A. Boot, Tropenbos International, Wageningen, The Netherlands by the authority of the Academic Board, Dr H. ter Steege, Naturalis, Leiden, The Netherlands in the presence of the Prof. Dr Ch.R. Clement, Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil Thesis Committee appointed by the Academic Board to be defended in public This research was conducted under the auspices of the C. T. de Wit Graduate School for Production on Wednesday 1 July 2015 Ecology and Resource Conservation (PE&RC). at 4 p.m. in the Aula.

Legacies of Amazonian Dark Earths

on Forest Composition, Structure and Dynamics

Thesis committee

Promotors Prof. Dr F.J.J.M. Bongers Estela María Quintero Vallejo Personal chair at the Forest Ecology and Forest Management Group Wageningen University

Prof. Dr L. Poorter Personal chair at the Forest Ecology and Forest Management Group Wageningen University

Co-promotors Dr M. Peña Claros Associate professor, Forest Ecology and Management Group Wageningen University

Dr M. Toledo

To Demetrio and Gloria

Estela María Quintero Vallejo Legacies of Amazonian Dark Earths on Forest Composition, Structure and Dynamics 168 pages.

PhD thesis, Wageningen University, Wageningen, NL (2015) With references, with summaries in English and Dutch

ISBN 978-94-6257-426-7

To Demetrio and Gloria

Estela María Quintero Vallejo Legacies of Amazonian Dark Earths on Forest Composition, Structure and Dynamics 168 pages.

PhD thesis, Wageningen University, Wageningen, NL (2015) With references, with summaries in English and Dutch

ISBN 978-94-6257-426-7 Table of Contents

Chapter 1...... 9

General Introduction

Chapter 2...... 25

Amazonian Dark Earths Shape the Understory Plant Community in a Bolivian Forest

Published in Biotropica (2015) 14(2): 89-96

Chapter 3...... 49

Effects of Amazonian Dark Earths on forest attributes and forest dynamics in a Bolivian forest

Submitted for publication

Chapter 4...... 77

Effects of Amazonian Dark Earths on growth and leaf nutrient balance of tropical tree seedlings

Accepted for publication in Plant and Soil

Chapter 5...... 103

General Discussion

Literature...... 117

APPENDIX...... 131

Characterization of soils at La Chonta

Summary...... 149

Samenvatting...... 153

Resumen...... 157

Acknowledgements...... 161

Short Biography...... 165

PE&RC Training and Education Statement...... 167

7 Table of Contents

Chapter 1...... 9

General Introduction

Chapter 2...... 25

Amazonian Dark Earths Shape the Understory Plant Community in a Bolivian Forest

Published in Biotropica (2015) 14(2): 89-96

Chapter 3...... 49

Effects of Amazonian Dark Earths on forest attributes and forest dynamics in a Bolivian forest

Submitted for publication

Chapter 4...... 77

Effects of Amazonian Dark Earths on growth and leaf nutrient balance of tropical tree seedlings

Accepted for publication in Plant and Soil

Chapter 5...... 103

General Discussion

Literature...... 117

APPENDIX...... 131

Characterization of soils at La Chonta

Summary...... 149

Samenvatting...... 153

Resumen...... 157

Acknowledgements...... 161

Short Biography...... 165

PE&RC Training and Education Statement...... 167 Chapter 1

General Introduction

Estela Quintero-Vallejo

9 Chapter 1

General Introduction

Estela Quintero-Vallejo Chapter 1 Introduction

The Amazonian rain forest is one of the most diverse biomes on the planet (Hoorn and The human footprint in the Amazonian Forest Wesselingh 2010); its high biodiversity, although not completely understood, is the result of complex interactions between geomorphologic and climatic processes throughout thousands The romantic view of the Amazonian forest as the archetype of ‘virgin’ forests, untouched by of years (Hoorn et al. 2010). It is this high diversity and complexity that fascinates people, humans, is far from reality (Clement and Junqueira 2010). Recent findings show increasing and attracts worldwide efforts to keep this forest intact as a ‘pristine’, untouched ecosystem evidence of landscape modification of tropical forests by humans around the world (Willis et (Clement and Junqueira 2010). The claim of conserving an ‘intact’ forest is made under the al. 2004) . Human influence has been pervasive through the whole American continent since assumption that the diversity of the Amazonian forest is mainly the result of natural processes around 12,000 years ago (McCann 1999); for instance, signals of inhabitation in the northern (Peres et al. 2010). But the Amazonian forest does not seems not pristine, in the literal sense Bolivian Amazonia Bolivian Amazonia are as old as 10.000 yr BP (Lombardo et al. 2013); of the word, as it has gone through a long, and perhaps prevalent history of human occupation earth works consisting of ring ditches in the province of Beni in northeast of Bolivia date and modification (Heckenberger et al. 2007, Arroyo-Kalin 2012, Carson et al. 2014). ~2300 yr BP (Erickson 2010); the presence of roads, moats and bridges at upper Xingu in Extensive presence of humans in the Amazonian region suggests an important contribution of Mato Grosso Brazil seem to have been built between 700 and 300 yr BP (Heckenberger et al. mankind in shaping the Amazonian forest as it currently is (Balée and Erickson 2006, 2003); and ADE in Central Amazonian forest have been established since ~2400 yr BP Heckenberger et al. 2007). (Sombroek 1966, Smith 1980, Lehmann et al. 2003b). Besides, signs of human use of fire Past human inhabitation in the Amazonian forest is confirmed by the presence of large (slash and burn) for incipient cultivations date from around 6000 yr BP in the Americas earth works in the South-West (Erickson 2006, Lombardo and Prümers 2010); and (Piperno 2009); the practice of slash-and-burn agriculture in the Amazon basin seems to be documented agricultural practices and use of fire for opening forested areas (Bush et al. 2007, associated with cultivation of maize in the Western Amazonian forest (around 5800 yr BP) Arroyo-Kalin 2012). Additional indication of human inhabitation in the Amazonian forest is and cassava in the North-West (around 5000 yr BP) (Arroyo-Kalin 2012). Thus, the the presence of anthropogenic soils or Amazonian Dark Earths- ADE (‘Terra Preta de Índio’ Amazonian forest by the time of the encounters with Europeans is a mosaic of natural and in Portuguese), named after the black, profoundly modified soil profile with increased organic recovered landscapes after periods of human transformation along pre-Columbian times. matter (Sombroek 1966, Lehmann et al. 2003a). These anthropogenic soils are, presumably, formed between approximately 2400 yr BP and around 500 to 300 yr BP (Neves et al. 2003) by the accumulation of kitchen middens over long periods of time (Schmidt et al. 2014). They are widely distributed across the region (Winklerprins and Aldrich 2010, McMichael et al. 2014), and they increase the soil fertility at small scales (i.e. < 2ha) (Glaser and Birk 2012). This thesis evaluates the effects of ADE soil fertility on the composition, structure and dynamics of a Bolivian forest that is located in the Southern boundary of the Amazon basin. Assessing if ADE have lasting effects on the re-grown forest after abandonment of indigenous settlements improves our ability to determine and understand the drivers of species composition, forest structure and dynamics in the Amazonian forest. In the following sections I will first introduce several historical aspects of human occupation of the Amazonian region with a focus on aspects that have resulted in ADE, especially in the case of North-East Bolivia (Llanos de Moxos) (Fig. 1.1); the aim is to contextualize my research in the historical ecology of the region. Secondly, I will highlight major aspects of soils and vegetation in the Amazonian forest with some emphasis on the findings of earlier studies that relate ADE with vegetation in Central Amazonian forest. Finally, I will present the research objectives, with a brief introduction to the study site and Figure 1.1 Map of Bolivia showing the location of Llanos de Moxos in the Northern region of the the thesis outline. country and the location of La Chonta, the study site of this thesis. After contact with Europeans in the early 16th century, the indigenous population was decimated due to diseases from the old world and slavery (Myers et al. 2003). Therefore, settlements were abandoned allowing forest regeneration. Besides, during the 17th and 18th

10 11 Chapter 1 Introduction

11 Chapter 1 Introduction

centuries the remaining indigenous populations went through a process of transformation in additions of cations (Ca, magnesium -Mg) to the soil (Costa et al. 2009). Nevertheless, there their demographic patterns and land uses practices (Denevan 1992, Cleary 2001). For is a large variability in nutrient values among different ADE sites (Falcão et al. 2009). The example, the decrease in population size, following indigenous depopulation, changed labour black colour of these soils is mainly caused by high concentrations of organic matter, charcoal intensities for provisions (Walker 2000). The introduction of metal tools (i.e. metal axes, and carbon (C) (Glaser et al. 2003). It is believed that it is the high charcoal concentration in ‘machetes’) changed the way how forests were cleared up for agriculture (Denevan 1992); the soil that contributes to nutrient retention over long periods of time (Glaser et al. 2003, and the introduction of animals such as cows and horses became important in the daily life of Lehmann et al. 2003b, Glaser 2007, Novotny et al. 2009). This explains that, even though the the missions in Moxos (Walker 2000). Consequently, the Amazonian forest has recovered age of these soils dates from ca. 2400 to 500 yr BP (Neves et al. 2003), the nutrient levels after abandonment by indigenous people after their contact with Europeans, but has gone seem to be stable over time. through more intensive landscape transformations by practices taught by the missionaries and Currently, ADE are used for cultivation of home garden, agroforestry, and slash-and-burn colonizers. agriculture (Hiraoka et al. 2003, Clement et al. 2009, Junqueira et al. 2010, Junqueira et al. 2011). People cultivate in these soils because crop biomass is higher, e.g., the production of Amazonian Dark Earths rice and beans doubled when cultivated on ADE compared with adjacent ferralsos (Oxisols) General characteristics (Lehmann et al. 2003b). Additionally, the time of the cropping period can be reduced until four times on ADE respect to surrounding non-ADE soils (German 2003, Major et al. 2005a, Amazonian Dark Earth or ‘Terra Preta’ (black earth in Portuguese) is a type of anthrosols Major et al. 2005b). In some sites, communities in the Amazonian region gather fruits and (man-modified soils) characterized by a thick dark or gray top layer with presence of ceramic wood from ADE sites that undergo a fallow period and are occupied by secondary forest pieces and small fragments of charcoal indicating a direct relation with past indigenous (Clement et al. 2009, Junqueira et al. 2010). inhabitation (Sombroek 1966, Smith 1980, Woods and Glaser 2004). It is still under debate whether these soils were intentionally created or not, and scientific evidence is accumulating Amazonian Dark Earths and ancient human forest use in Bolivia for support of both possibilities in ADE sites. Non-intentional origin of ADE is associated Past human history of the Bolivian Amazonian forest has been documented mainly in the with the accumulation of debris from daily life activities (WinklerPrins 2014). Intentional Llanos de Moxos in Beni department, Bolivia, where monumental earthworks composed of origin of ADE is associated with the piling up of kitchen remains and charcoal around the rings, causeways, and dikes are the landmark of the landscape (Erickson 2006, 2008, villages over hundreds of years; a practice that is also observed in current indigenous Lombardo and Prümers 2010). Recent studies have also documented the presence of ADE in communities in the Xingu Region in Mato Grosso state in Brazil (Schaan et al. 2009). But the area with presence of ceramics, high soil fertility (Walker 2011) and elevated values of regardless of its origin, what is relevant to the context of ADE is the fact that these soils are soil pH and P (Hastik et al. 2013). About 250 Km to the South-East of Llanos de Moxos (Fig. the result of sedentary patterns of inhabitation of the Amazonian forest (Arroyo-Kalin 2010, 1.1); there is evidence of ADE presence in a forested area ceramics and high levels of soil pH, WinklerPrins 2014). Evidence indicates that pre-Columbian indigenous people inhabiting P and Ca (Calla 2003, Paz-Rivera and Putz 2009). ADE areas were fisherman, hunters, fruits and nuts gatherers, and growers of manioc The human historical context of the region of Llanos de Moxos is associated with (Cassava sativa), maize (Zea mays), some tubers, and trees (Myers et al. 2003, Neves et al. populations of gatherers since ~10.000 yr BP (Lombardo et al. 2013), fisherman (355 yr BP), 2003, Bozarth et al. 2009, Rebellato et al. 2009). This implies a semi-permanent or permanent and farmers probably since ~1600 y BP (Whitney et al. 2014). Besides of the construction of cultivation that suggests human-induced openings and disturbances of the Amazonian forests. raised fields (Erickson 2006), inhabitants of the region used fire possibly associated with The sizes of ADE patches range from less than 2 ha to more than 100 ha throughout the agricultural practices (Whitney et al. 2014). These fire practices have been, however, dynamic Amazon basin (Winklerprins and Aldrich 2010). Recent estimates of the presence of ADE in through time decreasing around the middle of the 17th century (Whitney et al. 2014). the region account for around 3.2% (McMichael et al. 2014), being distributed in Bolivia, Chronicles of the 16th century that relate the encounters between Spaniards and missionaries Brazil, Ecuador, Guyana, Peru, and Venezuela (WinklerPrins 2014). The fertility of these with local inhabitants, describe the regions of Llanos the Moxos inhabited by a wide variety soils is high when they are compared with the surrounding Ferralsols (Oxisols) and Acrisols of small communities, apparently each with its own language and settlements conditioned to (Ultisols), typical of the Amazon basin. In general, ADE soils contain high concentrations of the geographical context. Thus, there where communities with ‘amphibian’ cultures used to total and available P (average of available phosphorus –P- higher than 35 ppm) and calcium -1 regular flooding, and practicing agriculture in elevated zones; other communities in the region (Ca) (average 7.1 cmol kg ), and elevated soil pH (values superior to 5.5) (Lehmann et al. of Baures opened small areas with fire to cultivate products such as bitter and sweet manioc, 2003b). The increase in these elements is a consequence of addition and accumulation in the peanuts, squash and peppers. Additionally, fishing when communities were living close to soil of animal bones (Lima et al. 2002), while the increase in soil pH can be a consequence of

12 13 Chapter 1 Introduction

13 Chapter 1 Introduction

rivers, and hunting of peccaries, deer, tapir, are described as the main ways of living (Block Ecuador and Colombia, distribution of more than one third of the studied tree species was 1994). associated with soil nutrients (John et al. 2007). Distribution of tree species in the Amazonian The human history before the contact with Europeans of the region outside from the forest responded to soil resources and hydrological conditions related to topography (Duque Llanos de Moxos (where this study took place) is hardly documented. It seems that by the end et al. 2002, Schietti et al. 2014). Similar associations with soils have been found for other of the 17th century, Jesuits missionaries explored the region, describing the presence of around plant groups. In western and central Amazonian forest, floristic composition of understory 60 different towns of around 60 people each (Van der Berg 2006). The inhabitants of this ferns, herbs and palms was correlated with clay percentage and cation concentration of Ca, region, which are called Guarayos, were closely related to the Tupi-Guaraní speaking Mg, K and Na (Tuomisto et al. 1998, Costa et al. 2005, Poulsen et al. 2006, Jones et al. 2008, indigenous people that migrated from the south, today Paraguay, around the 13th century Zuquim 2008). Besides, fern and Marantaceae diversity increased with cation concentration in (Calla 2003, Paz 2003). The Guarayos lived in small groups in the forest, and hunted and the soil in central and western Amazonian forest (Tuomisto et al. 1998, Costa et al. 2005, practiced agriculture (Calla 2003). With the influence of missionaries, they started to use Costa 2006). metal tools that allowed them to open the forest, creating gaps for cultivation of useful plants, and to establish home gardens (Van der Berg 2006). Explorers at the start of the 19th century Amazonian Dark Earth and species composition describe the practices of the Guarayos as a shifting cultivation system with crops of corn, Some studies have evaluated the effect of ADE on plant distribution in the central Amazonian cassava, and squash (D'Orbigny 1847). forests. In abandoned crop lands close to the city of Manaus, both diversity and biomass of weeds were high on ADE when compared to abandoned crop lands on adjacent non-ADE Soil and vegetation in the Amazonian forest soils (Major et al. 2005b). In another study carried out on secondary forest of until 30 years after abandonment, plots established on ADE and on adjacent soils (non-ADE) shared only Soils and species composition 46% of understory palm species and only 23% of the woody species (Diameter at Brast Forests in the Amazonian region are shaped mainly by a complex geomorphologic history Height [DBH]>5cm) (Junqueira et al. 2010). A study comparing an old growth forest that influences soil nutrients and climate that determines flooding and drought seasonality (between 300 and 700 years old) on ADE with forest of non-ADE found that only ~7% of (Hoorn and Wesselingh 2010, Stropp 2011). At large scale, tree diversity patterns and tree species (>10cm stem diameter) were shared between these forests, and that tree diversity was species composition can be explained by current climate and soil fertility levels. Broad lower on ADE soils (Clement et al. 2009).These results suggest that ADE affects the climate patterns range from seasonal sites (> 1 month with < 100mm of rain) in the South- distribution of some plant species, mainly due to cultivation of some useful species in sites of East to non-seasonal sites (0 to 1 month with <100mm of rain) in the North-West (ter Steege ADE or close to it (Junqueira et al. 2010, Junqueira et al. 2011). Nevertheless, results from an 2010, Stropp 2011). Soil fertility ranges from poor soils that originated from the Precambrian old growth forest (~ 480 yr BP) in the southern border of the Bolivian Amazonia showed that (Guyanese and Brazilian) shields in the Eastern region to richer soils that originated from density of man-used tree species was similar between ADE sites and non-ADE sites (Paz- sediments during the origin of the Andes in the Western region (Quesada et al. 2010, Quesada Rivera and Putz 2009). et al. 2011). Thus tree α-diversity is lower in the North-East and increases towards the west, In this thesis I evaluate the effect of ADE on understory herb, fern, and palm species with Eastern forests being composed by a higher proportion of Leguminosae, high wood composition and diversity (Fig. 1.2). Given that nutrient levels of ADE are high, fertility of density, and larger seeded species (ter Steege et al. 2006, ter Steege 2009, 2010, Quesada et the soils could have an effect on angiosperm herb, fern and palm species composition and al. 2012). Other plant groups show similar distributional patterns; for example, fern species richness (Costa et al. 2005, Major et al. 2005b, Costa 2006, Poulsen et al. 2006, Zuquim et al. diversity increases with a gradient of cation concentration in the soil that goes from north-east 2012) (Fig. 1.2). Additional to soil effects, composition, richness and abundance of palms can to south-west (Tuomisto et al. 2014). Similarly, changes in fern composition follow changes be the consequence of the use of these species by humans (Levis et al. 2012). in cation concentration in the soil observed in the Amazon basin (Zuquim et al. 2014). At local scale (mainly < 1 km2) diversity is constrained by dispersal limitation and Soils and forest structure and dynamics environmental filters (ter Steege 2010). When soils are studied at a small scale (50 ha), the Soil properties do not only affect species distribution and composition, but also drive forest ability to correlate plant distribution with soil properties is improved (John et al. 2007, structure. In the whole Amazonian forest basal area and liana density increase with the Dalling et al. 2012). In the case of the Lambir 50 ha plot in the peninsula of Malaysia, small- amount of soil nutrients (Malhi et al. 2004, Aragão et al. 2009, Quesada et al. 2009), while in scale variation in soil chemistry was correlated with tree distribution; in particular, Mg and P a regional study in the Bolivian Amazonia the density of trees and palms and the liana drove these patterns (Davies et al. 2005). In tropical regions such as forest of Panama, infestation show the opposite trend (Toledo et al. 2011a). At local scale, aboveground

14 15 Chapter 1 Introduction

15 Chapter 1 Introduction

biomass increased with increasing soil nitrogen (N), and percentage of clay (Laurance et al. sites covered a wide spatial gradient of soil fertility with plots located on sites with low 1999), and differences in texture caused by topography such as higher elevations (de Castilho nutrient soils such as white sands and on sites with high nutrients such as an ADE site. Thus, et al. 2006). Besides, trees > 30 cm DBH had higher aboveground biomass in more fertile on these contrasting soils, NPP increased almost twice in the soils with high nutrient soils compared with less fertile soils in western Amazonian forest (DeWalt and Chave 2004). availability specially available P, compared with the less fertile soil (Aragão et al. 2009). Similarly, a forest growing on ADE had higher gross primary productivity (GPP), NPP, and ADE high rates of fine root production compared with a forest growing on non-ADE (Doughty et al. 2014). In this thesis, I evaluate the effect of nutrients increases in ADE on forest structure and Human settlements High soil fertility dynamics. Increases in soil P, for example, directly could affect basal area (Quesada et al. 2009), tree growth (Aragão et al. 2009) and lianas (DeWalt et al. 2006). Increases in growth (Un)intentional and mortality rates (Quesada et al. 2012) could create gaps in the forest that could influence Over- Seed dispersal the presence of lianas, which in turn, could negatively affect tree mortality (Phillips et al. exploitation Cultivation/Gathering Species responses 2005). Likely, an increase in nutrients in the soil would shift species composition to more fast growing species, which will have an effect on the forest dynamics (Quesada et al. 2012) (Fig. 1.3).

ADE Palm composition Herb composition Fern composition Palm diversity Herb diversity Fern diversity Palm abundance Herb abundance Fern abundance Human settlements Increased soil fertility

Figure 1.2 Conceptual diagram of the relationships between ADE soils and species composition, Disturbance diversity and abundance. The increase in soil fertility on ADE could determine species composition through species responses to soil fertility. Similarly, soil fertility will affect herb and fern diversity and Open cleared areas abundances. These relationships are indicated with solid arrows and will be evaluated in this thesis. Gap formation

Species that historically had been used by humans, such as palms, can show responses to soil nutrients Changes in distribution but their composition, diversity and abundance can also be affected by management of past inhabitants of ecological guilds: Recruitment rate Growth rate Mortality rate Liana infestation Basal Area of the area of La Chonta. These relationships are indicated with dashed lines, and will not be directly Proportion of short-lived and long-lived pioneers Forest dynamics Forest structure evaluated in this thesis. In addition to the effects of soil in forest structure, forest dynamics have been shown to be strongly associated with soil characteristics. During the past 25 years, tree recruitment Figure 1.3 Conceptual diagram of the relationships between ADE soils and forest structure and and mortality rates have increased in the whole Amazon basin, with recruitment rates being dynamics. Increased soil fertility can affect forest structure (by increasing basal area and liana higher than mortality rates (Phillips et al. 2004). This increase was higher in the fertile infestation) and forest dynamics (by increase tree growth). Increases in tree growth could affect mortality rates which affect gap formation and modify forest structure. Increases in nutrients on ADE western than in the more infertile eastern Amazonian forest (Phillips et al. 2004). The could affect species composition favouring fast-growth species that affect forest dynamics. These mechanisms that explain this pattern are not fully understood yet, but the increased dynamics relationships are evaluated in this thesis and are represented by continuous arrows. Disturbance can be associated with changes in species composition along a gradient of soil fertility. On generated by human inhabitation also could influences species composition; this association is shown one hand, sites with higher growth and mortality rates are associated to species with low by dashed lines and will not be directly evaluated in this thesis. wood density (Poorter et al. 2008), perhaps because of the presence of more pioneer or long- Plant responses to soil nutrients lived pioneer species in the west. On the other hand, sites with lower mortality and growth rates are associated high wood density, in this case more shade tolerant species in the east (ter Plant responses to nutrients are intended to maximize growth rates (Poorter and Nagel 2000) Steege et al. 2006, Quesada et al. 2012). and minimize mortality (Walters and Reich 2000). Although there is no unique strategy for Forest productivity can be also affected by soils. Total net primary productivity (NPP) growth maximization, in poor nutrients environments plants generally respond in a was related to soil fertility across 10 1-ha plots distributed across the Amazonian forest. These conservative way by storing carbon and nutrients in denser and tougher tissues or increasing

16 17 Chapter 1 Introduction

ADE Palm composition Herb composition Fern composition Palm diversity Herb diversity Fern diversity Palm abundance Herb abundance Fern abundance Human settlements Increased soil fertility

17 Chapter 1 Introduction

organ life span (Reich et al. 1991, Eissenstat et al. 1997, Hill et al. 2006). In fertile Hypotheses environments, plants generally respond in an acquisitive way by increasing specific leaf area (SLA) and nitrogen (N) and phosphorus (P) concentration in leaves (Ordoñez et al. 2010). But Human-induced changes through ADE might change characteristics mainly related to these responses can be modulated by ways in which plants use and incorporate nutrients from soil fertility. For example, charcoal addition potentially leads to more stable organic matter, the soil leading to a specific elemental composition (Sardans and Peñuelas 2014). For higher nutrient levels and probably to higher soil water retention capacity in ADE compared example, the increase in soil fertility will increase the growth of saplings of tropical trees to non-ADE soils (Glaser et al. 2002), while the addition of organic waste leads to higher except for legume species (Baribault et al. 2012). Among nutrients, N and P are the major nutrient (P, N, and Ca) concentrations (Glaser 2007). Higher Ca concentrations increase the limiting elements for primary productivity of forests (Vitousek et al. 2010). Nevertheless, base exchangeable concentration and pH of the soil, which in turn also increases the recent studies have found that potassium (K) and Ca influence plant growth in tropical forest availability of other cations (Lambers et al. 2008a). The increase in fertility of ADE could affect forest composition through species-specific responses to soil nutrients and plant growth in similar ways as N and P (Wright et al. 2011, Baribault et al. 2012). In this thesis I evaluate the responses of tree seedlings to nutrients in ADE using a that ultimately determines forest structure and forest dynamics (Fig. 1.4). Finally, forest controlled experiment. Assessing changes in plant traits associated to fertility of ADE species composition can be also affected by human enrichment of useful plants in or close by provides a unique opportunity to understand tree responses to soil fertility, especially inhabitation (Fig. 1.4), but this is probably not the case in La Chonta forest because previous responses to specific nutrients such as P and Ca. These two elements are gaining importance studies did not found strong associations between species useful for humans and past settlements (Paz-Rivera and Putz 2009). in explaining the CO2 uptake of tropical trees (Asner et al. 2014). Previous studies on crops have shown that in fertile environments such as ADE, rice and beans have doubled the ADE production compared with crops cultivated on natural Ferralsols in the Amazonian region (Lehmann et al. 2003b). For this reason, ADE are often used by local farmers to grow crops faster and for longer periods of time compared with slash and burn agriculture done on Human settlements Increased soil fertility regular soils in the Amazon basin (German 2003).

Research objectives and hypotheses

This research aims to evaluate the effect of Amazonian Dark Earths soil fertility on species Plant responses composition and forest structure and dynamics in a southern Amazonian forest in Bolivia, and to understand possible mechanisms that explain patterns of plant distribution and forest Palm composition Fern, herb composition Tree composition Forest dynamics Forest structure dynamics at local scales. Species composition

Specifically, this study evaluates: Figure 1.4 Conceptual framework of the research reported in this thesis concerning the effect of 1) The effects of ADE on the composition, diversity and density of plant life forms that Amazonian Dark Earths (ADE) on vegetation attributes. Soil and vegetation are connected, in this respond to soil nutrients such as angiosperm herbs, ferns and palms. thesis, through changes in forest composition (Chapter 2), structure and forest dynamics (Chapter 3), that can be a consequence of specific relationships between the increase in fertility of ADE and plant 2) The effects of ADE on forest structure and dynamics; evaluated through empirical field responses (Chapter 4). Continuous arrows represent the relationships that are directly evaluated in this thesis. Dashed arrows represent weak effect of humans on species composition for La Chonta forest data; through selective harvesting and enrichment activities (Paz-Rivera and Putz 2009), which are not directly evaluated in this thesis. 3) The effects of ADE on the nutrients through the evaluation of growth and performance of tropical tree seedlings in a controlled experiment. With this experiment it will be possible to Thesis outline unravel the mechanism that explain species responses to ADE. This dissertation consists of five chapters including this general introduction (chapter 1), three research chapters (chapters 2-4) and a chapter that synthesizes the results (chapter 5). To identify sites where ADE were present, I screened the soils in 12, 27-ha permanent plots of La Chonta forest by characterizing soil colour and recording the presence of ceramics. I

18 19 Chapter 1 Introduction

Research objectives and hypotheses

19 Chapter 1 Introduction

established small plots to relate understory species composition (chapter 2) and forest contextualize the ADE characteristics in the southern Amazonian region (where background structural and dynamic variables (chapter 3) to ADE soils characteristics. Besides, I used a soil fertility is high) and discuss the main differences with ADE from the central Amazonian greenhouse experiment to test whether ADE soil collected at La Chonta affected tree forest (where the background soil fertility is much lower). Second, I discuss the effects of seedlings performance (chapter 4). nutrient addition on a fertile soil on individual plants and forest growth. Lastly, I discuss the In the Amazonian region, soil nutrients can play a role in the distribution of understory human legacies in the soil and in the vegetation and the indirect human legacies in the species (Costa 2006, Zuquim et al. 2014). In chapter 2, I evaluated the effect of ADE changes vegetation through the soil. in soil nutrients on understory species composition of an Amazonia Bolivian forest. I screened soils by looking for sites where ADE could be present and I recorded the composition of Study site terrestrial ferns, angiosperm herbs and understory palms. These groups of plants were selected because it has been shown that they respond to nutrients in the soil (Vormisto et al. 2004, This study was carried out in the forestry concession of Agroindustria Comercial La Chonta, Costa 2006, Costa et al. 2009, Zuquim et al. 2014). Additionally, by looking at understory on Guarayos province, Santa Cruz Department, Bolivia (15º47’S, 62º55’W). This is a 100,000 species such as ferns I could separate the effect of soil alone from the effect of human ha semi-deciduous tropical moist forest. Mean annual temperature is 24.3 °C and annual enrichment as, to this date, ferns have not been reported as being used by humans in the past, rainfall is 1580 mm (data from 2000-2006 from La Chonta sawmill) with a dry season (<100 which, in the contrary, is the case for palms and herbs. Subsequently, I correlated soils mm/month) from May through September (Peña-Claros et al. 2012). variables associated with ADE with species composition, diversity and abundance of these La Chonta is located in a confluence between old and young soils. The old soils are an three groups of plants. extension of the Precambrian Brazilian Shield, whose southern boundary is around 250 km Soil fertility has an effect on forest structure and dynamics throughout the Amazonian from the concession, and the young soils overlaid the basement rock with alluvial deposits forest. Nevertheless, at small scales the role of soils in forest dynamics in the Amazonian from the Tertiary and Quaternary (Burbridge et al. 2004, Gosling et al. 2005) that are a forest is less clear (Phillips et al. 2004, Malhi et al. 2006). Studying forest structure and consequence of the uplifting of the central Andes (Latrubesse et al. 2010). Soils at La Chonta dynamics at local scale will improve our ability to separate the effects of edaphic variables can be mostly Cambisols, although there are also Histosols, Gleysols, Leptosols/Regosols and from topographic variables and from successional sequences (Pan et al. 2013). Knowing the Anthrosols (see Appendix of this thesis). Cambisols are generally young, and not necessarily drivers of forest growth will contribute to improve forest production for C sequestration and very fertile. Histosols and Gleysols are soils associated with lower areas in the landscape. climate change mitigation (Malhi et al. 2009). In chapter 3, I tested the hypothesis that Leptosols are thin soils, not very deep, with coarse material, and presence of gravel very close increased fertility of ADE soils could have an effect on forest structure and forest dynamics as to the soil surface; they are found in the top of the small hills. Finally, Anthrosols are present a result of its effects on tree growth, tree recruitment and tree mortality. I used long term (~10 in La Chonta, indicated by the presence of a horizon with dark soil color (Munsell 7.5YR year) data on forest dynamics of 12 large permanent plots (~27 ha each), and a statistical 2.5/1) and remains of artefacts, such as pieces of ceramics or human generated charcoal. modelling approach to relate soil variables (pH, P, N) with forest attributes (stand basal area, The forest at La Chonta can be classified as a semi-deciduous tropical moist forest liana infestation, and successional composition) and dynamics (recruitment, mortality and (Peña-Claros et al. 2012). Floristic composition suggests that the forest at La Chonta is growth). The effect of soils was evaluated in two steps: first I determined the effect of soils on transitional between the forest of the northern Amazonian region and the southern Chiquitano forest attributes and then I determined the effect of soils and forest attributes on forest dry forest, with a higher abundance and frequency of Pseudolmedia laevis, Licaria triandria dynamics. and Ampelocera ruizi (Toledo et al. 2011a). This mixture of dry and wet forest species seems Increases in soil nutrients can drive plant responses such as increases in growth and to be related with the paleo-climatic history of the region that was a mixture of seasonal forest decreases in mortality, which can lead to successful establishment and species regeneration in and savanna between 50,000 and 11,400 yr BP and only had non-seasonal species and species the forest (Poorter and Nagel 2000, Walters and Reich 2000, Fine and Kembel 2011). In of humid and wet forest appearing in the paleo-palynological record between 6,000 and 2,000 chapter 4, I tested the hypothesis that the increase in nutrients in ADE increases seedling yr BP (Mayle and Whitney 2012). The forest has an average height of 27 m, and growth of selected tree species, and that this increase is a consequence of changes in plant approximately one third of the canopy species shed the leaves in the dry season (Markesteijn traits. To address this question I conducted a shade-house experiment with seedlings of 17 et al. 2010). The forest has, on average, 367 stems (>10 cm DBH) and 59 species per ha tree species. I planted them in ADE and non-ADE collected in La Chonta forest and grew (Peña-Claros et al. 2012). them for ~4 months. The forestry concession of La Chonta was established in 1974. During its first 20 Finally, in chapter 5 I discuss how ADE can be considered a human legacy in this years, the concession logged only mahogany trees (Swietenia macrophylla) and tropical cedar southern Amazonian forest, that affects species composition and forest structure. First, I (Cedrela fissilis). Before this time there was no evidence of forest management in the area.

20 21 Chapter 1 Introduction

21 Chapter 1 Introduction

Nevertheless, the presence of ADE soil with pieces of charcoal and pottery of 400 to 300 yr 1. The effect of Amazonian Dark Earths on the composition, structure and dynamics of a BP indicates that there were indigenous settlements in the area (Paz 2003). Currently, the Bolivian forest (this thesis) concession harvests around 3 trees/ha (7.7 m3/ha) belonging to 17-19 tree species. Besides, in this area the Bolivian Forest Research Institute (IBIF, in Spanish: Instituto Boliviano de 2. The current use of anthropogenic soils by local people in Central Amazonia (André Braga Investigacion Forestal) carries out the Long-term Silvicultural Research Program (LTSRP) Junqueira) that consist in 12 large-scale (27 ha) permanent sample plots distributed in three logging compartments, which were harvested by La Chonta company between 2001 and 2002, after 3. People, soils and manioc interactions in the upper Amazon region (Clara Patricia Venegas) plot establishment (Peña-Claros et al. 2008). The forest of La Chonta is an interesting forest to study ADE and test hypotheses 4. The recreation of Terra Preta ‘nova’ by understanding the effects of fire in processes of about the effects of ADE on forest composition, structure and dynamics because of the char production in Brazil (Paulo Paiva). following reasons: First, given its borderline location in the Amazon River basin, La Chonta is one of the most southern sites with ADE reported until present; Secondly, In La Chonta 5. Challenging the claims on the potential of biochar to mitigate climate change (Tatiana ADE is located in sites that are around 14 to 30 km distant from the rivers Blanco and Negro, Rittl) respectively (Paz-Rivera and Putz 2009), which is not common for ADE sites; Thirdly, differently from most of the reported ADE sites in the Amazonian region, ADE underlying 6. The role of black carbon (BC) and its interactions with soil Ca, and P in the stabilization of soil is fertile; Finally, the Instituto Boliviano de Investigacion Forestal (IBIF, in Spanish) has soil organic matter (SOM) in Brazil (Carlos Alho). established permanent plots since 2000 making regularly censuses that brings a unique opportunity to evaluate the effects of increases in soil fertility on forest dynamics and Social Sciences Natural Sciences structure.

Institutional context Future 3 & 6 Terra Preta Nova

This study was conducted within the framework of the Terra Preta Program (http://www.terrapretaprogram.org/). The program was initiated in 2010, with funds of INREF Interdisciplinary Research and Education Fund (The Netherlands), and associated Present 2 & 3 Agriculture 1 Forest partners from Brazil (EMBRAPA, Instituto Nacional de Pesquisas da Amazonia), Bolivia (IBIF, Instituto Boliviano de Investigacion Forestal), and Colombia (Universidad Nacional de Colombia). This research programme integrates social and natural sciences in order to understand 5 Nutrient Balance the context in which the Terra Preta phenomena originated, the current uses and practices on Terra Preta by Amazonian inhabitants; and its future as possible alternative for a sustainable Past & Food Web agriculture in the Amazonian region (Fig. 1.5). For these purposes, the program consist of 6 Ph.D. interdisciplinary projects aiming to integrate information on the opportunities and Figure 1.5 Diagram showing how the social and natural sciences are combined in the Terra Preta Program. Note that the different studies allow connecting different time scales: past, present and disadvantages of ADE for their sustainable use and management. The contents of expected future. Numbers refer to the six PhD research topics included in the program. Research topic 1 was dissertations are briefly outlined below: carried out in Bolivia, research topic 3 in Colombia, and research topic 2, 4, 5 and 6 in Brazil.

22 23 Chapter 1 Introduction

Institutional context Future 3 & 6 Terra Preta Nova

23 Chapter 2

Amazonian Dark Earths Shape the Understory Plant Community in a Bolivian Forest

Estela Quintero-Vallejo, Yannick Klomberg, Frans Bongers, Lourens Poorter, Marisol Toledo and Marielos Peña-Claros

Published in Biotropica (2015) 14: 89-96

25 Chapter 2

Amazonian Dark Earths Shape the Understory Plant Community in a Bolivian Forest

Estela Quintero-Vallejo, Yannick Klomberg, Frans Bongers, Lourens Poorter, Marisol Toledo and Marielos Peña-Claros

Published in Biotropica (2015) 14: 89-96 Chapter 2 ADE and Understory Species Composition

Abstract Introduction

Amazonian Dark Earths (ADE) are the result of human modification of the Amazonian Plant diversity and composition are driven by historical events, dispersal limitation, and landscape since pre-Columbian times. ADE are characterized by increased soil fertility biological (Fine et al. 2004), environmental (ter Steege 2009), and anthropogenic filters (Ross compared to natural soils. In the Amazonian forest, soil fertility influences understory herb 2011). These filters determine the locations that are adequate for germination, growth and and fern species composition. However, little research has been done to evaluate the effect of survival of plants (Fine and Kembel 2011). In Amazonian forest, edaphic characteristics are ADE on the composition of the understory community. We evaluated the effects of ADE and one of the most important environmental filters, shaping the floristic composition and soil in 36 plots (150x4m) established in a Bolivian moist forest (La Chonta). For each plot we diversity of understory communities. Several studies have shown that exchangeable base determined soil nutrients, and the composition, richness, and abundance of terrestrial fern, concentration and to some extent clay percentage in the soil influenced the distribution angiosperm herb, and understory palm species. We found that the presence of ADE created a patterns of understory fern, angiosperm herbaceous and palm species (Costa et al. 2005, gradient in soil nutrients and pH that affected the understory species composition, especially Poulsen et al. 2006, Costa et al. 2009, Kristiansen et al. 2012, Zuquim et al. 2014). Also it has of ferns and palms. Additionally, the higher nutrient concentration and more neutral soil pH been found that exchangeable base concentration positively affected species richness of on ADE soils caused a decrease of fern species richness. We therefore conclude that the Marantaceae and ferns (Costa 2006, Tuomisto et al. 2014) while it negatively affected palm current composition of the understory community in this particular Bolivian forest is a species richness in western Amazon basin (Vormisto et al. 2004 ). reflection of past human modifications of the soil. Plant distribution is not only shaped by biological and environmental factors, but also by anthropogenic factors. Humans may change the environmental conditions (e.g., light, soil nutrients), and hence plant distribution through disturbance of the vegetation (e.g., shifting Keywords: Anthrosols; terrestrial angiosperm herbs; terrestrial ferns; soil pH; understory cultivation) and soil modification. Humans also may directly impact species distribution by palms; Bolivia; Terra Preta. harvesting useful species or by deliberately planting them. In Central Amazon, for example, there is a higher abundance of useful tree and palm species in areas surrounding past human settlements (Levis et al. 2012). Pre-Columbian civilizations have strongly affected the environment in several ways: through the presence of roads, moats, and bridges at upper Xingu in Mato Grosso Brazil (Heckenberger et al. 2003); earth works in the province of Beni, Bolivia (Erickson 2010); and the pervasive existence of Amazonian Dark Earths (ADE) in the Amazon basin (Sombroek 1966, Glaser and Birk 2012). In this paper, we focus on ADE or black earth (‘Terra Preta’ in Portuguese), which is a type of anthrosol characterized by a thick dark or gray top layer with presence of ceramics indicating past indigenous settlements (Sombroek 1966, Woods and Glaser 2004). ADE are the product of kitchen remains, such as garbage deposition, over thousands of years (Schaan et al. 2009). ADE patch sizes range between 2 ha to more than 100 ha (Winklerprins and Aldrich 2010), and possess higher concentrations of total and available phosphorus (P) and calcium (Ca) (from the addition of animal bones to the soil), higher total nitrogen (N) (Lehmann et al. 2003b), and higher concentrations of charcoal and carbon (C) (Glaser et al. 2003) than the surrounding ultisols and oxisols from most of the Amazonia (Falcão et al. 2009). When ADE soils are present in a given area, they form a gradient of fertility from the center of the ADE patch to the surrounding natural soils (Fraser et al. 2011, Costa et al. 2013). Human-induced changes in soil structural, physical, and chemical properties, and soil processes have the potential to have long-lasting effect on the understory vegetation. For example, charcoal addition leads to more stable organic matter, nutrient levels and probably to higher soil water retention capacity in ADE compared to non-ADE soils (Glaser et al. 2002), while the addition of organic waste leads to higher nutrient concentrations (P, N, and

26 27 Chapter 2 ADE and Understory Species Composition

Abstract Introduction

27 Chapter 2 ADE and Understory Species Composition

Ca) (Glaser 2007). Higher Ca concentrations increase the base exchangeable concentration Peña-Claros et al. 2012). A previous study showed that ADE soils in La Chonta forest cover and pH of the soil which in turn, also increases the availability of other cations. In about 20 percent of a studied area of 216 ha (Paz-Rivera and Putz 2009). The forest at La combination, the increased nutrient storage and nutrient availability enhance plant nutrient Chonta can be classified as a semi-deciduous tropical moist forest (Peña-Claros et al. 2012). uptake (Neumann and Römheld 2012). The interplay between nutrient supply and species- Floristic comparisons of several lowland forests in Bolivia show that the forest at La Chonta specific nutrient demands determine in the end where species occur. At the lower end of the is transitional between the forest of the Northern Amazonia and the Southern Chiquitano dry soil fertility gradient an increase in nutrients has the potential to fuel plant growth, and forest (Toledo et al. 2011a). The forest canopy has an average height of 27 m, and enhance species abundance and richness. At the higher end of the soil fertility gradient approximately one third of the canopy species shed the leaves in the dry season (Markesteijn species richness may even decline, when nutrient availability becomes supra-optimal, or when et al. 2010). The forestry concession of La Chonta was established in 1974. Currently, they fast-growing species dominate and outcompete the others (Grime 1973). harvest around 3 trees/ha (7.68 m3/ha) belonging to 17-19 tree species. During its first 20 So far, few studies have examined the possible effect of ADE and associated human years, the concession logged only trees of mahogany (Swietenia macrophylla) and tropical activities on plant distribution and floristic composition. In recently abandoned croplands cedar (Cedrela odorata). Before this time there was no evidence of forest management in the there is a higher diversity and biomass of light-demanding weeds on ADE soils than in area. Nevertheless, the presence of ADE soil with pieces of charcoal and pottery of 400 to 300 adjacent non-ADE soils (Major et al. 2003, Major et al. 2005b). Young secondary forests on yr B.P. indicates that there were indigenous settlements in the area (Paz 2003). Research was ADE and non-ADE soils shared only 23% of woody species (palms and trees with diameter to carried out in twelve large-scale (27 ha) permanent sample plots established by the Bolivian breast height [DBH] > 5cm) and 46% of understory palm species. This low similarity was Forest Research Institute (IBIF, in Spanish), hereafter referred as the IBIF-permanent plots, attributed to differences in soil fertility and to the enrichment with useful tree and palm that are part of the Long Term Silvicultural Research Program, LTSRP (Peña-Claros et al. species at ADE sites (Junqueira et al. 2010, Junqueira et al. 2011). Finally, the only study 2008). Plots were distributed in three logging compartments, which were harvested by the performed in an old-growth forest showed that density of human-used tree species was similar company between 2001 and 2002, after plot establishment. between ADE and non-ADE sites in the Bolivian Amazonia (Paz-Rivera and Putz 2009). In this paper, we evaluate the effect of ADE soils on the composition and diversity of Data collection understory angiosperm herbs (hereafter referred to as herbs), ferns, and palms in an Amazonian Bolivian forest. We tested the hypothesis that the physical and chemical First, we performed a preliminary survey in the IBIF-permanent plots, using the grid system characteristics of soils differ along a gradient from ADE to non-ADE soils. We then tested the of the 27 ha plots to make an inventory of the presence of ADE soils. At each 50 x 50 m grid hypothesis that floristic composition, species richness and abundance of understory herbs, intersection of each plot, a soil sample was cored with an auger, providing 130 sample points ferns and palms differ along the gradient from ADE to non-ADE soils. We predict that soil per plot, for a total of 1750 points. Presence of ceramics and charcoal was evaluated visually, fertility and soil organic matter (and with that indirectly also soil water content) would and the soil colour was determined with Munsell Soil Color Charts. Subsequently, we made increase along the gradient from non-ADE to ADE soils; and that species richness and soil color maps using the Kriging interpolation function of ArcMap software (Klomberg abundance of herbs, ferns and palms would increase with soil fertility and soil organic matter 2012). Based on the soil color maps and the presence of ceramics and charcoal, we (and thus soil humidity), as has been reported elsewhere in mostly oligotrophic Amazonian established a total of 36 rectangular plots (150 x 4 m). Fourteen plots were established in forests (Tuomisto et al. 2002, Vormisto et al. 2004, Costa et al. 2005, Tuomisto et al. 2014). areas with dark soil (black or very dark brown color, mainly 7.5YR 2.5/1, 7.5YR 2.5/2 and 7.5YR 2.5/3 in the Munsell system); these plots were considered ADE plots. Fourteen additional paired plots were established on adjacent areas with lighter soil color (brown, Methods yellowish brown or reddish brown, mainly 7.5YR 4/3, 7.5YR 4/4, 7.5YR 5/4, 5YR 5/3); these Study site were considered as non-ADE soil plots. Distance between adjacent plots ranged between 120 and 400 m. Four additional pairs of plots on ADE sites previously determined by (Paz-Rivera The study was conducted in the 100,000 ha forestry concession of Agroindustria Comercial and Putz 2009) were established outside the IBIF-permanent plots, so that the total sample La Chonta, Guarayos province, Bolivia (15º47’S, 62º55’W). Mean annual temperature is 24.3 size was 36 plots. We took geographical reference points of every transect using a GPS °C and annual rainfall is 1580 mm (data from 2000-2006 from La Chonta sawmill) with a dry (Garmin Etrex Vista HCx, USA). season (< 100 mm/month) from May through September (Peña-Claros et al. 2012). The study In each plot, we counted individuals belonging to fern species and species of the site is located on the South-West border of the Brazilian Precambrian Shield (Navarro and Marantaceae and Arecaceae families, as these have been shown to respond to soil fertility Maldonado 2002) and has sandy-loam soils with neutral pH and rich in nutrients (Paz 2003, (Costa 2006, Poulsen et al. 2006, Andersen et al. 2010a). We also included other families that

28 29 Chapter 2 ADE and Understory Species Composition

29 Chapter 2 ADE and Understory Species Composition

were easy to recognize and abundant in the understory of La Chonta, such as Commelinaceae, Data analyses Costaceae, Heliconiaceae, Poaceae, and Zingiberaceae (Appendix2 Table A2.1). For species of small size, such as most fern species, we distinguished individuals separately. However, We explored overall variability across our plots using Principal Component Analysis (PCA) several species grow in clumps, therefore hampering the counting of individual plants. Stems based on eight chemical variables (pH, concentrations of P, exchangeable Ca, Mg, K, Na, of small clump-forming species (e.g., Bolbitis serratifolia) located within 20 cm of each other total N, and OM content) and three physical variables (percentage of sand, silt, clay), using were counted as part of the same clump; otherwise, they were counted as separate individuals the values from all 36 plots. Because variables had different measurement units, we (cf. Costa 2006). For large-sized species, such as Bactris major and Heliconia spp., we standardized them by subtracting from the mean and dividing by the standard deviation. PCA determined clump formation by excavating the roots and checking for underground was based on a correlation matrix. connectivity. We identified the species in the field when possible, using field guides for ferns, Marantaceae and Zingibelares (Zuquim 2008, Costa et al. 2011a, Costa et al. 2011b). For all Environmental effects on species composition species, we made botanical collections to confirm their identity at the Herbario del Oriente To evaluate the effect of ADE soils on species composition we selected a subset of edaphic Boliviano in Santa Cruz, Bolivia. variables considered as indicators of the presence of ADE soils (Glaser and Birk 2012). These To determine the soil properties, we collected seven soil samples, ~250g each, at variables, hereafter called ADE variables, are: pH, concentration of P and Ca, OM content, intervals of 25 m within each plot, sampling mineral soil to a 40 cm depth. We created a total N and presence of ceramics. To determine which factors drove understory species compound soil sample per transect, from which we characterized soil color using the Munsell composition, we performed Redundancy Analyses (RDA) using the six ADE variables as well System. Soil samples were further analysed at the Centro de Investigación Agrícola Tropical as UTM coordinates, light availability (canopy openness), and plot elevation as explanatory (CIAT) in Santa Cruz, for measurement of the following chemical and physical variables: variables; and species composition with their abundances on each transect as dependent 2+ 2+ + +3 concentrations of N, P, exchangeable Ca , Mg , K , Al ; pH; organic matter content (OM); variables. Geographical UTM coordinates were included to account for the spatial distances total exchangeable bases (TEB); cation exchangeable capacity (CEC); base saturation; among plots, given that the distance between plots varied from 0.25 to 8 km. Light acidity; and soil texture. Soil texture (percentage of sand, silt, and clay) was measured using a availability was included, as light can vary among plots due to logging and application of Bouyoucos hydrometer; pH and electric conductivity was determined in 1:5 distilled water silvicultural treatments (Peña-Claros et al. 2008); plot elevation accounted for topographical 2+ 2+ + + equilibrating for 90 min.; concentrations of exchangeable Ca , Mg , K and Na were differences in La Chonta. RDA is a constrained ordination method, in which the ordination 2+ 2+ determined in 1 M ammonium acetate at pH 7. After extraction, Ca and Mg were axes are linear combinations of all explanatory variables. RDA constrains the variability of + + measured with an Atomic Absorption Spectrophotometer; concentrations of K and Na were the data set (species composition per transect) to these ordination axes, and tests the 3+ measured with a flame photometer; exchangeable aluminium (Al ) was measured in 1 M significance of this ordination with Monte Carlo Permutation tests. Two Monte Carlo hydrochloric acid (HCl). Total P was determined by Olsen method; OM was determined by Permutation tests were performed: the first one was used to determine the significance of the Walkley-Black method, and total N was measured using the Micro-Kjeldahl method (Barber species composition variability explained by all canonical axes; the second was used to asses and Cochrane 1993). TEB was defined as the sum of the most important exchangeable cations the significance of the contribution of each explanatory variable (Lepš and Šmilauer 2003). 2+ 2+ + + (Ca , Mg , K and Na ). CEC was calculated as the sum of TEB plus acidity. Base The latter analysis allowed us to select those independent variables that significantly saturation was computed as the percentage of exchangeable cations or bases relative to total explained species composition in the plots. We did four RDA analyses: one for the complete CEC. dataset (all 37 species); one for herbs (Commelinaceae, Costaceae, Heliconiaceae, We measured canopy openness to account for influence of light intensity on species Marantaceae, Poaceae, and Zingiberaceae); one for ferns; and one for palms. We evaluated distribution. Canopy openness was determined with a convex spherical densiometer at four these groups separately as they may respond differently to soil fertility (Costa 2006, Andersen cardinal points by two independent observers that held the densiometer at ~1 m above ground et al. 2010a). level. Measurements were strongly correlated between observers (R2 = 0.83; p < 0.001). Canopy openness was measured every 5 m in each transect, and the 25 points were then Environmental effects on species richness and abundance averaged to give one estimate per plot (Englund et al. 2000, Paletto and Tosi 2009). Species richness per transect was modelled as a function of the six ADE variables, spatial location, plot elevation, and light intensity using multiple regression analysis based on generalized linear models (GLM). These models allow for not having non-linear combinations between expected values of response and explanatory variables, by including a

30 31 Chapter 2 ADE and Understory Species Composition

31 Chapter 2 ADE and Understory Species Composition

non-linear link function. In our case, we used the logarithm as a link function for count data. abundant family with 6,747 individuals, whereas the most abundant species were the ferns We used backward multiple regression to evaluate significance of the explanatory variables Bolbitis serratifolia (29,386 individuals) and Adiantum cf. pulverulentum (6,152 individuals). that were standardized with mean 0 and variance 1, to be able to compare parameters

estimated in the model. Regressions were performed for species richness of the whole 1.0 Soil Properties community (all group of plants combined), for herbs (all families combined), and ferns, but not for palms, since only a total of six palm species were found. GLM were also used to pH Ca Plots Total N assess the effect of ADE variables, spatial location, plot elevation, and light intensity on the ADE Non-ADE Silt K abundance of individuals per transect for herbs (all families combined), ferns and palms. Palm Clay Mg abundance was included in the analysis because there were enough individuals in the plots to OM do the analyses. Na

Finally, to evaluate the response of individual species to ADE soils, we analysed 22.42% species response curves. For each species that occurred in more than six plots (i.e., > 15 % of sample units: 11 herbs, 9 ferns and 5 palms) we determined response curves to pH, Sand P concentration of Ca and canopy openness. We tested only for pH and Ca because they are good indicators of the presence of ADE soils at La Chonta and those variables most influenced species composition, richness and abundance (see Results). We fitted linear and quadratic regressions for each explanatory variable, and selected for each species and for each -1.0 60.9% variable the model with the lowest Akaike Information Criterion (AIC). -1.0 1.0 RDA and response curves of individual species were done using CANOCO 5 software Figure 2.1 Results of Principal Component Analyses of soil parameters for Amazonian Dark Earths (ADE, 18 plots, black triangles) and non-ADE plots (18 plots, grey circles) in La Chonta, Bolivia. (Ter Braak and Šmilauer 2012); paired t-tests and GLM were performed using Genstat 15.1 Arrows represent the environmental variables used in the analyses and their magnitude of importance (VSN International Ltd). in the ordination.

Results Effect of ADE variables on plant composition Plant species composition was significantly influenced by soil and spatial variables (Fig. 2.2). Nutrients and texture of ADE soils RDA for all species combined and for each of the groups (herbs, ferns and palms) showed a The ADE soils were generally more fertile than non-ADE soils, as indicated by the PCA consistent patter The first axis that explained a higher percentage of variation in all combined based on soil chemical and physical characteristics which explained 82% of the variation species, herbs and palms was associated with spatial variables, elevation or light, depending among plots. The first axis, which explained 60.9% of the variance, separated plots with high of the group that we evaluated, but this axis did not separate ADE from non-ADE plots (Fig. concentration of P, Ca total N, and high pH (with positive score values) from plots with lower 2.2a, b, d). The second axis was associated with pH and variables related with fertility, such quantities of these elements (with negative score values). ADE and non-ADE plots distribute as concentration of soil exchangeable bases and total N. Interestingly, this axis dispersed continuously along the first PCA axis, indicating that there is a gradient of fertility between ADE and non-ADE, suggesting that there is a gradient of fertility along ADE and non-ADE ADE and non-ADE soils (Fig. 2.1). The second axis explained 22.4% of the variance, but soils, mainly determined by differences in pH and Ca concentration, which are significantly ADE and non-ADE plots were intermixed along this axis (Fig. 2.1). associated with changes in the floristic composition of the plots (Table 2.1; Fig. 2.2a, b, d). Fern composition was better explained by soil variables, and ADE and non-ADE plots General floristic observations showed an intermingled distribution along the main axes (Fig. 2.2c).

