University of São Paulo Luiz De Queiroz College of Agriculture Local and Landscape Drivers of Tropical Forest Regeneration in A

University of São Paulo Luiz de Queiroz College of agriculture

Local and landscape drivers of tropical forest regeneration in agricultural landscapes of the Atlantic Forest of Brazil

Ricardo Gomes César

Thesis presented to obtain the degree of Doctor in Sciences. Area: Forest Resources. Option in: Conservation of Forest Ecosystems

Piracicaba 2018 Ricardo Gomes César Forester

Local and landscape drivers of tropical forest regeneration in agricultural landscapes of the Atlantic Forest of Brazil versão revisada de acordo com a resolução CoPGr 6018 de 2011

Advisor: Prof. Dr. PEDRO HENRIQUE SANTIN BRANCALION

Thesis presented to obtain the degree of Doctor in Sciences. Area: Forest Resources. Option in: Conservation of Forest Ecosystems

Piracicaba 2018 2

Dados Internacionais de Catalogação na Publicação DIVISÃO DE BIBLIOTECA – DIBD/ESALQ/USP

César, Ricardo Gomes Chronossequence and landscape effect in tropical forest succession / Ricardo Gomes César. - - versão revisada de acordo com a resolução CoPGr 6018 de 2011. - - Piracicaba, 2018. 165 p.

Tese (Doutorado) - - USP / Escola Superior de Agricultura “Luiz de Queiroz”.

1. Florestas secundárias 2. Restauração florestal 3. Ecologia de paisagens 4. Regeneração natural I. Título

ACKNOWLEDGEMENTS

Conmigo vienen, vienen los de atras! - Calle 13

There are many, many people behind the dozens of pages and four years of this work. The name of a few of them should be in the cover, along with me and my tutors. However, this would violate scientific writing conventions and would lead to a long and awkward discussion about the restrictions of scientific publications and the relevant people for the development of science. In other words, a discussion that I would certainly lose. Besides, I already violated these conventions several times before getting to this final version so, to avoid further hassle for the library staff, mentioning these people that are very dear to me just in this section will have to do.

Regarding gratitude, the first time I felt it was for my family, so it would be fair to start from them. They were also the first to hear me talking about nature, science, the universe and everything else. For caring, hearing, inspiring, laughing and arguing (and not sleeping a single time while I was explaining my thesis), I thank my parents Francisco Ignácio Giocondo César and Marli Valverde Gomes, my sister Sofia Gomes César and my beloved Glaucia Zaina Gonsalves. I am also thankful for my grandparents Ricardo Gomes Filho and Antonieta Valverde Gomes for the weekends in the country house and for the first books about nature. I was also inspired by my grandfather and forester Geraldo de Barros Cesar, which I would like to have known better. Finally, I am thankful for my aunts Marisa and Magali Valverde Gomes, for being always there supporting me!

Glaucia, in a 14 billion-old universe that extends to who-knows-where, with millions of species in this 210 million km² planet, where seven billion people live in thousands of cities, you make the best place to be at your side, and the best time now.

I hope that the reader notices that this work was masterfully guided mainly by two people that pruned hedges, opened doors, pointed paths and donated themselves. For better or worse, all graduate students have a tutors. I was lucky to have tutors that became friends along this work. Every good tutor questions, research, write and apply. But you do all of this smiling and looking in the eyes of people. I have so much to say to both of you, that I prefer to keep it in simple words that encompass a lot: thank you Pedro and Robin! Your love move forests! (To Pedro this acknowledgement extends to the end of my undergraduate studies, when we first worked together).

These pages look clean, but they hide a lot of sweat, dust and cuts from many volunteers that faced the remaining and forgotten secondary forests, where we collected most of our data. I’m very thankful to GEPEM for their help since the early stages of this work. Thank you Bianca Torres, Hellen Pecchi, Kerolin Amarante, Julia Martins, Ana Carolina Yamaguchi and Karen Beneton. I also thank GADE and mainly Leandro Degrandi. I thank Paula Meli, Adriano Adinolfi Ito (best tapioca in the field!), Marina Peluci, Monica Borda Niño, Saulo E. X. F. de Souza, Felipe Brancalion, Carol Giudice Badari, Flávia Garcia Flórido (that endured several wasp stings), Renan Afoacy (that rescued me out of the forest when I injured my eye) and Henrique Sverzut Freire de Andrade. Interested and motivated people like you make this kind of project possible.