The studied groups consisted of 37 species: 14 ferns, 6 palms, and 17 species of herbs with 6 The RDA showed that, when all species were combined, species composition was Heliconiaceae, 5 Marantaceae, 2 Costaceae, 2 Poaceae, 1 Zingiberaceae, and 1 significantly explained by differences in pH, Ca concentration, spatial location and canopy Commelinaceae (see Appendix Table A2.2). Total abundance was 48,458 individuals, with openness (Table 2.1). When species groups were analysed separately, we found that canopy 36,108 ferns, 10,194 herbs, and 2,156 palms. Among herbs, Heliconiaceae was the most openness and concentration of Ca consistently explained the variance in all cases, but their

32 33 Chapter 2 ADE and Understory Species Composition

33 Chapter 2 ADE and Understory Species Composition

contribution differed amongst groups. For ferns, Ca and soil pH explained 42% of the Ca concentration and canopy openness. Palm abundance was negatively affected by pH and variation and for palms 20% of the variation in species composition. For herbs, Ca in the soil positively affected by Ca concentration. Finally, fern abundances were negatively affected by only accounted for 12% of the total variation. Spatial location significantly explained species canopy openness and spatial location (Table 2.2). These results are consistent with the RDA, composition of herbs and palms, whereas plot elevation significantly explained species which also indicated that spatial location, canopy openness, pH and concentration of Ca are composition of palms (Table 2.1). the most important variables explaining species composition, richness and abundance. 0 0 . Table 2.1 Results of a Redundancy Analyses (RDA) evaluating the effect of explanatory variables in . 1 1 b Herbs species composition of all species together, and separately for angiosperm herbs (Marantaceae, a All species Costaceae, Heliconiaceae, Poaceae), palms, and ferns. Data presented are the variation explained, R2, followed by the p-value (in patentherises). The p-value is associated with the Monte Carlo permutation test that evaluated the null hypothesis of no correlation between the species composition and Canopy openness environmental variables. The level of significance for the individual environmental variables was α = Canopy openness 0.05. Significant relations are shown in bold. UTMX UTMX % %

Groups 54 . 08 . 8 All Species Herbs Ferns Palms 13 OM UTMY OM P UTMY

All explanatory 53.8 (0.002) 53.7(0.002) 42.8(0.012) 52.6(0.002) Elevation Ceramics Elevation TotalN P Ca variables TotalN Ca Ceramics PH Individual variables PH 0 0 . . 1 - 1 pH 9.3 (0.002) 2.6 (0.19) 7.1 (0.016) 5.3 (0.03) - -1.0 21.49% 1.0 -1.0 29.11% 1.0

Total N (%) 3.6 (0.05) 3.0 (0.14) 2.7 (0.30) 7.4 (0.01) 0 . 0 . 1

1 c Ferns d Palms P (mg kg-1) 1.9 (0.36) 1.0 (0.81) 3.5 (0.16) 2.9 (0.17)

Ca (cmol kg-1) 4.8 (0.02) 6.5 (0.006) 15.0 (0.002) 5.2 (0.04) Canopy openness Ceramics OM Ca P OM (%) 1.5 (0.65) 2.1 (0.31) 1.7 (0.58) 2.1 (0.32) TotalN PH UTMY Ca UTMY

Ceramics 1.5 (0. 59) 1.6 (0. 50) 1.0 (0.9) 2.0 (0.38) % % OM TotalN Canopy openness 26 93 Ceramics . .

Canopy 7.3 (0.004) 4.8 (0.03) 6.0 (0.02) 6.7 (0.02) 9 UTMX PH openness P 13 Elevation X Coordinates 18.6 (0.002) 25.9 (0.002) 2.1 (0.48) 6.9 (0.01) Elevation UTMX Y Coordinates 2.4 (0.16) 2.6 (0.20) 1.4 (0.71) 1.5 (0.58) 0 0 . Plot elevation 2.8 (0.11) 3.4 (0.09) 2.4 (0.36) 12.6 (0.006) . 1 1 - - -1.0 19.29% 1.0 -1.0 28.31% 1.0 Effect of ADE variables on richness and abundance Figure 2.2 Biplots produced after the redundancy analyses, representing the distribution of the plots based in species composition and restricted to soil and environmental explanatory variables (Table Species richness decreased when soil pH increased, both when all species were combined, 2.1). ADE plots are represented with black triangles and non-ADE plots are represented with grey and for the fern species alone (Table 2.2). Abundance of individuals was explained by soil circles. Arrows represent explanatory variables. Biplot of all understory species (a); of herbaceous variables only in the case of herbs and palms. Herb abundances significantly increased with species (b); of fern species (c) and of palms (d).

34 35 Chapter 2 ADE and Understory Species Composition

Groups 54 . 08 . 8 All Species Herbs Ferns Palms 13 OM UTMY OM P UTMY

Total N (%) 3.6 (0.05) 3.0 (0.14) 2.7 (0.30) 7.4 (0.01) 0 . 0 . 1

1 c Ferns d Palms P (mg kg-1) 1.9 (0.36) 1.0 (0.81) 3.5 (0.16) 2.9 (0.17)

Ca (cmol kg-1) 4.8 (0.02) 6.5 (0.006) 15.0 (0.002) 5.2 (0.04) Canopy openness Ceramics OM Ca P OM (%) 1.5 (0.65) 2.1 (0.31) 1.7 (0.58) 2.1 (0.32) TotalN PH UTMY Ca UTMY

Ceramics 1.5 (0. 59) 1.6 (0. 50) 1.0 (0.9) 2.0 (0.38) % % OM TotalN Canopy openness 26 93 Ceramics . .

35 Chapter 2 ADE and Understory Species Composition

Individual species responses 600 700 a Herbs b Herbs Heliconia episcopalis We evaluated the response of the most frequent species to pH, Ca and canopy openness, since Heliconia episcopalis these variables consistently had significant effects on species composition, richness and/or abundance. Remarkably, of the 25 species evaluated, all but one (Pharus latifolius) showed a significant, although weak, response to pH and Ca (Appendix Table A2.3). Some palm species, e.g. Astrocaryum murumuru, the most abundant palm in our study site (1246 Heliconia metallica Abundance individuals), showed a type of bimodal response to Ca concentration and pH (Appendix, Abundance

Table A2.3; Fig. 2.3f). More species responded to pH (12 out of 25 species) than to Ca Costus scaber concentration (7 species) (Fig. 2.3a-f) or canopy openness (6 species; see R2 values in Heliconia stricta

Costus arabicus Pharus cf lappulaceus Heliconia sp. Calathea se r. comosae Appendix, Table A2.3). Many species were more frequent at lower pH, which could explain 0 0 0 20 the negative relationship between pH and overall species richness (Table 2.2). 5.5 PH 8.0 Ca (cmol/Kg) 250 200 c Ferns d Ferns Table 2.2 Results of backward multiple regression models evaluating the effect of explanatory Adiantum cf pulverulentum Adiantum argutum variables on species richness and abundance of individuals on each transect for all species combined, angiosperm herbs (Marantaceae, Costaceae, Heliconiaceae, Poaceae), palms, and ferns. Regression coefficients (a) of each explanatory variable that significantly fitted the model. For each model we present R2, intercept (b) and significance level (p). Slopes (a) and intercepts (b) are represented in log scale given that a logarithmic function was used as the link function to fit the model (LnY=b+ Abundance Abundance a1x+a2x+...+anx).

Diversity Abundance Blechnum sp. Asplenium cristatum Adiantum pectinatum All species Herbs Ferns Herbs Ferns Palms Thelypteris cf jamesonii Asplenium cf discrepans 0 0 5.5 8.0 0 20 Model PH Ca (cmol/kg) 7 Syagrus sancona parameters e Palms 120 f Palms Attalea phalerata R2 0.17 0.07 0.44 0.40 0.51 0.52

Astrocaryum aculeatum Astrocaryum murumuru b 2.72 1.95 1.40 5.57 6.82 4.01 p 0.007 0.069 <0.001 <0.001 <0.001 <0.001 Abundance Abundance Coefficients (a) of explanatory variables pH -0.07 -0.21 -0.36 OM (%) 0 0 5.5 PH 8.0 Ca (cmol kg-1) 0.13 0.17 0 Ca (cmol/Kg) 20 X Coordinates -0.32 -0.59 Figure 2.3 Responses of selected forest understory species to soil pH and Ca concentration in the soil at La Chonta, Bolivia. Modeled abundance values are shown for plots of 600m2: (a) herb species with 2 2 Y Coordinates -0.36 -0.29 the highest R when soil pH was used as the independent variable, (b) herb species with the highest R when Ca concentration in soil was used as the independent variable, (c) fern species with the highest Canopy openness 0.23 -0.42 R2 when evaluated with soil pH as the independent variable, (d) fern species with the highest R2 when Ca concentration in soil as the independent variable, (e) palm species with the highest R2 when soil pH Plot elevation -0.19 was used as the independent variable and (f) palm species with the highest R2 with Ca concentration in soil as the independent variable. The values of R2 are shown in Appendix. Table A2.2.

36 37 Chapter 2 ADE and Understory Species Composition

37 Chapter 2 ADE and Understory Species Composition

Discussion to a lesser extent they also responded to aboveground resources (canopy openness) and were affected by spatial distribution of the plots (Table 2.1; Fig. 2.2). We found that, among the We evaluated whether ADE soils have an effect on the understory plant community through ADE variables, Ca concentration and pH accounted for around 25% of the total variation in changes in soil properties. Along the gradient from non-ADE to ADE soils in La Chonta, soil species composition (Fig. 2.2). This was especially the case with fern species composition. cations, P and total N increased as well as soil pH. Species composition also carried along this Several studies on distribution patterns of ferns have shown the importance of soil gradient, with species distribution associated with soil pH, Ca concentration and, to a lesser exchangeable bases (Ca, Mg, K, Na) in explaining local (Costa et al. 2005, Tuomisto 2006) extent, total N. In general, species richness decreased with pH and abundance increased with and regional (Zuquim et al. 2012, Zuquim et al. 2014) fern species composition, indicating nutrient levels. Consequently, we found that gradients in soil pH and Ca concentration that ferns specialize for, and partition, the exchangeable bases gradient (Tuomisto and generated by ADE presence were significantly related to gradients in composition, diversity, Poulsen 1996, Costa et al. 2005). and abundance of the understory community. We also found that Ca concentration, soil pH, and total N had a significant effect on palm species composition (Fig. 2.2d, Table 2.1). Similarly in Western and Central Amazon, Nutrients and texture of ADE soils palm composition was associated with exchangeable base concentration in the soil, both at We hypothesized that past anthropogenic enrichment activities would have led to a higher local and meso-scales (Vormisto et al. 2004, Poulsen et al. 2006, Costa et al. 2009, fertility of ADE soils compared to non-ADE soils. We indeed found that 10 out of 12 Kristiansen et al. 2012), while in Panama palm species composition was most strongly chemical variables related to soil fertility were higher in ADE than non-ADE soils (Appendix associated with inorganic soil nitrogen (Andersen et al. 2010b). As for herbs, the effect of pH Table A2.1). Most studies in the Amazonia have found large differences between ADE soils was not significant, and Ca in the soil had only a weak effect on species composition (Table and surrounding soils in soil color, pH, organic matter content, CEC and concentrations of 2.1). This result is in agreement with another study in Central Amazonia, where exchangeable nutrients, such as P, Ca, N, Mg, K and Zn (Glaser and Birk 2012). Particularly, P and Ca base concentration in the soil explained a low percentage of the variance of herb species concentrations are 100 times higher in ADE soils compared to the surrounding soils; however, composition (Costa et al. 2005). In general, in the soil fertility gradient ranging from non- variability in the concentration of P among and within ADE sites can be high (Lehmann et al. ADE to ADE soils, we found species of ferns and palms represented mainly by Adiantum cf 2003b, Falcão et al. 2009). Interestingly, in La Chonta P and Ca concentrations were only 2.6 pulverulentum, Adiantum argutum and Attalea phalerata in the least fertile extremes and herb and 1.6 times higher in ADE compared to non-ADE soils, respectively. Similar results have species such as Heliconia episcopalis and Heliconia metallica in the most fertile extremes (Fig. 2.3). been found in other regions of the Bolivian Amazonia (Hastik et al. 2013). These smaller 2 differences between ADE and non-ADE are likely the result of higher background soil Even at the restricted spatial scale of this study (< 10 km ), geographical location of fertility levels in the study site. Given the geological history of this region (Southern Amazon the plots had an effect on palm and herb species composition but not on fern composition basin) that includes the pre-existing Brazilian Shield and geological processes of (Table 2.1). The spatial effect on composition could be related to dispersal limitation, past sedimentation and erosion originated by the uplift of the central Andes in the Quaternary human disturbance, and other environmental variables that we did not measure but that have a (Latrubesse et al. 2010), La Chonta soils vary widely in age. spatial pattern. Palms and herbs have probably a spatial component in their distribution We hypothesized that ADE and non-ADE soils are not so much two distinct groups because large palm seeds tend to disperse closer to the parent trees and many herb species but rather form the extremes of a continuum in soil fertility, which was indeed confirmed by studied here are clonal. On the other hand, fern spores are light and can disperse larger the ordination analysis (Fig. 2.1). In several sites in Central and South of the Amazon basin, it distances, and therefore a spatial effect on fern composition is not to be expected at this scale. has been suggested that inside archaeological ADE sites, variation in soil characteristics is Spatial location was also significantly correlated soil texture (sand: r = -0.39, p = 0.01; silt: r better described by a fertility gradient that goes from the darker “center” region with higher = 0.40, p = 0.01). Therefore, it is possible that herbs follow these differences in soil texture, as fertility to lighter areas in the surroundings ending in the natural less fertile adjacent soils do species of the Marantaceae family in Central Amazonia (Costa et al. 2005), which may (Fraser et al. 2011, Schmidt et al. 2014). also help explain the spatial structure in herb composition.

Effect of ADE and other explanatory variables on species composition and species responses Effect of ADE and environmental factors on species richness and abundance

We hypothesized that understory floristic composition is shaped by the presence of ADE We predicted that the increase in soil fertility of ADE soils would increase nutrient soils. Our findings partially supported these hypotheses: understory floristic composition availability and plant growth, and hence species abundance and richness. We based our indeed responded to belowground resources that were indicators of ADE presence. However, predictions on studies from relatively nutrient-poor sites in the Central and Western

38 39 Chapter 2 ADE and Understory Species Composition

Effect of ADE and other explanatory variables on species composition and species responses Effect of ADE and environmental factors on species richness and abundance

39 Chapter 2 ADE and Understory Species Composition

Amazonia, where an increase in exchangeable bases concentration led to a higher density of anonymous reviewers for their valuable comments that helped to improve previous versions fern species (number of species/area) and higher fern diversity (Tuomisto and Poulsen 1996, of this manuscript. Costa 2006). In contrast, we found that species richness was not directly related with nutrients, but that it decreased with an increase in pH. We think of two possible, not mutually exclusive, explanations for this pattern. First, increases in soil pH are probably the result of Ca addition through human activities (bones) in our study area, as these two variables are highly correlated (Fig. 2.1; Pearson correlation, r = 0.75; p < 0.001). These changes are rather recent, preventing a high fern diversity in these relatively ‘novel’ environmental conditions. Second, competition with herbs and palms might affect fern diversity. In our plots, an increase in Ca concentration significantly increased the abundance of palms and herbs (Table 2.2): species such as Astrocaryum murumuru (Fig. 2.3f), Heliconia epsicopalis and Heliconia metallica (Fig. 2.3b) increased in abundance towards the more nutrient rich extreme of the gradient whereas ferns such as Adiantum argutum and Adiantum cf pulverulentum decreased with fertility (Figs. 2.3c, 2.3d). Polypod ferns, such as those found in our plots, can absorb N and P when they grow in fertile environments as some herbs do. However, their absorption of cations, particularly Ca, is significantly lower than for herbs (Amatangelo and Vitousek 2008). This could imply that in environments with increased amount of exchangeable bases in the soil, ferns would be able to take less advantage of the improved conditions compared to other groups of species.

Conclusions

We found that soil fertility differed along a gradient ranging from non-ADE to ADE soils, but that these differences were not as large as those found in other studies in Central Amazonia. Even on the relatively fertile natural soils at La Chonta, the presence of ADE (with increase of Ca) extends the available natural soil fertility along a gradient that influences the understory species composition, especially of ferns and palms. Past human modifications of the soil probably led to changes in the amount of exchangeable bases in the soil and in soil pH, which increased the abundance of some herbs and palms, and decreased fern diversity. We therefore conclude that ADE soils shape the forest understory community in this Bolivian forest.

Acknowledgements

This research is part of the Terra Preta Program of Wageningen University funded by the Interdisciplinary Research and Education Fund (INREF). La Chonta Company provided us permits and logistic support to work in the concession. We thank our field assistants Ricardo Mendez, Lorgio Vaca, Bruno Vaca and the students Tjalle Boorsma and Jonna van Ulzen for help during field work. The Herbario del Oriente Boliviano (USZ) gave us access to their herbarium facilities for plant identification. Paul Maas, Flávia Costa, Gabriela Zuquim and Fernando Figueiredo helped with plant identification. We deeply thank IBIF staff for logistic facilities in Santa Cruz. Finally, we thank Agustí Muñoz-Garcia, Thom Kuyper and two

40 41 Chapter 2 ADE and Understory Species Composition

Conclusions

Acknowledgements

41 Chapter 2 ADE and Understory Species Composition

Appendix Table A2.2 List of herbs, palm and fern species and families found in the research plots of this study in “La Chonta” forestry concession, Guarayos Province, Bolivia. Table A2.1 Soil characteristics of Amazonian Dark Earths (ADE) and non-Amazonian Dark Earths (non-ADE) found in a moist semi-deciduous forest in lowland Bolivia. Results of paired t-test are Scientific name Family provided for each variable. Data provided are average ± standard deviation (SD). Variables in bold Astrocaryum murumuru Arecaceae type are significantly different between ADE and non-ADE at p< 0.05. Attalea phalerata Arecaceae Attalea glassmanii Arecaceae Soil variable ADE site Non-ADE site Astrocaryum aculeatum Arecaceae pH 7.12 ± 0.39 6.59 ± 0.51 Bactris major Arecaceae Syagrus sancona Arecaceae Electric Conductivity (μS cm-1) 88.83 ± 40.39 52.00 ± 29.06 Asplenium cf discrepans Aspleniaceae Asplenium cristatum Aspleniaceae Acidity 0.04 ± 0.06 0.11 ± 0.08 Blechnum sp. Blechnaceae Ca (cmol kg-1) 6.17 ± 3.12 2.82 ± 1.46 Dichorisandra hexandra Commelinaceae Costus scaber Costaceae Mg (cmol kg-1) 1.09 ± 0.24 1.04 ± 0.27 Costus arabicus Costaceae

-1 Bolbitis serratifolia Dryopteridaceae Na (cmol kg ) 0.09 ± 0.03 0.08 ± 0.02 Heliconia episcopalis Heliconiaceae Heliconia rostrata Heliconiaceae K (cmol kg-1) 0.25 ± 0.07 0.17 ± 0.06 Heliconia stricta Heliconiaceae Total Exchangeable Bases (cmol kg-1) 6.98 ± 2.63 4.73 ± 2.97 Heliconia metallica Heliconiaceae Heliconia x flabellata Heliconiaceae Cation Exchange Capacity (cmol kg-1) 7.02 ± 2.60 4.86 ± 2.92 Heliconia sp. Heliconiaceae Calathea ser comosae Marantaceae Base Saturation (%) 99.33 ± 1.14 96.33 ± 2.85 Monotagma juruanum Marantaceae P (mg kg-1) 12.94 ± 7.62 4.94 ± 1.70 Calathea sp1 Marantaceae Calathea sp2 Marantaceae Total N (%) 0.18 ± 0.04 0.13 ± 0.03 Calathea sp3 Marantaceae cf Ischnosiphon Marantaceae OM (%) 2.48 ± 0.62 2.22 ± 0.42 Pharus latifolius Poaceae Pharus cf lappulaceus Poaceae Sand (%) 28.78 ± 3.88 27.44 ± 5.57 Adiantum cf pulverulentum Pteridaceae Silt (%) 56.28 ± 3.47 58.06 ± 4.85 Adiantum pectinatum Pteridaceae Adiantum argutum Pteridaceae Clay (%) 14.94 ± 2.13 14.50 ± 2.01 Adiantum latifolium Pteridaceae Adiantum platyphyllum Pteridaceae Adiantopsis radiata Pteridaceae Pteris propinqua Pteridaceae Tectaria incisa Tectariaceae Thelypteris sp. Thelypteridaceae Thelypteris cf. jamesonii Thelypteridaceae Renealmia breviscapa Zingiberaceae

42 43 Chapter 2 ADE and Understory Species Composition

43 Chapter 2 ADE and Understory Species Composition

Table A2.3 Results of models fitted on species abundance (response curves fitting) with frequencies Species Predictor Model type R2 F p higher than 15% of the plots. Soil pH, Ca concentration (cmol kg-1)and canopy openness were evaluated. From linear and/or polynomial functions, the model with the lowest AIC criteria was Marantaceae selected for each species. Cells with bold characters represent the explanatory variable that had the highest R2 value for each species. Explanatory variables marked with (*) mean that the best fit of the Calathea ser. comosae pH quadratic 0.18 16.2 <0.001 models tested was a null model. Ca quadratic 0.23 10.4 <0.001 Species Predictor Model type R2 F p Canopy Openness quadratic 0.20 17.7 <0.001 Palms Monotagma cf. juruanum pH linear 0.02 18.2 <0.001 Astrocaryum aculeatum pH linear 0.08 11.5 0.002 Ca quadratic 0.18 67.7 <0.001 Ca quadratic 0.06 4.7 0.015 Canopy Openness quadratic 0.32 121.7 <0.001 Canopy Openness linear 0.04 5.4 0.026 Costaceae Astrocaryum murumuru pH quadratic 0.02 3.8 0.032 Costus arabicus pH quadratic 0.48 18.7 <0.001 Ca quadratic 0.13 27.6 <0.001 Ca linear 0.20 16.2 <0.001 Canopy Openness linear 0.08 33.1 <0.001 Canopy Openness quadratic 0.45 17.6 <0.001 Attalea phalerata pH quadratic 0.45 96.9 <0.001 Costus scaber pH linear 0.24 294.9 <0.001 Ca quadratic 0.49 107 <0.001 Ca quadratic 0.16 116.5 <0.001 Canopy Openness quadratic 0.19 41.3 <0.001 Canopy Openness quadratic 0.09 55.7 <0.001 Bactris major pH quadratic 0.08 20 <0.001 Heliconiaceae Ca quadratic 0.06 14.7 0.007 Heliconia episcopalis pH quadratic 0.38 773.7 <0.001 Canopy Openness quadratic 0.13 31.8 <0.001 Ca linear 0.38 1571 <0.001 Syagros sancona pH quadratic 0.25 21 <0.001 Canopy Openness quadratic 0.26 524.5 <0.0011 Ca quadratic 0.16 13.7 <0.001 Heliconia metallica pH quadratic 0.12 285.2 <0.001 Canopy Openness quadratic 0.21 18.2 <0.001 Ca quadratic 0.16 792.5 <0.001 Poaceae Canopy Openness quadratic 0.13 315.8 <0.001 Pharus latifolius pH quadratic 0.02 2.2 0.127 Heliconia rostrata pH quadratic 0.04 7.7 0.002 Ca linear 0.01 3.2 0.082 Ca linear 0.01 10.5 0.002 Canopy Openness quadratic 0.09 9.9 <0.001 Canopy Openness quadratic 0.10 20.1 <0.001 Pharus cf. lappulacens pH quadratic 0.21 37.3 <0.001 Heliconia stricta pH quadratic 0.22 277.4 <0.001 Ca quadratic 0.13 23.1 <0.001 Ca quadratic 0.23 268.9 <0.001 Canopy Openness quadratic 0.19 32.8 0.001 Canopy Openness quadratic 0.03 48.2 <0.001

44 45 Chapter 2 ADE and Understory Species Composition

45 Chapter 2 ADE and Understory Species Composition

Species Predictor Model type R2 F p Species Predictor Model type R2 F p

Heliconia sp. pH linear 0.13 15.5 <0.001 Pteris propincua pH linear 0.18 27.0 <0.001

Ca quadratic 0.07 4.3 0.022 Ca linear 0.17 25.8 <0.001

Canopy Openness quadratic 0.09 9.9 <0.001 Canopy Openness quadratic 0.25 19.1 <0.001

Ferns Thelypteris cf. jamesonii pH quadratic 0.43 9.2 <0.001

Adiantum cf. pulverulentum pH quadratic 0.21 379.7 <0.001 Ca linear 0.15 6.4 0.016

Ca quadratic 0.26 466.9 <0.001 Canopy Openness quadratic 0.38 8.1 0.001

Canopy Openness quadratic 0.15 270.9 <0.001

Adiantum pectinatum pH quadratic 0.12 21.1 <0.001

Ca quadratic 0.15 26.8 <0.001

Canopy Openness quadratic 0.18 33.0 <0.001

Adiantum argutum pH lineal 0.79 487.6 <0.001

Ca quadratic 0.54 167.3 <0.001

Canopy Openness quadratic 0.41 128.2 <0.001

Asplenium cf. discrepans pH quadratic 0.08 8.6 0.001

Ca quadratic 0.16 18.0 <0.001

Canopy Openness quadratic 0.04 4.7 0.016

Asplenium cristatum pH quadratic 0.40 6.7 0.004

Ca linear 0.15 5.2 0.028

Canopy Openness* null

Blechnum sp. pH quadratic 0.47 9.3 <0.001

Ca quadratic 0.40 7.5 0.001

Canopy Openness* null

Bolbitis serratifolia pH quadratic 0.07 346.7 <0.001

Ca quadratic 0.08 404.9 <0.001

Canopy Openness quadratic 0.43 2073 <0.001

46 47 Chapter 2 ADE and Understory Species Composition

Species Predictor Model type R2 F p Species Predictor Model type R2 F p

Heliconia sp. pH linear 0.13 15.5 <0.001 Pteris propincua pH linear 0.18 27.0 <0.001

Ca quadratic 0.07 4.3 0.022 Ca linear 0.17 25.8 <0.001

Canopy Openness quadratic 0.09 9.9 <0.001 Canopy Openness quadratic 0.25 19.1 <0.001

Ferns Thelypteris cf. jamesonii pH quadratic 0.43 9.2 <0.001

Adiantum cf. pulverulentum pH quadratic 0.21 379.7 <0.001 Ca linear 0.15 6.4 0.016

Ca quadratic 0.26 466.9 <0.001 Canopy Openness quadratic 0.38 8.1 0.001

Canopy Openness quadratic 0.15 270.9 <0.001

Adiantum pectinatum pH quadratic 0.12 21.1 <0.001

Ca quadratic 0.15 26.8 <0.001

Canopy Openness quadratic 0.18 33.0 <0.001

Adiantum argutum pH lineal 0.79 487.6 <0.001

Ca quadratic 0.54 167.3 <0.001

Canopy Openness quadratic 0.41 128.2 <0.001

Asplenium cf. discrepans pH quadratic 0.08 8.6 0.001

Ca quadratic 0.16 18.0 <0.001

Canopy Openness quadratic 0.04 4.7 0.016

Asplenium cristatum pH quadratic 0.40 6.7 0.004

Ca linear 0.15 5.2 0.028

Canopy Openness* null

Blechnum sp. pH quadratic 0.47 9.3 <0.001

Ca quadratic 0.40 7.5 0.001

Canopy Openness* null

Bolbitis serratifolia pH quadratic 0.07 346.7 <0.001

Ca quadratic 0.08 404.9 <0.001

Canopy Openness quadratic 0.43 2073 <0.001

47 Chapter 3

Effects of Amazonian Dark Earths on forest attributes and forest dynamics in a Bolivian forest

Estela Quintero-Vallejo, Lourens Poorter, Frans Bongers, Marisol Toledo, Alfredo Alarcón, Juan Carlos Licona, Marielos Peña-Claros

Submitted for publication

49 Chapter 3

Effects of Amazonian Dark Earths on forest attributes and forest dynamics in a Bolivian forest

Estela Quintero-Vallejo, Lourens Poorter, Frans Bongers, Marisol Toledo, Alfredo Alarcón, Juan Carlos Licona, Marielos Peña-Claros

Submitted for publication Chapter 3 ADE and Forest Structure and Dynamics

Abstract Introduction

Heterogeneity of soil fertility across the Amazonian region comes from geological history and Soil is an important driver of vegetation structure and dynamics at different spatial scales geomorphological processes. This heterogeneity drives forest structure and dynamics through (Paoli et al. 2008, Cleveland et al. 2011, Banin et al. 2014). For example, at large spatial the whole region, but at local scale the role of soils is unsolved. Amazonian Dark Earths scales across the Amazonian region, a soil fertility gradient runs from generally poor soils in (ADE) are anthropogenic soils related to the human occupation of the Amazonian region the east to generally rich soil in the west with profound influences on forest dynamics since pre-Columbian times; they contain high levels of P, Ca, and have high soil pH. ADE (Phillips et al. 2004, Malhi et al. 2006, Quesada et al. 2009, Quesada et al. 2012). This east- open an opportunity to test how increases in soil fertility could affect forest dynamics at local west gradient in soil fertility is caused by a combination of the geological history of scales. In this study, we evaluated the effect of ADE on forest attributes such as standing Amazonian soils and geomorphological processes (Hoorn et al. 2010, Quesada et al. 2010). basal area, tree liana infestation and successional composition defined by the relative presence On one hand, the older soils in the eastern and northern regions are heavily weathered and of pioneers, long-lived pioneers, partial shade tolerant and shade tolerant species in the forest. limited in nutrients but have good physical properties, such as deep soils and a texture that We also evaluated the effect of ADE on components of forest dynamics (basal area growth, allows for good drainage. On the other hand, the younger soils in the western and southern recruitment, and mortality). We found that soil fertility, most probably associated with ADE, regions have high bed rock-derived soil nutrient concentrations but poor physical properties, weakly affected basal area, liana infestation and successional composition of the plots. such as shallow soils and poor drainage (Quesada et al. 2011). The poor soils in the eastern Besides, it affected basal area of recruited trees and mortality of large trees. A higher soil pH Amazonian forest lead to slow tree growth, and less tree mortality related to deeper rooting on increased recruitment of intermediate-size trees (20-40 cm in stem diameter) and decreased more profound soils, leading to slow stand turnover rates. On the contrary, the rich and mortality of large trees (stem diameter > 40 cm). The most important effect of soils on forest shallow soils in the western Amazonian forests lead to faster tree growth and higher tree fall dynamics, however, is through the effects of soil pH on initial basal area and successional due to uprooting, giving rise to high stand-level turnover rates (Phillips et al. 2004, Quesada composition that directly affected basal area growth of intermediate-size trees. In this et al. 2012, Phillips and Lewis 2014). Among the soil variables, soil phosphorus (P), cation Amazonian forest ADE, and soils in general, are not a strong driver of forest dynamics, and (Ca, K, Mg) concentration, soil nitrogen (N) and physical characteristics (soil depth, soil other environmental factors, such as light availability and liana infestation, play a stronger structure, topography) seem to be the main factors driving Amazonian forest dynamics role. (Quesada et al. 2012). Rich soils with higher amounts of total and plant available phosphorus (P) increased net primary productivity of aboveground biomass (leaves, branches, stems and Key words: Soil pH, Bolivia, forest dynamics, forest structure volatile compounds) and below-ground biomass (fine roots, coarse roots and root exudates) in 9 1-ha plots across the Amazonian region (Aragão et al. 2009). Besides, these east-west soil gradients also affect forest structure (Quesada et al.2009) and species composition (ter Steege et al. 2006, Quesada et al. 2012). Aboveground biomass (AGB) is higher in the eastern poor soils regions, whereas lower AGB is more common in the rich soils of the west (Malhi et al. 2006, Quesada et al. 2009). This pattern seems to be related to tree species composition from north-east to south-west Amazon basin. North-eastern forests have more shade-tolerant species with higher wood density and larger seeds, while south-western forests have more light-demanding species with lower wood density and smaller seeds (ter Steege et al. 2006, Quesada et al. 2012). At smaller spatial scales, the strength of soils in explaining forest dynamics and forest structure decreases drastically. Comprehensive studies in Bolivian lowlands, including the Amazonian region of the country, show that rainfall is a stronger driver of forest structure and dynamics and of tree species composition than soil variables. For example, basal area growth was negatively related to soil concentration of Ca, P and percentage of N (Toledo et al. 2011b); tree and palm density and liana infestation were negatively related with soil fertility (Toledo et al. 2011c); and finally, calcium (Ca) and P in the soil affected tree species composition after having accounted for the major influence of rainfall (Toledo et al. 2011c).

50 51 Chapter 3 ADE and Forest Structure and Dynamics

Abstract Introduction

51 Chapter 3 ADE and Forest Structure and Dynamics

Similarly, small scale studies on the island of Borneo show that a combination of climatic and Preta’ in Portuguese) (Sombroek 1966, Glaser and Birk 2012), which is a type of Anthrosol soil variables affect forest structure and dynamics, and especially basal area and AGB were characterized by a thick dark or gray top layer with presence of ceramics indicating past positively related with soil fertility (Slik et al. 2010). At more reduced scales, around 15 km2, indigenous settlements (Sombroek 1966, Woods and Glaser 2004). ADE are the product of soils also play an important role on forest dynamics and structure in the Paleotropics. In a kitchen remains, such as garbage deposition, over thousands of years (Schaan et al. 2009). forest in Borneo, basal area and AGB was affected by soil nutrients (P and N) and the effect ADE patches are found throughout the Amazonian region, accounting for ca. 3.2% of the area was higher for large trees (DBH > 120 cm) and lower for intermediate-size trees (60-90 cm in (McMichael et al. 2014). ADE patch sizes vary from less than 2 to more than 100 ha, with DBH) (Paoli et al. 2008). In a forest in western Borneo, basal area and AGB growth of trees > most of them, around 80%, being ca. 2 ha (Winklerprins and Aldrich 2010). They possess 40 cm in DBH were positively correlated with total soil P and cation exchange capacity higher concentrations of total and available P and Ca, higher total N (Lehmann et al. 2003b), (CEC) (Paoli and Curran 2007). In the Neotropics few studies have found relations between and higher concentrations of charcoal and C (Glaser et al. 2003) than the surrounding Ultisols nutrients and forest structure at local scale. In central Amazonian forest AGB increased with (Acrisols in WRB -world reference base for soil resources) and Oxisols (Ferrasols in WRB) in increase in soil N and percentage of clay (Laurance et al. 1999); in Reserva Duke, in the most of the Amazonian region (Falcão et al. 2009). When ADE soils are present in a given central Amazonian region, AGB was higher in clayey soils that are located in high elevations area, they form a gradient of fertility from the center of the ADE patch to the surrounding (de Castilho et al. 2006); in the western Amazonian region, in the reserve of Cocha-Cachu natural soils (Fraser et al. 2011, Costa et al. 2013). Some of the main benefits in soil reserve, Peru, trees > 30 cm DBH had higher AGB in more fertile compared with less fertile properties of ADE are related to the addition of charcoal that leads to more stable organic soils (DeWalt and Chave 2004). Forest structural aspects and the association with soil matter, high nutrient levels and probably higher soil water retention capacity (Glaser et al. characteristics can be related to forest composition if, for example, large emergent tree species 2002). Moreover, the addition of organic waste leads to increase in nutrient concentrations (P, are dominant in sites with high nutrients (Paoli et al. 2008). Additionally, endogenous N, and Ca) (Glaser 2007), with Ca increasing the concentration of exchangeable bases and processes like gap formation (Ruslandi et al. 2012) and human perturbation could increase soil pH, which in turn also increases the availability of other cations. ADE provide, therefore, tree light availability that influences forest structure and dynamics (Peña-Claros et al. 2008) important human legacies, that can have potentially long-lasting impact on the vegetation, but and even forest composition (Carreño-Rocabado et al. 2012, Bourland et al. 2015). Besides, few studies have related ADE with forest structure and dynamics. Secondary forests (~30 y the management of man-useful trees through cultivation can influence forest composition old) that had been growing on ADE had lower tree density than secondary forest growing in (Levis et al. 2012). non-ADE soils especially at early stages of the forest succession (Junqueira et al. 2010). One important structural component of tropical forest are lianas (Schnitzer and Comparative studies across the Amazonian forest showed that net primary productivity (NPP) Bongers 2002, Schnitzer et al. 2015). Additionally, lianas can have an effect of forest of one forest plot established on ADE had higher NPP; notably this increase was due to higher dynamics through its influence on tree mortality (Phillips et al. 2005). Therefore, below-ground biomass and not to above-ground biomass (Aragão et al. 2009). Similarly, environmental factors that drive lianas can indirectly affect forest dynamics. The effect of Doughty et al. (2014) found an increase in gross primary productivity GPP) and high rates of soils on lianas seems not to be very strong and in many cases it depends on the scale. Across fine root production on a forest growing on ADE compared with a forest growing on non- the tropics, lianas are influenced by rainfall and climate seasonality (DeWalt et al. 2010, ADE. These local-scale studies evaluated the effects of ADE on some forest structural DeWalt et al. 2015), which can (at least partly) be explained by higher liana tolerance to variables and NPP, but they did not consider the underlying demographic processes that shape drought (Schnitzer 2005). Nevertheless, patterns at smaller spatial scales are different and forest dynamics such as tree growth, tree recruitment and tree mortality. even contrasting. Studies focussing on Neotropical ecosystems found very weak relationship Conceptually, at local scales, abiotic resources affect both forest structural attributes between climatic and soil variables in liana density and liana basal area (Van Der Heijden and and forest dynamics, while the forest attributes, on their turn, affect forest dynamics (Fig. Phillips 2008); but in Cocha-Cachu, Peru, liana density decreased in richer soils (DeWalt and 3.1a). Basal area is affected by abiotic factors such as soils (Paoli et al. 2008) and light Chave 2004). Recent detailed studies suggest that liana densities were not related with soil through forest perturbation (Saldarriaga et al. 1988, Peña-Claros et al. 2008). Also species nutrient variables but with water availability in the dry season and with gap formation composition may be driven by abiotic factors as species requirements can parallel a local (Dalling et al. 2012, Schnitzer et al. 2012) although few liana species are associated with gradient in resource availability (Paoli et al. 2006). At the same time, soils and light may richer soils in Barro Colorado Island, Panama suggesting species specific responses (Dalling positively affect liana infestation (Dalling et al. 2012) (Fig. 3.1a), while liana infestation et al. 2012). generally negatively affects basal area through increased tree mortality (Phillips et al. 2005, The main origin of P in the soil is the weathering of the parental material (Quesada et Peña-Claros et al. 2008, van der Heijden et al. 2015) (Figs. 3.1a, 3.1b). Demographic al. 2010). Nevertheless, in the Amazon basin, there are soils from anthropogenic origin that processes that determine forest dynamics can also be affected, at local scales, by abiotic and can have high levels of P. These soils are called Amazonian Dark Earths (ADE) (‘Terra biotic factors. Soil fertility (Paoli and Curran 2007) and light (Peña-Claros et al. 2008) are

52 53 Chapter 3 ADE and Forest Structure and Dynamics

53 Chapter 3 ADE and Forest Structure and Dynamics

shown to positively affect forest growth and recruitment (Fig. 3.1b). Species composition may presence of ADE soil with pieces of charcoal and pottery of ~480 to 250 years B.P. indicates increase forest dynamics through fast growing species that also die faster (Phillips and Lewis that there were indigenous settlements in the area (Paz 2003). 2014) (Fig. 3.1b). ADE provide an unique opportunity for testing if increases in soil nutrients can a) Effects of Abiotic Resources on Forest Attributes influence forest dynamics and structure at small scales. Therefore, in this study we evaluated the effect of ADE on forest attributes and forest dynamics of a lowland Bolivian forest. We Abiotic Resources Forest Attributes predicted that the presence of ADE increases soil fertility affecting positively forest attributes, Light + Liana Infestation such as liana infestation and standing basal area, and that sites with higher soil fertility (Crown exposure index) + - increase the proportion of species with more resource acquisitive strategies, such as pioneers (Fig. 3.1a). Furthermore, increased fertility of ADE soils positively affects forest dynamics by Initial Basal Area + - increasing tree growth (measured as basal area growth) and recruitment, and by increasing + mortality of fast growing species (Fig. 3.1b –left side–). Forest attributes are directly affected Soil Forest successional by soils but, simultaneously, forest attributes on their turn affect forest dynamics. Therefore, Soil chemistry -ADE + composition we predict that liana infestation has a negative effect on basal area growth and recruitment Soil Texture (Successional composition index) (basal area recruitment), and a positive effect on mortality (basal area mortality) (Fig. 3.1b – right side–). Additionally, we predict that forest successional composition is related with basal b) Effect of Abiotic Resources and Forest Attributes on Forest Dynamics area growth, basal area recruitment and basal area mortality, with the more dynamic plots Abiotic Resources Forest dynamics Forest Attributes having more fast growing species (Fig. 3.1b –right side–). Finally, we predict presence of - ceramics to be directly related to species composition, forest dynamics and presence of lianas, Light + Basal Area recruitment Liana Infestation Crown exposure index + as the ceramics reflect human perturbation that created areas with higher light, and/or + + enriched the forest with useful species (Fig. 3.1b). + - Basal Area growth + Initial Basal Area (Growth of survivors) Methods + + + + Study site Soil: + Forest successional Soil chemistry - ADE Basal Area mortality composition Soil texture + (Successional composition index) The study was conducted in the 100,000 ha forestry concession of Agroindustria Comercial La Chonta, Guarayos province, Bolivia (15º47’S, 62º55’W). Mean annual temperature in the area is 24.3 °C and annual rainfall is 1580 mm (data from 2000 - 2006 from La Chonta Ceramics sawmill) with a dry season (< 100 mm/mo) from May through September (Peña-Claros et al. 2012). The study site is located on the South-West border of the Brazilian Precambrian Figure 3.1 Conceptual framework showing a) the effects of abiotic resources (light, soils) on forest Shield. Soils at La Chonta are heterogeneous, they are a mosaic of old, more nutrient depleted attributes: liana infestation, initial plot basal area and forest successional composition (successional composition index). Successional composition index is based on the ecological guilds of the species soils from the Brazilian Precambrian Shield (Navarro and Maldonado 2002) and more fertile present in the plots that vary from shade tolerant to pioneer species (see Methods). Although our main soils that are the product of sedimentation and erosion originated from the uplift of central interest is the effect of soils (both chemical and physical characteristics), we include light in our Andes (Latrubesse et al. 2010). Furthermore, La Chonta contains anthropogenic soils that framework given its strong effects on forest dynamics. b) the effects of abiotic resources (light, soil account for about 20 percent of a studied area of 216 ha (Paz-Rivera and Putz 2009). chemistry and soil texture) on stand basal area growth (based on the trees surviving the whole monitoring period of 8 to 10 years), stand basal area recruitment (based on the basal area of the The forestry concession of La Chonta was established in 1974. Currently, they harvest around recruited trees that survive until the last census) and basal area mortality (based on the basal area of 3 3 trees/ha (7.7 m /ha) belonging to 17-19 tree species. During its first 20 years, the concession the trees sampled at plot establishment that died from natural death) –left side of the figure. The right logged only mahogany trees (Swietenia macrophylla) and tropical cedar (Cedrela odorata). side of the figure considers the effects of forest attributes (structure and successional composition) and Before this time there was no evidence of forest management in the area. Nevertheless, the ceramics (as the absolute proof of human occupation of the forest) on forest dynamics (basal area growth, basal area recruitment, basal area mortality).

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55 Chapter 3 ADE and Forest Structure and Dynamics

The forest at La Chonta is classified as a semi-deciduous tropical moist forest (Peña-Claros et Tropical (CIAT) in Santa Cruz (Bolivia):(Bolivia): concentrations of N, total P, exchangeable Ca2+, al. 2012). Floristic comparisons of several lowland forests in Bolivia show that the forest at Mg2+, K+, and Al+3; pH; organic matter content (OM); total exchangeable bases (TEB); cation La Chonta is transitional between the forest of the Northern Amazonian region and the exchangeable capacity (CEC); base saturation; and soil texture (percentage of sand, silt, and Southern Chiquitano dry forest (Toledo et al. 2011a). The forest has, on average, 367 stems clay (for extraction methods see Annex 1). (>10 cm diameter at breast height [DBH]) and 59 species per ha (Peña-Claros et al. 2012), an average tree height of 27 m, and approximately one third of the canopy species lose their Tree selection and forest dynamics variables leaves in the dry season (Markesteijn et al. 2010). To study forest dynamics we used the 400 soil points, located in the twelve 27 ha main plots, for which we had soil chemistry and texture information. From the point where the soil Permanent plots design and census sample was taken, we established a circular plot with a radius of 25 m and selected the trees This research was carried out in twelve large-scale 27-ha permanent sample plots established that were within this distance. These circular plots are referred as subplots hereafter. Because by the Bolivian Forest Research Institute (IBIF, due to acronym in Spanish) that are part of of the nested plot design (as described above), the subplots did not contain trees of all size the Long-term Silvicultural Research Program (LTSRP) (see Peña-Claros et al. 2008)for classes. Therefore, we divided the tree dataset as follows for further analysis: trees > 40 cm in details). Within each plot the mapping and measuring of trees was done using a nested design: DBH (in 387 subplots), hereafter referred to as large trees (which are all in the canopy); trees trees with a diameter > 40 cm (measured at 1.3m aboveground, DBH) were measured in the between 20 - 40 cm in DBH (in 214 subplots), referred to as intermediate-size trees (which whole (27ha) plot; trees with a diameter between 20 and 40 cm were measured in half of the are mostly in the sub-canopy); and trees between 10 - 20 cm in DBH (in 99 subplots), referred plot (13.5ha); and trees with a diameter between 10 and 20 cm were measured in four 1-ha to as small trees (which are mostly in the understory). This subdivision also allows to evaluate subplots (Peña-Claros et al. 2008). Establishment of the plots was done between September whether trees in different ontogenetic stages respond differently to abiotic resources and 2000 and December 2001. After plot establishment, plots were logged as part of the forest attributes. We determined forest dynamics using changes in basal area in a period of 8 treatments of the research program above mentioned. Plots were then measured 1 year after to 10 years depending on the date of plot measurement. We used basal area instead of number establishment and then around every two years until November 2011. of stems to account for soil points located in the borders of the 27-ha plots, for which we needed to use a measurement that allowed us to correct for subplot area. We divided the forest Soil sampling dynamics into three underlying components all expressed in m2 ha-1 y-1. For each subplot basal area growth was estimated by subtracting the final basal area (i.e. at the time of the final To map the presence of ADE soils in the twelve 27-ha plots, we sampled the soil at each census) from the initial basal area (i.e. at the time of plot establishment) of the trees that intersection of a 50x50 m grid the soil with an auger to 40cm depth. For each sample, we survived from the establishment until the last census, divided by the number of years between described the soil color with Munsell Soil Color Charts and recorded the presence of ceramics the plot establishment and the final census for each plot. Basal area recruitment was or charcoal as evidence of past human occupation. We then collected around 250 g of soil to determined by the sum of the basal area of all recruited trees on each subplot from the first make posterior chemical and physical analyses. We gathered around 130 samples per plot. census until the last census, divided by the time that passed between these two censuses. Once in the lab, we assessed soil pH for all samples by taking 25 mg of soil per sample and Basal area mortality was determined by the sum of the basal area of all trees that died from combining it with 10 ml of distilled water; we then measured pH with a Thermo Fisher the first until the last census and dividing it by the time passed between these censuses. Scientific Orion start meter (Beverly, MA, USA). Subplot basal area was determined by adding the basal area of all trees that belonged to the In La Chonta, ADE creates a gradient in soil characteristics, particularly in soil pH subplot (m2 ha-1). Because we were interested in natural dynamics, we excluded trees that (Quintero‐Vallejo et al. 2015). As we wanted to characterize this gradient on every plot but were logged or killed during logging operations or due to the application of silvicultural could not analyse all samples due to economic restrictions, we selected 20-40 soil points per treatments, or post logging mortality (caused by profound damage during the logging plot to perform further chemical and physical analyses, providing a total of 400 soil points. operations). We also excluded trees that died due to a wildfire in 2004 that affected partially On each plot we selected soil points in such a way that half of them had pH higher than 7, two of the 27-ha plots. dark color (black or very dark brown color, mainly 7.5YR 2.5/1, 7.5YR 2.5/2 and 7.5YR 2.5/3 in the Munsell system), and ceramics were present; whereas the other half of the soil Light, forest structure and forest composition points had pH lower than 7, lighter soil color (brown, yellowish brown or reddish brown, mainly 7.5YR 4/3, 7.5YR 4/4, 7.5YR 5/4, 5YR 5/3), and no ceramics. The following Forest dynamics can be affected by light, liana infestation and forest composition (Schnitzer chemical and physical variables were measured at the Centro de Investigación Agrícola and Bongers 2002, Peña-Claros et al. 2008, Quesada et al. 2012). We characterized light

56 57 Chapter 3 ADE and Forest Structure and Dynamics

57 Chapter 3 ADE and Forest Structure and Dynamics

received by each tree using the tree crown exposure index, which is ranked from 1 to 5 the human occupation of the forest. In the mixed models we used plots (12) nested in blocks (Dawkins and Field 1978): 1 = no direct overhead light and little lateral light; 2 = no direct (3) as random factors, to account for spatial correlation of subplots belonging to the plots and overhead light but moderate or substantial lateral light; 3 = vertical light on part of the crown; for internal variability of the plots due to the IBIF-research treatments. Since we found a 4 = vertical light on the whole crown; and 5 = exposed emergent crown. To calculate a crown strong correlation between pH and Ca concentration in the soil (Pearson correlation = 0.76), exposure at the plot level, we weighted the crown exposure index of each tree by its basal area we excluded Ca from the analyses to avoid effects of collinearity. Liana infestation was a ( × ) = response variable, nevertheless we included this as explanatory variable in modelling the and divided this amount by plot basal area as follows: � , where CEIp is ∑�=1 �� initial tree basal area since lianas can have a negative effect on tree growth and increase tree � the crown exposure index per plot p, CEIi is the crown ��exposure� index∑�=1 �� of� tree i; and BAi the mortality. Consequently, liana infestation is expected to reduce the initial tree basal area (Fig. basal area of tree i. 3.1). Besides, we fitted a series of linear mixed effects models to evaluate if forest dynamics Liana infestation per subplot was determined using a liana infestation index for every (basal area growth, basal area recruitment and basal area mortality) were affected by soil tree that goes from 1 to 4 as follows (Alder and Synnott 1992): 1 = no lianas; 2 = lianas on chemistry, soil texture, light, presence of ceramics, liana infestation and successional stem; 3 = lianas on stem and crown; and 4 = lianas completely covering the crown. To composition. determine the liana infestation per subplot we used the following equation: = From all possible models produced, we performed model selection by choosing those ( × ) � models that differed less than 2 Akaike’s Information Criterion (AIC) units from the best � , where LIIp is the liana infestation index per plot p, LIi stands for liana infestation�� ∑�=1 �� model after they were ranked from the best to the worst according to the AIC score. We of tree i, �and BA stands for basal area of tree i. Liana infestation index range from 0% (stand ∑�=1 �� i averaged the selected models (e.g. 4 or 5 models) to pick the explanatory variables with biomass has not liana infestation) to 100% (stand biomass is completely infested by lianas). higher relative importance value (maximum 1), which is the sum of the Akaike weights Additionally, we determined the successional composition index of every subplot using (Burnham and Anderson 2002) of the models that include the explanatory variable is present information on the ecological guild for each tree as follows: 1 = shade tolerant species; 2 = (Barton 2012). We run the model with the variables that showed the highest importance value partial-shade tolerant species; 3 = long-lived pioneer species; and 4 = (short-lived) pioneer to determine if their regression coefficients were statistically significant at level of α=0.05. In species (Jardim et al. 2003, Mostacedo et al. 2003, Justiniano et al. 2004, Poorter et al. 2006). cases where explanatory variables did not have significant regression coefficients, we dropped We then calculated a successional composition index per plot using the equation: = them to attain a simpler model, and we compared models to test whether the resulting model ( × ) � � , where SCp is the succession composition per plot p, EGi stands �for�� the was significantly different from the previous one. We fitted the final model (i.e. the simpler ∑�=1 �� model, which explanatory variables had statistically significant regression coefficients) and ecological∑�=1 �� guild of every tree i, and BAi stands for basal area of tree i. We scaled liana infestation and successional composition index from 0 to 100%. For successional composition we used restricted maximal likelihood estimation (REML) to determine regression coefficients. We calculate a pseudo-r2 for each model, squaring the Spearman correlation index values can range from 0% (stand biomass is fully represented by shade tolerant coefficient between the fitted vs. the empirical data. Models for each response variable were species); 33.3 (stand biomass represented mostly by partial-shade tolerant species); 66.6% (stand biomass represented mostly by long-lived pioneer species); and 100 (stand biomass is fitted for the tree different tree size classes: trees > 40 cm in DBH, trees between 20 - 40 cm in DBH and trees 10-20 cm in DBH. represented by short-lived pioneer species). Crown exposure (CEIi), liana infestation (LIi), Modelling procedures and statistical analyses where performed in R (version 3.1.2), and basal area (BAi) per tree were averaged over all censuses from the establishment of the plots until the last census (2011) before calculations of the indices. packages nlme, version 3.1.117 (Pinheiro et al. 2014) and MuMIn, version 1.10.5 (Barton 2012), using the functions lme, dredge , model.avg, and anova. Data analysis Results

To determine the variables that affected forest attributes (initial basal area, liana infestation Stand basal area was on average 9.0 ± 7.0 m2 ha-1 (mean ± SD) for large trees (DBH > 40 cm), and successional composition, see Fig. 3.1), we fitted a series of linear mixed effects models 6.1 ± 2.5 m2 ha-1 for intermediate-size trees (20 - 40 cm in DBH), and 2.9 ± 1.8 for small trees with all possible combinations of the explanatory variables (all-subset combinations). On (10-20 cm in DBH). In general, large trees had higher liana infestation index (59.7 ± 22.6 %) those models, explanatory variables were soil chemical variables that identify ADE such as than small trees (43.9 ± 20.7 %). Additionally, the community of large trees had larger soil pH, concentration of Ca, P, N, or OM content (hereafter called soil chemistry variables); proportion of (long-lived) pioneers (average SC = 45.1 ± 16.4%), the intermediate-size tree sand, and clay content (soil texture variables); light (crown exposure index); and by the community contained mostly partial shade tolerant (average SC = 33.7 ± 17.2%), whereas the presence of ceramics on the sampled soil. Presence of ceramics was included since it indicates

58 59 Chapter 3 ADE and Forest Structure and Dynamics

received by each tree using the tree crown exposure index, which is ranked from 1 to 5 the human occupation of the forest. In the mixed models we used plots (12) nested in blocks (Dawkins and Field 1978): 1 = no direct overhead light and little lateral light; 2 = no direct (3) as random factors, to account for spatial correlation of subplots belonging to the plots and overhead light but moderate or substantial lateral light; 3 = vertical light on part of the crown; for internal variability of the plots due to the IBIF-research treatments. Since we found a 4 = vertical light on the whole crown; and 5 = exposed emergent crown. To calculate a crown strong correlation between pH and Ca concentration in the soil (Pearson correlation = 0.76), exposure at the plot level, we weighted the crown exposure index of each tree by its basal area we excluded Ca from the analyses to avoid effects of collinearity. Liana infestation was a ( × ) = response variable, nevertheless we included this as explanatory variable in modelling the and divided this amount by plot basal area as follows: � , where CEIp is ∑�=1 �� initial tree basal area since lianas can have a negative effect on tree growth and increase tree � the crown exposure index per plot p, CEIi is the crown ��exposure� index∑�=1 �� of� tree i; and BAi the mortality. Consequently, liana infestation is expected to reduce the initial tree basal area (Fig. basal area of tree i. 3.1). Besides, we fitted a series of linear mixed effects models to evaluate if forest dynamics Liana infestation per subplot was determined using a liana infestation index for every (basal area growth, basal area recruitment and basal area mortality) were affected by soil tree that goes from 1 to 4 as follows (Alder and Synnott 1992): 1 = no lianas; 2 = lianas on chemistry, soil texture, light, presence of ceramics, liana infestation and successional stem; 3 = lianas on stem and crown; and 4 = lianas completely covering the crown. To composition. determine the liana infestation per subplot we used the following equation: = From all possible models produced, we performed model selection by choosing those ( × ) � models that differed less than 2 Akaike’s Information Criterion (AIC) units from the best � , where LIIp is the liana infestation index per plot p, LIi stands for liana infestation�� ∑�=1 �� model after they were ranked from the best to the worst according to the AIC score. We of tree i, �and BA stands for basal area of tree i. Liana infestation index range from 0% (stand ∑�=1 �� i averaged the selected models (e.g. 4 or 5 models) to pick the explanatory variables with biomass has not liana infestation) to 100% (stand biomass is completely infested by lianas). higher relative importance value (maximum 1), which is the sum of the Akaike weights Additionally, we determined the successional composition index of every subplot using (Burnham and Anderson 2002) of the models that include the explanatory variable is present information on the ecological guild for each tree as follows: 1 = shade tolerant species; 2 = (Barton 2012). We run the model with the variables that showed the highest importance value partial-shade tolerant species; 3 = long-lived pioneer species; and 4 = (short-lived) pioneer to determine if their regression coefficients were statistically significant at level of α=0.05. In species (Jardim et al. 2003, Mostacedo et al. 2003, Justiniano et al. 2004, Poorter et al. 2006). cases where explanatory variables did not have significant regression coefficients, we dropped We then calculated a successional composition index per plot using the equation: = them to attain a simpler model, and we compared models to test whether the resulting model ( × ) � � , where SCp is the succession composition per plot p, EGi stands �for�� the was significantly different from the previous one. We fitted the final model (i.e. the simpler ∑�=1 �� model, which explanatory variables had statistically significant regression coefficients) and ecological∑�=1 �� guild of every tree i, and BAi stands for basal area of tree i. We scaled liana infestation and successional composition index from 0 to 100%. For successional composition we used restricted maximal likelihood estimation (REML) to determine regression coefficients. We calculate a pseudo-r2 for each model, squaring the Spearman correlation index values can range from 0% (stand biomass is fully represented by shade tolerant coefficient between the fitted vs. the empirical data. Models for each response variable were species); 33.3 (stand biomass represented mostly by partial-shade tolerant species); 66.6% fitted for the tree different tree size classes: trees > 40 cm in DBH, trees between 20 - 40 cm (stand biomass represented mostly by long-lived pioneer species); and 100 (stand biomass is in DBH and trees 10-20 cm in DBH. represented by short-lived pioneer species). Crown exposure (CEIi), liana infestation (LIi), Modelling procedures and statistical analyses where performed in R (version 3.1.2), and basal area (BAi) per tree were averaged over all censuses from the establishment of the plots until the last census (2011) before calculations of the indices. packages nlme, version 3.1.117 (Pinheiro et al. 2014) and MuMIn, version 1.10.5 (Barton 2012), using the functions lme, dredge , model.avg, and anova. Data analysis Results

59 Chapter 3 ADE and Forest Structure and Dynamics

small tree community was mainly composed of (partial) shade-tolerant species (average SC = 3.2; Fig. 3.2c; Appendix). Additionally, light affected forest attributes differently for each size 24.5 ± 13.42%). category. For large trees, light had a negative effect on liana infestation and a positive effect Soil variables that are related with ADE and soil texture varied widely throughout our on successional composition index. For intermediate-size trees, light had a positive effect on sampling area (324 ha). Ca, P and OM varied most strongly amongst subplots (Table 3.1); Ca initial basal area, liana infestation and successional composition index (Fig. 3.2 b). For small and OM varied 100-fold and P 70-fold. The higher variability of P and Ca (which are trees, light positively affected liana infestation and successional composition index, but not indicators of ADE soils in the Amazonian region) plus the presence of ceramics indicate the initial basal area (Fig. 3.2c). presence of ADE soils in our study site (Table 3.1).