I’m super thankful to Vanessa Moreno, a strong ally during data gathering in the field. Thank you for your attention to details, for reviewing our data, helping in almost every field expedition of this work and having the energy to sample even more forests. Thank you for your help in the ups and downs, stresses and laughs, ticks and mud of the field work. And for the brainstorm to guess if it will rain or not.

I am very thankful for the help of Alex Mendes: the one that never spoiled Game of Thrones, the watcher of canopies, guardian of epiphytes, the one that went North and back, the wise of endless patience. Thank you Alex!

Research is always bigger than any single person, therefore I acknowledge for my colleagues that are also investigating the different aspects of second-growth forests in the Corumbataí watershed. Thank you Vanessa Moreno, Alex Mendes, Vanessa Oliveira, Daniella Schweizer, Monica Borda Niño, Danilo Almeida (thanks for the help with statistical analyses in the first chapter!), Juliano van Melis (thank you very very much for all the help in the second chapter and in many other analyses from other projects!), Marina Peluci, Eduardo Alexandrino, Alessandro Palmeira and Paulo Guilherme Molin. I just noticed that, even with so many people collecting data and asking questions about these forests, there is still so much more to collect and ask.

These pages also travelled a lot! The foreign collaborators that I had the privilege of working with were Prof. Jos Barlow, Prof. Fernando Espírito-Santo, Alessandro Palmeira and Leighton Reid. Many “obrigados” to all of you.

These pages are also stained with laughter, conversations and philosophic discussions. For enchanting the everyday routine, I am thankful to LASTROP: Andréia Moreno, Daniella Schweizer (and Carlos and Lucia), Paula Meli, Andreia Alves Erdmann, Carina Camargo Silva, Juliano van Melis, Daniel Palma Perez Braga (the main source of philosophical discussions), Vanessa Erler Sontag (who will build a time machine someday), Danilo Almeida, Fabrício Hernani Tinto (for the food supply), Frederico Domene, Luciana Maria Papp (who also gets extra points for the food supply), Marina Melo Duarte (for helping eating all the food supply with me), Monica Borda Niño, Nino Tavarez Amazonas, Vanessa Souza Moreno, Luis Eduardo Bernardini, Carol Giudice Badri, Prof. Edson José da Silva Vida and Pedro Henrique Santing Brancalion.

For making (much, much) more than their professional requirements in all material logistics, equipment, bureaucracy, keys, resources and conversations; I am very thankful to Andréia Moreno, Giovana Oliveira and Jeferson Polizel. You certainly help moving the wheels of graduate (and undergraduate) activities work better.

For contributing to my mental sanity and insanity during all this process, I thank my friends from “Raízes e Asas”, mainly Dante Moretti, Raquel Galvani, Fabio Camolesi, Amarílis Ibanez, Ivo Racca, Bárbara Contarini, Paulo Santini, Jéssica Telhada and Beatriz Abud.

Finally, I acknowledge the FAPESP for the funding granted by Processes #2014/14503-7 and 2017/05662-2.