Table 3.1 Soil properties of 400 soil samples (0 - 40 cm depth) taken in twelve 27-ha plots in lowland tropical forest of La Chonta, Bolivia. Average, standard deviation (SD) and range (minimum to maximum) are given. Soil samples were taken to represent the whole gradient in soil fertility that is present in La Chonta from non-Amazonian Dark Earths to Amazonian Dark Earths). CEC=cation exchange capacity, TEB=total exchangeable bases, OM=organic matter.

Soil Variable Average SD Range Soil pH 6.70 0.89 (4.8 - 8.3) Ca (cmol kg-1) 4.60 4.09 (0.2 - 22.8) K (cmol kg-1) 0.21 0.15 (0.04 - 1.2) Na (cmol kg-1) 0.05 0.05 (0 - 0.84) Mg (cmol kg-1) 1.06 0.81 (0.1 - 9) TEB (cmol kg-1) 5.69 4.51 (0.5 - 29.9) P (mg kg-1) 12.03 11.92 (1 - 79) N (%) 0.13 0.06 (0.01 - 0.37) OM (%) 2.05 1.70 (0.28 - 33.1) Sand (%) 58.74 9.45 (8 - 81) Clay (%) 13.39 4.89 (0 - 38) Silt (%) 27.90 7.72 (8 - 73)

Effects of soil and light on forest attributes

After model selection, the ‘best’ models that explain forest attributes had in general low predictive power determined by a low Akaike weight, which is a measure of predictive power of the model selected as the best (Burnham and Anderson 2002) (see Appendix, Table A3.1, for further details); and the models with significant explanatory variables had in general low pseudo-r2 values, especially those that included soil variables (Table 3.2). In general, soil chemistry and soil texture affected forest attributes in all tree size categories differently (Fig. 3.2a, b, c). For large trees, soil pH had a negative effect on initial basal area and successional composition index (Fig. 3.3a, d), while soil N had a negative effect on liana infestation (Table 3. 2; Fig. 3.3f). For intermediate-size trees, soil pH had a positive effect on initial basal area and a negative effect on the successional composition index (Table 3.2; Fig. 3.3b, e), while soil P had a negative effect on initial basal area (Fig. 3.3c). For small trees, percentage of clay and sand negatively affected initial basal area and liana infestation simultaneously; although clay was not statistically significant it was selected in the most parsimonious model (Table

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Effects of soil and light on forest attributes

61 Chapter 3 ADE and Forest Structure and Dynamics ) x e d i n l s n * a a

a * e o

n b 0.001). For For 0.001). * n e * Abiotic Resources Forest Attributes in DBH; b) o r Forest Attributes iti a Abiotic Resources b n i o ti c s < 03 A 03 bu t o . . i t a ss l 0 0 1 s r po cm 1 Trees 20 - 40 cm DBH 1 iti a - - − Light Liana Infestation Trees >40cm DBH 15 Trees 20 - 40 cm DBH − − Light 15

e Liana Infestation s s 1.5 tt m f a cce p=0.02 ha ha n o

ha p=0.03

(Crown exposure index) u po A I

B (Crown exposure index) 2 c 2 2 s

l ( - ) m l

a ( + ) t s t a

a -0.0031*** o s n -0.03** c e n 1.0 10 iti 10 i a r 0.01, *** 0.01, n i o o o re L I F F ss Initial Basal Area < Initial Basal Area 0.5 cce 5 5 u S (

large trees > 40 p=0.02 -0.03** 0.0 Initial Basal Area m Area Basal Initial Initial Basal Area m Area Basal Initial Initial Basal Area m Area Basal Initial

( - ) 0.05, ** 0 Forest successional Forest successional 0 < Soil composition 5.0 So5.6il 6.3 7.0 7.9 co5.0mposition5.6 6.3 7.1 7.9 1.0 3.1 10 31 (Soil chemistry) (Successional composition index) (Soil chemistry) (Successional composition index) −1 Soil pH Soil pH Soil P (mg Kg ) )

x e e d f d n i 100 100 c 80 Trees 20-40 cm DBH Trees >40cm DBH

s Trees >40cm DBH l n a e Abiotic Resources Forest Attributes a o n significant, * significant, n e ADE and Forest Structure and Dynamics o - r 80 80 o iti ) n i ti s bu t A 60 x o a

i Light Liana Infestation t ss l e r s s po iti a d 60 60 s s tt ) f e

(Crown exposure index) m a i n cce y n o rce A u po 40 estation (%) estation I B t r e c f s t r l m l a i s t 40 40 s u a t a s o n s sou m e c il n a re iti i r e e o o en (ns= not i gh po n 20 o i o h L Initial Basal Area I S 20 L R x 20 c F F ss Liana In Liana e p=0.02 p<0.001 p=0.008 c il n o

cce ( - )

0 ( - ) ( - ) 0 0 w S Succesional Composition (%)

aSuccesional Composition (%) b o ti

u c ( o S r b i ( 1 1 Trees 20 - 40 cm DBH 1 C − Trees >40cm DBH 15 Trees 20 - 40 cm DBH − − 15 ( 5.0 5.6 6.3 7.0 7.9 0.01 0.03 0.10 0.31

1.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 A p=0.02 ha ha Forest successional ha p=0.03 2 2 ( + ) Soil pH 2 ( - ) Soil compoSoilsiti opHn Soil N (%) b 1.0 10 (Soil texture) (Successional composition index) 10 Figure 3.3 Relation between forest attributes: initial subplot basal area; liana infestation: from 0 (subplots where stand biomass has not liana infestation) to 0.5 5 100 % (subplots where stand biomass is completely covered with lianas); and successional composition 5 from 0 (subplots with only shade tolerant trees) to Figure 3.2 Results of the effects of abiotic) resources (soils and light) on forest attributes for three size categories of trees a) large trees > 40 cm in DBH; b) x

e 100% (subplots with only short-live pioneer trees) and soil resources (pH, P and N) for the tree size classes where the relationships were significant. N = 377 intermediate-sized trees 20 - 40 cm in DBH;d c) small trees 10 - 20 cm in DBH. Arrowsp=0.02 indicate the effect of the explanatory variable on the forest attribute. 0.0 Initial Basal Area m Area Basal Initial Initial Basal Area m Area Basal Initial Initial Basal Area m Area Basal Initial ) i n * for trees( >- ) 40 cm DBH (a, d, f) and N = 205 for trees between 20 and 40 cm DBH (b, c, e). Regression coefficients for final modes are in Table 3.2. The p x s 0 0 l s e n

For each explanatorye variable, the absolute regression coefficient and significance level are given (ns= not-significant, * < 0.05, ** < 0.01, *** < 0.001). For ** a d o a

n values and the direction of the relationship are shown by the sign between brackets. Please note that the x-axes are log transformed (Table A3.1). n 10 e n 20 cm in DBH. Arrows indicate the effect of the explanatory variable on the forest attribute. on the variable forest in attribute. 20 cm DBH. of effect the the Arrows indicate explanatory o i ) 5.0 5.6 6.3 7.0 7.9 r iti rce 5.0 5.6 6.3 7.1 7.9 1.0 3.1 10 31 small trees, none of the soil chemical variablesn was important but soil texture (sand) was selected as predictors in the models. i o e bu t s ti e A o r i 0031 r ss r t a u l . −1 po t u t iti s a sou 0

tt Soil P (mg Kg )

s Soil pH Soil pH s x il e s - m e f cce e gh o a A t o i e n po u po f S R d c I B s L x il m s t l l t e a c o a a s o 100 n 100

80 Trees >40cm DBH S ti n e c Trees 20-40 cm DBH n Trees >40cm DBH ( r iti i a o o re w i o o n i L o I F F 80 b r 80 ss 63 C 60 A ( cce small trees 10 - 10 trees small u 60 60 S c) ( 40 estation (%) estation f c 40 40 20 20 20 p=0.02 p<0.001 In Liana p=0.008 ( - )

0 ( - ) ( - ) 0 0 Succesional Composition (%) Succesional Composition (%)

5.0 5.5 6.0 6.5 7.0 7.5 8.0 5.0 5.6 6.3 7.0 7.9 0.01 0.03 0.10 0.31 40 cm in DBH;

- Soil pH Soil pH Soil N (%) ) x s e

d Figure 3.3 Relation between forest attributes: initial subplot basal area; liana infestation: from 0 (subplots where stand biomass has not liana infestation) to ) i n rce y e t r

r 100 % (subplots where stand biomass is completely covered with lianas); and successional composition from 0 (subplots with only shade tolerant trees) to u t i s sou s e m 100% (subplots with only short-live pioneer trees) and soil resources (pH, P and N) for the tree size classes where the relationships were significant. N = 377 il e i gh po o R h L x S sized trees 20 sized

c for trees > 40 cm DBH (a, d, f) and N = 205 for trees between 20 and 40 cm DBH (b, c, e). Regression coefficients for final modes are in Table 3.2. The p e - c il n

o values and the direction of the relationship are shown by the sign between brackets. Please note that the x-axes are log10 transformed (Table A3.1). o ti w S o ( b i r C A ( a Chapter 3 a) trees of categories size three for attributes forest on light) and (soils resources abiotic of effects the of 3.2 Results Figure intermediate nd significance level are giv are level significance a nd coefficient regression a bsolute the variable, explanatory each For ls. the mode in predictors as selected was (sand) texture soil but important was variables chemical soil the of none trees, small 62 63 Chapter 3 small trees, none of the soil chemical variables was important but soil texture (sand) was selected as predictors in the models. For each explanatory variable, the absolute regression coefficient and significance level are giv intermediate Figure 3.2Results of the effects of abiotic resources (soils and light) on forest attributes for three size categories of treesa) 3 Chapter a ( A C r bi ( o S w oti

a b o n Abiotic Resources Forest Attributes il Forest Attributes

Abiotic Resources c - e c sized 20 trees S x L h R o po igh e

Light Liana Infestation il m Light Liana Infestatioe n s sou is t (Crown exposure index) (Crown exposure index) u r tr e y rce

-0.0031*** in ) -0.03** d e s x Initial Basal Area Initial B) asal Area - 40 cm in DBH; DBH; in cm 40 -0.03**

Forest successional Forest successional Soil composition Soil composition (Soil chemistry) (Successional composition index) (Soil chemistry) (Successional composition index) c ( c) S

c u small trees 10 - Abiotic Resources Forest Attributes cce ( A C ss r b F F I o L i n o io w

Light Liana Infestation ore o ia iti r ( n c e n ti S n o s a (Crown exposure index) a o c a e t l l st m il x L s B I c R S po u po n i o t A a o gh e cce f e m - s e il x s s tt 0 sou a s iti t u t po . l u ta r ss r 0031 i r o A Initial Base al Area ti s but e io n rce iti r ) i o 20 cm in DBH. Arrows indicate the effect of the explanatoryArrows indicate the the effect20 cmDBH.of attribute. in forest variable onthe n e n n a o d a ** e n e s l s x * in ) d e x

Forest successional ) Soil composition values and the direction of the relationship values and the direction of the relationship % 100% (subplots with only short 100 % (subplots where stand biomass is 3.3 Figure 100% (subplots with only short 100 % (subplots where stand biomass is 3.3 Figure for trees for trees

(Soil td exture) (Successional composition inded x) Succesional Composition (%) 2 −1 Succesionala Composition (%) 2 −1 a

Initial Basal Area m ha Initial Basal Area m ha b 0 20 40 60 80 100 0.0 0.5 1.0 1.5 0 20 40 60 80 100 0.0 0.5 1.0 1.5 Figure 3.2 Results of the effects of abiotic resources (soils and light) on forest attributes for three size categories of trees a) large trees > 40 cm in DBH; b) A p p p p ( =0.02 =0.02 =0.02 =0.02 ( - ) ( - ) ( - C ( -) ( -) ( > > 5.0 5.0 ( bi intermediate-sized trees 20 - 40 cm in DBH; c) small trees 10 - 205.0 cm in DBH. Arrows indicate the effect of the explanatory5.0 variable on the forest attribute. r S o ( u 40 cm DBH 40 cm DBH oti S w Relation between forest attribute Relation between forest attribute For each explanatory variable, the absolute regression coefficient and significancecce level are given (ns= not-significant, * < 0.05, ** < 0.01, *** < 0.001). For o n il c 5.5 5.5 e ss F F

small trees, none of the soil chemical variables was important but soil texture (sand) was selected as predictors in the modec ls. x R L S I L h o i o n po igh en (ns= not (ns= en o o e 5.6 5.6 e r i iti re a n il c e m sou s n o s a t 6.0 6.0 a u s t is a l m l r t s c e tr B I po u A rce o n y cce in a m fe ) Soil pH Soil pH Soil tt s s Soil pH Soil pH Soil (a, d,f) (a, d, f) (a, d 6.5 6.5 a iti po s s r 6.3 6.3 e l ss t i a o x A but s ti i n ) iti o r - o e n significant, * n o 7.0 7.0 a e a n l s - - T T i live pioneer trees) and soil resources live pioneer trees) and soil resources T T and N and N and n rees >40cm DBH rees >40cm DBH 7.0 7.0 rees >40cm DBH rees >40cm DBH rees d e 7.5 7.5 x ) = = 8.0 8.0 7.9 7.9 205 for 205 for trees 20and between 40cm DBH 205 for trees 20and between 40cm DBH completely covered with lianas); and successional composition from with only 0(subplots tolerant shade trees) to completely covered with lianas); and successional composition from with only 0(subplots tolerant shade trees) to are are < s: s: s: 0.05, ** shown by sign the between brackets. shown by sign the between brackets. initial subplo initial subplo large trees > 40 > trees large ( b b S e 2 −1 e 2 −1 Succesional Composition (%) Initial Basal Area m ha Succesional Composition (%) ADEu and ForestInitial Basal Structure Area m ha and Dynamics cce 0 20 40 60 80 0 5 10 15 0 20 40 60 80 0 5 10 15 < ss F F I L ore o io n 0.01, *** T T p p r ( +) ( p +) ( p ia iti rees 20- 40cm DBH rees 20- 40cm DBH <0.001 <0.001 n e c =0.02 =0.02 ( -) ( -) ( 5.0 5.0 n s o 5.0 5.0 a a st t a l m l s t basal area basal t area basal t c B I A po u o n cce a f m tt s s e - - a iti cm po r s 0 0 l ss ta i . . o but 03 A 03 < s ti io 5.6 5.6 n 5.6 5.6 iti r o in DBH;b) in

a b * e n * 0.001). For c n o e * a a * (pH, P and N) (pH, P and N) n s l 1 1 Trees 20 - 40 cm DBH 1 − Trees >40cm DBH 15 Trees 20 - 40 cm DBH in − − 15 ; ; 1.5 p=0.02 d ha liana infestation: from 0 (subplots where stand biomass has not liana infestation liana infestation: from 0 (subplots where stand biomass has not liana infestation ha ha p=0.03 Soil pH Soil pH Soil e 2 2 2 Soil pH Soil pH Soil 6.3 6.3 x ( + ) 6.3 6.3 ( - ) ) 1.0 10 10 T T rees 20-40 cm DBH cm 20-40 rees DBH cm 20-40 rees 0.5 7.0 7.0 Please note that the x Please note that the x 7.1 7.1 5 5 for the tree size classes where the relationships were significant. N for the tree size classes where the relationships were significant. N (b, c, e) c, (b, e) c, (b, p=0.02 0.0 Initial Basal Area m Area Basal Initial Initial Basal Area m Area Basal Initial ( - ) m Area Basal Initial 0 0 7.9 7.9 7.9 7.9 5.0 . Regression coefficients modes for final Tablein are 5.6 6.3 7.0 7.9 5.0 5.6. Regression coefficients modes for final Tablein are 6.3 7.1 7.9 1.0 3.1 10 31 −1 Soil pH Soil pH Soil P (mg Kg ) d e f c c f − f − Liana Infestation (%) Initial Basal Area m2 ha 1 Liana Infestation (%) Initial Basal Area m2 ha 1 100 100 Trees >40cm DBH 80 Trees 20-40 cm DBH Trees >40cm DBH - -

axes log are 0 20 40 60 80 100 axes log are 0 20 40 60 80 100 80

80 0 5 10 15 0 5 10 15 0.01 0.01 60 1.0 1.0 T T p p rees >40cm DBH >40cm rees DBH >40cm rees =0.008 =0.008 60 60 ( -) ( ( -) ( 40 estation (%) estation ADE andForest Structure andDynamics ADE andForest Structure andDynamics f 40 40 10 10 20 20 20 transformed (TableA3.1). transformed (TableA3.1). Liana In Liana p=0.008

p=0.02 p<0.001 3.1 3.1 0.03 0.03 ( - ) ( - ) 0 ( - ) 0 0 Soil P(mg Kg Soil P(mg Kg Succesional Composition (%) Succesional Composition (%)

5.0 5.5 6.0 6.5 7.0 7.5 8.0 5.0 5.6 6.3 7.0 7.9 0.01 0.03 0.10 0.31 Soil N(%) Soil N(%) Soil

Soil pH Soil pH Soil N (%) 10 10 − − 1 1 T T ) ) 0.10 0.10 rees 20 - 40cm DBH 20- rees Figure 3.3 Relation between forest attributes: initial subplot basal area; liana infestation: 40cm DBH 20- rees from 0 (subplots where stand biomass has not liana infestation) to 100 % (subplots where stand biomass is completely covered with lianas); and successional composition from 0 (subplots with only shade tolerant trees) to 100% (subplots with only short-live pioneer trees) and soil resources (pH, P and N) for the tree size classes where the relationships were significant. N = 377 for trees > 40 cm DBH (a, d, f) and N = 205 for trees between 31 20 and 40 cm DBH (b, c, e). Regression coefficients for31 final modes are in Table 3.2. The p ( - ) (- p ( - ) (- p values and the3.2. The direction of the relationship are shown by the sign between3.2. The brackets. Please note that the x-axes are log10 transformed (Table A3.1). =0.03 =0.03 0.31 0.31 = = 377 377 377 ) to to ) to ) 63 63 p p

63 Chapter 3 ADE and Forest Structure and Dynamics ). Table 3.2 Resultsp of the linear mixed model selected for each of the forest attributes (initial basal area, liana infestation, and successionalEffects composition)of soil, light onand forest attributes on forest dynamics each tree size category. For every explanatory variable, the value of the regression coefficient with significance level (p) are presented. AIC scores for the ‘best’ model (Annex 1) that had significant regression coefficients for the explanatory variables are presented as well as the squared Pearson correlation <0.001 0.69 <0.001 0.014 0.008 p 0.005 0.001 0.00 0.02 From the components of forest dynamics, basal area growth of intermediate-size trees was between the fitted model and the empirical data for each model. We also present the value of the model intercept with its significance levelhighest (p). (0.14 ± 0.08 m2ha-1y-1), lowest for large trees (0.074 ± 0.07 m2ha-1y-1) and intermediate for small trees (0.10 ± 0.09 m2ha-1y-1). Basal area recruitment was rather even for Forest Attributes Tree Size Category Explanatory Variables Coefficient p Model AIC Model Intercept p (Response Variable) pseudo-r2 all three size classes, just a bit higher in large and intermediate-size trees (large-size: 0.19 ± significance level ( level significance 2 -1 -1 2 -1 -1 2 -1 -1 Initial Basal Area DBH > 40 cm soil pH -0.71 0.02 227.15 0.12 1.52 0.15 m ha<0.001y ; intermediate-size: 0.18 ± 0.12 m ha y ; small-size: 0.14 ± 0.13 m ha y ). 39.13 Intercept 1.52 1.05 82.10 21.65 0.54 53.55 0.93 33.06 2 -1 -1 Liana Infestation -0.0031 <0.001 Basal area mortality was highest for large trees (0.27 ± 0.26 m ha y ), lowest for small trees 2 -1 -1

) are presented. AIC scores for the the scores for AIC presented. ) are (0.09 ± 0.06 m ha y ) and intermediate for intermediate-size trees (0.18 ± 0.14), across a p 20-40 cm in DBH soil pH 7.10 0.025 949.84 0.14 1.05 0.69

tes (initial basal area, liana infestation, and successional composition) on on composition) successional and infestation, liana area, basal (initial tes Sand -0.53 0.015 basal area growth (Table 3.3), which means that when trees are large, then plots that contain Succession Composition DBH > 40 cm soil pH -32.88 0.022 3157.29 0.06 39.13 less ‘pioneers’0.008 grow faster, but when trees are of intermediate-size, then plots that contain Crown Exposure 7.50 <0.001 more ‘pioneers’ grow faster. Only in the case of intermediate-size and small trees, light had a 32.88 0.71 0.03 13.97 0.53 1.00 8.26 0.0031 0.03 0.02 76.62 - Coefficient Coefficient - 7.10 - 0.79 - 9.08 - 7.50 12.77 ------0.14 17.58 20-40 cm in DBH soil pH -76.62 <0.001 1720.34 0.24 53.55 positive 0.001 effect on basal area growth (Fig. 3.4b, c; Table 3.3); and for intermediate-size and Crown Exposure 12.77 <0.001 large trees liana had a negative effect on basal area growth (Fig. 3.4a, c; Table 3.3). 10-20 cm in DBH Crown Exposure 0.14 0.04 114.63 0.04 0.93 Soil0.00 variables had only an effect on basal area recruitment of intermediate-size trees. Soil pH had a positive effect on basal area recruitment whereas N and clay had a negative effect (Fig.3.4b; Fig. 3.5a, b, c). Light had a positive effect on basal area recruitment for intermediate-size and for small trees (Fig. 3.4b, c). Among the forest attributes, successional composition had a positive effect on basal area recruitment of small trees and liana infestation oil pH oil pH oil pH oil pH S Explanatory Variables Explanatory Soil P Crown Exposure S Liana Infestation S Liana Infestation Composition Succession Crown Exposure Soil N Crown Exposure Sand Crown Exposure Crown Exposure Clay S Crown Exposure Crown Exposure did not have any effect (Table 3.3; Fig. 3.4c). Basal area mortality was only affected by soil variables in the case of large trees. Soil pH had a negative effect on basal area mortality (Fig. 3.4 a; Fig. 3.5d; Table 3.3), and the presence of ceramics in the soil had a negative coefficient

) that had significant regression coefficients for the explanatory variables are presented as well as the squared Pearson Pearson the squared as well as presented are variables explanatory for the coefficients regression significant had ) that related to basal area mortality of small trees (Fig. 3.4c; Table 3.3). Basal area mortality was 1 positively affected by initial basal area for all categories (Fig. 3.4a, b, c); positively affected A3. 40 cm in DBH in 40 cm DBH in 20 cm DBH in 40 cm DBH in 40 cm DBH in 20 cm 20 cm in DBH in 20 cm by successional composition in the case of intermediate-size and small trees (Fig. 3.4b, c); DBH > 40 cm Tree Size Category Size Tree DBH > 40 cm 20 - 10 - DBH > 40 cm 20 - 20 - 10 - 10 - and not affected by liana infestation. Lastly, presence of ceramics, included as explanatory variable in the models, was sometimes selected in the ‘best’ model (Annex 3, Table A3.1) but was only significant in explaining basal area mortality of small trees (Fig. 3.4c, Table 3.3). ppendix, Table ppendix, A Forest Attributes Forest Attributes Variable) (Response Initial Basal Area Infestation Liana Succession Composition Chapter 3 attribu the forest forof each selected model mixed the linear of Table 3.2 Results ( level significance with coefficient regression of the the value variable, explanatory every For category. size tree each ( ‘best’ model correlation between the fitted model and the empirical data for each model. We also present the value of the model intercept with itsintercept model the of value the present also We model. each for data empirical the and model fitted the between correlation 64 65 Chapter 3 ADE and Forest Structure and Dynamics ). Table 3.2 Resultsp of the linear mixed model selected for each of the forest attributes (initial basal area, liana infestation, and successionalEffects composition)of soil, light onand forest attributes on forest dynamics each tree size category. For every explanatory variable, the value of the regression coefficient with significance level (p) are presented. AIC scores for the ‘best’ model (Annex 1) that had significant regression coefficients for the explanatory variables are presented as well as the squared Pearson correlation p <0.001 0.69 0.005 <0.001 0.014 0.02 0.008 0.001 0.00 From the components of forest dynamics, basal area growth of intermediate-size trees was between the fitted model and the empirical data for each model. We also present the value of the model intercept with its significance levelhighest (p). (0.14 ± 0.08 m2ha-1y-1), lowest for large trees (0.074 ± 0.07 m2ha-1y-1) and intermediate for small trees (0.10 ± 0.09 m2ha-1y-1). Basal area recruitment was rather even for Forest Attributes Tree Size Category Explanatory Variables Coefficient p Model AIC Model Intercept p (Response Variable) pseudo-r2 all three size classes, just a bit higher in large and intermediate-size trees (large-size: 0.19 ± significance level ( level significance 2 -1 -1 2 -1 -1 2 -1 -1 Initial Basal Area DBH > 40 cm soil pH -0.71 0.02 227.15 0.12 1.52 0.15 m ha<0.001y ; intermediate-size: 0.18 ± 0.12 m ha y ; small-size: 0.14 ± 0.13 m ha y ).

Intercept 1.52 1.05 0.54 82.10 21.65 33.06 39.13 53.55 0.93 2 -1 -1 Liana Infestation -0.0031 <0.001 Basal area mortality was highest for large trees (0.27 ± 0.26 m ha y ), lowest for small trees 2 -1 -1

2 time window of 8 to 10 years. r - Soil P -1.00 0.035 Soil variables had a weak influence on forest dynamics. Some of the ‘best’ models Liana Infestation -0.03 0.006 that explained forest dynamics, selected pH, N, P, OM content and texture to explain basal Model pseudo 0.12 0.14 0.36 0.14 0.17 0.33 0.06 0.24 0.04 Succession Composition -0.03 0.005 area growth, basal area recruitment and basal area mortality (Appendix). Nevertheless, only in Crown Exposure 0.79 0.033 few cases soil variables were retained as significant in the final model (Table 3.3; Fig. 3.4 a, 10-20 cm in DBH Clay -0.02 0.097 122.53 0.36 0.54 b, c). Possi0.005bly the small size of our plots (radius= 25 m ca. 0.2 ha) prevented picking-up Liana Infestation DBH > 40 cm Soil N -13.97 0.011 3390.11 0.14 82.10 stronger <0.001patterns due to small amount of trees in the sampled area. In general, basal area Model AIC 227.15 949.84 122.53 3390.11 1767.97 804.01 3157.29 1720.34 114.63 Crown Exposure -8.26 0.001 growth of survivors was not affected by soil variables, it was only affected by light and forest 20-40 cm in DBH Crown Exposure 9.08 <0.001 1767.97 0.17 21.65 attributes.0.014 For large trees successional composition index negatively affected basal area p 100.02 -20 cm<0.001 in0.025 DBH0.035 0.006 Crown0.005 0.033 Exposure0.097 0.011 0.001 <0.001 <0.001 17.580.015 0.022 <0.001 <0.001 <0.001<0.001 0.04 804.01 0.33 33.06 growth and0.02 for intermediate-size trees, successional composition index positively affected tes (initial basal area, liana infestation, and successional composition) on on composition) successional and infestation, liana area, basal (initial tes Sand -0.53 0.015 basal area growth (Table 3.3), which means that when trees are large, then plots that contain Succession Composition DBH > 40 cm soil pH -32.88 0.022 3157.29 0.06 39.13 less ‘pioneers’0.008 grow faster, but when trees are of intermediate-size, then plots that contain Crown Exposure 7.50 <0.001 more ‘pioneers’ grow faster. Only in the case of intermediate-size and small trees, light had a 0.71 0.0031 1.00 0.03 0.03 0.02 13.97 8.26 0.53 32.88 76.62 Coefficient Coefficient - - 7.10 - - - 0.79 - - - 9.08 17.58 - - 7.50 - 12.77 0.14 20-40 cm in DBH soil pH -76.62 <0.001 1720.34 0.24 53.55 positive 0.001 effect on basal area growth (Fig. 3.4b, c; Table 3.3); and for intermediate-size and Crown Exposure 12.77 <0.001 large trees liana had a negative effect on basal area growth (Fig. 3.4a, c; Table 3.3). 10-20 cm in DBH Crown Exposure 0.14 0.04 114.63 0.04 0.93 Soil0.00 variables had only an effect on basal area recruitment of intermediate-size trees. Soil pH had a positive effect on basal area recruitment whereas N and clay had a negative effect (Fig.3.4b; Fig. 3.5a, b, c). Light had a positive effect on basal area recruitment for intermediate-size and for small trees (Fig. 3.4b, c). Among the forest attributes, successional composition had a positive effect on basal area recruitment of small trees and liana infestation oil pH oil pH oil pH oil pH Explanatory Variables Explanatory S Liana Infestation S Soil P Liana Infestation Composition Succession Crown Exposure Clay Soil N Crown Exposure Crown Exposure Crown Exposure Sand S Crown Exposure S Crown Exposure Crown Exposure did not have any effect (Table 3.3; Fig. 3.4c). Basal area mortality was only affected by soil variables in the case of large trees. Soil pH had a negative effect on basal area mortality (Fig. 3.4 a; Fig. 3.5d; Table 3.3), and the presence of ceramics in the soil had a negative coefficient

------by successional composition in the case of intermediate-size and small trees (Fig. 3.4b, c);

Tree Size Category Size Tree DBH > 40 cm 20 10 DBH > 40 cm 20 10 DBH > 40 cm 20 10 and not affected by liana infestation. Lastly, presence of ceramics, included as explanatory variable in the models, was sometimes selected in the ‘best’ model (Annex 3, Table A3.1) but was only significant in explaining basal area mortality of small trees (Fig. 3.4c, Table 3.3). ppendix, Table ppendix, A Forest Attributes Forest Attributes Variable) (Response Initial Basal Area Infestation Liana Succession Composition Chapter 3 attribu the forest forof each selected model mixed the linear of Table 3.2 Results ( level significance with coefficient regression of the the value variable, explanatory every For category. size tree each ( ‘best’ model correlation between the fitted model and the empirical data for each model. We also present the value of the model intercept with itsintercept model the of value the present also We model. each for data empirical the and model fitted the between correlation 65 Chapter 3 ADE and Forest Structure and Dynamics ) x e

40 cm of 40 cm i n

e s l

n a t b a n a > 1

1 c n e u r − − i t o t i o n b i o s y A -1 y s t a o s l r i s 1 1 y p e t i t o a e − − s t s c f m a c o n o -1 I u p A B

s ha ha a t m l t 2 2 n Trees 20-40 cm DBH a s o Trees 20-40 cm DBH Trees 20 - 40 cm DBH ha e c n i a i t a l 2 r r e s n L i o o I s F s e F o 0.31 0.32 0.31 c c u S large trees trees large ation and successional ation and successional ( uitment m uitment r 0.1 0.1 0.1 s i c m a r e 0.03 0.03 C p=0.03 0.03 p=0.04 p=0.002 (+) ( - ) ( - ) but that were not supported by the the by supported not that were but . t ) n s ,

r e 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 c s 0 5 10 15 20 25 30 t h o l i t y

i 5.0 5.6 6.3 7.1 7.9 Basal Area Rec Area Basal w i v t a i t m Basal Area Recruitment m Recruitment Area Basal r o v u r r o r a m g Basal Area Recruitment m u c

s Soil N (%) n e

a Soil Clay (%) r

f Soil pH a e

y r o e a 0.001)

r d e A

r t h A t l d A l a w < ty (dead trees) for a) for trees) (dead ty s a l o 1 s a a r e s − a s B G r a B y ( B F o 1 ) − x e

d ADE and Forest Structure and Dynamics ha Trees >40cm DBH i n 2

l e s 1.0 n a t a n e n r u i t o n t i o i o A b s 0.01, *** s o l t a s r i s a p e i t o t s e s c < t f m a c o n o u B p I A c

a m l t t a n o s c e n i t a l i a 0.1 r ) n r e s i o o x L I s e F s d r c e s e F o y c i n u

e c t r r e o u r i s t u t u S 0.05, ** 0.05, x s ( m p=0.039 h a b e e o 1 i g 1 c h p T R e s − c − < L x ( - ) i l y e 0.01 c -1 y i l

o i 1 o n 1 t y S − − S w o i o -1 r * s b ha ha * C i c ( 2 6 2 Basal Area Mortality m Mortality Area Basal Trees 20 - 40 cm DBH Trees 20-40 cm DBH A Trees 20-40 cm DBH ha 0 m 5.0 5.6 6.3 7.1 7.9 2 0 a . r 0 e C 0.31 0.32 0.31 Soil pH b t ) uitment m uitment n s

r e r c s 0.1 0.1 0.1 i t h o l i t y Figure 3. 5 Bivariate scatter plots of the relationship between abiotic resources soil chemistry and soil texture and basal area recruitment (panels a through c) w i v i t m t a o r v u r r a m r o ) g c u x

n m s and mortality (panel d). Panels a), b), and c) represent the effect of soil chemistry (pH, N) and texture (clay) on basal area gain; and panel d) shows the effect Figure 3.4 Effects of abiotic resources (soil chemistry,e soil texture and light) and forest attributes (initial basal area, liana infestation and successional e

r y f

e a d

r o a e small trees between 10 and 20 cm DBH. Arrows indicate the effect of the explanatory explanatory the of effect the indicate Arrows DBH. cm 20 and 10 between trees small d

= not significant, * significant, = not 0.03 r i n e

p=0.03 0.03 0.03 A

p=0.002 r t p=0.04 e s l t h of soil chemistry (pH) on basal area loss. For the tree size classes where the relationships were significant. Regression coefficients are in Table 3.3. The p A l composition) on forest dynamicsn measured as basal area recruitment, basal area growth and basal area mortality (dead trees) for a) large trees > 40 cm of t A a a a l w n ( - ) n s e l a

u ( - ) c) o r a s a r e s i t o

n (+) i o t i o s b a s A B s values and the direction of the relationship are shown by the sign between brackets. Please note that the x-axes are log

a transformed (Table A3.1). DBH; b) intermediate-sized trees between 20 and 40 cm DBHG r ; and c) small trees between 10 and 20 cm DBH. Arrows indicate the effect of the explanatory 10 o B t a s l ( r i B p s F o e i t o a t e s s c t m f a c o 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0 5 10 15 20 25 30 n o u

p 5.0 5.6 6.3 7.1 7.9 and A B variable I on the forest dynamic variable and dashed grey arrows represent relationships that we hypothesised (Fig. 1), but that were not supported by the Basal Area Rec Area Basal c

t m l Basal Area Recruitment m Recruitment Area Basal a t ws represent relationships that we hypothesised (Fig. 1) (Fig. hypothesised that we relationships represent ws ; a s o n e c n Basal Area Recruitment m i t a l r models. i a Regression coefficient and significance level are presented (ns = not significant, * < 0.05, ** < 0.01, *** < 0.001). Soil N (%)

r e s Soil Clay (%) n i o o

L Soil pH I s F s e F o c d c u 1 S − ( y 1 − ) x e r c e s ha d

) Trees >40cm DBH * u s 2 y i n *

67 1.0 o i c t r * e r 9 i s m t u 0 a . m s h r i l e 0 o e o R e s i g h p S C c L x c i e i l

t o n o S w i ( o b r 0.1 t C ) A ( n s

r e c s t h o i l i t y w t a i v i t m r o v u r r o r a m g u c

n p=0.039 s M e c

r f y e

a r o a e d

( - ) r e

0.01 A r t t h A l A a l w s l a o a s a r e s s a B G r a B ( Basal Area Mortality m Mortality Area Basal B F o 5.0 5.6 6.3 7.1 7.9 Soil pH sized trees between 20 and 40 cm DBH cm 40 and 20 between trees sized Figure 3. 5- Bivariate scatter plots of the relationship between abiotic resources soil chemistry and soil texture and basal area recruitment (panels a through c)

) and mortality (panel d). Panels a), b), and c) represent the effect of soil chemistry (pH, N) and texture (clay) on basal area gain; and panel d) shows the effect x e d ) r c e s y i n of soil chemistry (pH) on basal area loss. For the tree size classes where the relationships were significant. Regression coefficients are in Table 3.3. The p u

t r e o r i s t u s m h i l valuesinfest liana area, basal (initial attributes forest and light) and texture soil chemistry, (soil resources of abiotic Effects and the direction of the relationship are shown by the sign between brackets. Please note that the x-axes are log transformed (Table A3.1).

e 10 o o i g h p R e s S c L x e c intermediate i l

i o n t S w o ( o i r b) b C ( A a Chapter 3 Figure 3.4 mortali area basal and growth area basal recruitment, area basal as measured dynamics forest on composition) DBH; variable on the forest dynamic variable and dashed grey arro grey dashed and variable dynamic forest the on variable models. Regression coefficient and significance level are presented (ns presented are level significance and coefficient Regression models. 66 67 Chapter 3 models. Regression coefficient and significance level are presented (ns variable on the forest dynamic variable and dashed grey arro DBH; DBH; composition) on forest dynamics measured as basal area recruitment, basal area growth and basal area mortali 3.4 Figure 3 Chapter b) intermediate Effects of abiotic resources (soil chemistry, soil texture and light) and forest attributes (initial basal area, liana infest - sized trees between 20 and 40 cm DBH ; ws represent relationships we that hypothesised (Fig. 1) and and c) = not= significant, * small trees between 10 and 20 cm DBH. Arrows indicate the effect of the explanatory Figure 3. 5 Figure 3. 5 Figure values and the direction of the relatio and mortality (panel d). Panels a), b), and represent c) effect the of soil chemistry texture and N) (pH, values and the direction of the relatio and mortality (panel d). Panels a), b), and represent c) effect the of soil chemistry texture and N) (pH, of soil che soil of che soil of a a Basal Area Recruitment m2 ha−1 y−1 Basal Area Recruitment m2 ha−1 y−1 0.03 0.1 0.31 0.03 0.1 0.31

Figure 3.4 Effects of abiotic resources (soil chemistry, soil texture and light) andT forest attributes (initial basal area, liana infestation andT successional rees 20-40 cm DBH cm 20-40 rees DBH cm 20-40 rees

composition) on forest dynamics measured as basal area recruitment,5.0 basal area growth and basal area mortality (dead5.0 trees) for a) large trees > 40 cm of mistry (pH) on basal area loss. mistry (pH) on basal area loss. <

DBH; b) intermediateBivariate scatter plots of the relationship between abiotic resources soil chemistry and -soil texture and basal area recruitment sized trees between 20 and 40 cm DBH; and c) smallBivariate scatter plots of the relationship between abiotic resources soil chemistry and soil texturetrees and basal area recruitment between 10 and 20 cm DBH. Arrows indicate the effect of the explanatory

variable on the forest dynamic variable and dashed grey arro0.05, ** ws represent relationships that we hypothesised (Fig. 1), but that were not supported by the

models. Regression coefficient and significance level are presented5.6 (ns = not significant, * < 0.05, ** < 0.01, *** < 0.001)5.6 . < Soil pH Soil pH Soil 0.01, *** 6.3 6.3 7.1 7.1 ty (dead trees) for a) < 0.001) nship a nship a (+) p (+) p 7.9 7.9 , =0.04 =0.04 . F F but were that not supported by the re re or the tree size classes where the relationships were significant. Regression coefficients are in Table 3 or the tree size classes where the relationships were significant. Regression coefficients are in Table 3 shown by sign the between brackets. shown by sign the between brackets. ADE and Forest Structure and Dynamics b b d d Basal Area Mortality m2ha−1 y−1 Basal Area Recruitment m 2Basalha−1y−1 Area Mortality m2ha−1 y−1 Basal Area Recruitment m2ha−1y−1 ation and successional and ation 0.01 0.1 1.0 large trees 0.03 0.1 0.31 0.01 0.1 1.0 0.03 0.1 0.31 0.00 0.00 5.0 5.0 0.05 0.05 a b 1 1 c − − > y -1 y 0.10 0.10 1 1 y − − 5.6 5.6 40 cmof -1 ha ha 2 2 Trees 20-40 cm DBH Trees 20 - 40 cm DBH Trees 20-40 cm DBH ha 2 0.15 0.15 0.31 0.32 0.31 Soil N (%) N Soil (%) N Soil Soil pH Soil pH Soil 6.3 6.3 0.20 0.20 uitment m uitment r 0.1 0.1 0.1 T T Please note that the x Please note that the x rees 20- 40cm DBH rees 20- 40cm DBH T T rees >40cm DBH >40cm rees DBH >40cm rees 0.25 0.25 7.1 7.1 0.03 0.03 0.03 p=0.04 p=0.03 p=0.002 (+) ( - ) ( - ) 0.30 0.00 0.050.30 0.10 0.15 0.20 0.25 0.30 0.35 0 5 10 15 20 25 30 5.0 5.6 6.3 7.1 7.9 p p Basal Area Rec Area Basal p p =0.03 =0.03 ( - ) ( - ) ( - Basal Area Recruitment m Recruitment Area Basal 7.9 7.9 =0.039 =0.039 Soil pH Soil N (%) Basal Area Recruitment m Soil Cl ay (%) 0.35 0.35 ( - ) ( - ( - ) ( - d 1 − y (clay) (clay) 1 − - - c c axes log are axes log are 2 -1 -1 2 -1 -1 ha Trees >40cm DBH 2 Basal Area Recruitmentha my Basal Area Recruitmentha my 1.0 on area basal gain; and panel d)shows the effect 0.03on area basal gain; and panel d)shows the effect 0.1 0.32 0.03 0.1 0.32 ADE andForest Structure andDynamics ADE andForest Structure andDynamics 0 0 0.1 10 10 transformed (TableA3.1). transformed (TableA3.1). 5 5 p=0.039 ( - ) 0.01 10 10

Basal Area Mortality m Mortality Area Basal 5.0 5.6 6.3 7.1 7.9 Soil Cl Soil Cl Soil pH 15 15 a a T T y (%) Figure 3. 5 Bivariate scatter plots of the relationship betweeny (%) abiotic resources soil chemistry and soil texture and basal area recruitment (panels a through c) (panels a through c) (panels a through c) rees 20-40 cm DBH 20-40rees cm rees 20-40 cm DBH 20-40rees cm 20 and mortality (panel d). Panels a), b), and c) represent the effect of20 soil chemistry (pH, N) and texture (clay) on basal area gain; and panel d) shows the effect of soil chemistry (pH) on basal area loss. For the tree size classes where the relationships were significant. Regression coefficients are in Table 3.3. The p values and the direction of the relationship are shown by the sign between brackets. Please note that the x-axes are log10 transformed (Table A3.1). 25 25 .3 .3 p p =0.002 =0.002 ( -) ( -) The The 30 30 67 67 p p

67 Chapter 3 ADE and Forest Structure and Dynamics

Table 3.3 Results of the linear mixed model selected for each of the forest Tabledynamics 3.3 variables(continuation) (basal area growth, basal area recruitment, and basal area mortality) on each tree size category. For every explanatory variable, the value of the regression coefficient with significance level (p) are presented. Akaike’s p <0.001 <0.001 0.007 <0.001 <0.001 Information Criterion (AIC) scores for the ‘best’ model <0.001 (Annex 1) that had significanForest Dynamicst regression (Response coefficients Tree for Size the explanatoryExplanatory variables Variableare presented asCoefficient p Model AIC Model Intercept p 2 well as the squared Pearson correlation between the fitted model and the empiricalVariable) data for(Continuation) each model. We alsoCategory present the value of the model intercept with pseudo-r its significance level (p). Note that there is only a model in which the explanatoryBasal Area variableMortality does not haveDBH a statistically > 40 cm significantInitial Basal regression Area coefficient0.43 <0.001 132.51 0.19 0.45 0.15 (showed in bold). soil pH -0.77 0.039 1.63 1.77 1.78 0.92 1.72 ) are presented. Akaike’s Akaike’s presented. ) are Intercept 0.06 - - - - - ally significant regression regression significant ally Forest Dynamicsp (Response Tree Size Explanatory Variable Coefficient p Model 20AIC-40 cmModel in DBH InitialIntercept Basal Area p 0.06 0.015 81.00 0.37 -1.43 <0.001 Variable) Category pseudo-r2 Succession Composition 0.005 0.006

2 10-20 cm in DBH Initial Basal Area 0.08 <0.001 54.06 0.39 -1.53 <0.001

Basal Area Growth r DBH > 40 cm Initial Basal Area 0.10 <0.001 -899.34 0.33 0.06 <0.001 - Liana Infestation -0.02 <0.001 Succession Composition 0.006 0.005

Succession Composition -0.01 0.039 Ceramics -0.19 0.037 0.03 0.54 0.33 0.58 0.23 0.32 Model pseudo 20-40 cm in DBH Initial Basal Area 0.79 <0.001 -7.04 0.58 - 1.63 <0.001 Liana Infestation -0.003 <0.001 Succession Composition 0.004 <0.001 ADE and Forest Structure and Dynamics Crown Exposure 0.08 0.005 7.04 899.34 - - 82.87 132.51 91.42 30.16 Model AIC 10-20 cm in DBH Initial Basal Area 0.96 <0.001 30.16 0.54 -1.72 <0.001 Crown Exposure 0.14 0.034 Basal Area Recruitment DBH > 40 cm Initial Basal Area Table0.15 3.3 (continuation)0.082 82.87 0.03 -0.92 <0.001 <0.001 0.039 <0.001 <0.001 0.005 0.034 0.082 0.014 0.032 0.001 0.015 0.021 <0.001 <0.001 <0.001 0.002 0.004 p 20-40 cm in DBH soil pH 0.07 0.014 132.51 0.23 -1.77 0.007 Forest Dynamics (Response Tree Size Explanatory Variable Coefficient p Model AIC Model Intercept p Clay Variable)-0.02 (Continuation)0.002 Category pseudo-r2

Soil N Basal-1.11 Area Mortality0.032 DBH > 40 cm Initial Basal Area 0.43 <0.001 132.51 0.19 0.45 0.15 Crown Exposure 0.21 0.001 0.01 0.003 1.11 0.02 0.02 soil pH -0.77 0.039 0.10 - 0.79 - 0.08 0.14 0.07 - 0.21 0.07 0.006 - 0.004 0.96 0.15 - 0.20 Coefficient Coefficient 10-20 cm in DBH Initial Basal Area 0.07 0.015 91.42 20-40 cm0.32 in DBH Initial-1.78 Basal Area <0.001 0.06 0.015 81.00 0.37 -1.43 <0.001 Crown Exposure 0.20 0.004 Succession Composition 0.005 0.006

Succession Composition 0.006 0.021 10-20 cm in DBH Initial Basal Area 0.08 <0.001 54.06 0.39 -1.53 <0.001

Succession Composition 0.006 0.005 Ceramics -0.19 0.037

) that had significant regression coefficients for the explanatory variables are are variables explanatory the for coefficients regression significant that had Table ) A3.1 Appendix, pH

oil 69 Initial Basal Area Liana Infestation Composition Succession Initial Basal Area Liana Infestation Composition Succession Crown Exposure Crown Exposure S Soil N Crown Exposure Initial Basal Area Composition Succession Initial Basal Area Initial Basal Area Clay Crown Exposure Explanatory Variable Explanatory ). Note that there is only a model in which the explanatory variable does not have a statistic have not does variable the explanatory in which a model is only there that ). Note p earson correlation between the fitted model and the empirical data for each model. We also present the value of the model model of the value the We also present model. each for data the empirical and model the fitted between correlation earson 40 cm in DBH in 40 cm 20 cm in DBH in 20 cm DBH in 40 cm DBH in 20 cm - DBH > 40 cm 20 10 - DBH > 40 cm 20 - 10 - Tree Size Size Tree Category Recruitment nt, and basal area area nt,and basal recruitme area basal growth, area (basal variables dynamics forest the of each for selected model mixed linear the of Results Information Criterion (AIC) scores for for ( ‘best’ (AIC) the scores model Criterion Information intercept with its significance level ( level significance its with intercept presented as well as the squared P squared as the as well presented coefficient (showed (showed coefficient in bold). coefficient with significance level ( level significance with coefficient the regression of the value variable, explanatory every For category. size tree each on mortality) Chapter 3 Table 3.3 Forest Dynamics (Response Variable) Growth Area Basal Basal Area 68 69 Chapter 3 Basal Area Basal Basal Area Growth Variable) (Response Dynamics Forest significanceits level ( aswell the squared Pearson correlation between fittedthe model the and empirical for each data model. also present We the v Information Criterion (AIC) scores for the ‘best’ model (Annex 1) hadthat significan t regression coefficients for the explanatory variables are presented as mortality) on each tree size category. For every explanatory variable, valuethe of regressionthe coefficient with significa 3.3Table 3 Chapter (showed in bold). in (showed

Table 3.3 Results of the linear mixed model selected for each of the forest dynamics variables (basal area growth, basal area recruitment, and basal area Results of the linear mixed model selected for each of the forest dynamics variables (basal area growth, basal area recruitmebas and nt,

mortality) on each tree size category. For every explanatory variable, the value of the regressionRecruitment coefficient with significance level (p) are presented. Akaike’s Information Criterion (AIC) scores for the ‘best’ model (Appendix, Table A3.1) that had significant regression coefficients for the explanatory variables are presented as well as the squared Pearson correlation between the fitted model and the empirical data for each model. We also present the value of the model intercept with its significance level (p). Note that there is only a model in which the explanatory variable does not have a statistically significant regression coefficient (showed in bold). p ). Note that there is only a model which in the explanatory variable does not have astatistically significant regression coe Forest Dynamics (Response Tree Size Explanatory Variable Coefficient p Model AIC Model Intercept p Variable) Category pseudo-r2 20 cm 40 > DBH Category Tree Size 10 20 cm 40 > DBH 10 - - - - 40 cm in DBH Basal Area Growth DBH > 40 cm Initial Basal Area 20 cm in DBH 0.10 40 cm in DBH <0.001 20 cm in DBH -899.34 0.33 0.06 <0.001 Liana Infestation -0.02 <0.001 Succession Composition -0.01 0.039 20-40 cm in DBH Initial Basal Area 0.79 <0.001 -7.04 0.58 - 1.63 <0.001 Liana Infestation -0.003 <0.001

Succession CompositionSuccession Composition Exposure Crown Basal Initial 0.004 Exposure Crown N Soil Clay pH soil <0.001Area Basal Initial Exposure Crown Area Basal Initial Exposure Crown Succession Composition Infestation Liana Area Basal Initial Succession Composition Infestation Liana Area Basal Initial Explanatory Variable Crown Exposure 0.08 0.005 10-20 cm in DBH Initial Basal Area 0.96 <0.001 30.16 0.54 -1.72 <0.001 Crown Exposure 0.14 0.034 Area Basal Area Recruitment DBH > 40 cm Initial Basal Area 0.15 0.082 82.87 0.03 -0.92 <0.001 20-40 cm in DBH Soil pH 0.07 0.014 132.51 0.23 -1.77 0.007 Clay -0.02 0.002 Soil N -1.11 0.032 Crown Exposure 0.21 0.001 Area Basal (Continuation) Variable) (Response Dynamics Forest Table 3.3(continuation) Area Basal (Continuation) Variable) (Response Dynamics Forest Table 3.3(continuation) 0.006 0.20 0.07 0.21 - - 0.07 0.15 0.14 0.96 0.08 0.004 - 0.79 - - 0.10 Coefficient 10-20 cm in DBH Initial Basal Area 0.07 1.11 0.02 0.015 91.42 0.003 0.32 0.01 0.02 -1.78 <0.001 Crown Exposure 0.20 0.004 Succession Composition 0.006 0.021

Mortality Mortality 0.021 0.004 0.015 0.001 0.032 0.002 0.014 0.082 0.034 <0.001 0.005 <0.001 <0.001 <0.001 0.039 <0.001 <0.001 p 91.42 132.51 82.87 30.16 - - AIC Model 7.04 899.34 10 20 cm 40 > DBH Category Tree Size 10 20 cm 40 > DBH Category Tree Size - - - - 20 cm in DBH 40 cm in DBH 20 cm in DBH 40 cm in DBH 0.32 0.23 0.03 0.54 0.58 0.33 pseudo Model nce level (p - r 2 alue ofalue the model intercept with Initial Basal Area Basal Initial pH soil Area Basal Initial Explanatory Variable Ceramics Succession Composition Area Basal Initial Succession Composition Area Basal Initial pH soil Area Basal Initial Explanatory Variable ADECeramics andSuccession Composition ForestArea Basal Initial Succession Composition Structure and Dynamics - - - - - 0.06 Intercept ) are) presented. Akaike’s 1.78 1.77 0.92 1.72 1.63

Table 3.3 (continuation) <0.001 0.007 <0.001 <0.001 <0.001 <0.001 p Forest Dynamics (Response Tree Size Explanatory Variable Coefficient p Model AIC Model Intercept p Variable) (Continuation) Category pseudo-r2 fficient al area area al Basal Area Mortality DBH > 40 cm Initial Basal Area 0.43 <0.001 132.51 0.19 0.45 0.15

soil pH - 0.006 0.08 0.005 0.06 -0.77- 0.43 Coefficient 0.039 - 0.006 0.08 0.005 0.06 - 0.43 Coefficient 0.77 0.77 0.19 0.19 20-40 cm in DBH Initial Basal Area 0.06 0.015 81.00 0.37 -1.43 <0.001 Succession Composition 0.005 0.006 10-20 cm in DBH Initial Basal Area 0.08 <0.001 54.06 0.39 -1.53 <0.001 Succession Composition 0.006 0.005 0.037 0.005 <0.001 0.006 0.015 0.039 <0.001 p 0.037 0.005 <0.001 0.006 0.015 0.039 <0.001 p Ceramics -0.19 0.037

54.06 81.00 132.51 Model 54.06 81.00 132.51 Model AIC AIC ADE andForest Structure andDynamics ADE andForest Structure andDynamics 0.39 0.37 0.19 pseudo Model 0.39 0.37 0.19 pseudo Model - - r r 2 2 - - 0.45 Intercept - - 0.45 Intercept 1.53 1.43 1.53 1.43 <0.001 <0.001 0.15 p <0.001 <0.001 0.15 p 69 69

69 Chapter 3 ADE and Forest Structure and Dynamics

Discussion We hypothesized that nutrients on ADE could positively affect liana infestation. We found a rather negative effect of soil N in liana infestation of large trees. We do not have a What factors affects forest attributes on this forest? clear explanation for this finding, since when lianas show association with soil fertility, it is rather positive (DeWalt and Chave 2004), and it is expected that, as fast growing species, The effect of soils in forest attributes lianas perform better in richer soils (Kazda 2015).

We predicted that higher nutrient concentrations associated with ADE would lead to faster The effect of light on forest attributes growth and hence, a larger initial basal area and a larger infestation by lianas as they prefer higher amount of soil resources. We did not find support for our hypothesis, probably because We hypothesized that light should have a positive effect on the dominance of inherently light- the relationships between forest attributes and soils were more complex than expected. In our demanding species, such as (long-lived) pioneer trees and lianas. We confirmed this study, soil pH had a positive effect on initial basal area of intermediate-sized trees but a hypothesis since light was positively related to successional index for all size categories negative effect on that for large trees (Fig. 3.2a, b). Soil pH seems to be a good indicator of indicating that the more illuminated places had higher proportion of long-lived pioneers. overall soil fertility in la La Chonta (Pearson correlations for N: 0.52; for P: 0.34; for K: 0.49; Light also was positively correlated to liana infestation of intermediate and small trees for Ca: 0.74). Nevertheless, in our forest initial basal area of intermediate-size trees was although it negatively affected liana infestation of large trees. This last results was negatively related with P (Fig. 3.2b). This low productivity at increased soil P contrasts with unexpected. Possibly large canopy trees that received much light successfully overcome the idea that P is the main limiting nutrient of forest productivity (Vitousek et al. 2010) as lianas, whereas those with high liana infestation remain more suppressed in the canopy. The well as with findings from lowland tropical forests in Borneo (Paoli and Curran 2007, Paoli et effect of light on liana infestation was stronger for small trees suggesting that in the al. 2008) and across the Amazonian forest (Aragão et al. 2009, Quesada et al. 2012), where understory or sub-canopy of the forest, lianas might be limited by light and could be more soil P significantly increased forest biomass. It is in agreement, however, with other studies responsive to openings of forest gaps (Schnitzer et al. 2000, Dalling et al. 2012). Liana from lowland Bolivia that show negative relationship between stem density and soil nutrients infestation had a negative effect on the initial basal area of large and intermediate-size trees, (Toledo et al. 2011b). One possible explanation for this contradiction may be the effects of reflecting their negative effect on tree growth and the implication thereof in tree mortality soil pH on plant nutrition. For instance, an increase in Ca related to ADE may change soil pH, (Phillips et al. 2005, Peña-Claros et al. 2008). which in turn may limit plant nutrition, by decreasing the availability of some minerals such In conclusion, forest attributes are strongly influenced by light and much less by ADE as Fe, Zn, Mn, and P (Lambers et al. 2008b). Unfortunately we do not have data on soil soil characteristics. Although soils at La Chonta are heterogeneous, as a consequence of availability of Fe, Zn and Mn to confirm this. We have evidence that an increase in P does not geological history of the site, nutrient levels are in the range of high fertility with P, Ca, K and translate into an increase in plant growth, as it actually changes nutritional stoichiometry. This N levels similar to those in south-west Amazonian region (Quesada et al. 2010). Under such evidence comes from a greenhouse experiment, where we compared seedling growth of 17 conditions plants may not be limited by nutrients, and consequently, a human related increase species on ADE and non-ADE soil from the same site (Chapter 4 this thesis). The response of in soil nutrients will not have a substantial effect in forest productivity. large trees seemed to be in line with this argument because initial basal area and soil pH were negatively correlated. This physiological constraint related to soil pH may as well also What affects forest dynamics on this forest? influence the successional composition of the plots. Our successional composition index was The effect of soil on forest dynamics negatively related with soil pH, indicating that plots with lower pH have more long-lived pioneer species whereas plots with high pH have more shade tolerant species. Pioneer species We predicted that high soil nutrient concentrations associated with ADE would affect forest have more acquisitive strategies than shade tolerants and need higher availability of nutrients, dynamics through a positive effect on basal area growth and recruitment, but that faster which may not be available at higher soil pH. A previous study showed that a gradient in soil growth should also lead to a higher basal area mortality, and together to an increase in the pH in La Chonta affected understory species composition (Quintero‐Vallejo et al. 2015). Soil overall forest dynamics. The results do not support our hypothesis since we found that soils pH also was shown to affect tree species composition in a lowland tropical forest in Panama did not have a strong effect on forest dynamics and when there was an effect, it was negative. (Jones et al. 2013) and ‘cerrado’ vegetation in south-east Brazil (Viani et al. 2014). We Soil N was negatively related to basal area recruitment of intermediate-size trees (Fig. 3.4b). suggest that an interplay among soil pH, nutrient availability and changes in forest This negative relationship between forest dynamics and soil nutrients has been found earlier composition could contribute to disentangle the complex patterns found here, but recognize for the lowlands of Bolivia (Toledo et al. 2011b). The role of N in forest dynamics is debated that further research is needed. since recent studies indicated that N did not significantly affected tree growth in lowland

70 71 Chapter 3 ADE and Forest Structure and Dynamics

71 Chapter 3 ADE and Forest Structure and Dynamics

tropical forest in Panama (Wright et al. 2011) nor in Costa Rica (Baribault et al. 2012). Our with more pioneers have faster growth), which is in line with the expectations. The large negative relationship between N and basal area recruitment may be associated with the pioneer trees may be reaching their maximum age/size, when growth rate tends to slow down, complex interactions between soil pH, nutrient availability and species composition discussed while the more long-lived shade tolerant larger trees keep on growing in diameter. Plots with above (Chapter 4; Quintero-Vallejo et al. 2015). Interestingly, intermediate-size trees were the many pioneer of intermediate-size trees are probably also more open and as pioneers can take tree category more responsive to soils; this has been suggested for other tropical forest better advantage of this light, the overall growth is higher than in plots with more shade (DeWalt et al. 2006, Paoli and Curran 2007). Possibly small trees are more limited by light tolerant intermediate-size trees. than by soil, while large trees may have entered a senescent stage unrelated to soil conditions. The contribution of tree sizes to the whole-forest dynamics is different. Large trees Here, large trees had lower basal area mortality with increasing soil pH, possibly due to have a low net contribution, due to the combination of high mortality and low growth. effects of soil pH on species composition and basal area: sites with higher pH had a higher Intermediate-size trees mainly contribute to forest dynamics through their basal area growth proportion of shade tolerant species with inherently lower mortality rate (Poorter et al. 2008). (0.14 ± 0.08 m2 ha-1 y-1) since basal area recruitment and basal area mortality are the same. In contrast to our prediction, none of the soil variables affected basal area growth in Finally, small trees contribute to forest dynamics by having higher basal area of recruits any of the tree size categories (Fig. 3.4). This result is in contradiction with several studies compared to basal area growth and basal area of dead trees. Hence, for the overall-forest from the Amazonian region and other tropical forests where increases in soil nutrients, dynamics, those factors that contribute to the growth of the intermediate-size trees and especially soil P leads to an increase in forest biomass (Quesada et al. 2012) and net primary recruitment of small trees are crucial. Our results indicate that the growth of small trees is productivity (Paoli and Curran 2007, Paoli et al. 2008, Aragão et al. 2009). In fact, studies determined by standing basal area, light, and successional composition and that the growth of that evaluated 1-ha forest in sites of ADE in central Amazonian forest, which is characterised intermediate-size trees is determined by standing basal area, light, successional composition by a very low fertility of underlying soil, found that soil fertility of ADE, especially increases and liana infestation. Thus, the effect of soil variables in the dynamics of La Chonta forest is in soil P, had a positive effect on net primary productivity, mainly caused by an increase in mediated through the soil effects on standing basal area and successional composition of belowground biomass (Aragão et al. 2009, Doughty et al. 2014). intermediate-size and small size trees. Delimiting the boundaries of ADE patches in La Chonta forest based on soil color is challenging given the overall dark color of the natural soils. In our preliminary soil surveys Conclusions we added records on presence of ceramics, clearly indicating the presence of past human inhabitation in the sites. In all models we included presence of ceramics as explanatory In our study site, which is located at the higher end of the soil fertility gradient observed variable, but rarely it was selected in the ‘best’ model (Appendix, Table A3.1). Ceramics was across the Amazonian region, ADE extends soil fertility by increasing soil P, soil Ca, and soil only retained as explanatory variable in one model, and was only weakly significant in pH. But, this increased in soil fertility does not directly enhance forest growth. Our results explaining basal area mortality of small trees (Fig. 3.4c). We do not have an explanation for suggest that soils and light drive forest attributes at La Chonta. Light will affect the this result since we did not find significant correlations between ceramics and any of the soil successional composition of the forest and the level of liana infestation of trees. Soils will variable in the whole soil database. This could be related to not very accurate ceramics affect the standing basal area of large and intermediate-size trees, as well as the successional sampling that could show the extension of the human occupation. composition of the plots. The specific effects of soil nutrients on forest dynamics are negative, rather than positive and are mediated through the effect of soil on forest attributes rather than The effect of light and forest attributes on forest dynamics directly.

Basal area growth was more affected by light and forest attributes (Fig. 3.4) than by soil. Acknowledgements Light promoted the growth of small and intermediate-size trees occupying the middle strata of the forest, but it did not have any effect on large canopy trees, possibly because all large trees This research is part of the Terra Preta Program of Wageningen University funded by the are already well-exposed (average CI = 4.4). Liana infestation, however, affected growth of Interdisciplinary Research and Education Fund (INREF) La Chonta Company provided us intermediate-size and large trees but not small trees; maybe because small trees are used by permits and logistic support to work in the concession. We thank our field assistants Ricardo lianas only as support on their way up to the forest canopy. Therefore, liana effects in the Mendez, Lorgio Vaca, Bruno Vaca and MSc. student Yannick Klomberg for help during field shaded understory may be less strong. Finally, successional composition of the plots had, work. We deeply thank IBIF staff for logistic facilities while being in Santa Cruz and in La surprisingly, a negative effect on the growth of large trees (i.e. plots with more pioneers have Chonta. We also thank Wenceslau Texeira for providing facilities for pH measurements, for slower growth), while it was positively related to growth of intermediate-size trees (i.e. plots advise on the soil sampling strategy and for help with the interpretation of soil data and Dannaë Rozendaal for her input and help in the modelling analyses.

72 73 Chapter 3 ADE and Forest Structure and Dynamics

73 Chapter 3 ADE and Forest Structure and Dynamics

Table A3.1 Result of the best model after model selection. Models presented here correspond to the best selected model with the lowest AIC score. We also present the Akaike weight (Akaike w). Akaike w is referred as the ‘weight’ of evidence in favour of the model selected as being the ‘best’ model for the data Appendix. compared with a group of possible models. The closer to 1 the better predictive power the ‘best’ model have (Burnham and Anderson 2012). Note that variables are presented in the scale that they were evaluated (log transformed or without transformation). Bolded explanatory variables Shown in bold where Laboratory procedures for obtaining soil chemical and physicalthe variables variables that were retained for the final model after model averaging and selection based on their importance value (Barton 2012) and whose regression coefficient was statistically significant (Table 2 and Table 3). The concentrations of N, P, exchangeable Ca2+, Mg2+, K+ , Al+3, pH, organic matter content (OM), total exchangeable bases (TEB), cation exchange capacity (CEC), base saturationResponse Variable, Tree Size Category Model Random F Akaike w Forest acidity, and soil texture were analysed. Soil texture (percentage of sand,Attributes silt, and clay)Initial Basalwas area Trees bigger than 40 cm logInitialInitial BA= logpH + liana infestation + clay + Ceramics + logpH*Ceramics Plot, Block 0.029 measured using a Bouyoucos hydrometer; pH and electric conductivity were determined in Trees between 20 and 40 cm Initial BA= Succession composition + logpH +logP + liana infestation + Crown Exposure Plot, Block 0.058 2+ 2+ + 1:5 distilled water equilibrating for 90 min. Concentrations of exchangeable Ca , Mg , K Trees between 10 and 20 cm logInitial BA= clay Plot, Block 0.021 + 2+ 2+ and Na were determined in 1 M ammonium acetate at pH 7. After extraction, Ca andLiana Mg Infestation Trees bigger than 40 cm Liana Infestation = logN + Crown Exposure + Succession Composition Plot, Block 0.046 were measured with an Atomic Absorption Spectrophotometer; concentrations of K+ and Na+ Trees between 20 and 40 cm Liana Infestation= Crown Exposure + Clay Plot, Block 0.034 were measured with a flame photometer; exchangeable aluminium (Al3+) was measured in 1 Trees between 10 and 20 cm Liana Infestation= Crown Exposure + Sand + logOM + logpH Plot, Block 0.081 Succession Composition Trees bigger than 40 cm Succession Composition = logpH + Crown Exposure Plot, Block 0.064 M hydrochloric acid (HCl). Total P was determined by Olsen method; OM was determined by ADE and Forest Structure and Dynamics Walkley-Black method, and total N was measured using the Micro-Kjeldahl method (Barber Trees between 20 and 40 cm Succession Composition = logpH +Crown Exposure + Sand Plot, Block 0.06 and Cochrane 1993). TEB was defined as the sum of the most important exchangeable cations Trees between 10 and 20 cm Succession Composition = Crown Exposure + Clay Plot, Block 0.021 2+ 2+ + + Table A3.1 Result of the best model after model selection. Models presented here correspond to the best selected model with the lowest AIC score. We also (Ca , Mg , K and Na ). CEC was calculated as the sum of TEBpresentForest plus theacidity. Akaike Baseweight (Akaike w). Akaike w is referred as the ‘weight’ of evidence in favour of the model selected as being the ‘best’ model for the data saturation was computed as the percentage of exchangeable cations or comparedDynamicsbases relative withBasal toa group totalArea Growth of possible Treesmodels. bigger The than 40closer cm to 1 Basalthe betterArea Growth predictive = logInitial powerBA + Liana the Infestation‘best’ model + Succession have Composition(Burnham+ andCeramics Anderson 2012).Plot, Block Note that0.024 variables are presented in the scale that they were evaluated (lLogog transformedBasal Area Growth or = withoutlogInitial BAtransformation). + Liana Infestation Bolded +Succession explanatory Composition variables Shown in bold where CEC. Trees between 20 and 40 cm + Crown Exposure + logpH + logOM + logP + Ceramics + logpH*Ceramics Plot, Block 0.037 the variables that were retained for the final model after model averaging and selection based on their importance value (Barton 2012) and whose regression coefficient was statistically significantTrees (Table between 2 and10 and Table 20 cm 3). Log Basal Area Growth = logInitial BA + Crown Exposure Plot, Block 0.052 Basal Area Recruitment Trees bigger than 40 cm Log Basal Area Recruitment = logInitial BA Plot, Block 0.021

Response Variable TreeTrees Sizebetween Category 20 and 40 cm ModelLog Basal Area Recruitment = logpH+ Clay + Crown Exposure + soil N + Ceramics*logpH RandomPlot, Block F Akaike0.023 w Forest Trees between 10 and 20 cm Log Basal Area Recruitment = logInitial BA + Crown Exposure + Succession Composition Plot, Block 0.042 Attributes Initial Basal area Trees bigger than 40 cm logInitialInitial BA= logpH + liana infestation + clay + Ceramics + logpH*Ceramics Plot, Block 0.029 Basal Area Mortality Trees bigger than 40 cm Log Basal Area Mortality = logInitial BA + logpH Plot, Block 0.014 Trees between 20 and 40 cm Initial BA= Succession composition + logpH +logP + liana infestation + Crown Exposure Plot, Block 0.058 Trees between 20 and 40 cm Log Basal Area Mortality = Initial BA + Succession Composition + Crown Exposure + logOM + logpH Plot, Block 0.017 Trees between 10 and 20 cm LlogogInitialBasalBA= Area clay Mortality = Initial BA + Succession Composition + Ceramics + Liana Infestation + Plot, Block 0.021 Liana Infestation Trees biggerbetween than 10 40and cm 20 cm LianalogOM Infestation = logN + Crown Exposure + Succession Composition Plot, Block 0.0460.017 Trees between 20 and 40 cm Liana Infestation= Crown Exposure + Clay Plot, Block 0.034 Trees between 10 and 20 cm Liana Infestation= Crown Exposure + Sand + logOM + logpH Plot, Block 0.081 Succession Composition Trees bigger than 40 cm Succession Composition = logpH + Crown Exposure Plot, Block 0.064 Trees between 20 and 40 cm Succession Composition = logpH +Crown Exposure + Sand Plot, Block 750.06 Trees between 10 and 20 cm Succession Composition = Crown Exposure + Clay Plot, Block 0.021

Forest Dynamics Basal Area Growth Trees bigger than 40 cm Basal Area Growth = logInitial BA + Liana Infestation + Succession Composition + Ceramics Plot, Block 0.024 Log Basal Area Growth = logInitial BA + Liana Infestation +Succession Composition Trees between 20 and 40 cm + Crown Exposure + logpH + logOM + logP + Ceramics + logpH*Ceramics Plot, Block 0.037 Trees between 10 and 20 cm Log Basal Area Growth = logInitial BA + Crown Exposure Plot, Block 0.052 Basal Area Recruitment Trees bigger than 40 cm Log Basal Area Recruitment = logInitial BA Plot, Block 0.021 Trees between 20 and 40 cm Log Basal Area Recruitment = logpH+ Clay + Crown Exposure + soil N + Ceramics*logpH Plot, Block 0.023 Trees between 10 and 20 cm Log Basal Area Recruitment = logInitial BA + Crown Exposure + Succession Composition Plot, Block 0.042 Basal Area Mortality Trees bigger than 40 cm Log Basal Area Mortality = logInitial BA + logpH Plot, Block 0.014 Trees between 20 and 40 cm Log Basal Area Mortality = Initial BA + Succession Composition + Crown Exposure + logOM + logpH Plot, Block 0.017 Log Basal Area Mortality = Initial BA + Succession Composition + Ceramics + Liana Infestation + Trees between 10 and 20 cm logOM Plot, Block 0.017

74 75 Chapter 3 ADE and Forest Structure and Dynamics

Table A3.1 Result of the best model after model selection. Models presented herew correspond to the best selected model with the lowest AIC score. We also present the Akaike weight (Akaike w). Akaike w is referred as the ‘weight’ of evidence in favour of the model selected as being the ‘best’ model for the data 0.021 0.017 0.029 0.021 0.034 0.081 0.064 0.06 0.021 0.024 0.037 0.052 0.023 0.042 0.014 0.017 0.058 Akaike Akaike 0.046

Appendix. compared with a group of possible models. The closer to 1 the better predictive power the ‘best’ model have (Burnham and Anderson 2012). Note that 75 variables are presented in the scale that they were evaluated (log transformed or without transformation). Bolded explanatory variables Shown in bold where Laboratory procedures for obtaining soil chemical and physicalthe variables variables that were retained for the final model after model averaging and selection based on their importance value (Barton 2012) and whose regression coefficient was statistically significant (Table 2 and Table 3). The concentrations of N, P, exchangeable Ca2+, Mg2+, K+ , Al+3, pH, organic matter content Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Plot, Block Random F Random Plot, Block

(OM), total exchangeable bases (TEB), cation exchange capacity (CEC), base saturationResponse Variable, Tree Size Category We also score. IC Model Random F Akaike w Forest that Note n 2012). acidity, and soil texture were analysed. Soil texture (percentage of sand,Attributes silt, and clay)Initial Basalwas area Trees bigger than 40 cm logInitialInitial BA= logpH + liana infestation + clay + Ceramics + logpH*Ceramics Plot, Block 0.029 measured using a Bouyoucos hydrometer; pH and electric conductivity were determined in Trees between 20 and 40 cm Initial BA= Succession composition + logpH +logP + liana infestation + Crown Exposure Plot, Block 0.058 2+ 2+ + 1:5 distilled water equilibrating for 90 min. Concentrations of exchangeable Ca , Mg , K Trees between 10 and 20 cm logInitial BA= clay Plot, Block 0.021 + 2+ 2+ ton 2012) and whose regression and regression ton 2012) whose and Na were determined in 1 M ammonium acetate at pH 7. After extraction, Ca andLiana Mg Infestation Trees bigger than 40 cm Liana Infestation = logN + Crown Exposure + Succession Composition Plot,+ logOM logpH + Block 0.046 + Ceramics + were measured with an Atomic Absorption Spectrophotometer; concentrations of K+ and Na+ Trees between 20 and 40 cm Liana Infestation= Crown ExposureExposure + Clay Plot, Block 0.034 3+ Trees between 10 and 20 cm Liana Infestation= Crown Exposure + Sand + logOM + logpH Plot, Block 0.081 were measured with a flame photometer; exchangeable aluminium (Al ) was measured in 1 Exposure Succession Composition Trees bigger than 40 cm Succession Composition = logpH + Crown Exposure + Ceramics*logpH Plot, Block 0.064

M hydrochloric acid (HCl). Total P was determined by Olsen method; OM was determined by the data for model ‘best’ the as being ected

ADE and Forest Structure and Dynamics Structure and Forest ADE ADE and Forest Structure and Dynamics Trees between 20 and 40 cm Succession Composition = logpHlogpH*Ceramics +Crown Exposure + Sand Plot, Block 0.06 + Crown

Walkley-Black method, and total N was measured using the Micro-Kjeldahl method (Barber + Succession Composition + soil N and Cochrane 1993). TEB was defined as the sum of the most important exchangeable cations Trees between 10 and 20 cm Succession Composition = Crown Exposure + Clay Plot, Block 0.021 2+ 2+ + + Table A3.1 Result of the best model after model selection. Models presented here correspond to the best selected model with the lowest AIC score. We also (Ca , Mg , K and Na ). CEC was calculated as the sum of TEBForest plus acidity. Base present the Akaike weight (Akaike w). Akaike w is referred as the ‘weight’ of evidence in favour of the model selected as being the ‘best’Exposure model for the data saturation was computed as the percentage of exchangeable cations or comparedDynamicsbases relative withBasal toa group totalArea Growth of possible Treesmodels. bigger The than 40closer cm to 1 Basalthe betterArea Growth predictive = logInitial powerBA + Liana the Infestation‘best’ model + Succession+ Sand have Composition(Burnham+ andCeramics Anderson 2012).Plot, Block Note that0.024 Log Basal Area Growth = logInitial BA + Liana Infestation +Succession CompositionExposure CEC. variables are presented in the scale that they were evaluated (log transformed or without + Ceramics + clay + transformation). Bolded explanatory variables Shown in bold where + Succession Composition + Succession + Clay Trees between 20 and 40 cm + Crown Exposure + logpH + logOM + logP + Ceramics logOM+ + logpH + logpH*Ceramics Plot, Block 0.037 Exposure the variables that were retained for the final model after model averaging and selection based on their importanceExposure value (Barton 2012) and whose regression BA + CrownBA Exposure + Trees between 10 and 20 cm Log Basal Area Growth = logInitial BA + Crown Exposure BA Plot, Block 0.052

coefficient was statistically significant (Table 2 and Table 3). + Succession Composition BA logpH BA + + Clay + Sand BA + Crown BA + Liana Infestation +Succession Composition Composition +Succession Infestation Liana + BA

Basal Area Recruitment Trees bigger than 40 cm Log Basal Area Recruitmentinfestation = logInitial BA Plot, Block 0.021 + Liana Infestation + + Infestation + SuccessionBA Liana + Composition Ceramics + BA Exposure Exposure Initial Initial BA + Liana Infestation + Succession Composition Succession + Infestation Liana + BA Initial logpH+ Clay + Crown Crown + Clay logpH+ log Response Variable TreeTrees Sizebetween Category 20 and 40 cm ModelLog Basal Area Recruitment = logpH+ Clay + Crown Exposure + soil N + Ceramics*logpHlog RandomPlot, Block F Akaike0.023 w Initial Initial = = = Initial Initial Forest log logpH + Crown Crown logpH +Crown Exposure log log = = Trees between 10 and 20 cm Log Basal Area Recruitment = logInitial BA + Crown Exposure + Succession Composition Plot,= Block 0.042

Attributes Initial Basal area Trees bigger than 40 cm logInitialInitial BA= logpH + liana infestation + clay + Ceramics + logpH*CeramicsInitial Plot, Block 0.029

Basal Area Mortality Trees bigger than 40 cm Log Basal Area Mortality = logInitial BA + logpH log Plot, Block 0.014 logpH + liana Trees between 20 and 40 cm Initial BA= Succession composition + logpH +logP + liana infestation + Crown Exposure+ logpH + logOM logP + + Ceramics+ logpH*Ceramics Plot, Block 0.058 logN + Crown Crown + logN Crown Exposure Trees between 20 and 40 cm Log Basal Area Mortality = Initial BA + SuccessionCrown Composition + Crown Exposure + logOM + logpH Plot, Block 0.017 Recruitment Mortality Mortality Recruitment Mortality Recruitment BA= Trees between 10 and 20 cm LlogogInitialBasalBA= Area clay Mortality = Initial BAclay + Succession Composition + Ceramics + Liana Infestation + Plot, Block 0.021

Liana Infestation Trees biggerbetween than 10 40and cm 20 cm LianalogOM Infestation = logN + Crown Crown + infestation liana + +logP logpH + composition Succession Exposure + Succession Composition Plot, Block 0.0460.017 Exposure BA=

Trees between 20 and 40 cm Liana Infestation= Crown ExposureBA= + Clay Plot, Block 0.034 Basal Area Growth = Area Basal Area Basal Area Basal Basal Area Growth = Area Basal Area Basal Basal Area Area Basal InitialInitial Initial og og og og og og og Trees between 10 and 20 cm Liana Infestation= Crown Exposure + Sand + logOM + logpH og Plot, Block 0.081 og transformed or without transformation). Bolded explanatory variables Shown in bold where where in bold Shown variables explanatory Bolded transformation). without or transformed og log log Infestation= Liana = Composition Succession = Composition Succession Basal Area Growth = L L L L logOM Liana Infestation= Infestation= Liana = Composition Succession L + Crown L L L Initial Model Liana Infestation = Infestation Liana Succession Composition Trees bigger than 40 cm Succession Composition = logpH + Crown Exposure Plot, Block 0.064 Trees between 20 and 40 cm Succession Composition = logpH +Crown Exposure + Sand Plot, Block 750.06

Trees between 10 and 20 cm Succession Composition = Crown40 cm Exposure +40 cm Clay Plot, Block 0.021

Forest is referred as the ‘weight’ of evidence in offavour the sel model Dynamics Basal Area Growth Trees bigger than 40 cm Basal Area Growth = logInitial BA + Liana Infestation + Succession Composition + Ceramics Plot, Block 0.024 Log Basal Area Growth = logInitial BA + Liana Infestation +Succession Composition Trees between 20 and 40 cm + Crown Exposure + logpH + logOM + logP + Ceramics + logpH*Ceramics Plot, Block 0.037 Akaike w Trees between 10 and 20 cm 20 and 10 between Trees cm 40 and 20 between Trees cm 40 and 20 between Trees cm 20 and 10 between Trees cm 40 and 20 between Trees Trees bigger than 40cm Trees bigger than 40cm Trees between 20and cm 20 and 10 between Trees Trees between 20and Trees bigger than 40cm cm 20 and 10 between Trees Trees bigger than 40cm cm 20 and 10 between Trees cm 40 and 20 between Trees Trees bigger than 40cm cm 20 and 10 between Trees Tree Size Category Trees between 10 and 20 cm Log Basal Area Growth = logInitial BA + Trees bigger than 40cm Crown Exposure Plot, Block 0.052 ). Basal Area Recruitment Trees bigger than 40 cm w Log Basal Area Recruitment = logInitial BA Plot, Block 0.021 Trees between 20 and 40 cm Log Basal Area Recruitment = logpH+ Clay + Crown Exposure + soil N + Ceramics*logpH Plot, Block 0.023 Akaike

Trees between 10 and 20 cm ( Log Basal Area Recruitment = logInitial BA + Crown Exposure + Succession Composition Plot, Block 0.042

Basal Area Mortality Trees bigger than 40 cm Log Basal Area Mortality = logInitial BA + logpH Recruitment Plot, Block 0.014 Trees between 20 and 40 cm Log Basal Area Mortality = Initial BA + Succession Composition + Crown Exposure + logOM + logpH Plot, Block 0.017 Basal area Basal Log Basal Area Mortality = Initial BA + SuccessionInfestation Composition + Ceramics + Liana Infestation + Trees between 10 and 20 cm logOM Plot, Block 0.017 Basal Area Basal Response Variable Initial Composition Succession Basal Area Growth Mortality Area Basal Liana Liana Akaike weight Akaike ent was statistically significant (Table 2 and Table 3). 3). Table and 2 (Table significant statistically was ent

75 present the Result of the best model after model selection. Models presented here correspond to the best selected model with the lowest A lowest the with model selected to best the correspond here presented Models selection. model after model best the of Result Table A3.1 Forest Attributes Forest Dynamics rso and Ande (Burnham have model ‘best’ the power predictive the better 1 to closer The models. possible of a group with compared variables are presented in the scale that they were evaluated (l evaluated were they that scale the in presented are variables (Bar value importance their on based selection and averaging model after model final the for retained were that the variables coeffici Chapter 4 Effects of Amazonian Dark Earths on growth and leaf nutrient balance of tropical tree seedlings

Estela Quintero-Vallejo, Marielos Peña-Claros, Frans Bongers, Marisol Toledo, Lourens Poorter

Accepted for publication in Plant and Soil

77 Chapter 4 Effects of Amazonian Dark Earths on growth and leaf nutrient balance of tropical tree seedlings

Estela Quintero-Vallejo, Marielos Peña-Claros, Frans Bongers, Marisol Toledo, Lourens Poorter

Accepted for publication in Plant and Soil Chapter 4 ADE and Seedling Responses

Abstract Introduction

Amazonian Dark Earths (ADE) are ancient anthropogenic soils distributed in the Amazon Nutrient availability in tropical soils is the result of complex biogeochemical processes that basin. They are characterized by high nutrients concentrations, such as those of phosphorus, generate high spatial heterogeneity (Townsend et al. 2008). This heterogeneity is important, calcium, potassium and nitrogen and increases in soil pH when compared with natural soils. since it can determine tree species distribution at different spatial scales (John et al. 2007). We studied the effect of ADE on seedling growth, morphology and physiology in a However, the exact mechanisms that drive the underlying patterns of plant distribution are greenhouse experiment with seedlings of 17 tree species from a tropical moist forest, located still far from being fully understood. This lack of explanation is partly because plants respond in the southern boundary of the Amazon basin. Soils were collected in a forest located in the differently to a multitude of soil-related factors, such as water (Brenes‐Arguedas et al. 2013) province of Guarayos, Bolivia, where presence of ADE has been reported. Seedlings were and nutrients availability (Ordoñez et al. 2009). Among nutrients, nitrogen (N) and grown for 2-4 months on ADE and non-ADE. We evaluated soil nutrient concentration, phosphorus (P) are the major limiting elements for primary productivity of forests (Vitousek seedling growth, leaf and root functional traits, and leaf nutrient concentrations. We found et al. 2010). Nevertheless, recent fertilization studies have found that potassium (K) and that soil type affected 10 out of 24 evaluated seedling traits. Seedlings did not invest more in calcium (Ca) influence plant growth in tropical forest in similar ways as N and P (Wright et roots in non-ADE but they invested in leaves and leaf area in ADE, although this did not lead al. 2011, Baribault et al. 2012). to faster growth rate. Species responded differently to increases in soil calcium in the soil Plants respond to soil nutrient availability through a suite of traits, by adjusting their from ADE; some species seemed to suffer from calcium toxicity as is was indicated by allocation patterns, morphology, tissue chemistry and physiology. With an increase in soil seedlings mortality on ADE; others from nutrient imbalance observed from changes in nutrient availability, plants shift biomass allocation to aerial structures, such as leaves, instead nutrient stoichiometry in the leaves; whereas other species increased their leaf calcium, of underground structures such as roots (Poorter et al. 2012). This is usually accompanied by phosphorus and nitrogen concentrations in ADE. Only for this latter group of nutrient changes in other plant traits such as an increase in specific leaf area (SLA), N and P accumulators, there was a positive relationship between leaf calcium and seedlings growth concentration in leaves, and growth rates (Ordoñez et al. 2009, Ordoñez et al. 2010). rates. Contrary to our expectations, ADE did not lead to increased seedling growth. Ability of However, in soils with low-nutrient availability, plants grow slowly, have a smaller leaf area plants to colonize patches of ADE might depend on plant responses to increased soil calcium and allocate more biomass to roots, thus enhancing nutrient uptake (Baraloto et al. 2006). and their capacity to regulate internal tissue calcium to balance nutrition. Plant tissue chemistry changes with soil fertility, which is usually reflected in the nutrient concentration of leaves, particularly leaf N and P and their ratio (Ågren 2004, Townsend et al. 2007). Apart from soil fertility, foliar nutrient concentrations are shaped by a number of other Keywords: Amazonian Dark Earths; Bolivia; Calcium coping strategies; nutrient imbalance; factors, such as soil pH (Lambers et al. 2008a, Viani et al. 2014), plant demands for elements RGR such as calcium (Ca) to increase tissue toughness (Baribault et al. 2012), negative interactions between tissue P and aluminum (Al) when plants tend to accumulate Al (Metali et al. 2015), species phylogeny (Watanabe et al. 2007), and symbiotic relationships between plants and microorganisms (Nasto et al. 2014). As a result, plant nutrient concentrations and their stoichiometry can be uncoupled from those in the soil. Soil pH is an important factor in plant nutrition; it may limit the availability of a range of elements that are important for plant growth (e.g., iron [Fe] and zinc [Zn]), and may raise the availability of other elements, such as Al, that could cause plant mortality (Lambers et al. 2008a). For example, in soils with a low pH, increases in Al and Fe availability lead to decreases in P availability by reactions of Al and Fe with inorganic P to form plant insoluble compounds, unavailable for plants (McDowell et al. 2003). Furthermore, an increase in soil Al affects root elongation or plant uptake of other elements such as Ca and magnesium (Mg), causing reduced development of roots, depression of growth and potentially plant death (George et al. 2012). In soils with high pH, such as calcareous soils, increased concentrations of available Ca can react with inorganic P producing calcium phosphate, that leaves P unavailable for most plants (Tyler 1996). Besides, in soils with high pH, some elements such

78 79 Chapter 4 ADE and Seedling Responses

Abstract Introduction

79 Chapter 4 ADE and Seedling Responses

as Fe, manganese (Mn), Zn and copper (Cu) precipitate and become unavailable, thus limiting leaves, leaf N, Ca and P concentrations, specific leaf area and leaf area ratio; traits that plant growth (Lee 1998). enhance light capture and carbon gain. We also predict that seedlings growing on less fertile In general, soils in the tropics have low pH (Von Uexküll and Mutert 1995) leading to a non-ADE will have higher biomass allocation to (secondary) roots and will produce roots low availability of a range of nutrients. However, Neotropical soils can also be diverse in their with higher specific root length to enhance nutrient uptake. chemical properties as a result of different geological processes. In particular, the Amazon basin has experienced a long and dynamic geological history (Hoorn et al. 2010). In Methods combination with different biogeochemical processes (Quesada et al. 2010) this has led to a Species and collection site wide variation in soil characteristics and different availability of nutrients, essentially P and cations such as Ca2+, Mg2+, and K+. Despite the fact that most of the Amazon basin has a low Seventeen tree species were selected for the study based on seed availability. Hereafter soil fertility, there are patches of Amazonian Dark Earths (ADE) that are the product of past species will be referred by the genus name (Table 4.1). Most of the seeds were collected from human inhabitation of the region since pre-Columbian times, and have a high soil fertility a semi-evergreen forest (La Chonta forest concession) located in the province of Guarayos, (Glaser and Birk 2012). Bolivia (15º47’S, 62º55’W) in July and August of 2012. The site has a mean annual ADE are characterized by a thick dark or gray top layer (until 1.5m deep) with presence of temperature of 24.3°C and mean annual rainfall of 1580 mm (data from 2000-2006 from La ceramics that indicates past indigenous settlements (Sombroek 1966, Woods and Glaser Chonta) with a dry season (<100 mm/mo) from May through September (Peña-Claros et al. 2004). ADE have a black color due to high concentrations of organic matter and black carbon, 2012). Soils at La Chonta are a mosaic of poor soils from the Brazilian Precambrian Shield they have high P and Ca, and higher pH than the natural soils commonly found in the (Navarro and Maldonado 2002) and more fertile soils that are the product of sedimentation Amazon, such as Ultisols and Oxisols (Sombroek 1966, Falcão et al. 2009, Glaser and Birk and erosion originated from the uplift of central Andes (Latrubesse et al. 2010). A previous 2012). ADE seem to be the product of additions of charcoal and organic waste which change study showed that La Chonta also has Amazonian Dark Earths (ADE); anthropogenic soils soil physic-chemical characteristics (Schmidt et al. 2014). For example, charcoal addition that imply inhabitation of this forest in the past (Paz-Rivera and Putz 2009). leads to a more stable organic matter content, and a high soil nutrient retention capacity Study site (Glaser et al. 2002), whereas the addition of organic waste leads to high concentrations of P, N, and Ca (Glaser 2007). Compared with natural Central Amazonian soils, ADE are more The experiment was carried out in a shade house at the Agronomy Faculty of the Universidad fertile because of the combination of high pH and Ca and P concentrations. For this reason, Autónoma Gabriel René Moreno in Santa Cruz de la Sierra, Bolivia (16º30’S, 63º10’W). The ADE are often used by local farmers to grow crops faster for longer periods of time compared climate in this city is warm, with an average temperature of 24.2 ± 0.8 °C, and mean annual with slash and burn agriculture done on regular soils in the Amazon (German 2003). rainfall of 1308 ± 391 mm (Data from National Institute of Meteorology, Bolivia; 1949- Similarly, a forest growing on ADE increased gross primary productivity, net primary 2013). The 6 x 3 m shade-house was built and covered with black mesh (2 x 1.5 mm) to give productivity (NPP), and high rates of fine root production compared with a forest growing on an irradiance of about 10% of full sunlight (around 400 lux). non-ADE (Doughty et al. 2014). By stimulating productivity, the high fertility of ADE could promote changes in plant Soils and shade-house experiment traits associated with acquisition strategies, such as induction of a higher relative growth rate, The soil used for testing germination and plant growth was collected at La Chonta, where we higher leaf biomass and higher nutrients concentration in the leaves. Evaluating changes in previously identified a site with dark earth and the presence of pottery shards, hereafter ADE, plant traits associated to ADE provides a unique opportunity to understand tree responses to and a site at least 500 m away with more clear soils, hereafter non-ADE. Soils were mixed soil fertility as a consequence of past human inhabitation and soil modification. Besides, it with river sand at a ratio of 4:1 (w/w), to allow adequate drainage and to facilitate harvesting could help to understand how soil fertility influences species composition in habitats that do of the whole root system, including fine roots. Although the use of sand could dilute the effect not vary in climatic conditions and water availability. of soil nutrients, we considered that this dilution might not affect our experiment because the Here we present results of a greenhouse experiment, in which seedlings of 17 tropical differences between ADE and non-ADE were retained for most of the soil variables, except tree species were grown on ADE and non-ADE. We examined how soil fertility of ADE for total N (Appendix 4, Table A4.1). Chemical and physical characteristics of the original affected seedling growth, biomass allocation, and morphological and chemical traits of soils and of the mixture with sand were assessed in the Centro de Investigación Agrícola tropical tree species. We hypothesize that plants growing on ADE have higher growth rates Tropical (CIAT), in Santa Cruz, Bolivia (protocols for chemical and physical properties can compared with plants growing in non-ADE. Besides, we predict that the higher growth rates, be seen in Online Resource 2). The main differences in soil variables between ADE and non- of seedlings on ADE, will be associated with increases in biomass allocation to stem and

80 81 Chapter 4 ADE and Seedling Responses

81 Chapter 4 ADE and Seedling Responses

ADE sand mixes were found for Ca concentration, P concentration, total exchangeable base LIDE 20, Canon, USA) and leaf area was determined using the software program imageJ and cation exchange capacity (ratios between ADE and non-ADE were > 2 in all cases); soil (Rasband 2008). Leaf resistance to mechanical damage was measured for nine of thirteen pH was slightly alkaline (> 7) in both soils (ADE: 7.8; non-ADE:7.6) (Appendix 4, Table species using force to punch. Force to punch was determined on one or two of the newest A4.1). expanded leaves using a field penetrometer, which consisted of a flat-end nail of 3.2 mm in diameter; the nail was attached to the inner part of a syringe and a water basin on top. A leaf Table 4.1 Scientific names and families of tree species used for this study. was placed between two acrylic plates both having a 6 mm diameter hole. The holes were Scientific name Family located in the same position so that the nail could cross the leaf between the two plates similar to Aranwela et al. (1999). Force to punch the leaf was determined by the weight (converted to Albizia niopiodes (Spruce ex Benth.) Burkart Fabaceae Newton) of the water added to the basin that was necessary to penetrate the leaf, divided by Anadenanthera colubrina (Vell.) Brenan Fabaceae Enterolobium contortisiliquum (Vell.) Morong Fabaceae the circumference of the punch nail. Erythrina crista-galli L. Fabaceae Root length (RL) was determined following Newman (1966). Roots were placed in a Hymenaea courbaril L. Fabaceae tray filled with water. The tray was covered by a transparent sheet marked with 1 x 1 cm Machaerium villosum Vogel Fabaceae square grid and the number of intersections between roots and the gridlines were counted Ormosia nobilis Tul Fabaceae horizontally and vertically. Thereafter, total root length was determined as R = πNA/2H, Poeppigia procera C. Presl Fabaceae where R is the total length of the root (cm), N is the number of intersections between the root Samanea tubulosa (Benth.) Barneby & J.W. Grimes Fabaceae and the gridlines, A is the area of the rectangle (cm2) and H is the total length of the straight Schizolobium parahyba (Vell.)S.F. Blake Fabaceae Terminalia oblonga (Ruiz & Pav.) Steud. Combretaceae lines of the grid (cm) (Newman 1966). Root morphology was described by the Cariniana ianeirensis R. Knuth Lecythidaceae presence/absence of tap roots and by the number of secondary roots. When tap roots were Chorisia speciosa A. St.-Hil. Malvaceae present both the diameter of the base (close to the transition to stem) and the diameter of the Guazuma ulmifolia Lam. Malvaceae tip were measured. The diameter of three secondary roots was measured with a digital Cedrela fissilis Vell. Meliaceae calliper. Swietenia macrophylla King Meliaceae We measured stem length from the base to the apical bud, and top and base stem Sapindus saponaria L. Sapindaceae diameter. Stem volume was calculated using the formula for a section of a cone. Lamina and petioles, roots and stems were stored in separate paper bags, oven-dried at 70 °C for 72 h, and From April through September of 2013 seeds were germinated in trays of 25 x 30 cm weighted again to determine dry weight of plant sections. Using the fresh and dry weights we filled with either ADE or non-ADE sand mixes. When seedlings produced their first leaves, calculated root mass fraction (RMF, g g-1), leaf mass fraction (LMF, g g-1) and stem mass eight seedlings per species per soil type were harvested and 12 seedlings per species per soil fraction (SMF, g g-1) as the weight of each plant part over the total seedling dry mass. We also type were transplanted to pots of 650 ml (9.2 x 15 cm). The transplanted seedlings were calculated stem, root and leaf dry matter content (SDMC, RDMC, LDMC, respectively, in g placed in a completely randomized design in the shade-house and were re-distributed at g-1) as the dry mass over the fresh mass of that section. Specific leaf area (SLA, m2 kg-1) was random every three weeks to ensure that all seedlings were growing under similar light and calculated as the ratio between leaf area and leaf dry mass, leaf area ratio (LAR, m2 kg-1) as temperature conditions. Plants were watered daily or every other day depending on the the total leaf area over total seedling dry mass, specific root length (SRL, m kg-1) as root weather, since the shade house allowed rain water to pass through. Once seedlings were length over dry root mass and root length per plant mass (RLPM m kg-1) as the root length established, at around half of the growing period, the number of leaves on each seedling was over total seedling dry mass. counted and the newest full developed leaves were tagged with colored threads. At the time of These traits determine important functional characteristics for the plants. Biomass harvesting we counted the number of newly produced leaves. fractions of seedling sections describe how plants allocate biomass to light intercepting tissue A second harvest was done between 2 to 4 months after transplanting, depending on in the case of leaves, or nutrient capturing tissue in the case of roots. Stem, root and leaf dry the species. Differences in length of the growing period were due to differences in matter content, and force to punch indicate tissue toughness, which are thought to be good germination time, and lack of seed availability of some species at the time we started the proxies for tissue longevity. SLA and LAR indicate how efficiently plants invest in light experiment. At each harvest, we determined the fresh mass of roots, stems, and leaves; leaves interception. Measurements of SRL and PLPM indicate how the biomass that is allocated to were sectioned into the leaf lamina and the petiole, or rachis for compound leaves, and roots can be efficient in nutrient capture through increase of absorption surface (Markesteijn sections were weighed separately. Laminae were scanned with a desktop scanner (Cannon and Poorter 2009).

82 83 Chapter 4 ADE and Seedling Responses

83 Chapter 4 ADE and Seedling Responses

Leaf nutrient N, P, and Ca concentration was determined for three randomly selected species, as follows: DifferenceADE−nonADE = (Nutrienti,ADE − Nutrienti,non−ADE ) , where Nutrient seedlings per species per soil type. For 5 species (Albizia, Anadenanthera, Cedrela, stands for the concentration in the leaf, and i stands for Ca, N or P. Thereafter we tested, using Machaerium and Samanea) we selected a seedling that had a leaf dry weight > 50 mg, the the information of all species, whether the differences in leaf nutrient concentrations between minimum quantity required for leaf tissue analyses in the laboratory. Extraction of N, P and seedlings growing in the two soil types was related between pairs of leaf nutrients performing Ca were done using digestion with H2SO4, Se, and salicylic acid (Novozamsky et al. 1983). a linear regression procedure. After digestion, N and P content was measured with a Skalar San-plus auto analyzer, and Ca Based on the relationships found in the previous analyses (see results; Fig. 4), we content was measured using an Atomic Absorption Spectrometer (AAS) from Varian. Tissue separated the species into the following response groups: species in which the difference in analyses were done at the facilities of Wageningen University, The Netherlands. leaf Ca concentration was positively associated with the difference in leaf P concentration Data analyses. We quantified survival percentage for each species dividing the (hereafter refer as positive for P); species in which the difference in leaf Ca concentration was number of seedlings alive at the time of the second harvest by the number of transplanted negatively associated with the difference in leaf P concentration (negative for P); species in seedlings. We compared survival percentage between soil types using a chi-square test. which the difference in leaf Ca concentration was positively associated with the difference in ln h − ln h Relative height growth rate (RGRh) was calculated for each seedling as: RGRh = 2 1 , leaf N concentration (positive for N); and those species in which the difference in leaf Ca t − t 2 1 concentration was negatively associated with the difference in leaf N concentration (negative where hn represents the seedling height at time n; t1 is time at transplanting, t2 time at for N). harvesting, and (t2 - t1) is the number of days between transplanting and harvest. RGR based Using the four response groups previously defined, we tested how Ca uptake was on plant biomass (RGRb) was calculated for each species using the information of both related with RGRb. We evaluated Ca because it was the nutrient in the leaves that consistently increased in ADE for all species (Fig. 4.3a). Therefore, we performed a linear regression for ln M 2 − ln M1 harvests as: RGRb = , where ln M n is the average of the natural log of the dry each group between leaf Ca concentration in leaves as the independent variable and the t − t 2 1 average RGRb per species as the dependent variable. Finally, we also tested, using linear mass of each seedling at harvest n. regressions, how Ca uptake was related with leaf P uptake or leaf N uptake at the group level. Continuous variables were log10-transformed, whereas ratios were arcsine-transformed All statistical analyses were done using Genstat 16th ed. (VSN International Ltd). prior to statistical analyses. To test for the effect of ADE and non-ADE on total biomass, RGRh, and plant traits, we performed a two way ANOVA, with soil type and species as Results factors. For this analysis we used 13 species instead of 17 because we had significant mortality in 4 species that left us with few replicates per treatment to be able to make Eight out of 17 species showed some mortality during the experiment. Terminalia and 2 comparisons between the two soils. We used an unbalanced design because two species Poeppigia had a significantly higher mortality in ADE than in non-ADE soil (χ = 10.83, p < 2 (Cariniana and Hymenaea) had different number of seedlings per soil type. The interaction 0.05; χ = 12.08, p < 0.05, respectively), whereas Schizolobium had a significantly higher 2 between soil type and species was also evaluated. For all traits but RGRh, data of the second mortality in non-ADE (χ = 15.41, p < 0.05) (Fig. 4.1). harvest were used. The amount of variance that was explained by each factor was determined Plant responses to ADE by dividing the sum of squares associated with each factor (soil or species) and their interaction by the total sum of squares of the model. A separate analysis was done for each We found significant differences between soil types for ten out of 24 traits, but the proportion species using one way ANOVA, with soil type as factor. The effect of soil type on leaf of the variance explained by soils for each of these variables was very low (less than 2.8%, production rate was tested using a non-parametric Kruskal-Wallis test because the data were Table 4.2). Species had a significant effect on all variables, explaining 35% to 96% of the not normally distributed. variance (Table 4.2). We detected that the percentage of leaf Ca was always higher on ADE, whereas the percentages of leaf P and leaf N were similar or lower on ADE than in non-ADE, despite the higher concentration of these elements in ADE (see results; Fig. 4.3). Therefore, we tested for the existence of potential relationships between the amount of Ca, P and N acquisition from each soil. We averaged the Ca, N and P concentration in the leaves of seedlings growing in ADE and non-ADE, and calculated the difference between these amounts in each soil type per

84 85 Chapter 4 ADE and Seedling Responses

85 Chapter 4 ADE and Seedling Responses

a 3.50 b ADE 0.25 ADE NADE 100 3.00 ) ) 1 - g 0.20 * ( 2.50 90 N-ADE d ss 2.00 a 0.15 . mm m 1.50 ) o * 80 0.10 Bi ( mm

% 1.00 *

( * R l 70 0.50 * 0.05

R G 0 0.00 viva 60

Sur 50

40 Figure 4.2 Biomass at final harvest (a) and relative height growth rate (RGRh) (b) of seedlings of 13 species growing in Amazonian Dark Earths (ADE) (black bars) and non-Amazonian Dark Earths 30 (open bars). Means and standard errors are shown. Asterisks indicate significant responses of a species 20 to soil (p ≤ 0.05).

10 Overall, leaf production rate was similar for both soil types (Kruskal-Walis H = 2.97; p = 0.084), although some species, such as Cedrela, Chorisia, and Cariniana had a higher leaf 0 production rate in ADE than in non-ADE, whereas Swietenia had higher leaf production rate in non-ADE soil (Appendix, Table A4.2). We found that biomass allocation to leaves (LMF) was significantly higher in ADE than in non-ADE (Table 4.2). There were no significant differences in biomass allocation to roots (RMF) between soil types. There was a significant Figure 4.1 Seedling survival of 17 species of tropical trees in Amazonian Dark Earths (ADE) and interaction between soils and species in allocation to stems (SMF) (Table 4.2). Seedlings of non-Amazonian Dark Earths (non-ADE) soils after the growing period in the greenhouse. Anadenanthera had heavier stems in ADE (F = 14.71, p < 0.001) than in non-ADE, whereas Erythrina and Enterolobium seedlings had heavier stems in non-ADE than in ADE We expected soil fertility from ADE to have a positive effect on plant growth, but (Appendix, Table A4.2). Specific leaf area (SLA, a measure of area investment per leaf mass) most of the species had a similar growth rate on both soils. Total biomass (Fig. 4.2a), height and leaf area investment per plant mass (LAR) were significantly higher for seedlings in ADE growth (RGRh, Fig. 4.2b) and biomass growth (RGRb) did not differ significantly between (Table 4.2). We found a significant interaction term for specific root length (SRL) (a measure soil types (Table 4.2). However, the interaction between soil type and species was significant of plant investment in root length) (Table 4.2); Enterolobium seedlings had a significantly for total biomass and RGRh (Table 4.2), indicating that some species grew faster in ADE, higher SRL in ADE, whereas Swietenia seedlings had higher SRL in non-ADE (Appendix , whereas other species grew faster in non-ADE. Seedlings of Cedrela and Chorisia had Table A4.2). Leaf and root dry mass content were significantly higher in non-ADE (Table significantly more biomass in ADE (one-way ANOVA: Cedrela F= 7.29, p = 0.013; Chorisia 4.2). Leaf Ca concentration was significantly higher in ADE (Table 4.2, Fig. 4.3a). Leaf N F = 5.40, p = 0.03), whereas Machaerium had a significantly higher biomass in non-ADE concentration did not differ significantly between soil types (Table 4. 2, Fig. 4.3b). We found (one-way ANOVA, F = 4.87, p = 0.039) (Fig. 2a). Furthermore, Swietenia had a higher RGRh a significant interaction between soil and species for leaf P concentration (Table 2), caused by in ADE (one-way ANOVA, F = 9.04, p = 0.006) (Fig. 4.2b), whereas Chorisia had a a significantly higher leaf P concentration in ADE for Machaerium and in non-ADE for significantly higher RGRh in non-ADE (one-way ANOVA, F = 18.88, p < 0.001). Erythrina (Fig. 4.3c).