“A minha surpresa é só feita de fatos De sangue nos olhos e lama nos sapatos”

Chico Buarque de Hollanda

SUMMARY

RESUMO………………………………………………………………………………………………………….7 ABSTRACT………………………………………………………………………………………...... 8 1. INTRODUCTION ...... 9

2. EARLY ECOLOGICAL OUTCOMES OF NATURAL REGENERATION AND TREE PLANTATIONS FOR RESTORING AGRICULTURAL LANDSCAPES……………………...……..17 2.1. INTRODUCTION ...... 17 2.2. METHODS ...... 19 2.3. RESULTS ...... 24 2.4. DISCUSSION ...... 29

3. SURROUNDING LAND USE AND FOREST COVER AS MAJOR DRIVERS OF BIOMASS AND TREE DIVERSITY RECOVERY BY SECOND-GROWTH TROPICAL FORESTS IN AGRICULTURAL LANDSCAPES………………………………………………………………………..39

3.1. INTRODUCTION ...... 39 3.2. METHODS ...... 41 3.3. RESULTS ...... 48 3.4. DISCUSSION ...... 52 4. FINAL CONSIDERATIONS………………………………………………………………………...... 61

APPENDIXES ...... 65

RESUMO

Fatores locais e de paisagem sobre a regeneração natural em paisagens agrícolas da Mata Atlântica brasileira

Florestas estabelecidas pelo plantio de mudas de espécies nativas (PL) e por meio do estabelecimento de florestas secundárias pela regeneração natural (FS) são as principais comunidades geradas durante a restauração florestal em larga escala. A escolha dessas estratégias está condicionada potencial de regeneração natural do local, mas tão importante quanto a decisão sobre métodos de restauração, são as diferenças das comunidades que essas escolhas podem gerar. As FS são heterogêneas e, enquanto existe uma literatura crescente dos fatores que afetam a chance do estabelecimento das FS, poucos trabalhos abordam os fatores que influenciam os atributos dessas florestas. Nesse contexto, nosso trabalho busca identificar as diferenças entre PL e FS e as variáveis locais e de paisagem que afetam os atributos das FS. Para tal, amostramos a comunidade arbórea de florestas estacionais semideciduais de Mata Atlântica estabelecidas naturalmente (FS) e por PL em paisagens agrícolas na bacia do Rio Corumbataí, no estado de São Paulo. Observamos que os PL apresentam biomassa semelhante às SF e maior riqueza de espécies. No entanto, as PL também apresentam menor abundância de indivíduos jovens, indivíduos zoocóricos e lianas. A composição de espécies entre essas florestas também difere. As FS estabelecidas em plantios abandonados de eucalipto apresentaram riqueza de espécies e biomassa de espécies nativas semelhantes a outras florestas secundárias. No entanto, os atributos das SF variam consideravelmente. Nesse contexto, as FS apresentam elevado potencial de provimento de alimento para a fauna e estocagem de carbono de maneira custo-eficiente, enquanto que as PL podem ter sua permanência em longo prazo comprometida pela falta de indivíduos jovens. Em seguida, investigamos as variáveis que direcionam a heterogeneidade observada nas FS utilizando modelos mistos lineares generalizados para estimar a influência de variáveis locais e de paisagem na biomassa, densidade de espécies, área basal de árvores zoocóricas e estrutura filogenética das FS amostradas. Plantios de cana-de-açúcar próximos as FS reduzem a biomassa e área basal de indivíduos zoocóricos, enquanto que a cobertura florestal da paisagem aumentou a densidade de espécies e a diversidade filogenética. A idade da floresta apresentou importância secundária ou nula para os atributos estudados. Nossos resultados ressaltam a importância de práticas agrícolas que minimizem os danos em florestas próximas e de mecanismos que favoreçam a cobertura florestal nativa em paisagens agrícolas, a fim de fomentar o potencial dessas florestas em prover serviços ecossistêmicos e conservar a biodiversidade. A escolha entre facilitação do estabelecimento de FS ou PL visando a restauração florestal está condicionada ao contexto local e de paisagem onde serão realizadas as ações de restauração. Apesar de ambas as abordagens apresentarem potencial para cumprir os objetivos dos projetos de restauração, atenção especial deve ser dada ao recrutamento de novos indivíduos para manter a sustentabilidade de PL, enquanto que práticas agrícolas menos impactantes e paisagens agrícolas com maior cobertura florestal nativa podem aumentar o potencial de SF em prover serviços e conservar a biodiversidade.

Palavras-chave: Florestas secundárias; Restauração florestal; Ecologia de paisagens; Regeneração natural

ABSTRACT

Local and landscape drivers of tropical forest regeneration in agricultural landscapes of the Atlantic Forest of Brazil

Forests established through native seedling planting (PL) and the establishment of secondary forests through natural regeneration (SF) are the main outcomes of large scale forest restoration. The decision making process of these approaches is conditioned by resilience. But the different outcomes of these approaches are as important as the decision making. SF are heterogeneous and - although there is a growing literature of the drivers of forest establishment – few works analyzed drivers of attributes of these recently established forests. In this context, our work aims to identify the differences between PL and SF and the local and landscape variables that affect SF attributes. To do so, we sampled the tree community in seasonal semideciduous forests of the Atlantic Forest established naturally (SF) and PL in agricultural landscapes in the Corumbataí Watershed, São Paulo State, Brazil. We observed that PL has similar biomass to SF and higher species richness. However, PL also showed lower abundance of young trees, animal-dispersed trees and lianas. Species composition between PL and SF also differs. SF established in abandoned eucalypt plantings showed species richness and biomass of native species similar to other SF forests. However, SF attributes vary greatly. In this context, SF show a large potential for providing food for fauna and storing carbon in a cost-efficient way. While PL can also provide these benefits, it may have its long-term sustainability compromised by the lack of regenerating trees. We then proceeded to investigate drivers of the heterogeneity observed in SF using generalized linear mixed models to estimate the effect of local and landscape variables on the biomass, species density and basal area of animal-dispersed trees of the SF sampled. SF surrounded by sugarcane plantations had lower biomass and basal area of animal- dispersed trees, while native forest cover in the landscape increased species density of SF. Forest age showed little or no importance in predicting SF attributes. These results highlight the importance of low impact agricultural practices and of strategies that increase native forest cover in agricultural landscapes, in order to increase the potential of SF to provide ecosystem services and conserve taxonomic diversity. The choice between establishing PL or fomenting SF for forest restoration is conditioned to the local and landscape context where restoration actions will be carried out. Although both approaches can potentially fulfill the objectives of restoration projects, special attention must be given to the recruitment of new individuals to maintain PL sustainability, while less impacting agricultural practices and more forested agricultural landscapes may increase the SF potential to provide ecosystem services and conserve biodiversity.

Keywords: Second-growth forests; Forest restoration; Landscape ecology; Natural regeneration 9