86 87 Chapter 4 ADE and Seedling Responses

a 3.50 b ADE 0.25 ADE NADE 100 3.00 ) ) 1 - g 0.20 * ( 2.50 90 N-ADE d ss 2.00 a 0.15 . mm m 1.50 ) o * 80 0.10 Bi ( mm

% 1.00 *

( * R l 70 0.50 * 0.05

R G 0 0.00 viva 60

Sur 50

87 Chapter 4 ADE and Seedling Responses

a 45 b 5.0 ADE and non-ADE (Fig. 4.4c), indicating that species that had lower leaf N concentration ADE NADE 40 4.5 * tended to have lower P concentration on ADE and species that had higher leaf N 4.0 35 ) ) 1

1 concentrations also tended to have higher leaf P concentrations on ADE. - - 3.5 g 30 * 3.0 1 a 4 (mg 25 *

(mg g 2.5 b P 20 2.0 AnadenantheraCedrela Machaerium Cedrela * af Chorisia Ormosia af Ca e Sapindus 2 e 15 1.5 0 Anadenanthera * L * L Cariniana Swietenia nonADE

nonADE Albizia

1.0 0 Sapindus 10 Samanea Hymenaea Swietenia 0.5 Enterolobium Cariniana Ormosia 5 Leaf P Leaf N −1

− Hymenaea Machaerium

0 − 0 Enterolobium ADE ADE

−2 Albizia

−2 Samanea Leaf P Erythrina Leaf N y = 0.21− 0.18x Chorisia

2 −4 y = 0.43 − 0.49x c 55 r :0.26 Erythrina r2:0.17 −3 p=0.041 p=0.086 50 45 0 2 4 6 8 0 2 4 6 8 ) 1

- Leaf Ca ADE − Leaf CanonADE 40 Leaf Ca ADE −Leaf Ca nonADE g

35 1 c

(mg 30 Anadenanthera 25 Chorisia Machaerium Ormosia Cedrela Sapindus 0 af N 20 Cariniana Swietenia e nonADE Samanea Hymenaea L 15 Albizia Enterolobium

10 P Leaf −1 5 − ADE 0 −2 Leaf P Leaf Erythrina y = −0.06 + 0.21x r2 :0.39 p=0.014 −3

−4 −2 0 2 4

Figure 4.3 Leaf calcium (a), phosphorus (b) and nitrogen (c) concentrations of six seedlings of 13 Leaf NADE − Leaf NnonADE tropical tree species growing in Amazonian Dark Earths soils (black bars) and non-Amazonian Dark Earths soils (open bars). Twelve seedlings per species were used per each soil type. Bars represent the Figure 4.4 Linear regressions of the differences in leaf nutrient concentrations between Amazonian average per soil type, with standard errors. Asterisks indicate significant differences between the two Dark Earths and non-Amazonian Dark Earths. a) Differences in leaf P against differences in leaf Ca. soils (t-test, p ≤ 0.05). b) Differences in leaf N concentration against differences in leaf Ca concentration; c) Differences in leaf P concentration against differences in leaf N concentration. Results of linear regression are Nutrient stoichiometry in relation with growth rates provided. Concentrations units are mg g-1.

We explored the differences in leaf nutrient concentration between seedlings growing in ADE To explore the relationship between leaf Ca concentration and either RGRb or leaf P and non-ADE soils. We found that all species had a higher leaf Ca in ADE than non-ADE concentration, we divided the species in groups according to their responses to Ca and P or Ca (positive values on x axes, Fig. 4a), whereas species had either a higher (positive values on y and N. We found that “positive for P” species increase in RGRb with increasing leaf Ca 2 axes, Fig. 4.4a) or a lower leaf P in ADE than in non-ADE (negative values on y axes, Fig. concentration (Adj-R :0.64; p=0.034) (Fig. 4.5a) and that leaf P concentration increased with 2 4a). This resulted in a significant negative relationship between the difference in leaf Ca leaf Ca concentration, although this was at the edge of significance (Adj-R :0.56; p=0.052) concentration between ADE vs non-ADE and the concomitant difference in leaf P (Fig. 4.5b). No relation was found for “negative for P” species, neither for RGRb (Adj- 2 2 concentration at the species level (Fig. 4.4a). Similar trend was found for leaf Ca R :0.09; p=0.26) nor for leaf P concentration (Adj-R :-0.04; p=0.42) (Fig. 4.5e, b). concentrations in seedlings growing in ADE vs non-ADE and leaf N concentration (Fig. 4.4); Similarly, we grouped species as “positive for N” and “negative for N” (Fig. 4.4b) to species showed either a higher (positive values on y axes, Fig. 4.4b) or a lower leaf N explore the relationship between leaf Ca concentration and either RGRb or leaf N concentration in ADE than in non ADE soils (negative values on y axes, Fig. 4.4b). The latter concentration. We found that “positive for N” species significantly increased RGRb with 2 relationship, however, was only a trend, but not statistically significant (P = 0.086, Fig. 4.4b). increase in leaf Ca concentration (Adj-R : 0.86; p=0.01) (Fig. 4.5c) but they did not increase 2 We also found a positive relation between the difference in leaf P and leaf N concentrations in in leaf N concentration with increasing leaf Ca concentration (Adj-R : 0.47; p=0.12) (Fig. 4.5d). On the other hand, “Negative for N” species showed no relationship between leaf Ca

88 89 Chapter 4 ADE and Seedling Responses

1 concentrations also tended to have higher leaf P concentrations on ADE. - - 3.5 g 30 * 3.0 1 a 4 (mg 25 *

(mg g 2.5 b P 20 2.0 AnadenantheraCedrela Machaerium Cedrela * af Chorisia Ormosia af Ca e Sapindus 2 e 15 1.5 0 Anadenanthera * L * L Cariniana Swietenia nonADE

nonADE Albizia

1.0 0 Sapindus 10 Samanea Hymenaea Swietenia 0.5 Enterolobium Cariniana Ormosia 5 Leaf P Leaf N −1

− Hymenaea Machaerium

0 − 0 Enterolobium ADE ADE

−2 Albizia

−2 Samanea Leaf P Erythrina Leaf N y = 0.21− 0.18x Chorisia

2 −4 y = 0.43 − 0.49x c 55 r :0.26 Erythrina r2:0.17 −3 p=0.041 p=0.086 50 45 0 2 4 6 8 0 2 4 6 8 ) 1

- Leaf Ca ADE − Leaf CanonADE 40 Leaf Ca ADE −Leaf Ca nonADE g

35 1 c

(mg 30 Anadenanthera 25 Chorisia Machaerium Ormosia Cedrela Sapindus 0 af N 20 Cariniana Swietenia e nonADE Samanea Hymenaea L 15 Albizia Enterolobium

10 P Leaf −1 5 − ADE 0 −2 Leaf P Leaf Erythrina y = −0.06 + 0.21x r2 :0.39 p=0.014 −3

−4 −2 0 2 4

89 Chapter 4 ADE and Seedling Responses

Table 4.2. Averages of morphological, physiological and chemical variables measured on seedlings of 13 species of tropical trees growing on ADE (n = 12 seedlings per species) and non-ADE soils (n = 12). The overall R2 of the model and the ANOVA results are provided. The effect of soil, species or the interaction is presented with their F values and their significance level (p). The concentration and either RGRb (Adj-R2: 0.24; p=0.12) or leaf N concentrationpercentage (Adj of variation-R2: -0.05; explained by each factor or the interaction was calculated as the ratio between the factor sum of squares or interaction sum of squares and total sum of squares. p=0.46) (Fig. 4.5c, d). Average Soil Species Soil x Species

Trait ADE N-ADE R2 F p SSs/SSt F p SSspp/SSt F p SSsxspp/SSt a b Biomas (g) 0.932 0.94 0.89 0.39 0.531 0.0002 184.11 <.001 0.88 3 <.001 0.014 Albizia Chorisia RGRh (mm.mm.d-1) 0.0426 0.0422 0.87 0.09 0.767 0.0000 147.47 <.001 0.84 6.53 <.001 0.037 )

) -1 -1 -1 RGRbEryth r(g.g.dina ) 0.01615 0.01595 - 0.005 0.944 0.0000 ------Swietenia Cedrela -1 Enterolobium Root Mass Fraction (RMF) (g g ) 0.232 0.244 0.68 3.36 0.068 0.0040 46.54 <.001 0.67 0.58 0.859 0.008 Anadenanthe ra Sapindus Hymenaea Sapindus Chorisia Cariniana -1 Albizia Enterolobium Stem Mass Fraction (SMF) (g g ) 0.255 0.261 0.67 1.8 0.181 0.0022 43.4 <.001 0.64 2.15 0.015 0.032 Swietenia RGRb (g.g d (g.g RGRb Erythrina Leaf P (mg g Cedrela Samanea Ormosia Samanea -1 LeafAnadenanthera Mass Fraction (LMF) (g g ) 0.513 0.494 0.61 8.06 0.005 0.0117 33.19 <.001 0.58 1.14 0.331 0.020 Ormosia Cariniana Machaerium DiameterMachaerium secondary roots (mm) 0.481 0.49 0.61 0.08 0.776 0.0001 31.74 <.001 0.59 1.24 0.253 0.023 Positive to P Positive to P Hymenaea -1

Negative to P 012345 Secondary to primaryNegati rootve tomass P (g g ) 0.897 0.755 0.38 2.98 0.086 0.0069 12.57 <.001 0.35 1.05 0.401 0.029 0.000 0.010 0.020 0.030 0 10 20 30 40 0 10 Number20 of secondary30 roots40 29.17 30.66 0.53 1.43 0.233 0.0026 22.77 <.001 0.49 1.66 0.076 0.036 -1 -1 -1 Leaf Ca (mg g ) Leaf SpecificCa (mg g )Root Length (m kg ) 17860 15310 0.57 0.36 0.549 0.0006 26.88 <.001 0.52 ADE2.69 and Seedling0.002Responses0.052 -1 c d Enterolobium SamaneaRootAlbizia Length RatioChor isia(m kg ) 98740 74760 0.78 0.11 0.736 0.0001 79.19 <.001 0.77 1.74 0.059 0.017 Chorisia ErythrinaRoot Dry Matter Content (RDMC) (g g-1) 0.173 0.187 0.78 15.91 <.001 0.0128 78.82 <.001 0.76 0.78 0.672 0.008 TableCedrela 4.2. Averages of morphological, physiological and chemical variables measured on seedlings of 13 species of tropical trees growing on ADE (n = 12 seedlings per species) and non-ADE ) ) -1 -1 2 -1 soilsStemAnadenanthe (nDry= 12).Maratter TheContent overall (SDMC) R of the (g model g ) and0.282 the ANOVA0.293 results are0.91 provided. 7.81The effect0.006 of soil, species0.0027 or the interaction218.1 is presented<.001 with their0.90 F values and2.12 their significance0.016 level0.009 (p). The Swietenia Machaerium Cedrela Ormosia percentageSapindus of variation explained by each-1 factor or the interaction was calculated as the ratio between the factor sum of squares or interaction sum of squares and total sum of squares. Enterolobium Anadenanthera Leaf Dry Matter Content (LDMC) (g g ) 0.27 0.279 0.96 14.98 <.001 0.0022 551.38 <.001 0.96 0.98 0.464 0.002 Sapindus Swietenia Cariniana Albizia Hymenaea -3 CarinianaStem Density (g cm ) Average0.31 0.316 0.90 Soil1.6 0.207 0.0006 Species188.95 <.001 0.89 Soil1.93 x Species0.031 0.009 RGRb (g.g d Erythrina Samanea Leaf (mg N g 2 -1 Specific Leaf Area (SLA) (m kg ) 41.66 39.46 0.742 4.8 0.029 0.0046 63.23 <.001 0.73 0.83 0.624 0.010 Ormosia 10 20 30 40 50 Trait ADE N-ADE R F p SSs/SSt F p SSspp/SSt F p SSsxspp/SSt Machaerium Leaf Area Ratio (LAR) (m2 kg-1) 18.83 17.27 0.61 9.54 0.002 0.0140 32.52 <.001 0.57 1.17 0.306 0.021 Hymenaea Positive to N Biomas (g) Positive to N 0.932 0.94 0.89 0.39 0.531 0.0002 184.11 <.001 0.88 3 <.001 0.014 Negative to N 0 Force to Punch (N Negatimm-1v)e to N 0.229 0.244 0.69 4.31 0.039 0.0065 49.62 <.001 0.68 0.88 0.543 0.012 0.000 0.010 0.020 0.030 RGRh (mm.mm.d-1) 0.0426 0.0422 0.87 0.09 0.767 0.0000 147.47 <.001 0.84 6.53 <.001 0.037 0 10 20 30 40 0 10 Leaf20 Thickness30 (mm) 40 0.123 0.124 0.90 1.01 0.315 0.0005 202.28 <.001 0.89 1.87 0.058 0.008 Leaf Ca (mg g-1) Leaf CaRGRb (mg g (g.g.d-1) -1) 0.01615 0.01595 - 0.005 0.944 0.0000 ------Leaf Ca concentration (mg g-1) 19.2 16.8 0.95 26.52 <.001 0.0278 71.3 <.001 0.90 1.67 0.102 0.021 Root Mass Fraction (RMF) (g g-1) 0.232 0.244 0.68 3.36 0.068 0.0040 46.54 <.001 0.67 0.58 0.859 0.008 Figure 4.5 Relation between a) Relative biomass growth rate (RGRb) and leafLeaf Ca N concentrationconcentration; (mg gb-1)) 34.7 35.4 0.95 1.35 0.251 0.0012 88.88 <.001 0.94 0.87 0.579 0.009 Stem Mass Fraction (SMF) (g g-1) 0.255 0.261 0.67 1.8 0.181 0.0022 43.4 <.001 0.64 2.15 0.015 0.032 Leaf P concentration and leaf Ca concentration for species that responded positivelyLeaf P concentration or negatively (mg gto-1) 1.9 2.1 0.90 6.78 0.012 0.0134 32.42 <.001 0.77 5.02 <.001 0.119 P. Lines represent the significant relationship between RGRb and leaf Ca onlyLeaf for Mass spec Fractionies that (LMF) were (g g-1) 0.513 0.494 0.61 8.06 0.005 0.0117 33.19 <.001 0.58 1.14 0.331 0.020 2 Ca:P 11.06 9.95 0.83 3.74 0.059 0.0124 18.73 <.001 0.74 1.79 0.075 0.071 “positive for P” (RGRb =-0.0054+0.009(leaf Ca); R = 0.64; p=0.034). RelationDiameter between secondary c) Relative roots (mm) 0.481 0.49 0.61 0.08 0.776 0.0001 31.74 <.001 0.59 1.24 0.253 0.023 Ca:N 0.59 0.51 0.94 22.58 <.001 0.0258 65.31 <.001 0.90 1.41 0.191 0.019 biomass growth rate (RGRb) and leaf Ca concentration; d) Leaf P concentrationSecondary to andprimary leaf root Ca mass (g g-1) 0.897 0.755 0.38 2.98 0.086 0.0069 12.57 <.001 0.35 1.05 0.401 0.029 concentration for species that responded positively or negatively to N. The regressionN:P line represents 20.51 19.84 0.83 0.41 0.522 0.0013 19.86 <.001 0.77 1.52 0.146 0.059 Number of secondary roots 29.17 30.66 0.53 1.43 0.233 0.0026 22.77 <.001 0.49 1.66 0.076 0.036 the significant relationship between RGRb and leaf Ca only for species that were “positive for N”-1 (RGRb=-0.0003+0.0071(Leaf Ca); R2=0.86; p=0.014). Specific Root Length (m kg ) 17860 15310 0.57 0.36 0.549 0.0006 26.88 <.001 0.52 2.69 0.002 0.052 91 Root Length Ratio (m kg-1) 98740 74760 0.78 0.11 0.736 0.0001 79.19 <.001 0.77 1.74 0.059 0.017 Root Dry Matter Content (RDMC) (g g-1) 0.173 0.187 0.78 15.91 <.001 0.0128 78.82 <.001 0.76 0.78 0.672 0.008 Stem Dry Matter Content (SDMC) (g g-1) 0.282 0.293 0.91 7.81 0.006 0.0027 218.1 <.001 0.90 2.12 0.016 0.009 Leaf Dry Matter Content (LDMC) (g g-1) 0.27 0.279 0.96 14.98 <.001 0.0022 551.38 <.001 0.96 0.98 0.464 0.002 Stem Density (g cm-3) 0.31 0.316 0.90 1.6 0.207 0.0006 188.95 <.001 0.89 1.93 0.031 0.009 Specific Leaf Area (SLA) (m2 kg-1) 41.66 39.46 0.74 4.8 0.029 0.0046 63.23 <.001 0.73 0.83 0.624 0.010 Leaf Area Ratio (LAR) (m2 kg-1) 18.83 17.27 0.61 9.54 0.002 0.0140 32.52 <.001 0.57 1.17 0.306 0.021 Force to Punch (N mm-1) 0.229 0.244 0.69 4.31 0.039 0.0065 49.62 <.001 0.68 0.88 0.543 0.012 Leaf Thickness (mm) 0.123 0.124 0.90 1.01 0.315 0.0005 202.28 <.001 0.89 1.87 0.058 0.008 Leaf Ca concentration (mg g-1) 19.2 16.8 0.95 26.52 <.001 0.0278 71.3 <.001 0.90 1.67 0.102 0.021 Leaf N concentration (mg g-1) 34.7 35.4 0.95 1.35 0.251 0.0012 88.88 <.001 0.94 0.87 0.579 0.009 Leaf P concentration (mg g-1) 1.9 2.1 0.90 6.78 0.012 0.0134 32.42 <.001 0.77 5.02 <.001 0.119 Ca:P 11.06 9.95 0.83 3.74 0.059 0.0124 18.73 <.001 0.74 1.79 0.075 0.071 Ca:N 0.59 0.51 0.94 22.58 <.001 0.0258 65.31 <.001 0.90 1.41 0.191 0.019 N:P 20.51 19.84 0.83 0.41 0.522 0.0013 19.86 <.001 0.77 1.52 0.146 0.059

90 91 Chapter 4 ADE and Seedling Responses

Table 4.2. Averages of morphological, physiological and chemical variables measured on seedlings of 13 species of tropical trees growing on ADE (n = 12 seedlings per species) and non-ADE soils (n = 12). The overall R2 of the model and the ANOVA results are provided. The effect of soil, species or the interaction is presented with their F values and their significance level (p). The 91 - ADE ). The concentration and either RGRb (Adj-R2: 0.24; p=0.12) or leaf N concentrationpercentage (Adj of variation-R2: -0.05; explained by each factor or the interaction was calculatedp as the ratio between the factor sum of squares or interaction sum of squares and total sum of squares. - 0.009 0.009 0.017 0.021 0.014 0.023 0.036 0.008 0.071 0.059 SSsxspp/SSt 0.037 0.008 0.032 0.020 0.029 0.052 0.008 0.002 0.009 0.010 0.012 0.021 0.119 0.019 Average Soil Species Soil x Species

p=0.46) (Fig. 4.5c, d). Responses

Trait ADE N-ADE R2 F p SSs/SSt F p SSspp/SSt F p SSsxspp/SSt - 0.016 0.579 0.059 0.306 <.001 0.253 0.076 0.058 0.075 0.146 p <.001 0.859 0.015 0.331 0.401 0.002 0.672 0.464 0.031 0.624 0.543 0.102 <.001 0.191 a 5 b Biomas (g) 0.932 0.94 0.89 0.39 0.531 0.0002 184.11 <.001 0.88 3 <.001 0.014 0.030 Albizia Chorisia RGRh (mm.mm.d-1) 0.0426 0.0422 0.87 0.09 0.767 0.0000 147.47 <.001 0.84 6.53 <.001 0.037 4 )

) -1 -1 -1 RGRbEryth r(g.g.dina ) 0.01615 0.01595 - 0.005 0.944 0.0000 ------2.12 0.87 1.74 1.17 3 1.24 1.66 1.87 1.79 1.52 F 6.53 0.58 2.15 1.14 1.05 2.69 0.78 0.98 1.93 0.83 0.88 1.67 5.02 1.41 Soil x Species 0.020 Swietenia Cedrela 3 -1 Enterolobium Root Mass Fraction (RMF) (g g ) 0.232 0.244 0.68 3.36 0.068 0.0040 46.54 <.001 0.67 0.58 0.859 0.008 Anadenanthe ra Sapindus Chorisia Hymenaea Sapindus -1

Cariniana and Seedling ADE 12 seedlings per species) and non Albizia Stem Mass Fraction (SMF) (g g ) 0.255 0.261 0.67ues and their significance level ( 1.8 0.181 0.0022 43.4 <.001 0.64 2.15 0.015 0.032 2 Enterolobium Swietenia = RGRb (g.g d (g.g RGRb Erythrina Leaf P (mg g Cedrela Samanea Ormosia Samanea -1 0.010 LeafAnadenanthe Mass Fractionra (LMF) (g g ) 0.513 0.494 0.61 8.06 0.005 0.0117 33.19 <.001 0.58 1.14 0.331 0.020 Ormosia Cariniana Machaerium 1 DiameterMachaerium secondary roots (mm) 0.481 0.49 0.61 0.08 0.776 0.0001 31.74 <.001 0.59 1.24 0.253 0.023 - 0.90 0.94 0.64 0.77 0.89 0.57 0.88 0.59 0.49 0.89 0.74 0.77 Positive to P Positive to P SSspp/SSt 0.84 0.67 0.58 0.35 0.52 0.76 0.96 0.73 0.68 0.90 0.77 0.90 Hymenaea -1

Negative to P 0 Secondary to primaryNegati rootve tomass P (g g ) 0.897 0.755 0.38 2.98 0.086 0.0069 12.57 <.001 0.35 1.05 0.401 0.029 0.000 0 10 20 30 40 0 10 Number20 of secondary30 roots40 29.17 30.66 0.53 1.43 0.233 0.0026 22.77 <.001 0.49 1.66 0.076 0.036 -1 -1 -1 Leaf Ca (mg g ) Leaf SpecificCa (mg g )Root Length (m kg ) 17860 15310 0.57 0.36 0.549 0.0006 26.88 <.001 0.52 ADE2.69 and Seedling0.002Responses0.052 - <.001 <.001 <.001 <.001 <.001 <.001 p <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001

50 -1 c d Enterolobium Samanea Albizia Chorisia 0.030 Root Length Ratio (m kg ) 98740 74760 0.78 0.11 0.736 0.0001 79.19 <.001 0.77 1.74 0.059 0.017 Chorisia ErythrinaRoot Dry Matter Content (RDMC) (g g-1) 0.173 0.187 0.78 15.91 <.001 0.0128 78.82 <.001 0.76 0.78 0.672 0.008 40 TableCedrela 4.2. Averages of morphological, physiological and chemical variables measured on seedlings of 13 species of tropical trees growing on ADE (n = 12 seedlings per species) and non-ADE ) ) -1 -1 2 -1 soilsStemAnadenanthe (nDry= 12).Maratter TheContent overall (SDMC) R of the (g model g ) and0.282 the ANOVA0.293 results are0.91 provided. 7.81The effect0.006 of soil, species0.0027 or the interaction218.1 is presented<.001 with their0.90 F values and2.12 their significance0.016 level0.009 (p). The

0.020 Swietenia Machaerium 30 percentage of variation explained by each-1 factor or the interaction was calculated as the ratio between the factor sum of squares or interaction sum of squares and total sum of squares. - 218.1 88.88 43.4 79.19 188.95 32.52 F 147.47 46.54 33.19 12.57 26.88 78.82 551.38 63.23 49.62 71.3 32.42 65.31 184.11 31.74 22.77 202.28 18.73 19.86 Cedrela Ormosia Sapindus Species Enterolobium Anadenanthera Leaf Dry Matter Content (LDMC) (g g ) 0.27 0.279 0.96 14.98 <.001 0.0022 551.38 <.001 0.96 0.98 0.464 0.002 Sapindus Swietenia Cariniana Albizia Hymenaea -3 20 CarinianaStem Density (g cm ) Average0.31 0.316 0.90 Soil1.6 0.207 0.0006 Species188.95 <.001 0.89 Soil1.93 x Species0.031 0.009 RGRb (g.g d Erythrina Leaf (mg N g 2 -1 0.010 Samanea Specific Leaf Area (SLA) (m kg ) 41.66 39.46 0.742 4.8 0.029 0.0046 63.23 <.001 0.73 0.83 0.624 0.010 Ormosia 10 Trait ADE N-ADE R F p SSs/SSt F p SSspp/SSt F p SSsxspp/SSt 0.0000 0.0027 0.0012 0.0022 0.0001 0.0006 0.0140 SSs/SSt 0.0000 0.0040 0.0117 0.0069 0.0006 0.0128 0.0022 0.0046 0.0065 0.0278 0.0134 0.0258 Machaerium Leaf Area Ratio (LAR) (m2 kg-1) 18.83 17.27 0.61 9.54 0.0020.0002 0.0140 32.520.0001 0.0026 <.001 0.57 1.17 0.0005 0.306 0.0210.0124 0.0013 Hymenaea Positive to N Biomas (g) Positive to N 0.932 0.94 0.89 0.39 0.531 0.0002 184.11 <.001 0.88 3 <.001 0.014 Negative to N 0 Force to Punch (N Negatimm-1v)e to N 0.229 0.244 0.69 4.31 0.039 0.0065 49.62 <.001 0.68 0.88 0.543 0.012 0.000 RGRh (mm.mm.d-1) 0.0426 0.0422 0.87 0.09 0.767 0.0000 147.47 <.001 0.84 6.53 <.001 0.037 0 10 20 30 40 0 10 Leaf20 Thickness30 (mm) 40 0.123 0.124 0.90 1.01 0.315 0.0005 202.28 <.001 0.89 1.87 0.058 0.008 Leaf Ca (mg g-1) Leaf Ca (mg g-1) -1 RGRb (g.g.d ) -1 0.01615 0.01595 - 0.005 0.944 0.0000 ------0.944 0.006 0.251 0.181 p 0.767 0.068 0.005 0.086 0.549 0.736 <.001 <.001 0.207 0.029 0.002 0.039 <.001 0.012 <.001 Leaf Ca concentration (mg g ) 19.2 16.8 0.95 26.52 <.0010.531 0.0278 71.30.776 0.233 <.001 0.90 1.67 0.315 0.102 0.0210.059 0.522 Root Mass Fraction (RMF) (g g-1) 0.232 0.244 0.68 3.36 0.068 0.0040 46.54 <.001 0.67 0.58 0.859 0.008 Figure 4.5 Relation between a) Relative biomass growth rate (RGRb) and leafLeaf Ca N concentrationconcentration; (mg gb-)1) 34.7 35.4 0.95 1.35 0.251 0.0012 88.88 <.001 0.94 0.87 0.579 0.009 -1 Stem Mass Fraction (SMF) (g g ) 0.255 0.261 0.67 squares. of sum total and squares of sum interaction or squares of sum factor the between ratio e 1.8 0.181 0.0022 43.4 <.001 0.64 2.15 0.015 0.032 Leaf P concentration and leaf Ca concentration for species that responded positivelyLeaf P concentration or negatively (mg gto-1) 0.90 0.0134 32.42 <.001 0.77 0.119 -1 1.9 2.1 6.78 0.012 5.02 <.001 F 0.09 0.005 3.36 8.06 2.98 0.36 15.91 14.98 4.8 4.31 26.52 6.78 22.58 7.81 1.35 1.8 0.11 1.6 9.54 0.39 0.08 1.43 1.01 3.74 0.41 P. Lines represent the significant relationship between RGRb and leaf Ca onlyLeaf for Mass spec Fractionies that (LMF) were (g g ) 0.513 0.494 0.61 8.06Soil 0.005 0.0117 33.19 <.001 0.58 1.14 0.331 0.020 2 Ca:P 11.06 9.95 0.83 3.74 0.059 0.0124 18.73 <.001 0.74 1.79 0.075 0.071 “positive for P” (RGRb = -0.0054+0.009(leaf Ca); R = 0.64; p=0.034). RelationDiameter between secondary c) Relative roots (mm) 0.481 0.49 0.61 0.08 0.776 0.0001 31.74 <.001 0.59 1.24 0.253 0.023 Ca:N 0.59 0.51 0.94 22.58 <.001 0.0258 65.31 <.001 0.90 1.41 0.191 0.019 biomass growth rate (RGRb) and leaf Ca concentration; d) Leaf P concentration and leaf Ca -1

Secondary to primary root mass (g g ) 0.897 0.755 0.38 2.98 2 0.086 0.0069 12.57 <.001 0.35 1.05 0.401 0.029 R 0.87 0.68 0.61 0.38 0.57 0.78 0.96 0.74 0.69 0.95 0.90 0.94 - 0.91 0.95 0.67 0.78 0.90 0.61 concentration for species that responded positively or negatively to N. The regressionN:P line represents 20.51 19.84 0.83 0.41 0.5220.89 0.0013 19.860.61 0.53 <.001 0.77 1.52 0.90 0.146 0.0590.83 0.83 Number of secondary roots 29.17 30.66 0.53 1.43 0.233 0.0026 22.77 <.001 0.49 1.66 0.076 0.036 the significant relationship between RGRb and leaf Ca only for species that were “positive for N”-1 (RGRb=-0.0003+0.0071(Leaf Ca); R2=0.86; p=0.014). Specific Root Length (m kg ) 17860 15310 0.57 0.36 0.549 0.0006 26.88 <.001 0.52 2.69 0.002 0.052 91 Root Length Ratio (m kg-1) 98740 74760 0.78 0.11 0.736 0.0001 79.19 <.001 0.77 1.74 0.059 0.017 - ADE N 0.0422 0.01595 0.244 0.494 0.755 15310 0.187 0.279 39.46 0.244 16.8 2.1 0.51 0.293 35.4 0.261 74760 0.316 17.27 Root Dry Matter Content (RDMC) (g g-1) 0.173 0.187 0.78 15.91 <.0010.94 0.0128 78.820.49 30.66 <.001 0.76 0.78 0.124 0.672 0.0089.95 19.84 Stem Dry Matter Content (SDMC) (g g-1) 0.282 0.293 0.91 7.81 0.006 0.0027 218.1 <.001 0.90 2.12 0.016 0.009 Leaf Dry Matter Content (LDMC) (g g-1) 0.27 0.279 0.96 14.98 <.001 0.0022 551.38 <.001 0.96 0.98 0.464 0.002 0.01615 0.282 34.7 ADE 0.0426 0.232 0.255 0.513 0.897 17860 0.173 0.27 41.66 0.229 19.2 1.9 0.59 98740 0.31 18.83 0.932 0.481 29.17 0.123 11.06 20.51 Average Stem Density (g cm-3) 0.31 0.316 0.90 1.6 0.207 0.0006 188.95 <.001 0.89 1.93 0.031 0.009 2 -1 ) ) ) 1 1

Specific Leaf Area (SLA) (m kg ) 41.66 39.46 0.74 4.8 0.029 0.0046 63.23 <.001 0.731 0.83 0.624 0.010 - - - )

2 -1 1 Leaf Area Ratio (LAR) (m kg ) 18.83 17.27 0.61 9.54 0.002 0.0140 32.52- <.001 0.57 1.17 0.306 0.021 ) 1 ) ) -

-1 ) (g g 1 1 1 g - - )

Force to Punch (N mm ) 0.229 0.244 0.69 4.31 0.039 0.0065 - 49.62 <.001 0.68 0.88 0.543 0.012 1 k - ) of the model and the ANOVA results are provided. The effect of soil, species or the interaction is presented with their F val F their with presented is interaction the or species soil, of effect The provided. are results ANOVA the and model the of ) 2 1 g ) ) - 1 2 1 - k 1 -

Leaf Thickness (mm) 0.123 0.124 0.90 1.01 0.315 0.0005 202.28 <.001 0.89 1.87 0.058- 0.008 g 2 ) 1 k -

-1 ) g 1

Leaf Ca concentration (mg g ) 0.95 0.0278 71.3 oots <.001 0.90 1.67 0.102 0.021 19.2 16.8 26.52 <.001 - (mg g )

-1 3 - Leaf N concentration (mg g ) 34.7 35.4 0.95 1.35 0.251) 0.0012 88.88 <.001 0.94 0.87 0.579 0.009 1 - Content g (g (SDMC) Content (RDMC) (g g (g Content (RDMC) Content (LDMC) (g g (g Content (LDMC) Leaf P concentration (mg g-1) 1.9 2.1 0.90 6.78 0.012 0.0134 32.42 <.001 0.77 5.02 <.001 0.119 ) tter 1 tter Ca:P 11.06 9.95 0.83 3.74 0.059 - 0.0124 18.73 <.001 0.74 1.79 0.075 0.071 12). The overall R overall The 12). . Averages of morphological, physiological and chemical variables measured on seedlings of 13 species (n of tropical on ADE ng trees growi secondary roots (mm) roots secondary Ca:N 0.59 0.51 0.94= 22.58 <.001 0.0258 65.31 <.001 0.90 1.41 0.191 0.019 N:P 20.51 19.84 0.83 0.41 0.522 0.0013 19.86 <.001 0.77 1.52 0.146 0.059 percentage of variation explained eachby factor or the interactionwas calculated as th Table 4.2 Trait RGRh (mm.mm.d Fraction (RMF)Root g Mass (g Fraction Mass g Leaf (g (LMF) mass root primary to Secondary (m Length Root Specific Ma Root Dry DryLeaf Matter Specific Leaf (SLA)Area (m mm (N Punch to Force CaLeaf concentration g (mg PLeaf concentration Ca:N Biomas (g) Biomas (g.g.d RGRb g (g (SMF) Fraction Mass Stem Diameter r secondary of Number k (m Ratio Length Root Ma Dry Stem Stem Density (g cm Leaf Area Ratio (LAR) (m Leaf Thickness (mm) N concentrationLeaf g (mg Ca:P N:P soils (n 91 Chapter 4 ADE and Seedling Responses

Discussion 4.4b). Seedlings of seven species had, on average, 1.3 times higher leaf P concentrations in non-ADE than in ADE, even though ADE had more P than non-ADE soils (Appendix, Table The aim of this study was to evaluate whether tropical tree seedlings respond to the A4.1). This result suggests that there is an imbalance between soil and tissue elements in differences in nutrient concentrations between ADE and non-ADE. Seedlings indeed adjusted some of the species that we studied. There are several possible explanations for this result: their morphology and tissue chemistry in response to the increased fertility in ADE but this first, the effects of high Ca in soils and its consequences for P availability, second, the effects did not lead to higher seedling growth in ADE. This lack of growth response seems to be of a high Ca supply on plant tissue and the interaction with inorganic P, and finally, a determined by the interplay between the uptake of Ca and the uptake of other essential decrease in N-fixation leading to a decrease in P uptake by N-fixer species on ADE. nutrients, such as N and P. Firstly, soils that combine a neutral to alkaline pH with a high Ca concentration (as it Plant responses to ADE is the case in this Bolivian forest) may have, in fact, a low effective availability of P. The relatively high Ca in ADE could lead to the formation of mineral insoluble calcium In our study we found lower survival in seedlings of Poeppigia and Terminalia that were phosphate, thus reducing P availability as it is reported in calcareus soils with pH around 7.8 growing in ADE. Seedlings of Poeppigia were not able to develop a root system on ADE and (Lee 1998, Zohlen and Tyler 2004). In such conditions, only species that can exude organic the seedlings that could establish on non-ADE showed some chlorosis (E. Quintero, personal acids into the rhizosphere to increase the solubility of phosphate would be able to incorporate observation). These characteristics can be associated with the inability of plants to regulate Ca more P from the soil, as it has been shown in other studies (White and Broadley 2003). uptake when they grow in soils with high Ca concentration. Studies in calcifuges species have Further studies on rhizosphere biochemistry and detailed information about soil P could suggested that an excess of Ca in the tissues translates into Ca toxicity, that inhibits growth, confirm this mechanism. and causes mortality (Jefferies and Willis 1964). This inhibition could be caused by metabolic Secondly, high concentrations of Ca in the cytosol of plant cells can react with disorders related to enzyme inactivation and to a decrease of P availability for metabolism due inorganic P and produce calcium phosphate, decreasing the concentration of P available for to the excess of cytosolic Ca (Jefferies and Willis 1964, Grundon 1972, Zohlen and Tyler plant metabolic functions and consequently, the P concentration measured in leaves (Tyler 2004). 1996, Zohlen and Tyler 2004). Unfortunately, this mechanism has been mainly studied in We predicted that in high-fertility ADE plants would invest in aboveground light species that are not able to grow in calcareous soils with high soil Ca concentrations (i.e. capture, whereas in non-ADE plants would invest in belowground nutrient capture. This is in calcifuge species). Further exploration of the interaction between Ca and P deserve line with Brower’s hypothesis which states that plants invest in capturing the resource that is exploration in the ADE context where the major changes in the chemistry of the soils is in most limiting supply (Brouwer 1962). We found that seedlings growing on ADE allocated defined by increases of Ca and P (Glaser and Birk 2012). more biomass to leaves (LMF) than seedlings on non-ADE, but we did not find support for Third, some of the species belonging to Fabaceae (e.g., Erythrina, Samanea, and higher investment in root biomass on non-ADE. Additionally, we found higher investment on Albizia) might have a better N-fixation and higher P uptake on non-ADE than on ADE soils. leaf area per leaf mass (SLA) and per plant mass (LAR) on ADE. Greater biomass allocation These species had either more proportion of seedlings with nodules, or a higher number of to leaves in combination with higher SLA and LAR on ADE (Table 4.2) could increase nodules per root on seedlings growing on non-ADE than in ADE (data not shown). Thus, seedlings capacity to capture light and should enhance biomass growth (Poorter 1999). more nodules can increase N-fixation that can increase P uptake on non-ADE (Figs 3b and However, in our experiment, seedling RGR was similar on both soil types, suggesting that an 3c). Recent studies suggests that N-fixers can increase P uptake by being able to invest more increase in leaf light capture capacity does not translate into faster growth. There are other carbon for either associate with mycorrhizal fungi or release phosphatase into the soil which factors such as leaf tissue chemistry that could explain the limiting seedling growth found in can give them advantages to incorporate P (Nasto et al. 2014). this study (Figs. 4.4 and 4.5). Leaf Ca predicts species growth, but only for those species that can cope with Ca excess High Ca in ADE soils affect leaf nutrient stoichiometry Growth of seedlings in this experiment did not appear to be nutrient limited. In both soil One of the most interesting results of our study is the effect of ADE on leaf nutrient types, seedlings had higher leaf Ca and N concentration (Table 4.2) than reference levels concentrations of some of the most important elements for plant growth. ADE was more normally found to sustain plant growth (5 mg g-1 dry mass for Ca; 15 mg g-1 dry mass for N) fertile, with 3.2 times more P, 2.4 times more Ca, and 1.6 times more N than the non-ADE (Epstein and Bloom 2005). Leaf P concentrations were, however, close to reference values (2 (Appendix, Table A4.2). All species had indeed higher leaf Ca in ADE than in non-ADE (Fig. mg g-1 dry mass ) (Epstein and Bloom 2005), with some exceptions, such as Erytrhina (4.3 3a), but only 6 out of 13 species had higher leaf P and leaf N on ADE (Figs 4.3b, 4.3c, 4.4a, mg g-1 dry mass) and Albizia (4.5 mg g-1 dry mass) (Fig. 4.3b). But this “luxurious” nutrient

92 93 Chapter 4 ADE and Seedling Responses

93 Chapter 4 ADE and Seedling Responses

consumption of Ca, N and P did not translate into higher RGR of the species (Fig. 2b). Lack Ca in the soil. Therefore, the ability of plants to colonize and grow in patches of ADE of changes in plant growth with increased nutrient availability in ADE seems to be associated depends on their ability to cope with increases in soil nutrients, especially increases in Ca. with species-specific abilities in tissue nutrient regulation. Whereas some species increased In the Amazon basin, sites where the natural soils are nutrient limited, ADE soils can RGR when leaf Ca, P and N increased simultaneously (Figs. 4.5a-d), other species increased improve conditions for plant growth; whereas at sites where nutrients in the soils are not leaf Ca with no consequences on leaf N or P and RGR (Figs. 4.5a-d). This implies that limited, ADE potentially could cause nutrient imbalance and general nutritional disorders that increases of Ca in the soils and in plant tissues are not beneficial for some species. In general, do not improve plant growth. Ca enters in the plants by diffusing among cells or through cell cytoplasm in the roots, then it goes to the xylem and it accumulates in the leaves (White 2012). Plants that are not adapted to high Ca in the soils (such as calcifuge plants) cannot regulate the excess of Ca entering in Acknowledgements their tissues. This excess of Ca uptake is done at the expense of the uptake of other ions, such This research is part of the Terra Preta Program of Wageningen University funded by the as such as K, Fe and Mg, causing nutritional imbalance (Jefferies and Willis 1964). We Interdisciplinary Research and Education Fund (INREF). La Chonta Company provided us cannot confirm if our plants had a nutritional imbalance, since we did not measure other with permits and logistic support to work in the concession. We thank our field assistant nutrients in the leaves, although we did not notice any symptoms of nutrient deficiency. Ricardo Mendez, forest engineer Juan Carlos Licona and the students Delma Sarmiento, Additionally, excess of Ca can induce some metabolic problems. The excess of cytosolic Ca Miguel Angel Fernandes and Veronica Sisepe for their help in the field, in the greenhouse and can decrease cellular free P necessary for metabolic functions (Zohlen and Tyler 2004) or it in trait measurements. We deeply thank IBIF staff for logistic facilities in Santa Cruz. Finally, can inhibit some enzymatic action by competing with enzyme cofactors, such as Mg and Mn we thank Agustí Muñoz-Garcia, and Hans Lambers for valuable comments that helped to (Jefferies and Willis 1964, Webb 1999). Unless plants are able to regulate the excess of Ca improve this manuscript. through storing it in the vacuoles, in the cell wall (Kinzel 1989), in epidermal cells or in structures such as trichomes (Webb 1999), it is very likely that Ca can produce some metabolic dysfunctions. This has been suggested in studies that report poor plant development on soils with high concentrations of Ca (Jefferies and Willis 1964, Lee 1998). Trees at La Chonta do not seem nutrient limited since natural soils are fertile in terms of N, P, Ca and pH values. The improvement to the soil by increasing Ca and P levels, as it is the case in ADE, does not translate in higher tree RGR, and it can be even detrimental for some tree species such as Terminalia and Poeppigia that seemed to suffer from Ca toxicity. Our results suggest “luxury” consumption of Ca by tree seedlings, but the advantages of this consumption depend on the plant ability to maintain the balance among the various nutrients needed for performance. On one hand, it is likely that the species that did not increase RGR on ADE, such as Samanea, Erythrina and Albizia are unable to regulate excess of Ca and show some nutrient imbalance. On the other hand, species that had a higher RGR in ADE soils , such as Cedrela, and Sapindus, are able to regulate the excess of Ca and increase their leaf P and N concentrations on ADE as well. Nevertheless, we did not elucidate the exact mechanism of Ca accumulation or detoxification (He et al. 2014) .

Concluding remarks

Past inhabitants of the La Chonta forest modified the soils by increasing mostly Ca and P concentrations in ADE. Contrary to our expectation, this soil enrichment did not lead to a general increases in tree growth, probably because the background soil fertility of non-ADE at La Chonta is already high. Our results indicate three possible ways of regulating increase of

94 95 Chapter 4 ADE and Seedling Responses

Concluding remarks

95 Chapter 4 ADE and Seedling Responses

Table A4.2 Trait averages for all species on each soil type. RGR= Relative growth rate; RMF=Root mass fraction; SMF=Stem mass fraction; LMF=Leaf mass fraction; LDMC=Leaf dry mass content; SLA=Specific leaf Area; LAR=Leaf area ratio; SRL=Specific root length; RLR=Root length ratio; Appendix. RDMC=Root dry mass content; SDMC= Stem dry mass content.

Laboratory procedures for obtaining soil chemical and physical variables RGRbiomass RMF SMF LMF Diameter of (g.g d-1) (g g-1) (g g-1) (g g-1) secondary The concentrations of N, P, exchangeable Ca2+, Mg2+, K+, and Al+3; pH; organic matter content (OM); roots (mm) total exchangeable bases (TEB); cation exchangeable capacity (CEC); base saturation;Species acidity; and soil ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE texture were analysed. Soil texture (percentage of sand, silt, and clay) wasHymenaea measured courbaril using a 0.002 0.004 0.20 0.21 0.30 0.31 0.50 0.48 0.71 0.67 Bouyoucos hydrometer; pH and electric conductivity was determined Machaeriumin 1:5 distilled villosum water 0.006 0.007 0.27 0.30 0.28 0.29 0.47 0.46 0.42 0.44 equilibrating for 90 min.; concentrations of exchangeable Ca2+, Mg2+, K+ and NaOrmosia+ were determinednobilis in 0.009 0.009 0.23 0.23 0.27 0.27 0.49 0.48 0.49 0.59 1 M ammonium acetate at pH 7. After extraction, Ca2+ and Mg2+ were measuredSamanea with tubulosa an Atomic 0.014 0.012 0.32 0.32 0.29 0.30 0.41 0.41 0.42 0.42 Absorption Spectrophotometer; concentrations of K+ and Na+ were measured withErythrina a flame cristaphotometer;-galli 0.016 0.014 0.19 0.22 0.33 0.37 0.48 0.41 0.70 0.66 3+ exchangeable aluminium (Al ) was measured in 1 M hydrochloric acidAlbizia (HCl). niopoides Total P was 0.016 0.015 0.31 0.30 0.16 0.18 0.53 0.52 0.29 0.27 determined by Olsen method; OM was determined by Walkley-Black method,Enterolobium and total N was 0.017 0.017 0.30 0.31 0.21 0.25 0.49 0.44 0.54 0.55 measured using the Micro-Kjeldahl method (Barber and Cochrane 1993). TEB contortisiliquumwas defined as the sum ADE and Seedling Responses of the most important exchangeable cations (Ca2+, Mg2+, K+ and Na+). CEC wasAnadenanthera calculated as the colubrina sum 0.018 0.022 0.25 0.27 0.19 0.16 0.57 0.57 0.39 0.36 of TEB plus acidity. Base saturation was computed as the percentage of exchangeable cations or bases 0.017 0.019 0.24 0.22 0.28 0.30 0.49 0.48 0.67 0.66 TableCariniana A4.2 ianerensisTrait averages for all species on each soil type. RGR= Relative growth rate; RMF=Root mass fraction; SMF=Stem mass fraction; LMF=Leaf relative to total CEC. massSapindus fraction; saponaria LDMC=Leaf dry0.021 mass 0.018content; SLA=Specific0.20 leaf0.21 Area; LAR=Leaf0.25 0.24area ratio; SRL=Specific0.51 0.49 root length0.41; RLR=Roo0.53 t length ratio; RDMC=RootCedrela fissilis dry mass content; 0.023SDMC= 0.019Stem dry mass content.0.30 0.34 0.26 0.23 0.46 0.42 0.52 0.52 Table A4.1 Soil characteristics of Amazonian Dark Earths and non-Amazonian Dark Earths collected Swietenia macrophylla 0.025 0.024 0.12 0.11 0.27 0.29 0.63 0.65 0.38 0.37 in La Chonta forest concession, before and after mixing them with river sand. Ratios between the RGRbiomass RMF SMF LMF Diameter of values of the variables after mixing with river sand are presented. Chorisia speciosa 0.027 0.027 0.10 0.10 0.24 0.24 0.62 0.60 0.45 0.45 (g.g d-1) (g g-1) (g g-1) (g g-1) secondary ADE non- ADE / non-ADE / Ratio variables roots (mm) ADE River sand River sandSpecies on ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE HymenaeaADE: ncourbarilonADE 0.002 0.004 0.20 0.21 0.30 0.31 0.50 0.48 0.71 0.67 pH 7.4 7 7.8 7.6 Machaerium1 villosum 0.006 0.007 0.27 0.30 0.28 0.29 0.47 0.46 0.42 0.44 Electric conductivity 226 108 218 149 Ormosia1.5 nobilis 0.009 0.009 0.23 0.23 0.27 0.27 0.49 0.48 0.49 0.59 (μS/cm) Samanea tubulosa 0.014 0.012 0.32 0.32 0.29 0.30 0.41 0.41 0.42 0.42 Ca (cmol kg-1) 12.2 4.8 10 4.2 Erythrina2.4 crista-galli 0.016 0.014 0.19 0.22 0.33 0.37 0.48 0.41 0.70 0.66 Mg (cmol kg-1) 1.3 1 1.1 0.8 Albizia1.4 niopoides 0.016 0.015 0.31 0.30 0.16 0.18 0.53 0.52 0.29 0.27 Na (cmol kg-1) 0.11 0.04 0.09 0.05 Enterolobium1.8 0.017 0.017 0.30 0.31 0.21 0.25 0.49 0.44 0.54 0.55 -1 contortisiliquum K (cmol kg ) 0.38 0.26 0.28 0.2 1.4 97 -1 Anadenanthera colubrina 0.018 0.022 0.25 0.27 0.19 0.16 0.57 0.57 0.39 0.36 P (mg kg ) 36 12 35 11 3.2 Cariniana ianerensis 0.017 0.019 0.24 0.22 0.28 0.30 0.49 0.48 0.67 0.66 Total N (%) 0.31 0.16 0.18 0.11 1.6 Sapindus saponaria 0.021 0.018 0.20 0.21 0.25 0.24 0.51 0.49 0.41 0.53 Total Exchangable 14 6.1 11.5 5.3 2.2 Cedrela fissilis 0.023 0.019 0.30 0.34 0.26 0.23 0.46 0.42 0.52 0.52 bases 0.025 0.024 0.12 0.11 0.27 0.29 0.63 0.65 0.38 0.37 Cation Exchange 14 6.1 11.5 5.3 Swietenia2.2 macrophylla Capacity Chorisia speciosa 0.027 0.027 0.10 0.10 0.24 0.24 0.62 0.60 0.45 0.45 Base Saturation (%) 100 100 100 100 1 Organic Mater (%) 3.3 2.7 2.9 2.2 1.3 Sand (%) 49 57 68 73 0.9 Silt (%) 39 36 25 20 1.2 Clay (%) 12 7 7 7 1

96 97 Chapter 4 ADE and Seedling Responses

Table A4.2 Trait averages for all species on each soil type. RGR= Relative growth rate; RMF=Root mass fraction; SMF=Stem mass fraction; LMF=Leaf 97 mass fraction; LDMC=Leaf dry mass content; SLA=Specificratio; leaf Area; LAR=Leaf area ratio; SRL=Specific root length; RLR=Root length ratio; Appendix. RDMC=Root dry mass content; SDMC= Stem dry mass content. Responses RGRbiomass RMFlength SMF LMF Diameter of

Laboratory procedures for obtaining soil chemical and physical variables t -1 -1 -1 -1

(g.g d ) (g g ) ADE (g g ) (g g ) secondary - The concentrations of N, P, exchangeable Ca2+, Mg2+, K+, and Al+3; pH; organic matter content (OM); roots (mm) 0.45 total exchangeable bases (TEB); cation exchangeable capacity (CEC); base saturation;Species acidity; and soil ADE non-ADE ADE non-ADEnon 0.67 ADE0.44 0.59 non0.42 -0.66 ADE0.27 0.55 ADE0.36 0.66 non0.53 -ADE0.52 0.37 ADE non-ADE texture were analysed. Soil texture (percentage of sand, silt, and clay) was measured using a 0.002 0.004 0.20 0.21 0.30 0.31 0.50 0.48 0.71 0.67 Hymenaea courbaril ; RLR=Roo Bouyoucos hydrometer; pH and electric conductivity was determined Machaeriumin 1:5 distilled villosum water 0.006 0.007 0.27 0.30 0.28 0.29 0.47 0.46 0.42 0.44 ADE and Seedling ADE 0.45 equilibrating for 90 min.; concentrations of exchangeable Ca2+, Mg2+, K+ and NaOrmosia+ were determinednobilis in 0.009 0.009 0.23 0.23Diameter of secondary roots (mm) ADE 0.71 0.270.42 0.49 0.270.42 0.70 0.29 0.54 0.490.39 0.67 0.480.41 0.52 0.38 0.49 0.59 1 M ammonium acetate at pH 7. After extraction, Ca2+ and Mg2+ were measuredSamanea with tubulosa an Atomic 0.014 0.012 0.32 0.32 0.29 0.30 0.41 0.41 0.42 0.42 + +

Absorption Spectrophotometer; concentrations of K and Na were measured withErythrina a flame cristaphotometer;-galli 0.016 0.014 0.19 0.22 ADE 0.33 0.37 0.48 0.41 0.70 0.66 3+ - exchangeable aluminium (Al ) was measured in 1 M hydrochloric acidAlbizia (HCl). niopoides Total P was 0.016 0.015 0.31 0.30 0.16 0.18 0.53 0.52 0.29 0.27 0.60 determined by Olsen method; OM was determined by Walkley-Black method, and total N was 0.017 0.017 0.30 0.31 non 0.48 0.210.46 0.48 0.250.41 0.41 0.52 0.44 0.490.57 0.48 0.440.49 0.42 0.65 0.54 0.55

Enterolobium ) 1 measured using the Micro-Kjeldahl method (Barber and Cochrane 1993). TEB contortisiliquumwas defined as the sum - ADE and Seedling Responses 2+ 2+ + + 0.62 of the most important exchangeable cations (Ca , Mg , K and Na ). CEC wasAnadenanthera calculated as the colubrina sum 0.018 0.022 0.25 0.27LMF (g g ADE 0.50 0.190.47 0.49 0.160.41 0.48 0.53 0.49 0.570.57 0.49 0.570.51 0.46 0.63 0.39 0.36 of TEB plus acidity. Base saturation was computed as the percentage of exchangeable cations or bases 0.017 0.019 0.24 0.22 0.28 0.30 0.49 0.48 0.67 0.66 TableCariniana A4.2 ianerensisTrait averages for all species on each soil type. RGR= Relative growth rate; RMF=Root mass fraction; SMF=Stem mass fraction; LMF=Leaf relative to total CEC. massSapindus fraction; saponaria LDMC=Leaf dry0.021 mass 0.018content; SLA=Specific0.20 leaf0.21 Area; LAR=Leaf0.25 0.24area ratio; SRL=Specific0.51 0.49 root length0.41; RLR=Roo0.53 t length ratio; ADE CedrelaRDMC=Root fissilis dry mass content; 0.023SDMC= 0.019Stem dry mass content.0.30 0.34 - 0.26 0.23 0.46 0.42 0.52 0.52 Table A4.1 Soil characteristics of Amazonian Dark Earths and non-Amazonian Dark Earths collected 0.025 0.024 0.12 0.11 0.27 0.29 0.63 0.65 0.38 0.37 non 0.31 0.29 0.27 0.30 0.37 0.18 0.25 0.16 0.30 0.24 0.23 0.29 in La Chonta forest concession, before and after mixing them with river sand.Swietenia Ratios macrophyllabetween the 0.24

RGRbiomass RMF ) SMF LMF Diameter of

Chorisia speciosa 0.027 0.027 0.10 0.101 0.24 0.24 0.62 0.60 0.45 0.45 values of the variables after mixing with river sand are presented. - (g.g d-1) (g g-1) (g g-1) (g g-1) secondary 0.24 ADE non- ADE / non-ADE / Ratio variables SMF (g g ADE 0.30 0.28 0.27 0.29 0.33 0.16 0.21 0.19 0.28 0.25 0.26 0.27 roots (mm) ADE River sand River sandSpecies on ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE HymenaeaADE: ncourbarilonADE 0.002 0.004 0.20 0.21 0.30 0.31 0.50 0.48 0.71 0.67 ADE pH 7.4 7 7.8 7.6 Machaerium1 villosum 0.006 0.007 0.27 0.30 - 0.28 0.29 0.47 0.46 0.42 0.44 Ormosia nobilis 0.009 0.009 0.23 0.23 0.27 0.27 0.49 0.48 0.49 0.59 0.10 Electric conductivity 226 108 218 149 1.5 non 0.21 0.30 0.23 0.32 0.22 0.30 0.31 0.27 0.22 0.21 0.34 0.11

Samanea tubulosa 0.014 0.012 0.32 0.32) 0.29 0.30 0.41 0.41 0.42 0.42 (μS/cm) 1 - Ca (cmol kg-1) 12.2 4.8 10 4.2 Erythrina2.4 crista-galli 0.016 0.014 0.19 0.22 0.33 0.37 0.48 0.41 0.70 0.66 0.10 Mg (cmol kg-1) 1.3 1 1.1 0.8 Albizia1.4 niopoides 0.016 0.015 0.31 0.30RMF (g g ADE 0.20 0.160.27 0.23 0.180.32 0.19 0.31 0.30 0.530.25 0.24 0.520.20 0.30 0.12 0.29 0.27 Na (cmol kg-1) 0.11 0.04 0.09 0.05 Enterolobium1.8 0.017 0.017 0.30 0.31 0.21 0.25 0.49 0.44 0.54 0.55 -1 contortisiliquum K (cmol kg ) 0.38 0.26 0.28 0.2 1.4 97 -1 Anadenanthera colubrina 0.018 0.022 0.25 0.27 0.19 0.16 0.57 0.57 0.39 0.36 P (mg kg ) 36 12 35 11 3.2 0.017 0.019 0.24 0.22 0.28 0.30 0.49 0.48 0.67 0.66 Cariniana ianerensis ADE Total N (%) 0.31 0.16 0.18 0.11 1.6 - Sapindus saponaria 0.021 0.018 0.20 0.21 0.25 0.24 0.51 0.49 0.41 0.53 0.027 Total Exchangable 14 6.1 11.5 5.3 2.2 non 0.004 0.007 0.009 0.012 0.014 0.015 0.017 0.022 0.019 0.018 0.019 0.024 )