1. INTRODUCTION

Scaling up forest restoration is increasingly necessary in order to mitigate the current environmental crisis and reach ambitious international environmental goals (MELO et al., 2013a). Strategies to reach these objectives must evaluate the potential for natural regeneration in areas to be restored and prioritize approaches that establish the desired native communities at the best cost-efficiency possible (HOLL; AIDE, 2011). Interventions for the restoration of ecossystems can be classified in a gradient of human intervention, from abandoning cultivated areas for natural regeneration (low intervention) to high diversity seedling plantings (high intervention). Both approaches are complementary and valuable to fulfill the demands of forest restoration, and their contexts and costs are constantly discussed in the literature (DE GROOT et al., 2013; ZAHAWI; REID; HOLL, 2014). Nevertheless, the communities established by these approaches are the true legacy of restoration interventions and, ultimately, will determine the value of restoration actions for conservation of biodiversity and provision of ecosystem services. Thus, identifiying the differences in the communities established when both approaches were properly employed is key for long-term adaptative management of areas under restoration and to achieve their associated goals. Restoration ecology slowly consolidated as a science in the early works of Aldo Leopold (1949). Currently, the International Society for Ecological Restoration (SER) defines restoration as the “process of assisting the recoveryof an ecosystem that has been degraded, damaged or destroyed”. This definition emphasizes ecological restoration as an intentional human activity, differing it from ecosystem recovery through natural processes without human intervention. The investiment on ecological restoration only justifies itself if it the area restored is able to sustain itself indefinitely through time, an aspect that is just now being considered and analyzed (REID et al., 2017). In Brazil, restoration ecology and ecological restoration have historically developed through several dynamic phases which culminated in our current (but not consolidated) practices of active forest restoration (BELLOTTO et al., 2009). Brazil has the privilege of relying on relatively robust envinromental legislation, such as the Native Vegetation Protection Law (Law 12.651/2012). Such legislation and market pressures have historically acted as leverage mechanisms to foment restoration practices in pre-determined sites, mostly through large-scale plantations (RODRIGUES et al., 2009, 2011). In this context, desired restoration outcomes must be somewhat predictable and acquired as soon as possible. Thus, tree seedling plantings were broadly used near watercourses and on other environmental protection areas to establish forests. There are also many case studies of other restoration techniques, such as nucleation (BECHARA et al., 2016) and direct seedling (COLE et al., 2011) being successfully employed for forest restoration. Forest succession is a subject that has been extensively studied in ecology (CLEMENTS, 1916; PICKETT; CADENASSO; MEINERS, 2008), and was part of the foundation of the scientific investigations in this field, a long time before restoration ecology established as a science (CLEMENTS, 1916). The early works of forest succession focused on the alterations in the composition, structure and function of ecosystems on the long term, with the main objective to describe temporal changes in the plant community and to evaluate if and how these ecosystems attributes are recovered in second-growth forests (CHAZDON, 2014; MARTIN; NEWTON; BULLOCK, 2013; NORDEN et al., 2009). These early stages of successional studies focused only on natural disturbances (i.e. non-human mediated, such as gap openings by falling trees, landslides, underground soil exposure by falling trees and burrowing animals, river floods and dune movement) to explain sucessional processes. At this phase, we theorized that successional changes would ultimately lead to a somewhat predictable and stable climax community given enough time (CLEMENTS, 1916; PHILLIPS, 1934). 10

As successional research developed, the predictable and climax-based sucessional paradigm was increasingly questioned as several authors criticized the inflexibility of the stable climax concept and argued in favor of the different possible successional trajectories (CONNELL; SLATYER, 1977; EGLER, 1954; WHITTAKER, 1953). One notable essay that greatly fomented the change in ecology paradigm were the sucessional models based on facilitation, tolerance and inhibition proposed by (CONNELL; SLATYER, 1977). From the 1980s onward the paradigm of forest sucession gradually changed from a convergent, climax-based, stable perspective that did not encompass human disturbance, to a more fluid sucessional perspective that encompassed continuous change that could lead to several trajetories caused by abiotic conditions, species conditions, landscape context and human and natural disturbances. Forest succession research is frequently based on chronossequences, which is the analysis of similar forest formations in the same region, but with different ages (LETCHER; CHAZDON, 2009; WALKER et al., 2010). This method is more commonly used, since the alternative method (direct observation) is more difficult to monitor given the long duration of vegetation change, mainly in old-growth forests. The chronossequences method alone may not be ideal to study ecological succession (WALKER et al., 2010), given that remnants may have undergone distinct past disturbances and are currently subjected to distinct environmental and landscape context. Therefore, the successional pattern observed in one remnant may not occur in other remnants in the same region (ARROYO- RODRIGUEZ et al., 2015; WALKER et al., 2010). Such restriction highlights the role of several factors, such as land use before forest regeneration, landscape matrix, fragmentation and human disturbances in successional trajectories. Although these factors are considered as an unwanted “noise” for chronossequence studies, they also represent an opportunity to identify factors other than age that affect forest succession. Only more recently factors such as remnant size and connectivity, matrix, dispersal and establishment limitation were considered to determine successional trajectories and the potential of second-growth forests to recover biodiversity and ecosystem services (ARROYO-RODRIGUEZ et al., 2015; CHAZDON, 2014; LÔBO et al., 2011). The conceptual approaches of forest succession considered succession as a process that is more stochastic than deterministic (PICKETT; CADENASSO; MEINERS, 2008). However, recent approaches highlight the importance of considering several factors, including human and landscape factors, to study successional trajectories, in order to differentiate accurately between stochasticity and the factors that are indeed influencing successional pathways (ARROYO-RODRIGUEZ et al., 2015; MESQUITA et al., 2015). Landscape ecology has been showing that most ecological process are scale- and context-dependent, and are not influenced only at the local scale in a hermetic and non-interactive way (CRK et al., 2009). This field of research has been increasingly incorporated in restoration and sucessional studies (CROUZEILLES et al., 2016; SLOAN; GOOSEM; LAURANCE, 2015). Several methods and projects in the field of ecological restoration and restoration ecology act on the landscape level, by establishing, for example, corridors and stepping-stones in order to foment the recovery of biodiversity and ecosystem services of the native vegetation (METZGER; BRANCALION, 2016) Therefore, currently there is a knowledge gap about actions that aim to favor forest recuperation by manipulating the landscape (PEREIRA; DE OLIVEIRA; TOREZAN, 2013). The field of landscape ecology is increasingly used for conservation and restoration purposes due to the effects of fragmentation dynamics in natural habitat patches. Fragmentation is defined as the loss of continuity of natural habitat patches caused by habitat loss. The creation of patches of once continuous natural habitat implies not only forest loss, but also a series of alterations in populations connectivity, alterations in biotic and abiotic conditions and exposure to natural and human disturbances from the edge (know as “edge effect”). The effects of 11