Cedrela fissilis 0.023 0.019 0.30 0.341 0.26 0.23 0.46 0.42 0.52 0.52 bases - 0.025 0.024 0.12 0.11 0.27 0.29 0.63 0.65 0.38 0.37 Cation Exchange 14 6.1 11.5 5.3 Swietenia2.2 macrophylla 0.027 0.027 0.10 0.10 0.24 0.24 0.62 0.60 0.45 0.45 Capacity Chorisia speciosa RGRbiomass d (g.g ADE 0.002 0.006 0.009 0.014 0.016 0.016 0.017 0.018 0.017 0.021 0.023 0.025 0.027 Base Saturation (%) 100 100 100 100 1 Organic Mater (%) 3.3 2.7 2.9 2.2 1.3 Sand (%) 49 57 68 73 0.9 galli

Silt (%) 39 36 25 20 1.2 - Clay (%) 12 7 7 7 1 LMF=Leaf LMF=Leaf fraction; mass SMF=Stem fraction; mass RMF=Root rate; growth Relative RGR= type. soil each on species all for averages Trait courbaril Table A4.2 length root SRL=Specific ratio; area LAR=Leaf Area; leaf SLA=Specific content; mass dry LDMC=Leaf fraction; mass Species Hymenaea villosum Machaerium Ormosia nobilis tubulosa Samanea Erythrina crista Albizia niopoides Enterolobium contortisiliquum Anadenanthera colubrina ianerensis Cariniana Sapindus saponaria fissilis Cedrela macrophylla Swietenia Chorisia speciosa RDMC=Root dry mass content; SDMC= Stem dry content. mass dry Stem SDMC= content; mass dry RDMC=Root 97 Chapter 4 ADE and Seedling Responses

Trait (continuation) LDMC Stem Density TraitSLA (continuation) LAR Number of SecondaryForce to punchSRL RLR RDMC SDMC (g g-1) (g cm-3) (cm2 g-1) (cm2 g-1roots) (N mm-1) (cm g-1) (cm g-1) (g g-1) (g g-1) Species ADE non-ADE ADE non-ADE SpeciesADE non-ADE ADE ADEnon-ADEnon-ADEADE nonADE-ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE Hymenaea courbaril 0.32 0.34 0.33 0.45 Hymenaea202.32 courbaril203.11 97.68 14.33 93.3515.90 0.26 1043.630.28 779.28 42.85 36.80 0.23 0.27 0.33 0.36 0.28 0.18 0.32 0.16 0.17 0.33 0.30 0.22 0.26 0.26

ADE Machaerium villosum 13.92 18.55 2605.38 1999.71 792.89 347.29 0.24 0.25 0.34 0.35 Machaerium- villosum 0.31 0.32 0.30 0.30 252.36 240.80 110.28 102.47 0.30 0.33 Ormosia nobilis 0.28 0.29 0.19 0.20 Ormosia305.35 nobilis307.02 134.36 26.50 134.0521.17 0.31 850.400.30 1139.80 138.52 150.42 0.21 0.22 0.27 0.27 non )

1 42.50 41.17 432.81 568.52 228.57 319.78 0.18 0.21 0.36 0.33

- Samanea tubulosa 0.28 0.30 0.40 0.37 Samanea525.76 tubulosa487.05 187.16 171.84 0.19 0.22 Erythrina crista-galli 0.14 0.14 0.15 0.13 Erythrina541.99 crista516.19-galli 206.80 28.18 171.4536.00 0.17 1581.650.18 1297.06 304.50 279.89 0.10 0.11 0.15 0.16 0.26 0.17 0.35 0.13 0.14 Albizia0.30 niopoides0.31 0.19 0.34 0.23 0.360.25 0.53 0.53 Albizia388.68 niopoides388.71 191.75 29.25 188.9829.33 1975.07 1536.77 8106.05 5041.89 0.17 0.19 0.37 0.37 ADE Force to punch punch to Force (N mm Enterolobium Enterolobium 36.36 38.73 3570.32 2111.67 48.46 19.67 0.17 0.18 0.32 0.32 contortisiliquum 0.25 0.26 0.41 0.38 contortisiliquum500.48 451.96 188.45 151.80 93.35 AnadenantheraADE colubrina 0.49 0.49 0.57 0.50 Anadenanthera282.29 250.48 colubrina148.71 12.83 133.4026.33 2099.25 1590.84 499.74 423.14 0.23 0.26 0.38 0.41 - 171.45 151.80 138.88 199.79 315.95 102.47 134.05 171.84 188.98 133.40 162.52 248.64 Cariniana ianerensis 0.23 0.25 0.22 0.26 Cariniana355.07 ianerensis305.40 167.82 27.13 138.8828.17 0.35 3998.300.32 3519.98 86.55 82.74 0.15 0.16 0.25 0.30 non

) Sapindus saponaria 0.33 0.33 0.48 0.52 Sapindus344.63 saponaria329.73 177.21 51.11 162.5242.40 0.23 695.260.26 1056.07 28.76 65.32 0.21 0.22 0.41 0.43 1 - g Cedrela fissilis 0.19 0.21 0.16 0.17 Cedrela648.52 fissilis545.40 249.91 30.42 199.7929.75 0.13 2582.540.16 2601.03 745.21 880.43 0.12 0.13 ADE and0.16 Seedling0.18Responses 2 97.68 SwieteniaADE macrophylla 0.24 0.24 0.19 0.21 Swietenia392.67 macrophylla410.15 231.04 46.25 248.6447.08 0.25 526.640.26 1250.33 61.14 134.73 0.16 0.16 0.23 0.23 206.80 188.45 167.82 249.91 322.78 110.28 134.36 187.16 191.75 148.71 177.21 231.04 LAR (cm Chorisia speciosa 0.11 0.11 0.09 0.09 Chorisia610.36 speciosa609.98 322.78 23.50 315.9520.33 0.14 1878.840.17 1502.24 1205.45 1345.07 0.09 0.09 0.11 0.11 Trait (continuation) Number of Secondary SRL RLR RDMC SDMC roots (cm g-1) (cm g-1) (g g-1) (g g-1) ADE - 203.11 516.19 451.96 305.40 545.40 609.98 240.80 307.02 487.05 388.71 250.48 329.73 410.15 Species ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE

non Hymenaea courbaril 14.33 15.90 1043.63 779.28 42.85 36.80 0.23 0.27 0.33 0.36 ) 1 - Machaerium villosum 13.92 18.55 2605.38 1999.71 792.89 347.29 0.24 0.25 0.34 0.35 g

2 Ormosia nobilis 26.50 21.17 850.40 1139.80 138.52 150.42 0.21 0.22 0.27 0.27 ADE 202.32 541.99 500.48 355.07 648.52 610.36 252.36 305.35 525.76 388.68 282.29 344.63 392.67

SLA (cm Samanea tubulosa 42.50 41.17 432.81 568.52 228.57 319.78 0.18 0.21 0.36 0.33 Erythrina crista-galli 28.18 36.00 1581.65 1297.06 304.50 279.89 0.10 0.11 0.15 0.16 0.45 0.13 0.38 0.26 0.17 0.09 0.30 0.20 0.37 0.53 0.50 0.52 0.21 Albizia niopoides 29.25 29.33 1975.07 1536.77 8106.05 5041.89 0.17 0.19 0.37 0.37 ADE - Enterolobium 36.36 38.73 3570.32 2111.67 48.46 19.67 0.17 0.18 0.32 0.32

non contortisiliquum ) 3

- Anadenanthera colubrina 12.83 26.33 2099.25 1590.84 499.74 423.14 0.23 0.26 0.38 0.41 Cariniana ianerensis 27.13 28.17 3998.30 3519.98 86.55 82.74 0.15 0.16 0.25 0.30 0.33 0.15 0.41 0.22 0.16 0.09 0.30 0.19 0.40 0.53 0.57 0.48 0.19 (g cm

ADE 51.11 42.40 695.26 1056.07 28.76 65.32 0.21 0.22 0.41 0.43

Stem Density Sapindus saponaria Cedrela fissilis 30.42 29.75 2582.54 2601.03 745.21 880.43 0.12 0.13 0.16 0.18 46.25 47.08 526.64 1250.33 61.14 134.73 0.16 0.16 0.23 0.23 99 0.34 0.14 0.26 0.25 0.21 0.11 0.32 0.29 0.30 0.36 0.49 0.33 0.24 Swietenia macrophylla ADE - Chorisia speciosa 23.50 20.33 1878.84 1502.24 1205.45 1345.07 0.09 0.09 0.11 0.11 non ) 1 - 0.32 0.31 0.28 0.28 0.14 0.34 0.25 0.49 0.23 0.33 0.19 0.24 0.11 ADE LDMC (g g galli - Species Enterolobium Enterolobium Cedrela fissilis Cedrela Ormosia nobilis contortisiliquum Albizia niopoides Chorisia speciosa Samanea tubulosa Samanea Sapindus saponaria Hymenaea courbaril Cariniana ianerensis Cariniana Erythrina crista Machaerium villosum Machaerium Swietenia macrophylla Swietenia Anadenanthera colubrina Chapter 4 Trait (continuation) 98 99 Chapter 4 Trait (continuation) Trait 4 Chapter Anadenanthera colubrina Anadenanthera Trait (continuation) LDMC Stem Density SLA LAR Force to punch Swietenia macrophylla

-1 -3 2 -1 2 -1 -1 Machaerium villosum Erythrina crista Erythrina Cariniana ianerensis

(g g ) (g cm ) (cm g ) (cm g ) (N mm ) courbaril Hymenaea Sapindus saponaria Sapindus Samanea tubulosa

Species ADE non-ADE ADE non-ADE ADE non-ADE speciosa Chorisia ADE non-ADE ADE non-ADE Albizia niopoides Albizia contortisiliquum Ormosia nobilis Ormosia

Hymenaea courbaril 0.32 0.34 0.33 0.45 202.32 203.11 97.68Cedrela fissilis 93.35 0.26 0.28 Enterolobium Machaerium villosum 0.31 0.32 0.30 0.30 252.36 240.80 110.28 102.47 0.30 0.33 Ormosia nobilis 0.28 0.29 0.19 0.20 305.35 307.02 134.36 134.05 0.31 0.30 Samanea tubulosa 0.28 0.30 0.40 0.37 525.76 487.05 187.16 171.84 0.19 0.22 Species - Erythrina crista-galli 0.14 0.14 0.15 0.13 541.99 516.19 206.80 171.45 0.17galli 0.18 Albizia niopoides 0.34 0.36 0.53 0.53 388.68 388.71 191.75 188.98 (g g (g LDMC Enterolobium ADE 0.23 0.49 0.25 0.34 0.14 0.28 0.28 0.31 0.32 0.11 0.24 0.19 0.33

contortisiliquum 0.25 0.26 0.41 0.38 500.48 451.96 188.45 151.80 - 1 Anadenanthera colubrina 0.49 0.49 0.57 0.50 282.29 250.48 148.71 133.40 )

Cariniana ianerensis 0.23 0.25 0.22 0.26 355.07 305.40 167.82 138.88 0.35 0.32 non

Sapindus saponaria 0.33 0.33 0.48 0.52 344.63 329.73 177.21 162.52 0.23 0.26 - ADE Cedrela fissilis 0.19 0.21 0.16 0.17 648.52 545.40 0.11 249.910.24 0.21 0.33 0.25 199.790.49 0.26 0.130.36 0.14 0.30 0.29 0.32 0.160.34 Swietenia macrophylla 0.24 0.24 0.19 0.21 392.67 410.15 231.04 248.64 0.25 0.26 Stem Density Stem ADE Chorisia speciosa 0.11 0.11 0.09 0.09 610.36 609.98 322.78 315.95 0.14 0.17 cm (g 0.09 0.19 0.16 0.48 0.22 0.57 0.41 0.53 0.15 0.40 0.19 0.30 0.33 - 3 ) non - ADE 0.09 0.21 0.17 0.52 0.26 0.50 0.38 0.53 0.13 0.37 0.20 0.30 0.45 Chorisia speciosa Chorisia Swietenia macrophylla Cedrela fissilis saponaria Sapindus Cariniana ianerensis colubrina Anadenanthera contortisiliquum Enterolobium niopoides Albizia crista Erythrina Samanea tubulosa nobilis Ormosia Machaerium villosum courbaril Hymenaea Species (continuation) Trait speciosa Chorisia Swietenia macrophylla Cedrela fissilis saponaria Sapindus Cariniana ianerensis colubrina Anadenanthera contortisiliquum Enterolobium niopoides Albizia crista Erythrina Samanea tubulosa nobilis Ormosia Machaerium villosum courbaril Hymenaea Species (continuation) Trait (cm SLA 610.36 392.67 648.52 344.63 355.07 282.29 500.48 388.68 541.99 525.76 305.35 252.36 202.32 ADE 2 g - 1 ) non 609.98 410.15 545.40 329.73 305.40 250.48 451.96 388.71 516.19 487.05 307.02 240.80 203.11 - ADE - - galli galli (cm LAR 322.78 231.04 249.91 177.21 167.82 148.71 188.45 191.75 206.80 187.16 134.36 110.28 97.68 ADE 2 g - 1 14.33 ADE Number of 23.50 46.25 30.42 51.11 27.13 12.83 36.36 29.25 28.18 42.50 26.50 13.92 14.33 ADE Number of 23.50 46.25 30.42 51.11 27.13 12.83 36.36 29.25 28.18 42.50 26.50 13.92 roots roots ) non 315.95 248.64 199.79 162.52 138.88 133.40 151.80 188.98 171.45 171.84 134.05 102.47 - 93.35 ADE 20.33 47.08 29.75 42.40 28.17 26.33 38.73 29.33 36.00 41.17 21.17 18.55 15.90 non 20.33 47.08 29.75 42.40 28.17 26.33 38.73 29.33 36.00 41.17 21.17 18.55 15.90 non - - S S ADE ADE econdary econdary (N mm (N Forceto punch ADE 0.14 0.25 0.13 0.23 0.35 ADE 0.17 and0.19 Seedling0.31 0.30 0.26 Responses - 1 )

Trait (continuation) Number of Secondary SRL RLR RDMC SDMC non ADE g (cm SRL roots 1878.84 526.64 2582.54 695.26 3998.30 (cm2099.25 g-1) 3570.32 1975.07 1581.65 432.81 850.40 (cm2605.38 1043.63 g-1ADE ) g (cm SRL 1878.84 526.64 2582.54 (g 695.26 g-13998.30 ) 2099.25 3570.32 1975.07 1581.65 (g432.81 g850.40 -1) 2605.38 1043.63 - ADE

Species ADE non-ADE ADE non-ADE ADE non-ADE0.17 0.26 0.16 ADE0.26 0.32 non-ADE 0.18 ADE0.22 0.30 0.33 non0.28 -ADE - - 1 1

Hymenaea courbaril 14.33 15.90 1043.63 779.28 42.85 36.80) 0.23 0.27 0.33 0.36 )

Machaerium villosum 13.92 18.55 1502.24 1250.33 2601.03 1056.07 3519.98 2605.381590.84 2111.67 1999.711536.77 1297.06 568.52 1139.80 792.891999.71 779.28 non 347.291502.24 1250.33 2601.03 0.241056.07 3519.98 0.251590.84 2111.67 1536.77 1297.06 0.34568.52 1139.80 1999.71 0.35779.28 non

Ormosia nobilis 26.50 21.17 850.40 1139.80 138.52 - 150.42 0.21 0.22 0.27 0.27 - ADE ADE Samanea tubulosa 42.50 41.17 432.81 568.52 228.57 319.78 0.18 0.21 0.36 0.33 Erythrina crista-galli 28.18 36.00 1581.65 1297.06 304.50 279.89 0.10 0.11 0.15 0.16 61.14 745.21 28.76 86.55 499.74 48.46 8106.05 304.50 228.57 138.52 792.89 42.85 ADE g (cm RLR 1205.45 61.14 745.21 28.76 86.55 499.74 48.46 8106.05 304.50 228.57 138.52 792.89 42.85 ADE g (cm RLR Albizia niopoides 29.25 29.33 1205.45 1975.07 1536.77 8106.05 5041.89 0.17 0.19 0.37 0.37 Enterolobium 36.36 38.73 3570.32 2111.67 48.46 19.67 0.17 0.18 0.32 0.32

contortisiliquum - - 1 1 Anadenanthera colubrina 12.83 26.33 2099.25 1590.84 499.74 423.14) 0.23 0.26 0.38 0.41 ) 1345.07 134.73 880.43 65.32 82.74 423.14 19.67 5041.89 279.89 319.78 150.42 347.29 36.80 non 1345.07 134.73 880.43 65.32 82.74 423.14 19.67 5041.89 279.89 319.78 150.42 347.29 36.80 non Cariniana ianerensis 27.13 28.17 3998.30 3519.98 86.55 82.74 0.15 0.16 0.25 0.30 - - Sapindus saponaria 51.11 42.40 695.26 1056.07 28.76 ADE 65.32 0.21 0.22 0.41 0.43 ADE Cedrela fissilis 30.42 29.75 2582.54 2601.03 745.21 880.43 0.12 0.13 0.16 0.18 Swietenia macrophylla 46.25 47.08 526.64 1250.33 61.14 134.73 0.16 0.16 0.23 0.23 0.16 0.12 0.21 0.15 0.23 0.17 0.17 0.10 0.18 0.21 0.24 0.23 ADE g (g RDMC 0.09 0.16 0.12 0.21 0.15 0.23 0.17 0.17 0.10 0.18 0.21 0.24 0.23 ADE g (g RDMC Chorisia speciosa 23.50 20.33 0.09 1878.84 1502.24 1205.45 1345.07 0.09 0.09 0.11 0.11 - - 1 1 ) ) 0.16 0.26 0.18 0.19 0.11 0.21 0.22 0.25 0.27 non 0.09 0.16 0.13 0.22 0.16 0.26 0.18 0.19 0.11 0.21 0.22 0.25 0.27 non 0.09 0.16 0.13 0.22 - - ADE ADE ADE andSeedling ADE andSeedling 0.25 0.38 0.32 0.37 0.15 0.36 0.27 0.34 0.33 ADE g (g SDMC 0.11 0.23 0.16 0.41 0.25 0.38 0.32 0.37 0.15 0.36 0.27 0.34 0.33 ADE g (g SDMC 0.11 0.23 0.16 0.41 - - 1 1 ) ) 0.30 0.41 0.32 0.37 0.16 0.33 0.27 0.35 0.36 non 0.11 0.23 0.18 0.43 0.30 0.41 0.32 0.37 0.16 0.33 0.27 0.35 0.36 non 0.11 0.23 0.18 0.43 - - Responses Responses ADE ADE 99 99

99 Chapter 4 ADE and Seedling Responses

Trait (continuation) Leaf thickness Leaf Ca content Leaf N content Leaf P content Ca:P (mm) (mg g-1) (mg g-1) (mg g-1) Species ADE non-ADE ADE non-ADE TraitADE (continuation)non-ADE ADE non-ADECa:N ADE nonN:P-ADE Leaf Production -1 Hymenaea courbaril 0.14 0.13 0.80 0.65 2.14 2.17 0.21 0.23 3.83 2.88 (leaves d )

ADE Species ADE non-ADE ADE non-ADE ADE non-ADE Machaerium- villosum 0.16 0.17 2.09 1.81 2.97 3.04 0.12 0.11 17.05 17.28 Ormosia nobilis 0.15 0.14 1.17 0.90 Hymenaea2.63 2.62 courbaril 0.14 0.13 0.37 0.318.39 7.0910.24 9.49 0.02 0.04 3.98 non 2.88 17.28 7.09 11.59 3.78 2.70 14.63 11.54 11.07 16.20 12.94 13.62 Samanea tubulosa 0.09 0.09 1.56 1.52 Machaerium4.70 4.96 villosum 0.11 0.15 0.70 0.6014.42 11.5924.39 28.67 0.02 0.02 Erythrina crista-galli 0.14 0.14 2.33 1.71 Ormosia3.97 4.41 nobilis 0.19 0.43 0.45 0.3412.05 3.9818.90 20.69 0.02 0.02 12.05 AlbiziaCa:P niopoidesADE 3.83 17.05 8.39 14.42 6.27 4.27 12.79 13.36 11.81 12.45 14.26 12.80 2.44 1.71 Samanea4.65 4.93 tubulosa 0.41 0.45 0.33 0.306.27 3.7842.90 33.47 0.07 0.06 Enterolobium 0.66 0.55 Erythrina4.62 4.74 crista-galli 0.16 0.21 0.59 0.394.27 2.7020.53 10.22 0.09 0.09 contortisiliquum Albizia niopoides 0.53 0.35 11.34 10.94 0.05 0.05 2.04 2.01 Enterolobium3.53 3.34 contortisiliquum0.16 0.14 0.14 0.1212.79 14.6329.12 22.44 0.07 0.06 AnadenantheraADE colubrina - Cariniana ianerensis 0.10 0.11 1.61 1.39 Anadenanthera1.82 1.83 colubrina0.12 0.13 0.58 0.6013.36 11.5421.47 24.25 0.05 0.05 ) 0.43 non 0.23 0.11 0.13 0.15 0.45 0.21 0.14 0.13 0.22 0.15 0.17 0.23

1 Cariniana ianerensis 0.88 0.76 15.06 14.11 0.09 0.08 Sapindus- saponaria 0.08 0.08 2.63 2.31 2.87 2.77 0.22 0.22 11.81 11.07 Cedrela fissilis 0.10 0.10 2.30 2.21 Sapindus4.11 3.84 saponaria 0.18 0.15 0.91 0.8412.45 16.2012.87 12.87 0.08 0.07 ADE and Seedling Responses 0.19 SwieteniaLeaf content P (mg g ADE macrophylla0.21 0.12 0.14 0.11 0.41 0.110.16 0.16 0.110.12 0.22 0.18 0.16 0.25 2.27 2.18 Cedrela2.54 2.48fissilis 0.16 0.17 0.56 0.5814.26 12.9422.24 25.66 0.05 0.03 Chorisia speciosa 0.15 0.15 3.08 2.95 Swietenia4.52 4.89 macrophylla 0.25 0.23 0.90 0.8812.80 13.6215.93 14.70 0.08 0.09 Chorisia speciosa 0.70 0.61 18.22 21.06 0.12 0.09 Trait (continuation) Ca:N N:P Leaf Production ADE - (leaves d-1) ) 4.41 non 2.17 3.04 2.62 4.96 4.93 4.74 3.34 1.83 2.77 3.84 2.48 4.89 1 Species ADE non-ADE ADE non-ADE ADE non-ADE - Hymenaea courbaril 0.37 0.31 10.24 9.49 0.02 0.04

(mg g Machaerium villosum 0.70 0.60 24.39 28.67 0.02 0.02 3.97 Leaf N content ADE 2.14 2.97 2.63 4.70 4.65 4.62 3.53 1.82 2.87 4.11 2.54 4.52 Ormosia nobilis 0.45 0.34 18.90 20.69 0.02 0.02 Samanea tubulosa 0.33 0.30 42.90 33.47 0.07 0.06 Erythrina crista-galli 0.59 0.39 20.53 10.22 0.09 0.09 ADE - Albizia niopoides 0.53 0.35 11.34 10.94 0.05 0.05 non 0.65 1.81 0.90 1.52 1.71 1.71 0.55 2.01 1.39 2.31 2.21 2.18 2.95 ) Enterolobium contortisiliquum 0.14 0.12 29.12 22.44 0.07 0.06 1 - Anadenanthera colubrina 0.58 0.60 21.47 24.25 0.05 0.05 Cariniana ianerensis 0.88 0.76 15.06 14.11 0.09 0.08 2.33 Leaf Ca content (mg g ADE 0.80 2.09 1.17 1.56 2.44 0.66 2.04 1.61 2.63 2.30 2.27 3.08 Sapindus saponaria 0.91 0.84 12.87 12.87 0.08 0.07 Cedrela fissilis 0.56 0.58 22.24 25.66 0.05 0.03 101 Swietenia macrophylla 0.90 0.88 15.93 14.70 0.08 0.09 ADE - Chorisia speciosa 0.70 0.61 18.22 21.06 0.12 0.09 0.14 non 0.13 0.17 0.14 0.09 0.11 0.08 0.10 0.11 0.15 Leaf thickness (mm) ADE 0.14 0.16 0.15 0.09 0.14 0.10 0.08 0.10 0.11 0.15 galli - Chapter 4 Trait (continuation) Species Hymenaea courbaril villosum Machaerium Ormosia nobilis tubulosa Samanea Erythrina crista Albizia niopoides Enterolobium contortisiliquum Anadenanthera colubrina ianerensis Cariniana Sapindus saponaria fissilis Cedrela macrophylla Swietenia Chorisia speciosa 100 101 Chapter 4 Cariniana ianerensis colubrina Anadenanthera contortisiliquum Enterolobium niopoides Albizia crista Erythrina Samanea tubulosa nobilis Ormosia Machaerium villosum courbaril Hymenaea Species (continuation) Trait 4 Chapter Chorisia speciosa Chorisia Swietenia macrophylla Cedrela fissilis saponaria Sapindus Trait (continuation) Leaf thickness Leaf Ca content Leaf N content Leaf P content Ca:P (mm) (mg g-1) (mg g-1) (mg g-1) Species ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE ADE non-ADE Hymenaea courbaril 0.14 0.13 0.80 0.65 2.14 2.17 0.21 0.23 3.83 2.88 Machaerium villosum 0.16 0.17 2.09 1.81 2.97 3.04 0.12 0.11 17.05 17.28 - Ormosia nobilis 0.15 0.14 1.17 0.90 2.63 2.62 0.14 0.13 8.39galli 7.09 Samanea tubulosa 0.09 0.09 1.56 1.52 4.70 4.96 0.11 0.15 14.42 11.59 Erythrina crista-galli 0.14 0.14 2.33 1.71 3.97 4.41 0.19 0.43 12.05 3.98 Albizia niopoides 2.44 1.71 4.65 4.93 0.41 0.45 6.27 3.78

Enterolobium 0.66 0.55 4.62 4.74 0.15 0.160.11 0.10 0.08 0.210.10 4.270.14 0.09 2.700.15 0.16 0.14 ADE (mm) thickness Leaf contortisiliquum Anadenanthera colubrina 2.04 2.01 3.53 3.34 0.16 0.14 12.79 14.63 Cariniana ianerensis 0.10 0.11 1.61 1.39 1.82 1.83 0.15 0.120.11 0.10 0.08 0.130.11 13.360.14 0.09 11.540.14 0.17 0.13 non - Sapindus saponaria 0.08 0.08 2.63 2.31 2.87 2.77 0.22 0.22 11.81 11.07 ADE Cedrela fissilis 0.10 0.10 2.30 2.21 4.11 3.84 0.18 0.15 12.45 16.20 Swietenia macrophylla 0.11 0.11 2.27 2.18 2.54 2.48 0.16 0.17 14.26 12.94

Chorisia speciosa 0.15 0.15 3.08 2.95 4.52 4.89 3.08 0.252.27 2.30 2.63 0.231.61 2.04 0.66 2.44 12.802.33 1.56 13.621.17 2.09 0.80 ADE g (mg content Ca Leaf - 1 ) 2.95 2.18 2.21 2.31 1.39 2.01 0.55 1.71 1.71 1.52 0.90 1.81 0.65 non - ADE Chorisia speciosa Chorisia Swietenia macrophylla Cedrela fissilis saponaria Sapindus Cariniana ianerensis colubrina Anadenanthera Enterolobium contortisiliquum niopoides Albizia Erythrina Samanea tubulosa nobilis Ormosia Machaerium villosum courbaril Hymenaea Species (continuation) Trait speciosa Chorisia Swietenia macrophylla Cedrela fissilis saponaria Sapindus Cariniana ianerensis colubrina Anadenanthera Enterolobium contortisiliquum niopoides Albizia Erythrina Samanea tubulosa nobilis Ormosia Machaerium villosum courbaril Hymenaea Species (continuation) Trait 4.52 2.54 4.11 2.87 1.82 3.53 4.62 4.65 3.97 4.70 2.63 2.97 2.14 ADE content N Leaf (mg g (mg - 1 4.89 2.48 3.84 2.77 1.83 3.34 4.74 4.93 4.41 4.96 2.62 3.04 2.17 non ) crista crista - ADE - - galli galli 0.25 0.16 0.18 0.22 0.12 0.16 0.16 0.41 0.19 0.11 0.14 0.12 0.21 ADE g (mg P content Leaf - 1 0.23 0.17 0.15 0.22 0.13 0.14 0.21 0.45 0.43 0.15 0.13 0.11 0.23 non ) - ADE 0.70 0.90 0.56 0.91 0.88 0.58 0.14 0.53 0.59 0.33 0.45 0.70 0.37 ADE Ca:N 0.70 0.90 0.56 0.91 0.88 0.58 0.14 0.53 0.59 0.33 0.45 0.70 0.37 ADE Ca:N 0.34 0.60 0.31 non 0.61 0.88 0.58 0.84 0.76 0.60 0.12 0.35 0.39 0.30 0.34 0.60 0.31 non 0.61 0.88 0.58 0.84 0.76 0.60 0.12 0.35 ADE0.39 0.30 and Seedling Responses 12.80 14.26 12.45 11.81 13.36 12.79 4.27 6.27 12.05 14.42 8.39 17.05 3.83 ADE Ca:P - - ADE ADE 13.62 12.94 16.20 11.07 11.54 14.63 2.70 3.78 3.98 11.59 7.09 17.28 2.88 non N:P 18.22 15.93 22.24 12.87 15.06 21.47 29.12 11.34 20.53 42.90 18.90 24.39 10.24 ADE N:P 18.22 15.93 22.24 12.87 15.06 21.47 29.12 11.34 20.53 42.90 18.90 24.39 10.24 ADE - Trait (continuation) Ca:N N:P Leaf Production ADE (leaves d-1)

Species ADE21.06 14.70 non25.66 -12.87 ADE14.11 24.25 ADE22.44 10.94 10.22 non33.47 -ADE20.69 28.67 9.49 ADEnon non-ADE21.06 14.70 25.66 12.87 14.11 24.25 22.44 10.94 10.22 33.47 20.69 28.67 9.49 non Hymenaea courbaril 0.37 0.31 10.24 9.49 0.02 0.04 - - Machaerium villosum 0.70 0.60 24.39 28.67 0.02ADE 0.02 ADE Ormosia nobilis 0.45 0.34 18.90 20.69 0.02 0.02 0.05 0.08 0.09 0.05 0.07 0.05 0.09 0.07 0.02 0.02 0.02 ADE Production Leaf 0.12 0.08 0.05 0.08 0.09 0.05 0.07 0.05 0.09 0.07 0.02 0.02 0.02 ADE Production Leaf Samanea tubulosa 0.330.12 0.08 0.30 42.90 33.47 0.07 0.06 (leaves d (leaves d (leaves Erythrina crista-galli 0.59 0.39 20.53 10.22 0.09 0.09 Albizia niopoides 0.53 0.35 11.34 10.94 0.05 0.05 0.03 0.07 0.08 0.05 0.06 0.05 0.09 0.06 0.02 0.02 0.04 non 0.09 0.09 0.03 0.07 0.08 0.05 0.06 0.05 0.09 0.06 0.02 0.02 0.04 non Enterolobium contortisiliquum 0.140.09 0.09 0.12 29.12 22.44 0.07 0.06 - - 1 1 - - ) )

Anadenanthera colubrina 0.58 0.60 21.47 24.25 0.05ADE 0.05 ADE Cariniana ianerensis 0.88 0.76 15.06 14.11 0.09 0.08 Sapindus saponaria 0.91 0.84 12.87 12.87 0.08 0.07 Cedrela fissilis 0.56 0.58 22.24 25.66 0.05 0.03 Swietenia macrophylla 0.90 0.88 15.93 14.70 0.08 0.09 Chorisia speciosa 0.70 0.61 18.22 21.06 0.12 0.09 ADE andSeedling ADE andSeedling Responses Responses 101 101

101 Chapter 5

General Discussion

Estela Quintero-Vallejo

103 Chapter 5

General Discussion

Estela Quintero-Vallejo Chapter 5 General Discussion

Introduction thesis regarding to the effects of increased soil pH in the understory species composition, forest structure and dynamics, and plant responses to changes in soil Ca and pH. The myth of tropical forests as pristine and untouched ecosystems is rapidly changing in favour of the idea that large extensions of tropical forest are rather old growth forests that Soils in the Amazonian region, fertility in the south-west and ADE in context could have likely gone through human influence in the past (Willis et al. 2004, Ghazoul and Sheil 2010). Processes of permanent inhabitation, expansion and retreat of human populations Recent soil characterizations of the whole Amazonian basin have reported high variation in have not always been obvious in those ecosystems, leaving sometimes weak and overlooked soil composition and properties (Quesada et al. 2011). This variation is mainly caused by the imprints in the landscape (Bush and Silman 2007, Carson et al. 2014). However, a growing diverse geological history of the region (Hoorn et al. 2010), which determines soil fertility. list of evidence reveals important modifications generated by humans in the Amazonian Thus, soils from the older regions in the eastern and central Amazonian forest are less fertile, region, ranging in scale from scant openings in the forest to changes in the entire landscape with a lower concentration of available and total P and lower total base content (sum of Ca, (McMichael et al. 2012, Heckenberger 2013). Whereas regions in central Amazon basin and Mg, Na, K) than soils from younger regions in the south-west. sites close to rivers were more intensively and extensively inhabited (Erickson 2008), there is Our study site in the lowlands of Bolivia, is located in a region where soils have been evidence that in western Amazonian region humans created only sparse clearings in their classified as Ferralsols and Lixisols, which are usually identified as ancient poor soils settlements for agriculture (McMichael et al. 2012, Urrego et al. 2013). Colossal earthworks (Quesada et al. 2011, Gardi et al. 2014). The classification in Quesada et al (2009, 2011) has and massive movement of earth to make artificial mounds, were used to protect small villages been based on the world soil database (SOTERLAC-ISRIC -version 2.0, 1.5 million scale). In from enemies and for cultivation in inundated sites, for example in the region of Llanos de this thesis, a detailed description of soil profiles allowed us to propose a reclassification of the Moxos, Bolivia (Erickson 2006, Erickson 2010, Lombardo and Prümers 2010, Dickau et al. soils at La Chonta as a combination of Cambisols, Histosols/Gleysols, Leptosols and 2012, Whitney et al. 2013). In savannahs, humans used fire to eliminate trees and allow Anthrosols, which in general are soils that possess high nutrient concentrations (See agricultural activities (Carson et al. 2014). Across several sites in the Amazon basin, the appendix; Gardi et al. (2014)). Discrepancies between our characterization and current soil opening of the forest for cultivation (Arroyo-Kalin 2012) or the introduction and enrichment classification can be the result of the non-formal, published descriptions of the soils of this of useful species such as palms and trees, were common practices (Clement 1999a ). These area, based on only one site about 300 km north-east from La Chonta (Noel Kempff Mercado modifications have produced legacies that, in one way or another, are currently reflected in National Park) (Quesada et al. 2011). I did not find Ferralsols in La Chonta, despite their the forest (Levis et al. 2012, Chazdon 2014). alleged presence in the area. Ferralsols are mainly associated with old-aged soils, but in La Besides of the more or less noticeable aboveground effects of human occupation in Chonta, soil history seems to be more complex because of the geological and tropical forests, less visible belowground changes in the soil are also part of the footprint of geomorphological processes of the region. Thus in La Chonta, there is a mixture of old and humans in these environments. One of these soil modifications, referred here as Amazonian young soils originated from the south corner of the ancient Precambrian Brazilian shield; the Dark Earths (ADE), is the product of the accumulation of pieces of charcoal, organic matter more recent fluvial sediments caused by the uplifting of the Andes (Hoorn et al. 2010, and some elements, such as P, Ca, and N, that changed soil conditions and favoured Latrubesse et al. 2010); flooding and drought events during the mid-Holocene (Taylor et al. agricultural practices (Glaser and Birk 2012, WinklerPrins 2014). Studies that measured crop 2010, Urrego et al. 2013); and modifications of the soil due to human occupation in the area productivity in fallows have already established that ADE increase soil fertility (German around 400 yr BP (Paz-Rivera and Putz 2009). Thus, further soil surveys in the region will 2003), but little is known about the effect of increased soil fertility in natural forests produce a more accurate classification of the soils in La Chonta and the surroundings area, (Junqueira et al. 2010, Doughty et al. 2014). The aim of this thesis is to evaluate the effects of that complement the information presented in this thesis. soil changes due to ADE on the forest that has grown after abandonment by indigenous A previous study found that ca. 20% of a surveyed area of 216 ha in La Chonta populations declined or abandoned the areas. First, I characterized the soils to assess the contained Anthrosols, ‘terra preta’ or ADE (Paz-Rivera and Putz 2009), indicating that these magnitude of the changes in the soil as a consequence of human occupation. Then, I studied soils are relatively common in the forest. Although the World Reference Base of Soil how soil changes affected species composition in the understory (Chapter 2) and forest Classification has recognized ‘terra preta’ as a type of Anthrosol, there is still debate about dynamics (Chapter 3), and how plants respond to anthropogenic changes in soil properties their unique characteristics, mainly because of the high variability of the soil parameters (Chapter 4). In the current chapter, I synthesize the results of this thesis and discuss how the between and within archaeological ADE sites (Arroyo-Kalin 2012, Glaser and Birk 2012, acquired knowledge helps to contextualize the phenomenon of ADE in the Amazonian forest Schmidt et al. 2014). Although this discussion is beyond the scope of this thesis, the and its contribution to the discussion of the creation of ADE. Besides, I discuss the extent to information presented here might contribute to future considerations regarding the which ADE corresponds to a human legacy in the forest. Finally, I discuss the results of this classification of ADE.

104 105 Chapter 5 General Discussion

105 Chapter 5 General Discussion

Most studies in the Amazonian region have found large differences in soil color, pH, fertility of the underlying soils of La Chonta, can make difficult a clear separation among the organic matter content, CEC and concentrations of nutrients, such as P, Ca, N, Mg, K and Zn, points of Fig. 5.2. between ADE soils and surrounding soils (Glaser and Birk 2012). Particularly, P and Ca 100000 concentrations are hundreds and even thousands of times higher in ADE soils than in adjacent Total nutrients (Lehman et al. 2003) Available nutrients (Lehman et al. 2003) natural soils (Glaser and Birk 2012). However, variability in the concentration of P between Available nutrients La Chonta (This thesis) and within ADE sites can also be high (Lehmann et al. 2003b, Falcão et al. 2009)(Fig. 5.1).

Interestingly, differences in P, N, Ca, K and Mg concentrations between ADE and 10000 neighbouring soils in La Chonta, are lower (about 10 times) than the differences found in al soils [log%] other sites in the Amazonian region where the largest differences can be more than 100 times, r as illustrated in Fig. 5.1 (that includes information from Chapter 2). The same pattern holds true for soil pH; differences in soil pH between ADE and non-ADE sites were less 1000 pronounced in La Chonta (increase by 0.4 units) than in other sites in the Amazonian region (increase of around 1 unit, Table 5.1). Our data suggest that, at a larger scale, soil modification through ADE in La Chonta seems not to be as intense as in other sites in central 100 erence natu from adjacent Amazonian expanse because the underlying soil fertility in La Chonta is already high. f However, La Chonta is not unique in this respect, as other studies north of our site in Baures Dif and Huacaraje municipalities (province of Iténez, Bolivia) have also reported modest contrasts between ADE and adjacent soils (Hastick et al 2013). Additional evidence of the similarity between Anthrosols and natural soils in La Chonta comes from Principal N N N P P P P P P P P P P P S S S Ca Ca 700 Component Analyses (PCA) on soil characteristics described in Chapter 3. In the PCA, soil Total nutrients (Lehman et al. 2003) samples where we found ceramics are interspersed with soil samples without ceramics (Fig. Available nutrients (Lehman et al. 2003) 600 Available nutrients La Chonta (This Thesis) 5.2), indicating that human activities have not always enhanced soil fertility or that ceramic remains are not always found where human activities have changed soil fertility. Since our 500 al soils [%] soils al r survey of the occurrence of ceramics was not designed to rigorously assess the extension of settlement sites, we cannot be strictly certain about the extension of ADE in La Chonta. 400 Nevertheless, our PCA can be interpreted in three alternative ways: First, the area of soil 300 modification was large (we sampled over 324 ha dispersed in an area of around 33.7 km2). This is consistent with findings of wide spread ADE in La Chonta (around 20%) (Paz-Rivera 200 and Putz 2009), and evidence of long and extended history of human occupation recorded in Llanos de Moxos (north from our site in Bolivia) (Erickson 2006, Lombardo and Prümers 100 erence from adjacent natu adjacent from erence f

2010, Walker 2011, Lombardo et al. 2013). Second, natural soils in La Chonta have a higher Dif 0 and variable amount of nutrients and the addition of minerals by human occupation has not substantially changed soil fertility. Finally, it is also possible that human habitation of the area K K Mg Mg Fe Fe Zn Zn Mn Mn Cu Cu was not long enough to make more substantial transformations to the soil. Further Figure 5.1 Differences in percentage of nutrients between ADE and adjacent natural (mostly archaeological studies are needed to clarify the history of habitation patterns in this forest. Ferralsols) soils in several Amazonian sites, mostly in Central and Eastern Amazonian There are some discrepancies between this PCA (Fig. 5.2) and the one showed in region (where most natural soils are Ferralsols). Modified from Lehmann et al. (2003).

Chapter 2 (Fig. 2.1). Whereas in Fig. 2.1 each data point consisted of a compound sample of a [%] = 100[%] . I �� −�� −�� soil core taken every 25 m in a 150 m transect, in Fig. 5.2 each point corresponds to a unique included my own data from La Chonta as red bars. The calculated difference of our soils in �� −�� −�� soil core taken somewhere in the 12 permanent plots. The latter sampling scheme possibly La Chonta correspond with the average of 36 transects (18 in ADE with presence of captures a wider range of soil variability within ADE sites, a factor that needs to be ceramics and 18 non-ADE) in La Chonta forestry concession. Differences for P=172,7; considered when characterizing ADE soil properties (Schmidt et al. 2014). Besides, the high Ca=159,4; N=45,3; Mg=12,6; K=62,0 % (Chapter 2).

106 107 Chapter 5 General Discussion

107 Chapter 5 General Discussion

Table 5.1 Values of soil pH (based on H2O) from different sites distributed through the Amazon River In summary, I found that differences in soil composition between natural soils and Basin, including La Chonta. The sites are ordered from lowest to highest pH values. ADE at La Chonta were not large when we compare them with other sites of the Amazon basin. Still, it seems that past human settlements in La Chonta resulted in the addition of Study site pH (H2O) Reference ADE Non-ADE minerals, such as P and Ca that enhanced soil nutrients and soil pH (Appendix, Chapter 2). In Araracuara, Amazon Department 4.8 4.4 (Eden et al. 1984) the next sections, I will examine the effect of these changes on important forest attributes, (Colombia) such as forest composition and community dynamics. Costa de Açutuba (Brazil) 5.7 4.8 (Falcão et al. 2009) Fazenda Juquitaia (Brazil) 5.5 4.8 (Falcão et al. 2009) Effects of soil nutrient concentration on forest composition, forest dynamics, and Costa do Laranjal (Brazil) 6.0 5.0 (Falcão et al. 2009) plant growth. Hatahara, close to Manaus (Brazil) 6.6 4.7 (Falcão et al. 2009) Iranduba District (Brazil) 5.9 4.6 (Lima et al. 2002) Species composition Tranquilidad and Embolla, Iténez Province 6.8 5.8 (Hastik et al. 2013) (Bolivia) Soil nutrients play an important role in shaping forest composition and dynamics. The La Chonta, Guarayos Province (Bolivia) 7.1 6.6 (Quintero‐Vallejo et al. concentration of soil nutrients explain differences in species composition of understory herbs 2015) (Costa 2006), ferns (Tuomisto et al. 2014, Zuquim et al. 2014) and tropical trees (John et al. La Chonta, Guarayos Province (Bolivia) 7.4 7.0 This thesis Chapter 4 2007, Toledo et al. 2011b, Baldeck et al. 2013, Condit et al. 2013). However, studies that relate species composition with soil characteristics rarely include changes in soil properties as a consequence of past human modification and, if they do, they focus on species that are

non-Ceramics species (Junqueira et al. 2010). In this thesis, I studied how plant species respond to soil nutrient concentrations, by Mg Ceramics focusing both on species that are little or not used by humans, such as herbs and ferns and species that are used by humans (for example palms) to try to separate effects associated with the intentional cultivation of species from effects associated with plant responses to soil K changes, as is the case of ferns (Tuomisto et al. 2014) (see Chapter 2). I found that changes in

OM soil pH (which most likely indicates an increase in soil Ca) affected the composition of fern, herb and palm species that inhabit the understory, and that it decreased fern diversity (Chapter P Ca 2: Table 2.2, 2.3, Fig. 2.3). Although I did not explore in full detail the underlying pH mechanisms, it seems that fern species respond to changes in the concentration of soil nutrients Chapter 2: Fig. 2.3) (Amantangelo and Vitousek, 2008). Changes in the abundance Sand of palms might be determined by the history of human populations in the region. For example, the occurrence of the palm Astrocaryum aculeatum has been associated with past human settlements in La Chonta and other sites in central Amazonian region (Major et al. -1.0 2005a, Paz-Rivera and Putz 2009). Palms and other domesticated species are recognized as -1.0 1.0 species that have been intensively used by inhabitants of the Amazon basin in the past Figure 5.2 Principal Component Analysis of eight soil properties of 398 soil samples collected from12 (Clement 1999a, b, Morcote-Ríos and Bernal 2001), and recent research provides evidence of permanent plots in La Chonta forestry concession, Guarayos Province, Bolivia. For details of soil the influence of humans in their current distribution (Levis et al. 2012). However, we lack sampling and analyses see Chapter 3 (Appendix 3.1). The presence of ceramics in the soil sample is information on the effects of human-modified soils on the current distribution of plants in the indicated (filled squares = ceramics found, open circles=no ceramics found). Presence of ceramics was not included in the variables to run the analyses, it was only used to distinguish the points. P Amazonian forest. Research done in other regions of the world indicate human-induced soil correspond to values of available phosphorus. modification effects on plant distribution (Dambrine et al. 2007). For example, shell middens

108 109 Chapter 5 General Discussion

1.0 Soil Properties useful for humans (Clement et al. 2009, Paz-Rivera and Putz 2009, Levis et al. 2012, Ross et Clay al. 2014). The only study that did not target useful species only, still found that species Plots composition of ADE sites was characterized by high abundance of useful and domesticated non-Ceramics species (Junqueira et al. 2010). In this thesis, I studied how plant species respond to soil nutrient concentrations, by Mg Ceramics focusing both on species that are little or not used by humans, such as herbs and ferns and species that are used by humans (for example palms) to try to separate effects associated with the intentional cultivation of species from effects associated with plant responses to soil K changes, as is the case of ferns (Tuomisto et al. 2014) (see Chapter 2). I found that changes in

109 Chapter 5 General Discussion

left by ancient inhabitants in the East coast of the United States of America led to a shift from formation (Ruslandi et al. 2012) and with human perturbation (Peña-Claros et al. 2008, less woody to a more herbaceous community as a result of a particular composition of soil Bourland et al. 2015). Few studies have looked at forest structure on ADE. One of those nutrients (Cook-Patton et al. 2014). This thesis shows that even small changes in soil studies looked at younger forests (~30 yr old) growing on ADE (Junqueira et al. 2010). They properties of a place located in the south-western Amazonian region changed understory found that forest on ADE had lower tree density at early stages of the forest succession than species composition (Chapter 2). It would be interesting to explore whether large changes in similar aged forest on non-ADE. This could be caused by high dominance of fast grow soil properties, as those observed in ADE in the central Amazonian expanse, would have species that rapidly colonize and establish on high nutrient sites (Junqueira et al. 2010) as it similar effects. has been suggested by high dominance of annual herbaceous species in ADE sites close to In summary, the legacies of human occupation on the composition of plant species in Manaus city (Major et al. 2005a). Besides, older forests on ADE are more productive than the La Chonta forest seem to be the result of an interplay between changes in soil made by forest on non-ADE, i.e. they have higher net primary productivity (Aragão et al. 2009, humans and the modification in the distribution of those species used by humans (Fig. 5.3). Doughty et al. 2014). So, it seems that over both relatively short and long terms ADE has an an effect on forest structure. ADE I studied the effect of ADE on forest attributes and components of forest dynamics, such as tree growth, mortality and recruitment (Chapter 3) and found weak soil effects on forest structure and dynamics. I expected that concentration of nutrients, such as P would be Human settlements High soil fertility the main determinant of forest structure. Surprisingly, I found that soil pH and not P emerged Soil pH as the soil property that consistently had a significant effect on forest structure and dynamics (Un)intentional Soil Ca (Chapter 3: Figs. 3.2, 3.3; Fig. 5.4). Soil pH had a positive effect on tree recruitment, a Over- Seed dispersal negative effect on tree mortality of the plot, and a positive or negative effect on tree basal area exploitation Cultivation/Gathering Species responses of the plot, depending on the tree size class considered (Chapter 3: Fig. 3.2).

One remarkable aspect of soils in La Chonta, is that they have, in general, high pH values in comparison with other soils in the Amazon basin (Table 5.1; Table 3.1). Many tropical Palm composition Herb composition Fern composition forest grow in sub-optimal acid soil conditions. Therefore soil amendments to increase pH, Palm diversity Herb diversity (-)Fern diversity such as addition of ashes or charcoal (Glaser et al. 2002) will be beneficial for tree growth (+)Palm abundance (+)Herb abundance Fern abundance because nutrients such as P become more available in less acid soils. Additionally, Al cations, which can be toxic for plants will become less available in the soil (Nkana et al. 1998). Nevertheless, Ca addition in ADE soils in La Chonta increased soil pH to neutral or higher Figure 5.3 Conceptual diagram of human legacies on understory species composition. ADE is values (Chapter 3: Table 3.1). Although theoretically the increase in pH would enhance tree associated with changes in species composition through increases in soil Ca and soil pH that affect species composition of ferns, herbs and palms, and negatively affect fern diversity. Grey boxes growth, I argue that high values of pH could result in supra-optimal conditions. Studies that correspond to factors that we did not measure but we recognize that it could have an effect on the have explored the effect of increases of soil pH beyond neutral values are scarce. One such a attribute. Grey text in italics correspond to attributes for which we did not find a significant effect. study, added wood ash to forest stands (Augusto et al. 2008). Addition of ashes to acidic soils Continuous arrows correspond to the attributes for which we found effects and dashed arrows increased soil pH and, in the long term, increased the availability of nutrients such as Fe, Ca, correspond to attributes for which we found a possible effect but we did not evaluate this directly. Signs in parentheses show the direction of the effect; (+) indicates a positive effect and (-) indicates a P, thus favouring tree growth. However, when ashes were added to soils with a pH close to 7, negative effect. tree growth did not increase (Pitman 2006, Augusto et al. 2008). Moreover, the response of trees to an increase in soil pH seems to be species-specific. In temperate forests it has been Forest structure, dynamics and plant growth shown that broadleaved species perform better in higher pH soils and have, in general, higher In the Neotropics few studies have found significant relationships between nutrient nutrient uptake of K, Ca and Mg than coniferous species (Vance 1996). Although rigorous concentrations and forest structure at a local scale (Laurance et al. 1999, DeWalt and Chave comparisons have not yet been made in more diverse tropical forests, the observed 2004). At small scales, forest structure can be associated with soil characteristics such as relationships between the availability of minerals in the soil and species composition suggests available P in the soil (Paoli et al. 2008) but also with endogenous processes such as gap that different species respond to soil resources in a different way (John et al. 2007, Baldeck et

110 111 Chapter 5 General Discussion

111 Chapter 5 General Discussion

al. 2013). This was confirmed in Chapter 3 (Table 3.2, Figs. 3.3, 3.4), where the dominance of leaf P and leaf N on ADE but all species had higher leaf Ca on ADE (Chapter 4: Figs 4.3b, shade-tolerant species increased with soil pH and the dominance of (long-lived) pioneers 4.3c, 4.4a, 4.4b). These results suggested that there is an imbalance between soil and tissue decreased with soil pH. elements in some of the species that we studied when they grow on ADE. These imbalances could be caused by reduced P availability for plants at high pH because reactions between soil ADE P and Ca result in insoluble salts, as has been found in calcareous soils (Lee 1998, Zohlen and Tyler 2004). Alternatively, the nutrient inbalance in the plant is causedr by reactions between

Human settlements Increased soil fertility inorganic P and Ca in the interior of the plant cells resulting in a lower availability of Pi for metabolic functions (Zohlen and Tyler 2004).

Disturbance +/-Soil pH -Soil N Table 5.2 Species used in Chapter 4 for the greenhouse experiment, with the ecological guilds to Open cleared areas Soil pH Soil pH which they belong according to the IBIF database. P: Pioneers; LLP: Long-lived pioneers; PST: (-) Basal Area (-) +Soil pH Partial shade tolerant. Responses that species show in ADE are indicated with an X. In the case of (+) - Soil P (+) mortality some species died in ADE, others on non-ADE and others in both soils (Chapter 4; Fig. 4.1). Mortality rate Growth rate Recruitment rate In the case of nutrient imbalance, some species had less P and N in the leaf tissue of seedlings growing Changes in distribution of on ADE, other species had less P, and others had less N (Chapter 4: Figs 4.3b, 4.3c, 4.4a, 4.4b). (+) (-) Forest dynamics ecological guilds: Proportion of Short-lived and Gap formation Response in Greenhouse Experiment long-lived pioneers Soil N (-) (+) Scientific name Ecological Mortality Nutrient Not guild imbalance affected Liana infestation Albizia niopiodes (Spruce ex Benth.) Burkart LLP X (N and P) Figure 5.4 Conceptual diagram of human legacies on forest dynamics in La Chonta. Amazonian Dark Anadenanthera colubrina (Vell.) Brenan LLP X Earths (ADE) affect forest dynamics by increasing soil pH and the concentrations availability of P and Enterolobium contortisiliquum (Vell.) Morong P X (N and P) N. Successional guilds composition (i.e. the distribution of individuals such as pioneer and shade- Erythrina crista-galli L. P X (N and P) tolerant species) can influence forest dynamics (i.e. a higher proportion of pioneers led to increased Hymenaea courbaril L. PST X (N and P) basal area growth rate of the plot). However, we also found that soil pH was negatively related with the successional guild composition of forest stands (Chapter 3: Fig. 3.2, Fig. 3.3) with a lower Machaerium villosum Vogel LLP X (N) proportion of pioneers on soils with high pH. Liana infestation was positively affected by light and Ormosia nobilis Tul PST X negatively affected by soil N. Liana infestation had, in turn, a negative effect on tree growth rate Poeppigia procera C. Presl LLP X (> on ADE) (Chapter 3; Figs 3.2, 3.3). Current forest successional composition seems a consequence of past Samanea tubulosa (Benth.) Barneby & J.W. Grimes PST X (N and P) human perturbation (Chapter 3). Black arrows indicate processes that were affected by soils or human settlements, grey solid arrows indicate processes for which no direct effect of soils was observed, and Schizolobium parahyba (Vell.)S.F. Blake LLP X (> on non-ADE) dashed grey arrows indicate processes that were not evaluated but could have an effect. Grey boxes Terminalia oblonga (Ruiz & Pav.) Steud. PST X (> on ADE) correspond to factors that we did not measure but we recognize that it could have an effect on the Cariniana ianeirensis R. Knuth PST X (P) attribute. Signs in parenthesis show the direction of the effect; (+) indicates a positive effect, and (-) Chorisia speciosa A. St.-Hil. LLP X (N) indicates a negative effect. Guazuma ulmifolia Lam. LLP X (= on both soils) Moreover, I have shown in Chapter 4 that seedlings of 17 species responded Cedrela fissilis Vell. LLP X differently to ADE soils. Surprisingly, ADE had a negative rather than a positive effect on Swietenia macrophylla King PST X (P) seedling growth of Terminalia oblonga and Poeppigia procera, and changed the nutrient Sapindus saponaria L. PST X balance in 9 other species (Table 5.2; Chapter 4: Fig. 4.4). At first sight it seems paradoxical that nutrient addition to soil has a negative effect on plant performance and functioning, but Amongst nutrients, nitrogen (N) and phosphorus (P) are the major limiting elements these negative effects could be explained by the possible change of either soil stoichiometry for primary productivity of forests (Vitousek et al. 2010). In the forest of La Chonta, values of and soil pH, or the internal plant nutrient stoichiometry, emanating from the Ca addition to -1 available P are relatively high (average 12.03 mg kg ) compared with a sample of 71 forests the soils (Chapter 4). In a greenhouse experiment I found that in spite of higher P, N and Ca across the Amazon basin (Quesada et al. 2010). The results from Chapter 3, in which concentration in ADE (Chapter 4: Appendix, Table A4.1), only 6 out of 13 species had higher increases in P and N in the soils did not translate into increases in tree growth suggest that soil

112 113 Chapter 5 General Discussion

Human settlements Increased soil fertility inorganic P and Ca in the interior of the plant cells resulting in a lower availability of Pi for metabolic functions (Zohlen and Tyler 2004).

113 Chapter 5 General Discussion

pH could modify the availability of those nutrient for the plants (cf.Lambers et al. 2008a) remarkable different from surrounding soils (Cook-Patton et al. 2014). Legacies in the forest which, in turn, could affect plant distribution. Further studies on tropical tree nutrition and the are the persistent presence of certain plant species, like in the Maya forest, in which plant interactions between soil pH and nutrients in the soils could help to understand these results in distribution is a reflection of the agroforestry practices of the Maya culture around 1000 yr BP depth. There is growing evidence that soil pH is playing an important role in species (Ross 2011, Ross et al. 2014); and in relatively young forest that grow on ADE, where there is distribution in the tropics (John et al. 2007, Baldeck et al. 2013) a higher proportion of domesticated and semi domesticated species (Clement et al. 2003).

Implications of soil fertility for plant performance The most recent evidence of past human inhabitation in the La Chonta forest is from around 400 year BP (Paz-Rivera and Putz 2009). The possible imprint of this occupation can Ecologist are still in the process of understanding many aspects of plant-soil interactions; i.e. be inferred from the age of trees and their ecological characteristics. Studies using annual the relationship between nutrients in the soils and in plant tissue (Metali et al. 2015), and this rings have shown that species typical of early to intermediate stages of succession, such as the is particularly the case for plant nutrition in tropical forest (Baribault et al. 2012). Information long-lived pioneer Sweetia fruticosa, have a maximum age of around 220 years. Partial-shade on this topic comes mainly from ‘special ecosystems’, such as extremely poor soils, P tolerant species, such as Cariniana ianerensis and Hura crepitans have a maximum age of deficient soils, such as the campos rupestres in Central Brazil (Oliveira et al. 2015); from around 170 and 163 years, respectively, and shade tolerant species such as Ampelocera ruizii, poor old soils in the Guyana Shield (Raaimakers and Lambers 1996) and in western Australia have a maximum age of around 82 years (sampled in 92 individuals DBH<65 cm) (Vlam et (Lambers et al. 2008b). Studies on the association between soil characteristics and tree al. 2014). I show in Chapter 3 that tree species composition suggests that this forest is still in a distributions (John et al. 2007, Jones et al. 2008, Condit et al. 2013) and studies in responses successional stage. I found that large trees (>40 cm stem diameter at 1.3 m height) are of tree species to soil fertilization allow us to determine for example, how low-wood density represented mainly by long-lived pioneers, the intermediated size trees are represented by species respond more to P additions in the soil than species of high wood density (Baribault et partial-shade tolerant species and the smaller trees were mostly represented by shade-tolerant al. 2012). Most ecological studies so far have focused on N and P as the main soil nutrients species that showed, on average, the highest tree growth (Chapter 3). Thus, it is possible that limiting plant growth and productivity, because they are needed to produce Rubisco and ATP openings in the forest made by ancient inhabitants of La Chonta were spread in the area and (Lambers et al. 2008a). In plant nutrition and agriculture there is a long tradition on focusing that the forest is still recovering from these perturbations. Consequently, these patches are still on other limiting elements (Epstein and Bloom 2005, Marschner and Marschner 2012). Now, in a late-succesisonal stage. It is also possible, however, that natural process such as ecologists also recognize the importance of other elements such as K, Ca, Zn, and B and their transformation from dry forest to humid forest that occurred around 1200 yr BP (Taylor et al. interactions for plant nutrition (Watanabe et al. 2007, Wright et al. 2011, Baribault et al. 2010) could be playing a role in this successional composition. I would argue, against the 2012). Likewise, more attention has been given to soil pH on tropical plant distribution, latter because with 100 years of succession, enough time must have passed for the forest to growth and nutrition (Jones et al. 2013, Viani et al. 2014). In temperate zones, plant nutrition show characteristics of mature stands (cf. Saldarriaga et al. 1988). A better understanding on studies in relation with calcareous high pH soils have allowed to determine plant strategies how ecosystems change and develop over the long-term, as well as a better knowledge of the that cope successfully or unsuccessfully with P deficiencies (Zohlen and Tyler 2004). ancient inhabitation of La Chonta could help to clarify this aspect. However, in tropical forest work on soil pH has mainly been done in acid soils (Viani et al. 2014), mainly because, in general, tropical forest tend to have low values of soil pH. In this In summary, in the old-growth forest of La Chonta, legacies of ADE are direct and sense my thesis can make a modest contribution to the field of plant nutrition. Thus, from this indirect. Direct legacies include: 1) persistent changes in concentrations of soil nutrients such thesis we know that increases in soil pH can be negative for diversity cf. ferns (Chapter 2) and as Ca and P, and associated changes in soil pH; 2) changes in the abundance of some palms, that high pH in combination with high Ca can potentially generate nutrient imbalance that such as Astrocarium acculeatum. Indirect legacies include: 3) changes in species composition negatively affect plant growth (Chapter 4). of understory species, and the successional composition of the forest through the increase in the concentration of Ca and pH (Figure 5.5). Legacies of past inhabitants in the soil and the forest

Legacies are the imprints that humans leave in their environment in the course of their existence. Human legacies are determined by the intact memory (Vogt et al. 1997) of any past modification. Legacies in the soil are persistent changes in chemical and physical characteristics (Vogt et al. 1997) such as the presence of shell middens accumulation in the East coast of the United States, in which the concentration of Ca in the soil and soil pH are

114 115 Chapter 5 General Discussion

115 Chapter 5 Literature

ADE 1 Literature

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Palm composition Fern, herb composition Tree composition Forest dynamics Forest structure nitrogen addition. Functional Ecology 24:1171-1180. Andersen, K. M., B. L. Turner, and J. W. Dalling. 2010b. Soil‐based habitat partitioning in Species composition understorey palms in lower montane tropical forests. Journal of biogeography 37:278-292. Aragão, L. E. O. C., Y. Malhi, D. B. Metcalfe, J. E. Silva-Espejo, E. Jiménez, D. Navarrete, S. Figure 5.5 Conceptual diagram relating components that explain forest composition and dynamics, Almeida, A. C. L. Costa, N. Salinas, O. L. Phillips, L. O. Anderson, E. Alvarez, T. R. Baker, evaluated in each chapter, and their relationship with components that represent the legacies of human P. H. Goncalvez, J. Huamán-Ovalle, M. Mamani-Solórzano, P. Meir, A. Monteagudo, S. occupation of La Chonta. Black arrows point out attributes and processes that were directly evaluated Patĩo, M. C. Peñuela, A. Prieto, C. A. Quesada, A. Rozas-Dávila, A. Rudas, J. A. Silva Jr, and in this thesis and for which I found a direct effect of the soils. Solid grey arrows represent attributes R. Vásquez. 2009. Above- and below-ground net primary productivity across ten Amazonian for which I found an indirect effect of the soils. Dashed grey arrows represent attributes for which I forests on contrasting soils. Biogeosciences 6:2759-2778. found a possible effect of human settlements but which I did not evaluate directly. Numbers represent Aranwela, N., G. Sanson, and J. Read. 1999. Methods of assessing leaf‐fracture properties. New the different legacies of past human inhabitation on the forest of La Chonta: 1. Legacies in the soil; 2. Phytologist 144:369-383. Legacies of ‘cultivated’ species on current species composition; and 3. Legacies through changes in Arroyo-Kalin, M. 2010. The Amazonian formative: Crop domestication and anthropogenic soils. the soil, in the attributes and processes of the forest. Diversity 2:473-504. Arroyo-Kalin, M. 2012. Slash-burn-and-churn: Landscape history and crop cultivation in pre- Recommendations for future research Columbian Amazonia. Quaternary International 249:4-18. Asner, G. P., R. E. Martin, R. Tupayachi, C. B. Anderson, F. Sinca, L. Carranza-Jiménez, and P. Results of the present thesis highlight the importance of a better understanding of the effects Martinez. 2014. Amazonian functional diversity from forest canopy chemical assembly. of soil fertility and soil manipulation on forest dynamics. Nevertheless, far from a solved Proceedings of the National Academy of Sciences 111:5604-5609. issue, this thesis opens some new questions that can direct future research in plant and soil Augusto, L., M. Bakker, and C. Meredieu. 2008. Wood ash applications to temperate forest ecosystems—potential benefits and drawbacks. Plant and Soil 306:181-198. interactions, and hopefully bring better understanding of the legacies of the past inhabitants of Baldeck, C. A., K. E. Harms, J. B. Yavitt, R. John, B. L. Turner, R. Valencia, H. Navarrete, S. the Amazonian forest. Some specific future research questions are: First, could tree responses Bunyavejchewin, S. Kiratiprayoon, and A. Yaacob. 2013. Habitat filtering across tree life to soil Ca and soil pH explain the distribution patterns of species in the Amazonian forest?; stages in tropical forest communities. Proceedings of the Royal Society B: Biological Sciences Second, to what extent is the distribution of domesticated or man-used species in the 280:1-8. Balée, W., and C. L. Erickson. 2006. Time and complexity in historical ecology. Columbia University Amazonian region affected or favoured by human induced changes in the soil? Press, New York. Banin, L., S. L. Lewis, G. Lopez‐Gonzalez, T. R. Baker, C. A. Quesada, K. J. Chao, D. F. Burslem, R. Concluding Remarks Nilus, K. Abu Salim, and H. C. Keeling. 2014. Tropical forest wood production: a cross‐ continental comparison. Journal of Ecology 102:1025-1037. The history of a Bolivian forest, what I refer in this thesis as the southern limit of the Amazon Baraloto, C., D. Bonal, and D. E. Goldberg. 2006. Differential seedling growth response to soil basin, has gone through a long and intense history of human occupation. In this region, the resource availability among nine neotropical tree species. Journal of Tropical Ecology 22:487. Barber, R., and T. Cochrane. 1993. Análisis de suelo y plantas tropicales. Centro de Investigación formation of Amazonian Dark Earths probably did not imply soil improvement given the high Agrícola Tropical - CIAT and Misión Brintánica en Agricultura Tropical - MBTA, Santa fertility of the underlying natural soils. Still, small changes in the soil as a consequence of Cruz, Bolivia. human inhabitation, have left long lasting impacts on the diversity of ferns and the Baribault, T. W., R. K. Kobe, and A. O. Finley. 2012. Tropical tree growth is correlated with soil composition of plants in the forest. Thus, through subtle changes in the soil, humans can have phosphorus, potassium, and calcium, though not for legumes. Ecological Monographs 82:189- 203. long lasting impacts on the forest. Barton, K. 2012. Package 'MuMIn' Multimodel Inference. Block, D. 1994. Mission culture on the upper Amazon: Native Tradition, Jesuit interprise & Secular Policy in Moxos 1660-1880. University of Nebraska, Lincold, USA.

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Ion Uptake Mechanisms of Individual cells and Roots: Short-distance Transport. Tuomisto, H., and A. D. Poulsen. 1996. Influence of edaphic specialization on pteridophyte Pages 7-49 in P. Marschner, editor. Marschner's Mineral Nutrition of Higher Plants. Academic distribution in neotropical rain forests. Journal of biogeography 23:283-293. Press, London, UK. Tuomisto, H., A. D. Poulsen, and R. C. Moran. 1998. Edaphic Distribution of Some Species of the White, P. J., and M. R. Broadley. 2003. Calcium in plants. Annals of Botany 92:487-511. Fern Genus Adiantum in Western Amazonia. Biotropica 30:392-399. Whitney, B. S., R. Dickau, F. E. Mayle, J. D. Soto, and J. Iriarte. 2013. Pre-Columbian landscape Tuomisto, H., K. Ruokolainen, A. D. Poulsen, R. C. Moran, C. Quintana, G. Cañas, and J. Celi. 2002. impact and agriculture in the Monumental Mound region of the Llanos de Moxos, lowland Distribution and Diversity of Pteridophytes and Melastomataceae along Edaphic Gradients in Bolivia. Quaternary Research 80:207-217. Yasuní National Park, Ecuadorian Amazonia1. Biotropica 34:516-533. Whitney, B. S., R. Dickau, F. E. Mayle, J. H. Walker, J. D. Soto, and J. Iriarte. 2014. Pre-Columbian Tuomisto, H., G. Zuquim, and G. Cárdenas. 2014. Species richness and diversity along edaphic and raised-field agriculture and land use in the Bolivian Amazon. The climatic gradients in Amazonia. Ecography 36:1034–1046. Holocene:0959683613517401. Tyler, G. 1996. Mineral nutrient limitations of calcifuge plants in phosphate sufficient limestone soil. Willis, K. J., L. Gillson, and T. M. Brncic. 2004. How “Virgin” Is Virgin Rainforest? Science Annals of Botany 77:649-656. 304:402-403. Urrego, D. H., M. B. Bush, M. R. Silman, B. A. Niccum, P. La Rosa, C. H. McMichael, S. Hagen, and WinklerPrins, A. 2014. Terra Preta: The Mysterious Soils of the Amazon. Page 400 in G. J. M. Palace. 2013. Holocene fires, forest stability and human occupation in south‐western Churchman and E. R. Landa, editors. The Soil Underfoot: Infinite Possibilities for a Finite Amazonia. Journal of biogeography 40:521-533. Resource. CRC Press, Florida, USA. Van der Berg, H. 2006. Las misiones Yuracares y Gurayos, Snata Cruz de la Sierra, Bolivia. Winklerprins, A. M. G. A., and S. P. Aldrich. 2010. Locating Amazonian Dark Earths: Creating an Van Der Heijden, G. M., and O. L. Phillips. 2008. What controls liana success in Neotropical forests? interactive GIS of known locations. Journal of Latin American Geography 9:33-50. Global Ecology and Biogeography 17:372-383. Woods, W. I., and B. Glaser. 2004. Towards an Understanding of Amazonian Dark Earths. Pages 1-8 van der Heijden, G. M., O. L. Phillips, and S. A. Schnitzer. 2015. Impacts of lianas on forest‐level in B. Glaser and W. Woods, editors. Amazonian Dark Earths: Explorations in space and time. carbon storage and sequestration. Ecology of Lianas:164-174. Springer-Verlag, New York. Vance, E. D. 1996. Land application of wood-fired and combination boiler ashes: an overview. Wright, S. J., J. B. Yavitt, N. Wurzburger, B. L. Turner, E. V. J. Tanner, E. J. Sayer, L. S. Santiago, Journal of Environmental Quality 25:937-944. M. Kaspari, L. O. Hedin, K. E. Harms, M. N. Garcia, and M. D. Corre. 2011. Potassium, Viani, R. A. G., R. R. Rodrigues, T. E. Dawson, H. Lambers, and R. S. Oliveira. 2014. Soil pH phosphorus, or nitrogen limit root allocation, tree growth, or litter production in a lowland accounts for differences in species distribution and leaf nutrient concentrations of Brazilian tropical forest. Ecology 92:1616-1625. woodland savannah and seasonally dry forest species. Perspectives in Plant Ecology, Zohlen, A., and G. Tyler. 2004. Soluble inorganic tissue phosphorus and calcicole–calcifuge Evolution and Systematics 16:64-74. behaviour of plants. Annals of Botany 94:427-432. Vitousek, P. M., S. Porder, B. Z. Houlton, and O. A. Chadwick. 2010. Terrestrial phosphorus Zuquim, G. 2008. Guide to the ferns and lycophytes of REBIO Uatamã, Central Amazonia. Attema limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecological Design Ed. Applications 20:5-15. Zuquim, G., H. Tuomisto, F. R. C. Costa, J. Prado, W. E. Magnusson, T. Pimentel, R. Braga-Neto, and Vlam, M., P. J. Baker, S. Bunyavejchewin, G. M. Mohren, and P. A. Zuidema. 2014. Understanding F. O. G. Figueiredo. 2012. Broad Scale Distribution of Ferns and Lycophytes along recruitment failure in tropical tree species: Insights from a tree-ring study. Forest Ecology and Environmental Gradients in Central and Northern Amazonia, Brazil. Biotropica 44:752-762. Management 312:108-116.

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Zuquim, G., H. Tuomisto, M. M. Jones, J. Prado, F. O. Figueiredo, G. M. Moulatlet, F. R. Costa, C. A. Quesada, and T. Emilio. 2014. Predicting environmental gradients with fern species composition in Brazilian Amazonia. Journal of Vegetation Science 25:1195–1207. APPENDIX Characterization of soils at La Chonta

This appendix contains data on the characterization of the soils of the forestry concession of La Chonta, Guarayos province, Bolivia (15º47’S, 62º55’W).

Methodology

Data for the characterization of the soils were obtained during a field trip made to La Chonta from January 31st to February 3rd of 2013. The weather during the trip was overcast intermixed with heavy rains. I visited two of the permanent plots that are part of the Long- term Silvicultural Research Program (LTSRP) (see (Peña-Claros et al. 2008), and chapter 4 for details). The first plot, Block 2 Intensive, is located at 2.64 km (north-east) from the camping facilities that IBIF has in the concession; the second plot, located at 2 Km (north- east) from the camping, is denominated Block 2 Normal. In previous soil surveys made from September to December of 2011 on the 12 ha permanent plots, we determined soil color and presence of ceramics on grids of 50x50 m (Klomberg 2012). Based on the previous description of soil color and records of ceramics in the plots, we established a transect of around 275 m length in each plot. Using an auger, I collected soil samples at depth intervals of 20 cm, every 75 m, but I shortened this distance (25 to 50 m) when I were close to points that previously were recorded as having ceramics (see map in Fig. A1a). Besides, I collected an extra sample in the point (250,100) where I noticed changes in the terrain topography, so I could get a better description of soil variability (see map in Fig. A1a). Because of heavy rains during the trip, we could only take three samples from plot Block 2 Normal from a site that had dark soils and records of ceramics (see map in Fig. A1b). Additionally, we took a sample

(P12) from a site that was previously described by Paz-Rivera and Putz (2009) as having abundant ceramics and containing anthropic soil horizon. To establish the profiles, I removed the litter layer from the top soil and I dug a one-meter depth pit using a Dutch auger; thereafter, I divided the profiles at intervals of depth of 20 cm (0-20; 20-40; 40-60; 60-80; 80- 100 cm). In the field notes I recorded the depth of the limit of horizons in the soil, based in change in color and texture (done by finger methods in the field). I collected a sample of each depth and I saved it for further analysis in Santa Cruz, Bolivia.

Once in Santa Cruz, I characterized the physical and chemical properties of the soil at each depth on all the profiles. I characterized soil color using the Munsell Color guide over a wet sample. Soil texture was characterized by taking one table spoon of soil and adding water until sticky consistency was reached; then the soil was rolled in a ball and texture was determined following the method in McGarry (2006). I assessed soil pH and magnetic susceptibility for all samples by taking 25 mg of soil per sample and combining it with 10 ml

130 131 Literature APPENDIX

This appendix contains data on the characterization of the soils of the forestry concession of La Chonta, Guarayos province, Bolivia (15º47’S, 62º55’W).

Methodology

131 APPENDIX APPENDIX

of distilled water; I then measured pH with a Thermo Fisher Scientific Orion start meter (Beverly, MA, USA). Finally, the soil samples were analyzed with additional chemical and physical parameters at the Centro de Investigación Agrícola Tropical (CIAT) in Santa Cruz, Bolivia. Concentrations of N, available P (Olsen), exchangeable Ca2+, Mg2+, K+ and Al+3; pH in water (1:2,5 v/v); soil carbon (organic matter content -OM-); total exchangeable bases (TEB); cation exchange capacity (CEC); base saturation; and particle size distribution for sand, silt, and clay were determined (for detailed methods on chemical and physical characterization of soils, see Appendix of Chapter 2).

Location of profiles in the permanent plots

The location of the auger profiles is showed in Fig. A1a. Note that when I approached a point where ceramic had been recorded, I shortened the distance between points. Fig. A1b, shows three profiles that were taken in plot Block 2 Normal; Figs. A3 and A4 show pictures of the soil profiles, where soil color can be appreciated; finally, Figs A5 and A6 show some of the chemical and textural variables of the soil of each profile.

Soil classification

In general, some morphological detail is necessary to classify soils; moreover, the profiles should be analyzed based on horizons and not by “artificial” layers. Nevertheless, I am confident that the data of the physical and chemical analyses presented here, together with the field notes, allowed us to ascertain the soil classes that I found in La Chonta.

Soils at La Chonta are mostly Cambisols, although there are also Histosols, Gleysols, Leptosols/Regosols and Anthrosols. Presence of other soils typical of the region, such as Acrisols, Lixisols and Luvisols, is improbable because there is not a gradient in clay content (argic horizon), which is necessary to characterize these soil classes. I also ruled out Nitisols because there was not enough clay content (nitic horizon) that characterizes this class of soil. I can also discard the presence of Ferralsols because soils at La Chonta present a ratio silt:clay ratio higher than that of soils that are highly weathered like Ferralsols. Likewise, the amount of some primary minerals, visible in soil samples, is high in La Chonta soils making the presence of Ferralsols less likely. Finally, base saturation in soils of La Chonta is high enough that I can discard the presence of Alilsols (Gardi et al. 2014).

Figure A.1 Maps from two permanent plots at forestry concession La Chonta, Guarayos Province, Bolivia. Color of the maps was obtained using ordinary Kriging function with a spherical semi- variogram model within the Interpolation box of the Spatial Analyst Tool in ArcMAP sotware based in field soil color descriptions using the Munsell Chart Color guide (See, Klomberg 2012 for details). Presence of ceramics and charcoal is shown. a) location of profiles where soils were collected (Profiles P) in the plot Block 2 Intensive. Notice that Profile 5 (P5) and profile 6 (P6) had ceramics. b) location of soil collection profiles in the plot Block 2 Normal.

132 133 APPENDIX APPENDIX

Location of profiles in the permanent plots

Soil classification

133 APPENDIX APPENDIX

Cambisols have, in general, incipient formation indicated by a cambic (change) in the concession. I thank Wenceslau Texeira for visiting the field site and for guidance on horizon with variation in color, structure and clay content. These soils are generally young, how to characterize the soils at La Chonta and and the interpretation of soil information for not necessarily very fertile, but they can contain minerals from bed rock. In La Chonta they soil classification. I also thank the student Victor Diego Rojas and the matero Angel Mendez seem to be present in plains and small slopes (Fig. A2). Histosols and Gleysols are soils for help in the field; as well as Jorge Carvajal, who helped in driving us to the camping sites present in lower areas in the landscape and usually they are associated with ground water, or and to the plots. The maps with the soil colors were made by Yannick Klomberg, who low soil drainage. In La Chonta they could be present in low areas (Fig. A2) (Profile 3). additionally contributed to gather the preliminary field information on the 12 on the soil of the Leptosols are thin soils, not very deep, with coarse material and presence of gravel very close 12, 27-ha plots. to the soil surface; they are found in sites with some dissected topography; therefore, soil fertility is variable and depending of the bed rock, they can have high base saturation. In La Chonta some of these soils are present in the top of the hills, normally associated to rock in the surface (Fig. A2) (Profile 8). Finally, Anthrosols are present in La Chonta, indicated by the presence an horizon with dark soil color (munsell 7.5YR 2.5/1) and remains of artefacts, such as pieces of ceramics or human generated charcoal; they present high concentration of P and high total base concentration (Profile 6, 9, 10, 12). The profile P12 is an example of a Anthrosol with enrichment of P, high organic matter content until 60 cm, very dark color and with ceramic bricks (Fig. A7). The values of pH are high, possibly indicating anthropic modification (Woods, 2009). A previous study in La Chonta found that ca. 20% of surveyed area of 216 ha had Anthrosols, or ‘terra preta’ (Paz-Rivera and Putz 2009), indicating that these soils are relatively common. A strict classification of the soil in low levels (as pretic, hortic, plaggic Anthrosols) was not the objective of this study and some additional information that I did not collect is needed for this task.

Figure A.2 General scheme of distribution of soils classes in the landscape of the forestry concession La Chonta, Guarayos. Province, Bolivia. Based on a scratch scheme made during the field trip (Wenceslau Texeira). Scales are approximated. Acknowledgments

I thank to Terra Preta Program of Wageningen University funded by the Interdisciplinary Research and Education Fund (INREF), for providing the economic support for the field visit of Wenceslau Texeira. La Chonta Company provided me permits and logistic support to work

134 135 APPENDIX APPENDIX

135 APPENDIX are less less are 8 APPENDIX and P 7 Guarayos Province, Province, Guarayos

APPENDIX ofile is indicated at the top. Notice top. that the at P indicated is ofile

137 Daniel Alarcon & Carmen Mateu. Carmen & Alarcon Daniel Photo:

Figure A.4 Soil profiles from Plot Block 2 Normal (P9 to P11) and from the site Chonta 7 (P12) at the forestry concession La Chonta, Guarayos Province, Bolivia. Numbers on the left side represent the depth of the sample in cm. and the label of each profile is in the top. Notice that P11 is less deep La Chonta, concession the forestry from 2 Intensive plotfrom Block layers at different collected samples soil of Photograph than the other two profiles from Block 2 Normal. Profile 12 also shows differentiation in horizons (Wenceslau Texeira). Pieces of . 3 ceramics were found in this profile in a depth of 0-30 cm. Photo: Daniel Alarcon & Carmen Mateu. Bolivia. Numbers on the left side represent the depth of the sample (in cm). and the label of each pr each label of the and cm). (in the of sample the depth represent side left the on Bolivia. Numbers Figure A deep than the rest of the profiles. the than rest deep of the profiles. 136 137 APPENDIX ceramics were found in this profile a in ceramics depth ofwere 0 found this in less deeptwo profiles than the otherfrom 2Normal. Block Prof profile a in ceramics depth ofwere 0 found this in less deeptwo profiles than the otherfrom 2Normal. Block Prof Province, Bolivia. onthe the left depth Numbers side of represent the sample cm. in and the label of each theNotice in top. is thatP profile A Figure Province, Bolivia. onthe the left depth Numbers sideof represent the sample cm. in and the label of each theNotice in top. is thatP profile A Figure

Figure A.3 Photograph.4 of soil samples collected at different layers from .4 plot Block 2 Intensive from the forestry concession La Chonta, Guarayos Province,

Bolivia. NumbersSoil profilesfrom PlotBlock Normal (P 2 on the left side represent the depth of the sample (in cm).Soil profilesfrom PlotBlock Normal (P 2 and the label of each profile is indicated at the top. Notice that P7 and P8 are less deep than the rest of the profiles. Photo: Daniel Alarcon & Carmen Mateu.

APPENDIX - - 30 cm. 30 cm. 9 9 to P to P to : Photo : Photo 11 11 ) and from the site Chonta 7 (P ) 7 Chonta andsite from the (P ) 7 Chonta andsite from the Daniel Alarcon & Carmen Mateu Daniel Alarcon & Carmen Mateu ile 12 also shows differentiation in horizons (Wenceslauile 12also horizons showsdifferentiationin Texeira). Pieces of (Wenceslauile 12alsohorizons showsdifferentiationin Texeira). Pieces of 12 12 . . the forestry concession La Chonta, Guarayos La Guarayos Chonta, the forestryconcession at ) the forestry concession La Chonta, Guarayos La Guarayos Chonta, the forestryconcession at )

Figure A.4 Soil profiles from Plot Block 2 Normal (P9 to P11) and from the site Chonta 7 (P12) at the forestry concession La Chonta, Guarayos APPENDIX Province, Bolivia. Numbers on the left side represent the depth of the sample in cm. and the label ofAPPENDIX each profile is in the top. Notice that P11 is less deep than the other two profiles from Block 2 Normal. Profile 12 also shows differentiation in horizons (Wenceslau Texeira). Pieces of ceramics were found in this profile in a depth of 0-30 cm. Photo: Daniel Alarcon & Carmen Mateu. 11 11 137 137 is is is

137 APPENDIX lower panel APPENDIX

APPENDIX

Figure A.6 Chemical and texture variables in the different depth intervals in the profiles collected in the permanent plot Block 2 Normal of the forestry concession of La Chonta, Guarayos province, Bolivia. The upper panel shows chemical variables: pH, P and Ca; and he lower panel shows texture variables: percentage of sand, clay and silt. and silt. and 139

Figure A.6 Chemical and texture variables in the different depth intervals in the profiles collected in the permanent plot Block 2 Normal of the forestry concession of La Chonta, Guarayos province, Bolivia. The upper panel shows chemical variables: pH, P and Ca; and he lower panel he profiles collected in the permanent plot Block 2 Intensive of the of plot collected inpermanent the Block 2 profiles t he in Intensive intervals depth the in different variables and texture Chemical

shows texture. 5 variables: percentage of sand, clay and silt. forestry concession of La Chonta, Guarayos province, Bolivia. The upper panel shows chemical variables: pH, P and Ca; and the PCa; and pH, and shows chemical variables: upper panel The province, Bolivia. Chonta, Guarayos concession of La forestry Figure A shows texture variables: percentage of sand, clay 138 139 APPENDIX shows texture variables:shows texture percentageand clay silt. of sand, andvariables:forestryLa panel Guarayos and Chonta, Bolivia. upper province, pH, of shows chemicalP Ca; concession The A Figure variables:shows texture percentageand claysilt. of sand, andvariables:forestryLa panel Guarayosand Chonta, Bolivia.upper province, pH, of showschemicalP Ca; concession The A Figure

Figure A.5 Chemical and texture variables in the different depth intervals in the profiles collected in the permanent plot Block 2 Intensive of the forestry concession.6 of La Chonta, Guarayos province, Bolivia. The.6 upper panel shows chemical variables: pH, P and Ca; and the lower panel shows texture variables: variables texture Chemical the and in different the in profiles depth collected intervals the in permanent Block plot 2Norma percentage of sand, clay and silt. variables texture Chemical the and in different the in profiles depth collected intervals the in permanent Block plot 2Norma

APPENDIX

Figure A.6 Chemical he panel lower and texture variables in the different depth he panel lower intervals in the profiles collected in the permanent plot Block 2 Normal of the forestry concession of La Chonta, Guarayos province, Bolivia. The upper panel shows chemical variables: pH, P and Ca; and he lower panel APPENDIX APPENDIX shows texture variables: percentage of sand, clay and silt. l ofl the ofl the 139 139

139 APPENDIX erra erra Table A.. Physical and physicochemical variables of 12 soil profiles collected in two permanent plots at La Chonta forestry concession, in Guarayos province, Bolivia. Electric conductivity units are μS -1 APPENDIX cm . B2 I = Block 2 Intensive; B2 N = Block 2 Normal and Chonta 7 is an Amazonian Dark Earths ure variables: site (Paz-Rivera and Putz, 2009)

Electric Conductivity Sand Silt Clay Depth (cm) (μS cm-1) Soil color Soil texture (%) (%) (%) Site B2 I -Profile 1- Coordinates (UTM) 525768, 8265150.3 0-20 87.9 7.5 YR 3/4 silty loam 66 25 9 20-40 31 7.5 YR 4/4 sandy loam 65 24 11 40-60 18.01 7.5 YR 4/4 sandy loam 67 23 10 60-80 17.26 10 YR 5/4 sandy loam 76 15 9 80-100 19.05 10 YR 5/4 sandy loam 67 22 11 Site B2 I -Profile 2- Coordinates (UTM) 525843, 8265150.3 0-20 85.73 7.5 YR 3/3 silty loam 58 33 9 20-40 29.1 7.5 YR 3/4 silty loam 66 24 10 40-60 17.78 7.5 YR 4/4 sandy loam 78 12 9 60-80 16.34 7.5 YR 4/6 sandy loam 81 10 9 80-100 11.8 7.5 YR 4/6 sandy loam 78 13 9 Site B2 I -Profile 3- Coordinates (UTM) 525918, 8265150.3 0-20 175.67 7,5 YR 2,5/2 loam 64 28 8 20-40 79.97 7,5 YR 2,5/3 loam 64 26 10 40-60 68.67 7,5 YR 3/3 silty loam 65 26 9 60-80 50.23 7.5 YR 3/4 sandy loam 69 21 10 80-100 53.57 7,5 YR 4/4 sandy 67 23 10 Site B2 I -Profile 4- Coordinates (UTM) 525988, 8265149 0-20 102.23 7,5 YR 2,5/2 silty loam 65 28 7 20-40 108.73 7,5 YR 2,5/2 silty loam 63 26 11 40-60 85 7,5 YR 2,5/3 sandy loam 65 25 10 60-80 86.37 7,5 YR 3/4 sandy loam 56 24 20 clayley 64 80-100 51 7,5 YR 4/6 loamy 28 8 Site B2 I -Profile 5 - Coordinates (UTM) 526045, 8265145 0-20 140.37 7,5 YR 2,5/1 silty loam 60 32 8 20-40 64.67 7,5 YR 2,5/1 silty loam 63 27 10 40-60 38.57 7,5 YR 2,5/2 silty loam 63 25 12 60-80 31.3 7,5 YR 4/4 sandy loam 68 21 11 80-100 28.94 7,5 YR 4/4 4/3 loam 69 21 10 9) in the forestry concession of Chonta, theLa survey, province, Bolivia.field Guarayos pieces of inforestry Putz, the 200 9) ceramics and In Rivera Chemical and texture variables in the different depth intervals in the profile collected in a site that previously described asT “ described previously that site a in collected profile the in intervals depth the in different variables and texture Chemical - 7 ” (Paz reta Figure A P panel PCa; pH, shows and lower the shows chemical variables: and text upper panel associated to The thisfound were profile. perc sand, entage of clay silt.and 140 141 APPENDIX

Figure A7 Chemical and texture variables in the different depth intervals in the profile collected in a site that previously described as “Terra Preta” (Paz-Rivera and Putz, 2009) in the forestry concession of La Chonta, Guarayos province, Bolivia. In the field survey, pieces of ceramics were found associated to this profile. The upper panel shows chemical variables: pH, P and Ca; and the lower panel shows texture variables: percentage of sand, clay and silt. cm Laat Chonta forestry concession,μS in Guarayos province, Bolivia. Electric conductivity units are A Table site (Paz- 80- 60- 60- 80- 80- 60 40- 20 0- 80- 60- 40- 20 0- 40- 20- 0- 80 40- 20- 0- 60- 40- 20- 0- Depth (cm) Depth - 20 20 20 20 20 1 - - - - . B2 I. B2 Intensive; Block = B2 2 N Block = 2Normal Chonta and 7 is Amazonianan Dark Earths 100 80 80 100 100 80 60 40 100 80 60 40 60 40 100 60 40 80 60 40 . Physical and physicochemical variables of 12 profiles soil collected in two permanent plots Rivera Putz, and 2009) Site B2 I I B2 Site - I B2 Site - I B2 Site Site B2 I - I B2 Site - I B2 Site Conductivity (μS cm Electric Electric - 108.73 140.37 102.23 175.67 Profile 3 Profile Profile 2 Profile 1 Profile 53.57 50.23 16.34 19.05 28.94 38.57 86.37 68.67 79.97 17.78 17.26 18.01 64.67 85.73 Profile 5 - 5 Profile 4 Profile - 1 11.8 31.3 29.1 87.9 ) 51 85 31 7,5 7.5 YR 3/4 7.5 YR 4/6 7.5 YR 5/4 10 YR 7,5 YR 4/4 4/3 4/4 7,5 YR 4/4 7,5 YR 2,5/2 7,5 YR 2,5/1 7,5 YR 3/4 7,5 YR 2,5/2 7,5 YR 3/3 7,5 YR 2,5/3 7,5 YR 4/6 7.5 YR 4/4 7.5 YR 3/4 7.5 YR 5/4 10 YR 4/4 7.5 YR 7,5 YR 2,5/1 7,5 YR 4/6 7,5 YR 2,5/3 7,5 YR 2,5/2 7,5 YR 2,5/2 7,5 YR 3/3 7.5 YR 4/4 7.5 YR 3/4 7.5 YR - - - - Coordinates 8265150.3 (UTM) 525918, Coordinates 8265150.3 (UTM) 525843, Coordinates (UTM) 525768, Soil color Soil YR 4/4 YR Coordinates 8265145 (UTM) 526045, Coordinates 8265149 (UTM) 525988, sandy sandy loam sandy loam sandy loam sandy loam loam sandy silty loam silty loam loamy clayley loam sandy silty loam silty loam loam sandy loam sandy silty loam loam sandy loam sandy silty loam loam sandy silty loam loam silty loam loam sandy silty loam Soil texture loam 8265150.3 Sand (%) 67 69 81 67 66 69 68 63 63 64 56 63 65 64 78 78 66 76 67 60 65 65 64 58 65 (%) Silt Silt 23 21 10 22 21 25 28 24 26 12 24 15 23 25 21 27 32 25 26 28 26 28 13 33 24 APPENDIX Clay Clay (%) 10 10 11 10 12 20 10 10 11 10 11 10 10 11 141 9 8 9 9 9 9 9 8 7 8 9 APPENDIX APPENDIX

Table A.1 (Continued) Table A.1 (Continued)

Electric Electric Conductivity Sand Silt Clay Conductivity Sand Silt Clay Depth (cm) (μS cm-1) Soil color Soil texture (%) (%) (%) Depth (cm) (μS cm-1) Soil color Soil texture (%) (%) (%) Site B2 I - Profile 6 - Coordinates (UTM) 526039, 8265200 Chonta 7 -Profile 12- Coordinates (UTM) 524520, 8263152 0-20 57.63 7,5 YR 2,5/1 silty loam 58 31 11 0-20 216.77 7,5 YR 2,5/1 loam 56 35 9 20-40 24.44 7,5 YR 2,5/2 silty loam 65 23 12 20-30 79.1 7,5 YR 2,5/1 silty loam 62 28 10 40-60 18.19 7,5 YR 2,5/3 silty loam 72 18 10 30-40 59.2 7,5 YR 3/2 silty loam 65 26 9 60-80 15.8 7,5 YR 3/4 sandy loam 68 20 12 40-60 43 7,5 YR 3/2 silty loam 67 23 10 80-100 18.8 7,5 YR 3/4 silty loam 68 19 13 60-80 39.15 7,5 YR 4/2 silty loam 61 24 15 Site B2 I -Profile 7- Coordinates (UTM) 525978, 8265169 0-20 155.23 7,5 YR 2,5/2 loam 57 34 9 20-40 33.57 7,5 YR 2,5/2 loam 58 30 12 40-60 16.29 7,5 YR 2,5/2 loam 57 30 13 clayley 59 30 60-65 24.97 7,5 YR 3/4 loamy 11 Site B2 I Profile 8 Coordinates (UTM) 525984, 826846 0-20 88.83 7,5 YR 3/2 heavy clay 73 19 8 20-40 13.52 7,5 YR 4/3 fine clay 53 25 22 clayley 60 23 40-60 11.69 7,5 YR 3/4 loamy 17 Site B2 N -Profile 9- Coordinates (UTM) 525227, 8265018 0-20 222.9 7,5 YR 2,5/2 loam 62 28 10 20-40 41.67 7,5 YR 2,5/2 silty loam 68 23 9 40-60 16.39 7,5 YR 3/3 silty loam 67 24 9 60-80 12.03 7,5 YR 3/3 silty loam 52 35 13 80-100 15.83 7,5 YR 3/4 silty loam 71 20 9 Site B2 N -Profile 10- Coordinates (UTM) 525213, 8264991 0-20 149.75 10 YR 2/2 loam 53 37 10 20-40 18.48 10 YR 2/2 loam 59 26 15 40-60 29.45 10 YR 3/2 fine clay 58 28 14 60-80 14.08 10 YR 3/2 silty loam 66 20 14 80-100 13.15 10 YR 3/3 silty loam 63 22 15 Site B2 N -Profile 11- Coordinates (UTM) 525033, 8264976 0-20 101.93 7,5 YR 3/2 loam 63 29 8 20-40 40.23 7,5 YR 3/2 silty loam 64 27 9 40-50 38.6 7,5 YR 3/3 sandy loam 65 26 9

142 143 APPENDIX APPENDIX

Table A.1 (Continued) Table A.1 (Continued)

143 APPENDIX 1 . Units of . Units of 1 - 1.9 1.3 0.4 2.5 0.1 0.9 0.6 0.4 2.2 0.3 0.3 0.5 0.3 0.2 g in Guarayos in Guarayos APPENDIX k (%) OM Table A.2 (Continued) 6 4 3 4 7 5 3 2 3 4 3 4 2 3 3 Depth pH Sat bas OM (cm) (H O) Ca Mg Na K TEB CEC (%) Acidity Al P (%) P 2 Site B2 I -Profile 4- Coordinates (UTM) 525988, 8265149 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0-20 6.05 3.8 0.9 0.05 0.19 4.9 5.1 98 0.1 0 9 3 EC, and areEC, cmol Al

Al 20-40 6.21 2.3 0.8 0.04 0.19 3.3 3.3 100 0 0 9 1.9 40-60 6.25 1.7 0.8 0.07 0.12 2.7 2.7 100 0 0 10 0.9 Rivera and Putz, 2009) 2009) and Rivera Putz, - 1 0 0 0 0 1 1 0 1 60-80 6.31 1.2 0.8 0.05 0.09 2.1 2.1 100 0 0 6 0.7 0.2 0.9 0.2 0.2 1.2 0.9 80-100 6.34 1.7 1.5 0.05 0.19 3.4 3.4 100 0 0 19 0.6

Acidity Site B2 I - Profile 5- Coordinates (UTM) 526045, 8265145 APPENDIX 0-20 6.33 8.6 1.7 0.04 0.28 10.6 10.6 100 0 0 21 3.4 52 94 60 55 93 47 50 95 46 61 100 100 100 100 20-40 100 6.53 4.4 1.6 0.04 0.15 6.2 6.2 100 0 0 14 2.5 (%)

Sat bas 40-60 6.47 2.9 2.4 0.05 0.26 5.6 5.6 100 0 0 9 1 2 2 2 60-80 6.5 2.4 1.7 0.05 0.26 4.4 4.4 100 0 0 9 0.4 2.7 2.3 2.2 2.9 1.8 6.6 2.7 2.3 1.8 3.4 2.1 2.4 Table A80.2-100(Continued)6.48 3.3 1.8 0.05 0.25 5.4 5.4 100 0 0 10 0.4 CEC Depth pH Site B2 I -Profile 6- Coordinates (UTM) 526039,Sat bas 8265200 OM 0-20 5.65 6.2 1.9 0.04 0.34 8.5 8.6 98 0.2 0 19 3.2 1 2 1 1 (cm) (H2O) Ca Mg Na K TEB CEC (%) Acidity Al P (%) 1.2 0.9 2.3 1.8 2.5 1.4 2.7 6.6 2.7 3.3 1.5 20-40 5.69 3.4 0.9Site B20.05 I -Profile0.17 4- Coordinates4.5 (UTM)4.7 525988, 968265149 0.2 0 16 2.6 TEB 040-20-60 6.055.85 3.82 0.90.6 0.050.07 0.190.13 4.92.8 5.12.9 9895 0.1 0 169 1.33 Coordinates (UTM) 525843, (UTM) 8265150.3 Coordinates Coordinates (UTM) 525768, (UTM) 8265150.3 Coordinates Coordinates (UTM) 525918, (UTM) 8265150.3 Coordinates

- 60-80 5.84 2.3 0.7 0.07 0.14 3.2 3.4 95 0.2 0 13 1

- - 20-40 6.21 2.3 0.8 0.04 0.19 3.3 3.3 100 0 0 9 1.9 0.1 0.1 0.09 0.05 0.03 0.12 0.11 0.16 0.07 0.05 0.19 0.04 0.11 0.03 0.04 K 4080-60100 6.255.85 1.72.7 0.8 0.07 0.120.14 2.73.7 2.73.9 10096 0.10 0 1014 0.9 60-80 6.31 1.2 0.8 0.05 0.09 2.1 2.1 100 0 0 6 0.7 Profile 1 Profile 2 Profile 3 80-100 6.34 1.7 1.5 0.05 0.19 3.4 3.4 100 0 0 19 0.6 0.03 0.04 0.03 0.04 0.05 0.05 0.03 0.03 0.04 0.05 0.04 0.04 0.05 0.04 0.05 Na Site B2 I - Profile 5- Coordinates (UTM) 526045, 8265145 145 0-20 6.33 8.6 1.7 0.04 0.28 10.6 10.6 100 0 0 21 3.4 Site B2 I - 1 Site B2 I - Site B2 I - 0.4 0.6 0.4 0.9 0.8 0.9 0.8 0.7 1.7 1.2 0.9 1.1 0.5 0.6 20-40 6.53 4.4 1.6 0.04 0.15 6.2 6.2 100 0 0 14 2.5

Mg 40-60 6.47 2.9 2.4 0.05 0.26 5.6 5.6 100 0 0 9 1 60-80 6.5 2.4 1.7 0.05 0.26 4.4 4.4 100 0 0 9 0.4 2 0.5 0.5 0.4 1.2 0.8 0.9 1.6 0.6 0.9 5.2 1.7 0.4 0.3 0.4 80-100 6.48 3.3 1.8 0.05 0.25 5.4 5.4 100 0 0 10 0.4 Ca Site B2 I -Profile 6- Coordinates (UTM) 526039, 8265200 0-20 5.65 6.2 1.9 0.04 0.34 8.5 8.6 98 0.2 0 19 3.2 O) 5.9 6.1 5.8 2 5.65 5.62 5.62 6.37 6.42 6.39 5.59 5.51 5.63 6.32 5.61 5.64 pH 20-40 5.69 3.4 0.9 0.05 0.17 4.5 4.7 96 0.2 0 16 2.6 (H 40-60 5.85 2 0.6 0.07 0.13 2.8 2.9 95 0.1 0 16 1.3 . B2 I = Block 2 Intensive; B2 N = Block 2 Normal and Chonta 7 is an Amazonian Dark Earths site (Paz site Earths Dark Amazonian an is 7 and Chonta 2 Normal = Block N B2 = Block 2 Intensive; . B2 I 1 - Physical and physicochemical variables of 12 soil profiles collected in two permanent plots at La Chonta forestry concession, forestry Chonta at La plots permanent two in collected profiles soil of 12 variables physicochemical and Physical 60-80 5.84 2.3 0.7 0.07 0.14 3.2 3.4 95 0.2 0 13 1 60 60 80 60 80 100 40 40 40 80 100 100 g 20 20 20 k (cm) . 2 Depth Depth 80-100 5.85 2.7 0.8 0.07 0.14 3.7 3.9 96 0.1 0 14 0.9 40 - 40 - 60 - 40 - 60 - 80 - 0 - 20 - 0 - 20 - 0 - 20 - 60 - 80 - 80 - province, Bolivia. TEB = Total exchangeable bases, and CEC = cation exchange capacity. Units of Ca, Mg, Na, K, TEB, C TEB, K, Na, Mg, Ca, of Units capacity. exchange cation = CEC and bases, exchangeable Total = TEB Bolivia. province, P are mg are mg P Table A 144 145 APPENDIX

Table A.2 Physical and physicochemical variables of 12 soil profiles collected in two permanent plots at La Chonta forestry concession, in Guarayos province, Bolivia. TEB = Total exchangeable bases, and CEC = cation exchange capacity. Units of Ca, Mg, Na, K, TEB, CEC, and Al are cmol kg-1. Units of P are mg kg-1. B2 I = Block 2 Intensive; B2 N = Block 2 Normal and Chonta 7 is an Amazonian Dark Earths site (Paz-Rivera and Putz, 2009)

Depth pH Sat bas OM (cm) (H2O) Ca Mg Na K TEB CEC (%) Acidity Al P (%) Site B2 I -Profile 1- Coordinates (UTM) 525768, 8265150.3 0-20 5.9 1.6 0.8 0.03 0.1 2.5 2.7 94 0.2 0 6 1.9 20-40 5.59 0.6 0.7 0.03 0.07 1.4 2.3 60 0.9 0 4 1.3 40-60 5.62 0.5 0.6 0.04 0.05 1.2 2.2 55 1 0 3 0.4 60-80 5.61 0.4 0.5 0.04 0.04 1 2 50 1 0 3 0.3 80-100 5.8 0.4 1 0.05 0.04 1.5 2.4 61 0.9 0 3 0.2 Site B2 I -Profile 2- Coordinates (UTM) 525843, 8265150.3 0-20 5.51 2 1.1 0.05 0.11 3.3 3.4 95 0.2 0 4 2.2 20-40 5.63 0.9 1.7 0.04 0.05 2.7 2.9 93 0.2 0 3 1 40-60 5.65 0.5 0.4 0.03 0.09 1 2 52 1 0 4 0.4 60-80 5.62 0.4 0.4 0.03 0.03 0.9 1.8 47 1 0 3 0.3 80-100 5.64 0.3 0.6 0.04 0.03 1 2.1 46 1.2 1 4 0.3 Site B2 I -Profile 3- Coordinates (UTM) 525918, 8265150.3

0-20 6.1 5.2 1.2 0.05 0.19 6.6 A Table 6.6 100 0 0 7 A Table 2.5 20-40 6.32 1.7 0.9 0.04 0.1 2.7 2.7 100 0 0 5 0.1

4080 -6060 40 20 6.370 80 1.2 60 40 0.920 0 0.0480 60 40 80 0.1220 60 0 40 20 2.30 80 2.360 40 20 1000 80 60 0 40 20 0 3 0.9 Depth Depth ------.2 .2 (cm) (cm) 20 20 20 20 20 20 ------100 80 60 40 100 80 60 40 100 80 60 100 40 80 60 40 100 80 60 40 100 80 60 40

60-80 6.42 0.8 0.8 0.05 0.11 1.8 (Continued) 1.8 100 0 0 2 (Continued) 0.6 80-100 6.39 0.9 0.9 0.05 0.16 2 2 100 0 0 2 0.5 (H (H pH pH pH 5.85 5.84 5.85 5.69 5.65 6.48 6.47 6.53 6.33 6.34 6.31 6.25 5.85 6.21 5.84 6.05 5.85 5.69 5.65 6.48 6.47 6.53 6.33 6.34 6.31 6.25 6.21 6.05 2 2 6.5 6.5 O) O) Ca Ca 2.7 2.3 3.4 6.2 3.3 2.4 2.9 4.4 8.6 1.7 1.2 1.7 2.7 2.3 2.3 3.8 3.4 6.2 3.3 2.4 2.9 4.4 8.6 1.7 1.2 1.7 2.3 3.8 2 2 Mg Mg 0.9 0.8 0.7 0.6 0.9 1.9 1.8 1.7 2.4 1.6 1.7 1.5 0.8 0.8 0.8 0.8 0.7 0.9 0.6 0.9 1.9 1.8 1.7 2.4 1.6 1.7 1.5 0.8 0.8 0.8 Site B2 I I B2 Site I B2 Site Site B2 I I B2 Site S I B2 Site S ite B2 I I B2 ite I B2 ite APPENDIX Na Na 0.04 0.07 0.05 0.07 0.05 0.04 0.05 0.05 0.05 0.04 0.04 0.05 0.05 0.07 0.04 0.05 0.07 0.07 0.07 0.05 0.04 0.05 0.05 0.05 0.04 0.04 0.05 0.05 0.07 0.07 ------Profile 6 Profile 5 Profile 4 Profile Profile 6 Profile 5 Profile 4 Profile K K 0.14 0.14 0.13 0.17 0.34 0.25 0.26 0.26 0.15 0.28 0.19 0.09 0.12 0.14 0.19 0.14 0.19 0.13 0.17 0.34 0.25 0.26 0.26 0.15 0.28 0.19 0.09 0.12 0.19 0.19 ------

Table A.2 (Continued) Coordinates8265200 (UTM) 526039, Coordinates8265149 (UTM) 525988, Coordinates8265200 (UTM) 526039, Coordinates8265149 (UTM) 525988, Coordinates 8265145 (UTM) 526045, Coordinates 8265145 (UTM) 526045, TEB TEB

Depth pH 10.6 Sat bas 10.6 OM 3.4 2.1 2.7 3.7 3.3 3.2 4.9 2.8 4.5 8.5 5.4 4.4 5.6 6.2 3.4 2.1 2.7 3.3 4.9 3.7 3.2 2.8 4.5 8.5 5.4 4.4 5.6 6.2 (cm) (H2O) Ca Mg Na K TEB CEC (%) Acidity Al P (%) Site B2 I -Profile 4- Coordinates (UTM) 525988, 8265149 CEC CEC

0-20 6.05 3.8 0.9 10.6 0.05 0.19 4.9 5.1 9810.6 0.1 0 9 3 3.4 2.1 2.7 3.9 3.3 3.4 5.1 2.9 4.7 8.6 5.4 4.4 5.6 6.2 3.4 2.1 2.7 3.3 5.1 3.9 3.4 2.9 4.7 8.6 5.4 4.4 5.6 6.2 20-40 6.21 2.3 0.8 0.04 0.19 3.3 3.3 100 0 0 9 1.9 40-60 6.25 1.7 0.8 0.07 0.12 2.7 2.7 100 0 0 10 0.9 Sat bas bas Sat bas Sat 60-80 6.31 1.2 0.8 0.05 0.09 2.1(%) 2.1 100 0 0 (%) 6 0.7 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 8096 -10095 95 96 6.3498 1.7 1.5 0.05 96 0.1995 98 95 96 3.498 3.4 100 0 098 19 0.6 Site B2 I - Profile 5- Coordinates (UTM) 526045, 8265145 Acidity Acidity 0-20 6.33 8.6 1.7 0.04 0.28 10.6 10.6 100 0 0 21 3.4 0.1 200.1 -400.2 0.1 0.2 6.530.2 4.4 1.6 0.04 0.1 0.150.2 0.1 0.1 0.2 6.20.2 6.2 100 0 0 14 2.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 40-60 6.47 2.9 2.4 0.05 0.26 5.6 5.6 100 0 0 9 1 Al 60-80 6.5 2.4 1.7 0.05 0.26 4.4Al 4.4 100 0 0 9 0.4 0 0 0 0 800 -1000 0 0 6.480 3.30 0 0 1.80 0 0.050 0 0 0 0.250 0 0 0 0 5.40 0 5.40 0 0 1000 0 0 0 10 0.4 Site B2 I -Profile 6- Coordinates (UTM) 526039, 8265200 P 0-20 5.65 6.2 1.9 0.04 0.34 8.5P 8.6 98 0.2 0 19 3.2 10 14 13 16 16 19 10 14 21 19 10 14 13 16 16 19 10 14 21 19 6 9 9 20-40 5.69 3.4 9 9 0.9 0.05 6 0.179 9 4.5 4.79 9 96 0.2 0 16 2.6 40-60 5.85 2 0.6 0.07 0.13 2.8 2.9 95 0.1 0 16 1.3 OM OM OM OM (%) (%) APPENDIX 60-80 5.84 2.3 0.7 0.07 0.14 3.2 3.4 95 APPENDIX 0.2 0 13 1 0.7 0.9 1.9 0.9 1.3 2.6 3.2 0.4 0.4 2.5 3.4 0.6 0.7 0.9 0.9 1.9 1.3 2.6 3.2 0.4 0.4 2.5 3.4 0.6 3 80-1001 5.85 2.7 1 0.8 0.07 0.141 3 3.7 3.9 1 96 0.1 0 14 0.9 145 145

145 APPENDIX 0.3 0.5 0.6 1.1 0.7 0.7 1.7 2.9 1.3 0.9 0.6 3.2 APPENDIX (%) OM Table A.2 (Continued) 3 2 2 2 2 2 3 Depth pH Sat bas OM 18 16 11 13 14 (cm) (H2O) Ca Mg Na K TEB CEC (%) Acidity Al P (%) P Site B2 I -Profile 4- Coordinates (UTM) 525988, 8265149 0 0 0 0 0 0 0 0 0 0 0-20 6.05 3.8 0.9 0.05 0.19 4.9 5.1 98 0.1 0 9 3 0.9 1.1

Al 20-40 6.21 2.3 0.8 0.04 0.19 3.3 3.3 100 0 0 9 1.9 40-60 6.25 1.7 0.8 0.07 0.12 2.7 2.7 100 0 0 10 0.9 0 0 0 60-80 6.31 1.2 0.8 0.05 0.09 2.1 2.1 100 0 0 6 0.7 0.1 0.2 1.2 0.1 0.1 0.1 0.1 0.1 1.4 80-100 6.34 1.7 1.5 0.05 0.19 3.4 3.4 100 0 0 19 0.6

Acidity Site B2 I - Profile 5- Coordinates (UTM) 526045, 8265145 APPENDIX 0-20 6.33 8.6 1.7 0.04 0.28 10.6 10.6 100 0 0 21 3.4 94 97 51 95 98 96 96 93 52 100 100 100 20-40 6.53 4.4 1.6 0.04 0.15 6.2 6.2 100 0 0 14 2.5 (%)

Sat bas 40-60 6.47 2.9 2.4 0.05 0.26 5.6 5.6 100 0 0 9 1 60-80 6.5 2.4 1.7 0.05 0.26 4.4 4.4 100 0 0 9 0.4 2 1.6 5.1 2.4 2.3 4.7 3.2 3.2 2.8 3.1 9.1 Table A.2 (Continued)

10.3 80-100 6.48 3.3 1.8 0.05 0.25 5.4 5.4 100 0 0 10 0.4

CEC Depth pH Site B2 I -Profile 6- Coordinates (UTM) 526039,Sat bas 8265200 0-(cm)20 (H5.652O) Ca6.2 Mg1.9 Na0.04 K0.34 TEB8.5 CEC8.6 (%)98 Acidity0.2 Al 0 P 19 OM (%)3.2 3 1.5 4.9 1.2 2.1 3.1 1.8 4.6 1.5 3.1 9.1

10.3 20-40 5.69 3.4 0.9Site B2 0.05N -Profile0.17 10- Coordinates4.5 (UTM)4.7 525213,96 8264991 0.2 0 16 2.6 TEB 040-20-60 5.975.85 6.92 1.70.6 0.040.07 0.180.13 8.82.8 8.82.9 10095 0.10 0 16 2.91.3 Coordinates (UTM) 525984, (UTM) 826846 Coordinates Coordinates (UTM) 525978, (UTM) 8265169 Coordinates Coordinates (UTM) 525227, (UTM) 8265018 Coordinates - - - 2060-4080 5.695.84 2.82.3 0.40.7 0.040.07 0.110.14 3.43.2 3.4 9895 0.10.2 0 137 1.21 0.1 0.1 0.19 0.24 0.09 0.36 0.16 0.15 0.09 0.22 0.09 0.13 K 4080-60100 5.555.85 3.12.7 0.70.8 0.040.07 0.130.14 3.74 4.13.9 9896 0.1 0 143 1.40.9 60-80 5.91 2.6 0.3 0.04 0.11 3.1 3.2 96 0.1 0 4 0.9 Profile 8 Profile 9 Profile 7 80-100 6.16 2.1 0.4 0.05 0.12 2.7 2.8 95 0.1 0 5 0.8 0.04 0.04 0.05 0.11 0.03 0.09 0.08 0.04 0.05 0.05 0.09 0.03 Na Site B2 N -Profile 11- Coordinates (UTM) 525033, 8264976 145 0-20 5.86 4.9 1 0.04 0.26 6.2 6.2 100 0 0 11 2.7 Site B2 I - Site B2 I - Site B2 N - 1 20-40 6.21 2 0.6 0.05 0.12 2.8 2.8 100 0 0 4 1.4 0.5 0.4 0.4 1.3 0.3 0.3 0.5 0.7 0.4 1.7 0.5

Mg 40-50 6.32 1.8 0.5 0.04 0.12 2.5 2.5 100 0 0 4 1.4 Chonta 7 -Profile 12- Coordinates (UTM) 524520, 8263152 0.9 3.6 0.6 1.6 8.5 2.5 2.6 1.2 3.6 0.9 7.2 2.4 0-20 7.43 15.4 0.8 0.04 0.43 16.7 16.7 100 0 0 41 3.1 Ca 20-30 7.21 6.3 0.4 0.04 0.38 7.1 7.1 100 0 0 30 1.8 30-40 7.35 3 0.2 0.03 0.24 3.5 3.5 100 0 0 24 1.4 O) 5.4 6.9 2 6.73 6.77 6.72 6.21 6.58 6.43 6.03 6.65 6.49 5.42

pH 40-60 7.28 2 0.2 0.03 0.15 2.4 2.4 100 0 0 20 0.8 (H 60-80 7.18 1.9 0.2 0.03 0.13 2.3 2.3 100 0 0 19 0.7 100 80 60 60 60 65 40 40 40 20 20 20 (cm) . 2 (Continued) - Depth Depth 80 - 60 - 40 - 40 - 40 - 60 - 0 - 20 - 0 - 20 - 0 20 - Table A 146 147 APPENDIX

Table A.2 (Continued)

Depth pH Sat bas OM (cm) (H2O) Ca Mg Na K TEB CEC (%) Acidity Al P (%) Site B2 I -Profile 7- Coordinates (UTM) 525978, 8265169 0-20 6.21 8.5 1.3 0.09 0.36 10.3 10.3 100 0 0 18 2.9 20-40 6.49 3.6 0.7 0.05 0.22 4.6 4.7 98 0.1 0 16 1.3 40-60 6.58 2.5 0.3 0.08 0.16 3 3.2 96 0.1 0 11 0.9 60-65 6.43 2.6 0.3 0.04 0.15 3.1 3.2 96 0.1 0 13 0.6 Site B2 I -Profile 8- Coordinates (UTM) 525984, 826846 0-20 6.03 1.2 0.5 0.05 0.09 1.8 2 93 0.1 0 2 0.7 20-40 5.42 0.9 0.4 0.09 0.09 1.5 2.8 52 1.4 1.1 2 0.7 40-60 5.4 0.6 0.4 0.11 0.09 1.2 2.4 51 1.2 0.9 2 0.6 Site B2 N -Profile 9- Coordinates (UTM) 525227, 8265018 0-20 6.65 7.2 1.7 0.04 0.19 9.1 9.1 100 0 0 14 3.2 20-40 6.9 2.4 0.5 0.03 0.13 3.1 3.1 100 0 0 3 1.7 40-60 6.72 1.6 0.4 0.03 0.1 2.1 2.3 95 0.1 0 2 1.1

60-80 6.77 3.6 1 0.05 0.24 4.9 5.1 A Table 97 0.2 0 2 A Table 0.5 80-100 6.73 0.9 0.5 0.04 0.1 1.5 1.6 94 0.1 0 3 0.3 60 40 30 20 0 40 20 0 80 60 40 80 20 60 0 40 20 0 80 60 40 20 0 80 60 40 20 0 Depth Depth ------.2 .2 (cm) (cm) 20 20 20 20 20 20 ------80 60 40 30 50 40 100 80 60 100 40 80 60 40 100 80 60 40 100 80 60 40 (Continued) (Continued) (H (H pH pH pH 7.18 7.28 7.35 7.21 7.43 6.32 6.21 5.86 6.16 5.91 5.55 5.85 5.69 5.84 5.97 5.85 5.69 5.65 6.48 6.47 6.53 6.33 6.34 6.31 6.25 6.21 6.05 2 2 6.5 O) O) Ca Ca 15.4 1.9 6.3 1.8 4.9 2.1 2.6 3.1 2.7 2.8 2.3 6.9 3.4 6.2 3.3 2.4 2.9 4.4 8.6 1.7 1.2 1.7 2.3 3.8 2 3 2 2 Mg Mg 0.9 0.2 0.2 0.2 0.4 0.8 0.5 0.6 0.4 0.3 0.7 0.8 0.4 0.7 1.7 0.6 0.9 1.9 1.8 1.7 2.4 1.6 1.7 1.5 0.8 0.8 0.8 1 Site B2 N N B2 Site N B2 Site Chonta 7 Chonta Site B2 I I B2 Site Site B2 I I B2 Site S ite B2 I I B2 ite APPENDIX Na Na 0.04 0.07 0.04 0.07 0.05 0.04 0.05 0.05 0.05 0.04 0.04 0.05 0.05 0.07 0.04 0.05 0.03 0.03 0.03 0.04 0.04 0.04 0.05 0.04 0.05 0.04 0.04 0.07 ------Profile 12 Profile Profile 11 Profile 10 Profile Profile 6 Profile 5 Profile 4 Profile K K 0.13 0.15 0.24 0.38 0.43 0.12 0.12 0.26 0.12 0.11 0.13 0.14 0.11 0.14 0.18 0.13 0.17 0.34 0.25 0.26 0.26 0.15 0.28 0.19 0.09 0.12 0.19 0.19 ------

Table A.2 (Continued) Coordinates8265200 (UTM) 526039, Coordinates8265149 (UTM) 525988, Coordinates 8265145 (UTM) 526045, Coordinates 8263152 (UTM) 524520, Coordinates 8264976 (UTM) 525033, Coordinates 8264991 (UTM) 525213, TEB TEB

Depth pH 16.7 Sat bas 10.6 2.7 3.1 3.7 3.4 3.2 8.8 2.8 4.5 8.5 5.4 4.4 5.6 6.2 3.4 2.1 2.7 3.3 4.9 2.3 2.4 3.5 7.1 2.5 2.8 6.2

(cm) (H2O) Ca Mg Na 4 K TEB CEC (%) Acidity Al P OM (%) Site B2 N -Profile 10- Coordinates (UTM) 525213, 8264991 CEC CEC

0-20 5.97 16.7 6.9 1.7 0.04 0.18 8.8 8.8 10010.6 0 0 16 2.9 2.8 3.2 4.1 3.9 3.4 3.4 8.8 2.9 4.7 8.6 5.4 4.4 5.6 6.2 3.4 2.1 2.7 3.3 5.1 2.3 2.4 3.5 7.1 2.5 2.8 6.2 20-40 5.69 2.8 0.4 0.04 0.11 3.4 3.4 98 0.1 0 7 1.2 40-60 5.55 3.1 0.7 0.04 0.13 4 4.1 98 0.1 0 3 1.4 Sat bas bas Sat bas Sat 60-80 5.91 2.6 0.3 0.04 0.11 3.1 (%) 3.2 96 0.1 0 (%) 4 0.9 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 80-100 6.16 2.1 0.4 0.0595 96 98 96 0.1298 95 95 96 2.798 2.8 95 0.1 098 5 0.8 Site B2 N -Profile 11- Coordinates (UTM) 525033, 8264976 Acidity Acidity 0-20 5.86 4.9 1 0.04 0.26 6.2 6.2 100 0 0 11 2.7 0.1 20-40 6.21 2 0.6 0.050.1 0.1 0.1 0.1 0.120.1 0.2 0.1 0.2 2.80.2 2.8 100 0 0 4 1.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 40-50 6.32 1.8 0.5 0.04 0.12 2.5 2.5 100 0 0 4 1.4 Al Chonta 7 -Profile 12- CoordinatesAl (UTM) 524520, 8263152 0 0 0 0 0-20 0 0 7.430 0 15.40 0 0.80 0 0.040 0 0 0 0.430 0 0 0 16.70 0 16.70 0 0 0 1000 0 0 0 41 3.1 20-30 7.21 6.3 0.4 0.04 0.38 7.1 7.1 100 0 0 30 1.8 P 30-40 7.35 3 0.2 0.03 0.24 3.5 P 3.5 100 0 0 24 1.4 10 19 20 24 30 41 11 14 13 16 16 16 19 10 14 21 19 6 9 9 40-60 7.28 2 4 0.24 0.035 4 3 0.157 2.4 2.49 9 100 0 0 20 0.8

60-80 7.18 1.9 0.2 0.03 0.13 2.3 (%) OM 2.3 100 0 0 19 0.7 OM OM (%) APPENDIX APPENDIX 0.7 0.9 1.9 0.7 0.8 1.4 1.8 3.1 1.4 1.4 2.7 0.8 0.9 1.4 0.9 1.2 2.9 1.3 2.6 3.2 0.4 0.4 2.5 3.4 0.6 3 1 1 147 145

147 Summary

Summary

Amazonian forest is seen as the archetype of pristine forests, untouched by humans, but this romantic view is far from reality. In recent years, there is increasing evidence of long and extensive landscape modification by humans. Processes of permanent inhabitation, expansion and retreat of human populations have not always been obvious in those ecosystems, leaving sometimes weak and overlooked imprints in the landscape. An example of one of these inconspicuous alterations are the modifications in the soil known as Amazonian Dark Earths (ADE) or ‘terra preta’ (black earth in Portuguese), which are the product of the accumulation of residuals from permanent or semi-permanent human inhabitation. They are named after the black color of the soils, which is a consequence of the accumulation of charcoal pieces and organic matter in the soil. These soils also contain higher levels of phosphorous, calcium (mainly originated from bone residuals), and nitrogen that increase fertility of the naturally poor soils, thus favouring agricultural practices. Amazonian Dark Earths are distributed in Brazil, Bolivia, Colombia, and Peru, and it is estimated that they could occupy 3% of the area of the Amazon basin. With the decrease in human population in the Americas after the encounter with European colonists, sites where ADE had been formed were abandoned and the vegetation recovered. So far, the effects of ADE on old growth forest had not been widely examined and we are just starting to understand the consequences of past human inhabitation on forest composition and structure. In this thesis, I evaluated the effects of ADE on the forest that has re-grown after abandonment by indigenous people in the La Chonta forest, situated at the southern edge of the Amazon basin, in Bolivia. First, I assessed the magnitude of the changes in the soil as a consequence of human occupation. For that, I did a detailed soil survey on 12 plots of ~27-ha each, and I evaluate the differences between ADE and non-ADE soils. Then, I studied how soil changes affected plant species composition in the understory (Chapter 2), forest structure and forest dynamics (Chapter 3), and finally I determined how seedlings of tree species respond to anthropogenic changes in soil properties (Chapter 4). Detailed information on soil characteristics and its heterogeneity in the landscape is needed to evaluate the effects of soil on the vegetation. This was even more important for my study site, as soil heterogeneity can be increased by the presence of ADE. Therefore, I did detailed soil surveys (Appendix, Chapter 2, Chapter 3) that allowed me to understand the relationship between past human occupation and alteration in the concentration of soil nutrients. I found that natural soils in the southern Amazonian forest are more fertile than their Central and Eastern Amazon counterparts. Past human presence in the area resulted in soil enrichment, due to increases in the concentration of phosphorus, calcium, potassium, and increases in soil pH. Thus, with this information I could test specific hypothesis about the effects of soil fertility on the vegetation that occurs in these sites. In the Amazonian forest in general, soil characteristics influences the composition of understory angiosperm herbs, ferns and palm species. Thus, increases in soil fertility in ADE

149 Summary

Summary

149 Summary

could affect the distribution of understory angiosperm herbs, ferns and palm species. In higher seedling mortality on ADE; others suffered from nutrient imbalance as observed from Chapter 2, I evaluated the effect of ADE on composition, richness and abundance of changes in nutrient stoichiometry in the leaves; whereas other species increased their leaf Ca, understory species (ferns, angiosperm herbs, and palms). First, I did a detailed soil survey that P and N concentrations in ADE. Only for this latter group of nutrient accumulators, there was allowed me to determine sites in the forest with higher nutrients and with evidence of past a positive relationship between leaf Ca concentration and the growth rates of seedlings. human occupation (i.e., presence of ceramics). Then, I correlated soil variables associated Contrary to expectations, ADE did not lead to increased seedling growth. The ability of plants with ADE, such as Ca, P, and soil pH, with species composition, richness and abundance. I to colonize patches of ADE might depend on plant responses to increased soil Ca and their found that the presence of ADE created a gradient in soil nutrients and pH, which changed the capacity to regulate internal tissue calcium to balance nutrition. composition of understory species, especially of ferns and palms. Additionally, the higher In summary, in this southern Amazon forest the increased soil nutrient concentrations nutrient concentration and the more neutral pH on ADE soils were associated with a decrease are a legacy of the humans that inhabited the area. This nutrient addition caused changes in in the richness of fern species. I therefore conclude that the current composition of the understory species composition and decreased fern species richness and had modest effects on understory community in La Chonta is a reflection of past human modification of the soil. forest structure and dynamics. Increases in nutrients, specifically Ca, can cause positive and Heterogeneity of soil fertility across the Amazonian region arises from geological history and negative responses of tree species, resulting in potentially long term effects on the tree species geomorphological processes. This heterogeneity drives forest structure and forest dynamics composition of the forest. across the Amazon region, but at a local scale the role of soils on forest dynamics is not well understood. The study of Amazonian Dark Earths (ADE) opens an opportunity to test how increases in soil fertility could affect forest structure and dynamics at local scales. In chapter 3, I evaluated the effect of ADE on forest attributes, such standing basal area, tree liana infestation and successional composition, defined by the relative presence of pioneers, long- lived pioneers, partial shade tolerant and shade tolerant species in the forest. I also evaluated the effect of ADE on individual components of forest dynamics: basal area growth, recruitment, and mortality. Surprisingly, I found that these fertile ADE affected only few forest attributes and components of forest dynamics. Soil pH was one of the edaphic variables that significantly explained forest structure and dynamics. A higher soil pH increased recruitment of intermediate-sized trees (with stem diameter between 20 and 40 cm) and decreased mortality of large trees (stem diameter > 40 cm). The most important effect of pH, however, was on initial basal area and successional composition, which directly affected growth in basal area of intermediate-sized trees. In conclusion, I found that in this southern Amazonian forest, other environmental factors, such as light availability and liana infestation are stronger drivers of forest dynamics than ADE or soils in general. Increases in soil nutrients can drive plant responses promoting higher growth rates and lower mortality. Plants respond to soil nutrient availability through a suite of traits, by adjusting their biomass allocation patterns, morphology, tissue chemistry and physiology, which allow them successful establishment and regeneration. The higher amount of nutrients found on ADE compared to natural soils could improve the growth of tropical tree species. In chapter 4, I studied the effect of ADE on seedling growth, morphology and physiology in a greenhouse experiment with seedlings of 17 tree species from La Chonta. I found that soil type affected 10 out of 24 evaluated seedling traits. Seedlings did not invest more in roots in non-ADE (to take up scarce soil resources) but they invested in leaves and leaf area in ADE (to enhance light capture), although this did not lead to faster growth rate. Tree species responded differently to an increase in soil Ca concentration, which was 2.4 times higher in ADE than in non-ADE soils. Some species seemed to suffer from Ca toxicity as indicated by

150 151 Summary

151 Samenvatting

Samenvatting

Het Amazonewoud wordt vaak gezien als hét voorbeeld van een ongerept bos, onaangetast door menselijke beschaving. Maar dit romantische beeld klopt niet. De afgelopen jaren is het bewijs toegenomen dat mensen dit landschap intensief beïnvloed hebben gedurende een lange periode in het verleden. De gevolgen van permanente bewoning en de toe- en afname van menselijke bewoning hebben meestal slechts vage sporen achtergelaten in dit landschap, die gemakkelijk over het hoofd kunnen worden gezien. Een voorbeeld van deze onopvallende sporen zijn de bodems die bekend staan als ‘Amazonian Dark Earths’; (ADE), ‘terra preta’. Deze bodems zijn het gevolg van een opeenstapeling van resten van de permanente en semipermanente bewoning door mensen. De bodems danken hun naam aan hun zwarte kleur, die het gevolg is van de aanwezigheid van houtskoolstukjes en organisch materiaal. Ze bevatten ook een hoge concentratie fosfor, calcium (voornamelijk afkomstig uit botfragmenten) en stikstof. Door aanwezigheid van deze nutriënten wordt plaatselijk de bodemvruchtbaarheid verhoogd in de van nature arme bodems in de Amazonebekken en daardoor kan de landbouwopbrengst hoger zijn. Amazonian Dark Earths bodems worden gevonden in Brazilië, Bolivia, Colombia en Peru en bedekken naar schatting 3% van het oppervlak van het Amazonebekken. Na de ineenstorting van menselijke populaties op het Amerikaanse continent, na de komst van Europese kolonisten, zijn de plaatsen waar ADE gevormd werd verlaten en weer overgroeid met vegetatie. Tot op heden zijn de effecten van ADE op volgroeide bossen nog nooit uitgebreid onderzocht en we beginnen pas recent te begrijpen hoe menselijke bewoning in het verleden de compositie en structuur van bossen in het heden heeft bepaald. In deze thesis heb ik onderzocht hoe ADE het bos heeft beïnvloed dat is terug gegroeid na de verdwijning van inheemse bewoners. Ik heb dit gedaan in het bos ‘La Chonta’, dat ligt in het zuidelijke grensgebied van het Amazonebekken in Bolivia. In dit gebied heb ik allereest onderzocht hoe ADE bodems en gewone bodems (niet-ADE bodems) verschillen in hun fysische en chemische karakteristieken. Hiervoor heb ik de bodem in 12 percelen van elk ~27 hectare in kaart gebracht. Vervolgens heb ik bestudeerd hoe veranderingen in de bodem effect hebben op de samenstelling van planten in de onderlaag van het bos (hoofdstuk 2), de structuur en dynamiek van het bos (hoofdstuk 3) en tenslotte, hoe zaailingen van bomen reageren op antropogene veranderingen van bodemeigenschappen (hoofdstuk 4). De beschikbaarheid van gedetailleerde informatie over bodemeigenschappen en bodem-heterogeniteit is noodzakelijk om de effecten van bodems op vegetatie te kunnen bestuderen. Deze informatie is met name belangrijk voor mijn studiegebied, omdat de heterogeniteit van de bodem kan toenemen door de aanwezigheid van ADE. Om die reden, heb ik uitgebreide bodempeilingen uitgevoerd (appendix, hoofdstuk 2 en 3), die me vervolgens in staat stelden om de relatie te bestuderen tussen precolumbiaanse bewoning en veranderingen in de concentratie van voedingstoffen in de bodem. Ik stelde vast dat bodems in het bos in de zuidelijke Amazone vruchtbaarder zijn dan hun tegenhangers in de centrale en oostelijke Amazone. Daarnaast heeft precolumbiaanse bewoning in het studiegebied lokaal geleid tot een verrijking van de bodem, middels een toename van de fosfor-, calcium- en kaliumconcentratie en door een verhoging van de pH. Met deze informatie kon ik specifieke hypotheses testen over de effecten van bodemvruchtbaarheid op de vegetatie.

153 Samenvatting

Samenvatting

153 Samenvatting Samenvatting

Over het algemeen beïnvloedt de bodemvruchtbaarheid de samenstelling van kruiden morfologie en fysiologie van zaailingen van 17 boomsoorten uit La Chonta in een kas (Angiospermae), varens en palmen in de ondergroei van het Amazonewoud. De toename van experiment. Ik vond dat bodemtype een effect had op 10 van de 24 gemeten eigenschappen de bodemvruchtbaarheid op ADE zou dus ook een effect kunnen hebben op de samenstelling van de zaailingen. De zaailingen investeerden niet sterker in de productie van wortels (voor de van deze kruiden, varens en palmsoorten. In hoofdstuk 2, heb ik de effecten onderzocht van opname van schaarse voedingstoffen) wanneer ze groeiden in niet-ADE dan in ADE, maar ze ADE op de compositie, rijkdom en abundantie van soorten in de onderlaag van het bos investeerden wel meer in bladeren en bladoppervlakte wanneer ze groeiden in ADE (om de (varens, kruiden en palmen). Allereest, heb ik uitgebreide bodempeilingen uitgevoerd die me opname van licht te vergroten). Dit leidde echter niet tot een verhoogde groei in ADE. De vervolgens in staat stelde plekken in het bos aan te wijzen met een hogere beschikbaarheid boomsoorten reageerden verschillend op de toename van de Ca concentratie in de bodem, die van voedingsstoffen en met bewijs voor menselijke bewoning in het verleden (bv. de 2.4 keer zo hoog was in de ADE dan in de niet-ADE. Sommige soorten leken te lijden aan aanwezigheid van potscherven). Vervolgens, heb ik bodemvariabelen die geassocieerd calciumvergiftiging, hetgeen vooral duidelijk werd door de verhoogde sterfte van zaailingen worden met ADE, zoals Ca, P, en de pH van de bodem, gecorreleerd met de compositie, in ADE. Andere soorten lijden aan een disbalans in de verhouding van concentraties rijkdom en abundantie van plantensoorten. Ik vond dat de aanwezigheid van ADE een voedingsstoffen in de bladeren; weer andere soorten kregen een verhoogde Ca, P en N gradiënt creëert in bodemvoedingstoffen en pH, en zo de compositie van planten in de concentratie in hun bladeren. Alleen in deze laatste groep, de zogenaamde voedingsstof- onderlaag van het bos beïnvloedt, met name die van varens en palmen. De hogere accumulatoren, was er een positieve relatie tussen Ca concentratie in het blad en de concentratie voedingsstoffen en meer neutralere pH van ADE bodems kwamen overeen met groeisnelheid van zaailingen. In tegenstelling tot onze verwachting, zorgde ADE niet voor een een afname in de rijkdom aan soorten varens. Mijn conclusie is dat de huidige compositie van verhoogde groeisnelheid van zaailingen. Het vermogen van planten om ADE te koloniseren de plantengemeenschap in de onderlaag van het bos in La Chonta een afspiegeling is van hangt mogelijk af van hun reactie op hogere Ca concentratie in de bodem en van hun veranderingen van de bodem door precolumbiaanse bewoners. capaciteit om de calcium concentraties in hun weefsel te reguleren en zo hun voeding te Heterogeniteit in bodemvruchtbaarheid in het Amazonebekken is tot stand gekomen balanceren. door variaties in de geologische geschiedenis en geomorfologische processen. Deze In dit bos in het zuidelijke deel van het Amazonebekken is de toename van heterogeniteit veroorzaakt verschillen op grote schaal in de structuur en dynamiek van bossen voedingstoffen in de bodem een nalatenschap van precolumbiaanse bewoning. De hierdoor in de Amazone. Op een kleinere schaal is het effect van bodem op de bosdynamiek echter nog ontstane toevoeging van voedingsstoffen veroorzaakt een verandering in de samenstelling van slecht onderzocht. De studie van ADE biedt de mogelijkheid om te onderzoeken hoe een plantensoorten in de onderlaag van het bos, het vermindert de diversiteit van varensoorten, en toename in bodemvruchtbaarheid de structuur en dynamiek van bossen beïnvloedt op een heeft een bescheiden effect op de structuur en dynamiek van het bos. De toename van lokale schaal. In hoofdstuk 3, heb ik het effect van ADE op boseigenschappen geëvalueerd, voedingsstoffen, en dan met name die van Ca, kan zowel een positief als negatief effect zoals lichtbeschikbaarheid, het oppervlak van de stammen van de aanwezige bomen hebben en zo resulteren in lange-termijn effecten op de soortensamenstelling van het bos. (grondvlak), de hoeveelheid lianen in het bos en het successiestadium. Het laatste werd bepaald aan de hand van de aanwezigheid van pioniers, langlevende pioniers, gedeeltelijk- schaduwtolerante en schaduwtolerante boomsoorten in het bos. Daarnaast heb ik onderzocht hoe ADE effect heeft op componenten van de bosdynamiek: boomgroei (in grondvlak), aanwas en sterfte van bomen. Verassend was dat ik slechts voor enkele boseigenschappen en componenten van de bosdynamiek een effect vond van de vruchtbare ADE. De pH van de bodem had een significante invloed op bosstructuur en bosdynamiek. Een hogere pH van de bodem leidde tot een hogere ingroei van middelgrote bomen (met een stamdiameter tussen de 20 en 40 cm) en tot een lagere sterfte van grote bomen (stamdiameter > 40 cm). Het belangrijkste effect van de bodem pH was echter op het grondvlak van de stammen van de aanwezige bomen en het successie stadium. Deze factoren hadden weer een direct effect op de groei van middelgrote bomen. Samenvattend heb ik gevonden dat in dit bos aan de zuidelijke rand van het Amazonebekken, andere factoren dan ADE (of de bodem in zijn algemeen) een sterkere invloed hebben op de bosdynamiek, zoals bijvoorbeeld de lichtbeschikbaarheid en hoeveelheid lianen in het bos. Een hogere beschikbaarheid van voedingsstoffen in de bodem kan de groeisnelheid van planten vergroten en hun sterftekans verlagen. Planten reageren op de beschikbaarheid van voedingsstoffen door aanpassingen in diverse eigenschappen: biomassa-allocatiepatronen, morfologie, chemische samenstelling en fysiologie van weefsels. Deze aanpassingen maken een succesvolle vestiging en regeneratie van planten mogelijk. De hogere concentratie voedingstoffen in ADE in vergelijking tot natuurlijke bodems zou de groei van tropische bomen kunnen versterken. In hoofdstuk 4 heb ik de effecten van ADE bestudeerd op de groei,

154 155 Samenvatting Samenvatting

155 Resumen

Resumen

El bosque Amazónico es usualmente visto como el arquetipo del bosque prístino, nunca transformado por los humanos; pero esta visión romántica está lejos de ser una realidad. En los últimos años, se ha incrementado la evidencia de prolongada y extensa modificación del paisaje por los humanos. El registro de procesos tales como la habitación permanente, expansión y contracción de poblaciones humanas no han sido siempre obvios in la Amazonía, dejando leves huellas en el paisaje que no son evidentes a simple vista. Un ejemplo de este, menos evidente, registro es la modificación del suelo conocida como Suelos Negros Amazónicos (ADE por sus siglas en inglés) o terra preta (tierra negra en Portugués), las cuales son el producto de la acumulación de residuos de la habitación humana permanente o semipermanente. Su nombre hace referencia al color negro de estos suelos, lo cual es una consecuencia de la acumulación de materia orgánica y piezas de carbón. Estos suelos contienen altos niveles de fósforo, calcio (originados principalmente de residuos de huesos), y nitrógeno que incrementa la fertilidad de suelos que, por lo general, son pobres en nutrientes, y por lo tanto, favorecen las actividades agrícolas en ellos. Los suelos negros Amazónicos (ADE) se distribuyen en Brasil, Bolivia, Colombia y Perú, y se estima que podrían ocupar hasta un 3% de la cuenca Amazónica. Una de las consecuencias de la disminución en las poblaciones humanas tras el encuentro con los colonizadores provenientes de Europa, ha sido el abandono de sitios donde ADE ha sido formada, generando un nuevo crecimiento de la vegetación. Hasta la fecha, los efectos de ADE en el bosque que ha crecido después del abandono de las tierras por los indígenas, no han sido extensamente evaluados y solo actualmente comenzamos a entender las consecuencias de la habitación de los humanos en el pasado en la composición y estructura del bosque en la Amazonía. En la presente tesis, evalué los efectos de ADE en el bosque que ha crecido después del abandono por los indígenas en el sitio conocido como La Chonta, situado en el límite sur de la cuenca Amazónica en Bolivia. Inicialmente, evalué la magnitud de los cambios en el suelo como consecuencia de la ocupación humana. Para este fin, hice un mapa detallado de los suelos de 12 parcelas de cerca de 27ha cada una. Posteriormente, estudie como los cambios en el suelo afectan la composición de especies en el sotobosque (Capitulo 2), la estructura y la dinámica del bosque (Capitulo 3), y finalmente determine como las plántulas de las especies de árboles responden a los cambios en las propiedades del suelo que son causados por el hombre (Capitulo 4). Información detallada de las características y la heterogeneidad del suelo in el paisaje es necesaria para evaluar los efectos del suelo en la vegetación. Esto fue incluso más importante en el sitio de mi investigación, ya que la heterogeneidad del suelo, se incrementa con la presencia de ADE. Por lo tanto, realice una descripción detallada (Apéndice, Capitulo 2, Capitulo 3) esto me ayudo a entender la relación entre la ocupación humana en la chonta y los cambios en la concentración de los nutrientes del suelo. Gracias a esta información, pude determinar que los suelos en la región sur de la Amazonía son más fértiles que en las regiones

157 Resumen

Resumen

157 Resumen Resumen

central y este. También encontré que la presencia de humanos en el pasado resulto en el determinada por la disponibilidad de luz y la infestación por lianas de los árboles, que por enriquecimiento del suelo, especialmente por un incremento en la concentración de fosforo, ADE o por los suelos en general. calcio, potasio, y un incremento en el pH del suelo. Así, usando esta información, pude El incremento en los nutrientes del suelo puede causar respuestas en las plantas que a evaluar hipótesis especificas acerca de los efectos de la fertilidad del suelo en la vegetación la vez promueven el incremento en tasas de crecimiento y disminución en las tasas de que crece en estos suelos. mortalidad. Las plantas responden a la disponibilidad de nutrientes en el suelo por medio de En el bosque Amazónico en general, las características del suelo tiene influencia en la un conjunto de rasgos: ajustes en los patrones de redistribución de biomasa a las raíces o los composición de plantas herbáceas, helechos y palmas del sotobosque. Así, un incremento en tallos, ajustes morfológicos, ajustes en la fisiología en general y en la química de los tejidos, la fertilidad del suelo en ADE se espera que afecte la distribución de las especies herbáceas, lo cual permite que las plantas tengan un establecimiento exitoso y se puedan regenerar en el los helechos y las palmas del sotobosque. En el Capítulo 2, evalué el efecto de ADE en la bosque. Una mayor cantidad de nutrientes como la que se encuentra en las ADE, en composición, riqueza y abundancia de las especies del sotobosque (helechos, hierbas y comparación con los suelos naturales, podrían mejorar el crecimiento de las especies de palmas). Primero determine los sitios del bosque que contenían mayor cantidad de nutrientes árboles tropicales. En el Capítulo 4, estudie el efecto de ADE en el crecimiento, la morfología y evidencia de ocupación humana en el pasado (por ejemplo, presencia de cerámica). y la fisiología de plántulas en un experimento de invernadero en el cual cultive 17 especies de Posteriormente, correlacione las variables del suelo que usualmente se asocian con ADE, tales árboles tropicales del bosque de La Chonta en ADE y no-ADE. Como resultado de este como Ca, P, y pH del suelo con la composición de especies, la riqueza y la abundancia de experimento, encontré que el tipo de suelos provocó un cambio en 10 de 24 rasgos evaluados individuos. Encontré que la presencia de ADE crea un gradiente de nutrientes y pH en el en las plántulas. Las plántulas no invirtieron más biomasa en las raíces en no-ADE (para suelo, que cambia la composición de especies del sotobosque, especialmente de helechos y tomar recursos más escasos del suelo) pero invirtieron más biomasa en hojas y área foliar en palmas. Además, una mayor cantidad de nutrientes en el suelo y un pH más neutral en ADE ADE (para incrementar captura de luz). Sin embargo, estos cambios morfológicos no está relacionado con una disminución en la riqueza de especies de helechos. En conclusión, la conllevaron a un incremento en la tasa de crecimiento. De forma interesante, las especies composición actual del sotobosque en La Chonta puede ser considerada como un reflejo de la respondieron diferente a un incremento en la concentración de calcio en el suelo, el cual fue modificación del suelo realizada por los humanos en el pasado. 2.4 veces mayor en ADE que en non-ADE. Al parecer, algunas especies fueron afectadas por La heterogeneidad de los suelos en la Amazonía proviene de su historia geológica y de toxicidad de Ca como sugiere un incremento en la mortalidad de plántulas en ADE; otras los procesos geomorfológicos de esta área. Esta heterogeneidad determina la estructura y la especies mostraron un desbalance en los nutrientes lo cual fue observado por cambios en la dinámica del bosque a través de toda la región. Sin embargo, a escala local, el papel que estequiometria de los nutrientes en las hojas. Por ultimo otras especies incrementaron Ca, P y desempeñan los suelos en la dinámica del bosque aun no es completamente entendido. El N concomitantemente en las hojas cuando crecieron en ADE. Solo este último grupo de estudio de ADE representa una oportunidad para evaluar como el incremento en la fertilidad especies, las cuales tienden a acumular nutrientes, mostró una relación positiva entre la del suelo puede afectar la estructura y la dinámica del bosque a escala local. En el Capítulo 3, concentración de Ca en las hojas y la tasa de crecimiento de las plántulas. En contradicción evalué el efecto de ADE en los atributos del bosque, tal como el área basal del bosque, la con las predicciones establecidas, ADE no determina un incremento en el crecimiento de las infestación de lianas en los árboles, y la composición relacionada con la sucesión secundaria plántulas. La habilidad de las plantas para colonizar sectores de ADE en el bosque, podría del bosque, que está definida por la presencia relativa de especies pioneras, pioneras de larga depender de las respuestas de las especies al incremento en el Ca del suelo y su capacidad vida, especies parcialmente tolerantes a la sombra, y especies completamente tolerantes a la para regular el Ca en los tejidos que permita un equilibrado balance de otros nutrientes en la sombra. Adicionalmente, evalué el efecto de ADE en los componentes individuales de la planta. dinámica del bosque: crecimiento del área basal, reclutamiento de árboles y mortalidad. En resumen, en este bosque localizado en el sur de la Amazonía, el incremento en la Sorprendentemente, entontare que ADE afecto pocos atributos del bosque y pocos concentración de nutrientes en el suelo es el legado de los humanos que lo han habitado desde componentes de la dinámica del bosque. El pH del suelo fue una de las variables edáficas que tiempos pasados. La adición de nutrientes en el suelo causo cambios en la composición de las significativamente explico la estructura y la dinámica del bosque. Un suelo con alto pH especies del sotobosque, y disminuyo la riqueza especies de helechos y tuvo un efecto menor incremento el reclutamiento de árboles de tamaño intermedio (con diámetro del tallo entre 20 en la estructura y la dinámica del bosque. Incrementos de los nutrientes en el suelo, y 40 cm) y disminuyó la mortalidad de árboles grandes (con diámetro del tallo mayor a 40 específicamente en Ca, pueden causar respuestas positivas y negativas en las especies de cm). Sin embargo, el efecto más importante del pH del suelo fue en el área basal inicial en las árboles, resultando en efectos que podrían tener impacto a largo plazo en la composición de parcelas evaluadas y la composición relacionada con la sucesión secundaria; lo cual, especies arbóreas en el bosque. directamente afecto el crecimiento en el área basal de árboles de tamaño intermedio. En conclusión, con este estudio encontré que la dinámica del bosque está más fuertemente

158 159 Resumen Resumen

159 Acknowledgements

Acknowledgments

Gabriel García Marquez (the famous Colombian writer) wrote: “los seres humanos no nacen para siempre el día que sus madres los alumbran, sino que la vida los obliga a parirse a sí mismos una y otra vez” (Humans are not born forever the day that their mothers deliver them; life makes them labor themselves time after time). These words perfectly describe what I have felt during my PhD. I have gained so much during this 4.5 years that it feels like I am born again both in science and personally, and in this process many people have contributed significantly. My PhD research would not have been possible without the support of the Interdisciplinary Research and Education Fund (INREF) from Wageningen University that through the Terra Preta program provided the necessary funding. The support received by my supervision team in the Netherlands (Frans Bongers, Marielos Peña Claros and Lourens Poorter) was very valuable; they were very patient with my slow pace, which I really appreciate. Frans, thanks for sharing all your experience and enthusiasm and many moments of sensibility, which meant a lot. Your comprehensive point of view pulled me out from the local forest in Bolivia to the broader picture about the implications of my findings for the Amazonian forest. You not only shared all your wisdom and knowledge but also your music, your house, and introduced me to your adorable and charming wife Yvonne who was always very kind to me. Lourens, thanks a lot for being so supportive. Your sharp perspective in science (straight to the point) made me aware of every possible detail that will matter to fit all pieces of the big picture on each ecological process and pattern; part of my rebirth in science is thanks to you. Thanks for being enthusiastic and trying to encourage me all the time, even when I was on the foggiest path. Marielos, I do not have words to thank you, not only for your academic supervision with such an insightful mind and lots of experience but also for personal support. Thanks for Second workshop Terra Preta Program, Manaus, Brazil (2012) your trust and unconditional embrace since my arrival to the Netherlands. You and your family (Lourens and Franka) took me to trips, museums, dinners, family moments that meant a lot to me; as a candle, you warmed the coldest moments in the high latitudes. I am fully thankful with my local supervisor, Marisol Toledo, who contributed not only with the logistics needed in Santa Cruz and at IBIF but also with her feedback on manuscripts and chapters. My PhD is part of an interdisciplinary program: the Terra Preta program. My program’s colleagues: Wellinton, Tatiana, Angela, Isabel, Paulo, Carlos, Clarita, Camilo and specially André, made the experience of living in The Netherlands easier to carry. Since our arrival to The Netherlands, we, ‘el Cardumen’, tried to support each other in all possible ways, for which I want to thank you. I also want to acknowledge the enlightening discussions in our regular meetings in Wageningen and in the Terra preta workshops that increased my knowledge and challenged my ecological background to be extended in the context of these special anthrosols. The director of the Terra preta Program, Thom Kuyper, deserves special

160 161 Acknowledgements

Acknowledgments

161 Acknowledgements Acknowledgements

acknowledgement for being supportive in the moments when was needed. Thom was Leo Goudzwaard, Jan den Ouden, Ellen Wilderink, Gustavo Schwartz, Patrick Jansen, Milena objective and critical when it was necessary to confront my own ideas and brought them to Holmgren, Emiru Birhane, Abeje Eshete, Tefera Mengistu, Motuma Tolera, Gabriel Muturi, the wider perspective of the entire program. All the experts and researchers associated to the Canisius Mungunga, Mathieu Decuyper, Venceslas Goudiaby, Han Van Tiep, Hooz Angela program contributed significantly to my thesis, especially Ellis Hoffland, Tjeerd-Jan Stomph, Chaparro, Hellen Esser, Monique Weemstra, Huicui Lu, Paul Copini, Merel Jansen, Charles Clement, James Fraser, Morgan Schmidt, Anne Rapp, Eduardo Neves, Manuel Addisalem Bekele, Peter van der Sleen, Peter Groenendijk, Linar Akhmetzyanov, Kwame Arroyo-Kalin, and Simon Jeffery. Wenceslau Teixeira deserves all my gratitude since he was Oduro, Jamir Alfonso, Federico Alice, Kathelyn Paredes, Tanvir Shovon. Thanks to Peter van a key contributor to my work. His knowledge about terra preta, and soils in general was der Sleen for helping me to translate my summary to Dutch. A special acknowledgement to fundamental. I knew very little about soils when I started this project, and a big proportion of more closer colleagues Lucy Amissah, Geovana Carreño, Marlene Soriano, Masha van der what I know today is thanks to Wenceslau. Sande, Madelon Lohbeck, Mart Vlam, and to my ‘compañera’ Catarina Jakovac, for full The Instituto Boliviano de Investigación Forestal (IBIF) provided me the logistic support, discussions, and company. Joke Jansen, you have been always very kind and willing support needed in Bolivia. People at IBIF became like a family when I lived in Bolivia. Just to help me with everything, thank you so much. Claudius van de Vijver and Lennart by sharing the daily routine they made my days more enjoyable. Thanks To Fabiola Clavijo Suselbeek were always very supportive in all aspects concerning to the meaning of achieving for being a nice office mate, to Alfredo Alarcón for facilitating help when needed, to Daniel a PhD in The Netherlands, thanks. Espinosa for teaching me and help me with ARCMAP, to Sandra Velasco for being always The two and half years that I lived in Bolivia were rather short to assimilate such nice very helpful, to Marlene Soriano for being always so positive and enthusiastic and to Marisol experience, nice country and nice people. There, I met incredible persons that made me feel Toledo for your support. Special thanks go to Nataly Ascarrunz for full support as executive really at home. Mariely Negrette, Andrea Fernandez, Miriam Coraite, Steffen Reichle and director of IBIF. Thanks to Jaqueline Tapia for support in all issues related with computers Yuvinka Gareca, Don Wolfango Peña and Doña Matilde Claros, Esther Peña Claros and her and also for her friendship. I just do not have more words to express my gratitude to Laly family, thanks for your hospitality during all my time in Santa Cruz. Maikol Muñoz, Mario Dominguez, who really made me feel like at home, and to Juan Carlos Licona who offered me Diego Lilienfeld, Rene Mendez, Carmen Mateu and Daniel Alarcon, thanks for your unconditional support, help and friendship. Many students associated to IBIF helped me in friendship and support when needed. Hanna Dahlstrom and Doña Alicia de Arana, thanks for my research: thanks to Delma Sarmiento for help with the greenhouse, to Alejandra Romero the unforgettable dance adventures and the best company. for help with plant labels, to Veronica Sisepe for help with trait measurements, to Victor Living in the high latitudes was cold, but there were people that made it warmer. Diego for help in the field, to Maya Cuchallo for help with soil sieving, and specially to Thanks a lot to all the friends in The Netherlands, Natalia Patiño, Diana Londoño, Abdul Miguel Angel Fernandez for measurements of soil pH and plant traits. Drame, Olivier de Coster, Popko Wiersma, Giovani Melo, Oscar Castellanos, especially to In the field the support given by personnel of La Chonta company was very crucial, the dancers, Domenico Badalati, Elvis, Arjan Stroo, Anton Versteeg, Pentcho Bodegom and especially Don Luis Vaca, and Ing. Ronald Hurtado, who were always helpful with the field Imke Bertrams. logistics and solved all possible problems that we had in the field station. Being far from my homeland was difficult but it would have been harder if I would Digging soils and carrying earth in the field is not an easy work, and this could not not have had the support, from long distance of my family and friends. Father and mother, have been possible without the field assistants. Very important in my research was the thanks for your support always, I could not have gone this far without your kind and inspiring collaboration of Don Ricardo Mendez, not only for the work done in the field and also in the words that gave me the necessary energy for not giving up (Padre y madre, gracias por su greenhouse in Santa Cruz, but for lots of long and joyful chats in the field. All my gratitude apoyo siempre, no hubiese podido llegar tan lejos sin sus palabras cálidas e inspiradoras que also to Lorgio Vaca, Don Bruno Vaca, Don Ruddy Rodriguez, Don Angel Mendez (Don me dieron la energía necesaria para no desfallecer). Antonio and Cesar thanks for your Wicho), Don Jorge Carvajal , and Don Adrian Dominguez. brotherhood and positive support. My sister Mary, thanks for your kind and encouraging The first chapter of my thesis was a team work. Thanks to Yannick Klomberg, not words always. Olguita, Mauricio, Janeth, and Juan, thanks for your reinforcement. My close only for all the field work (digging holes, carrying soils and plant counting), also for friends in Colombia, Cristy, Anita, Jaime, and in USA Douglas thank you always. The source significant contribution in data analyses and writing. Jonna van Ulzen and Tjalle Boorsma of my inspiration in ecology and botany, Carolina Murcia and Ricardo Callejas, thanks. The were also very important help in the field, so thank you. person that helped me to be born again, Patricia Quijano, thanks. My very good friend Agus In Wageningen, my days pass by in the Lumen building with all my colleagues from Muñoz was always close (although at long distance), thanks for your advice, discussion, FEM. Collaboration, nice and enlightening discussions, support, company, warm social critics, encouragement, patience and friendship, I owe you a lot. events, laughs, coffees, cakes, all those things made me feel very fortunate to be part of this group. Frank Sterck, Ute Sass-Klaassen, Frits Mohren, Pieter Zuidema, Danaë Rozendaal,

162 Acknowledgements Acknowledgements

163 Bibliography

Short Biography

Estela Maria Quintero Vallejo was born in Medellin, Colombia on May 2nd,1975. She studied elementary and high school at Colegio San Juan Bosco. She studied Biology at Universidad de Antioquia, Medellin where she was active in student organizations, such as the National Association of Biological Science Students (ANECB, in Spanish).

During her studies, she did an internship at the Corporations for Biological Research (CIB, in Spanish) where she carried out her Bachelor’s thesis on Mutagenesis of Bacteria to be used as safe biological control. In 2000, she participated in the Tropical Ecology and Conservation Course organized by Organization for Tropical Studies (OTS) in Costa Rica.

After graduating in 2000, she joined a young research program funded by Wildlife Conservation Society (WCS) in Cali, Colombia under the supervision of Carolina Murcia and Gustavo Kattan. In this three year program she carried out research on the restoration ecology of watersheds in the Andean cloud forests. She travelled to the USA in 2004 for her MSc studies at Department of Ecology, Evolution and Organismal Biology of Ohio State University. For her MSc thesis she worked on the life history and physiological strategies of native and invasive species of the prairies. In 2008 she returned to Medellin, where she was appointed as part time associate professor at the University of Antioquia, regional Campus. She taught courses on general ecology, evolution and plant physiology for 2 years. In 2011 she started her PhD in the Terra Preta interdisciplinary program at Wageningen University together with six other PhD students from Colombia and Brazil. This dissertation forms the result of her research work.

165 Biography

Short Biography

165 PE&RC Training and Education Statement

PE&RCWith the Training training and and Education education activitiesStatement listed below the PhD candidate has complied with the requirements set by the C.T. de Wit Graduate School for Production Ecology and Resource WithConservation the training (PE&RC) and education which activitiescomprises listed of a minimumbelow the total PhD of candidate32 ECTS has (= complied 22 weeks with of activities) the requirements set by the C.T. de Wit Graduate School for Production Ecology and Resource ConservationReview of literature(PE&RC) (4.5which ECTS) comprises of a minimum total of 32 ECTS- (=The 22 effectsweeks of of anthropogenic activities) dark earth on the composition, structure and dynamics of an Amazonian forest ReviewWriting of literature of project (4.5 proposal ECTS) (4.5 ECTS)

- -TheThe effects effects of ofanthropogenic anthropogenic dark dark earth earth on on the the composition, composition, structure structure and and dynamics dynamics ofof anan AmazonianAmazonian forest forest (2011) WritingPost-graduate of project courses proposal (5.7 (4.5 ECTS) ECTS)

- -TheLinear effects models; of anthropogenic PE&RC (2011) dark earth on the composition, structure and dynamics of an -AmazonianGeneralized forest models; (2011) PE&RC (2011) Post-graduate- Linear courses mixed models;(5.7 ECTS) PE&RC (2011) - Bayesian statistics; PE&RC (2011) - -LinearIntroduction models; toPE&RC R; PE&RC (2011) (2012) - -GeneralizedMultivariate models; statistics; PE&RC PE&RC (2011) (2012) - -LinearSoil mixedtaxonomy; models; ISRIC PE&RC (2014) (2011) Invited- Bayesian review statistics; of (unpublished) PE&RC (2011)journal manuscript (2 ECTS) - Introduction to R; PE&RC (2012) - -MultivariateBiotropica: statistics; shrub cover PE&RC influence (2012) on seedling growth and survival following logging of a - Soiltropical taxonomy; forest ISRIC (2014) Invited- reviewBiotropica: of (unpublished) how tightly linkedjournalare manuscript pericopsis elata (2 ECTS)(Fabaceae) patches to anthropogenic disturbances in South-eastern Cameroon? Some elements of a long story Deficiency,- Biotropica: refresh, shrub brush cover-up influence courses on(2 seedlingECTS) growth and survival following logging of a tropical forest - -Biotropica:Advanced how course tightly on forestlinked ecologyare pericopsis and forest elata management(Fabaceae) patches to anthropogenic disturbances in South-eastern Cameroon? Some elements of a long story Deficiency,Competence refresh, strengthening brush-up /courses skills courses (2 ECTS) (2.4 ECTS)

- -AdvancedCompetence course assessment; on forest ecologyWageningen and forestGraduate management School (2011) - Information literacy and Endnote course; Wageningen Library (2011) - Scientific writing; Wageningen in’to Languages (2014) Competence strengthening / skills courses (2.4 ECTS)

- Competence assessment; Wageningen Graduate School (2011) - Information literacy and Endnote course; Wageningen Library (2011) - Scientific writing; Wageningen in’to Languages (2014) 167

167 PE&RC Training and Education Statement

- -TheLinear effects models; of anthropogenic PE&RC (2011) dark earth on the composition, structure and dynamics of an -AmazonianGeneralized forest models; (2011) PE&RC (2011) Post-graduate- Linear courses mixed models;(5.7 ECTS) PE&RC (2011) - Bayesian statistics; PE&RC (2011) - -LinearIntroduction models; toPE&RC R; PE&RC (2011) (2012) - -GeneralizedMultivariate models; statistics; PE&RC PE&RC (2011) (2012) - -LinearSoil mixedtaxonomy; models; ISRIC PE&RC (2014) (2011) Invited- Bayesian review statistics; of (unpublished) PE&RC (2011)journal manuscript (2 ECTS) - Introduction to R; PE&RC (2012) - -MultiBiotropica:variate statistics; shrub cover PE&RC influence (2012) on seedling growth and survival following logging of a - Soiltropical taxonomy; forest ISRIC (2014) Invited- reviewBiotropica: of (unpublished) how tightly linkedjournalare manuscript pericopsis elata (2 ECTS)(Fabaceae) patches to anthropogenic disturbances in South-eastern Cameroon? Some elements of a long story Deficiency,- Biotropica: refresh, shrub brush cover-up influence courses on(2 seedlingECTS) growth and survival following logging of a tropical forest - -Biotropica:Advanced how course tightly on forestlinked ecologyare pericopsis and forest elata management(Fabaceae) patches to anthropogenic disturbances in South-eastern Cameroon? Some elements of a long story Deficiency,Competence refresh, strengthening brush-up /courses skills courses (2 ECTS) (2.4 ECTS)

- Competence assessment; Wageningen Graduate School (2011) - Information literacy and Endnote course; Wageningen Library (2011) - Scientific writing; Wageningen in’to Languages (2014) 167

167 PE&RC Annual meetings, seminars and the PE&RC weekend (2.4 ECTS) - PE&RC PhD Weekend (2011) - PE&RC Day (2012) - PE&RC Weekend (2014) - PE&RC Day (2014)

Discussion groups / local seminars / other scientific meetings (6.95 ECTS) - Terra Preta discussion group (2011) - Terra Preta workshop; Wageningen, the Netherlands (2011) - Wageningen evolution and ecology seminars (2011) - Terra Preta workshop; Manaos, Brasil (2012) - Ecological theory and application (2012) - Terra Preta workshop; Leticia, Colombia (2013)

International symposia, workshops and conferences (3.8 ECTS) - Annual meeting of Association for Tropical Biology; Bonito, Brasil (2012) - Netherlands Annual Ecology Meeting (NAEM); Lunteren, the Netherlands (2014)

Supervision of a MSc student - The effect of Amazonian dark earths on composition, diversity and density of understory herbs, ferns and palms in a Bolivian tropical forest

169 168 PE&RC Annual meetings, seminars and the PE&RC weekend (2.4 ECTS) - PE&RC PhD Weekend (2011) - PE&RC Day (2012) - PE&RC Weekend (2014) - PE&RC Day (2014)

Supervision of a MSc student - The effect of Amazonian dark earths on composition, diversity and density of understory herbs, ferns and palms in a Bolivian tropical forest MART VLAM MART INVITATION WAGENINGEN PARANYMPHS: [email protected] AT 16:00 IN THE AULA OF AT 16:00 CRISTINA LOPEZ GALLEGO WAGENINGEN UNIVERSITY, UNIVERSITY, WAGENINGEN [email protected] STRUCTURE AND DYNAMICS STRUCTURE AND DYNAMICS GENERAAL FOULKESWEG 1A, 1A, GENERAAL FOULKESWEG OF MY PHD THESIS ENTITLED: OF MY PHD [email protected] ATTEND THE PUBLIC DEFENSE THE PUBLIC DEFENSE ATTEND ON WEDNESDAY JULY 1ST 2015 1ST JULY ON WEDNESDAY YOU ARE CORDIALLY INVITED TO ARE CORDIALLY YOU LEGACIES OF AMAZONIAN DARK DARK LEGACIES OF AMAZONIAN EARTHS ON FOREST COMPOSITION, ON FOREST COMPOSITION, EARTHS VALLEJO ESTELA MARÍA QUINTERO TERRA PRETA PROGRAM TERRA PRETA STRUCTURE AND DYNAMICS LEGACIES OF AMAZONIAN DARK DARK AMAZONIAN OF LEGACIES EARTHS ON FOREST COMPOSITION, COMPOSITION, ON FOREST EARTHS Estela María Quintero Vallejo

LEGACIES OF AMAZONIAN DARK EARTHS ON FOREST COMPOSITION, STRUCTURE AND DYNAMICS | TERRA PRETA PROGRAM Estela María Quintero Vallejo