fragmentation are vast and have been studied since the coinage of the term in 1980s with still much ground to cover (HAILA, 2002). Some examples of ecosystem alterations caused by fragmentation include alteration in temperature, moisture and wind regimes (ARROYO-RODR??GUEZ et al., 2016; MAGNAGO et al., 2016), tree and liana biomass (MAGNAGO et al., 2016), species composition and distribution, by favoring species more adapted to the new disturbance and environmental regime (SFAIR et al., 2016; TABARELLI; PERES; MELO, 2012) and reduction in species density (ARROYO-RODRÍGUEZ et al., 2012) among others. Additionally, the effect of fragmentation in a given patch are species-specific (DA SILVA; ROSSA-FERES, 2016) and depends on the biome studied and patch surroundings. Nevertheless, less intensive agricultural practices and high native habitat cover can mitigate most of fragmentation effects (ARROYO-RODR??GUEZ et al., 2016; MELO et al., 2013b; VILLARD; METZGER; SAURA, 2014). Despite the negative effects of habitat fragmentantion, the conservation potential of forest patches in agricultural landscapes in Brazil cannot be ignored, as these forests house a great number of native species, many of them not found in conservation units, and are a valuable source of propagules for forest restoration and natural regeneration (CHAZDON et al., 2009a; FARAH et al., 2017). However, many of the remnant species in these forest patches are very rare and their offspring is unable to establish under the new biotic and abiotic regimes of the fragmented habitat, therefore they may be extinct in the future without intervention (ARROYO-RODRÍGUEZ et al., 2013; LÔBO et al., 2011). A global meta-analysis by Meli et al. (2017) showed that, when natural regeneration occurs, it develops in forests similar to actively planted forests for restoration purposes. Chazdon and Guarigata (2016) argues that, overall, forests established without human intervention show less structural development, but similar species density when compared to planted native forests. However, site specific results vary: SHOO et al. (2016) observed that native tree plantings had greater and higher forest cover, more species richness and more wind-dispersed and large-seeded species than naturally established forests in tropical Australia. Working on 10-years-old cloud forests in Equador, Wilson and Rhemtulla (2016) concluded that seedling planting favored natural regneration while forests established without human intervention seemed to be in arrested sucession. Understanding the local and landscape drivers of forest succession could colaborate to seize its beneftis to ecological restoration projects (WALKER; WALKER; HOBBS, 2007). Understanding site-specific potential for natural regeneration could prevent over-interviening in a given restoration site, which would not only increase restoration costs but also may hinder spontaneous vegetation (SAMPAIO; HOLL; SCARIOT, 2007). Tropical forest restoration can be a expensive activity, but these costs are highly variable (from ten to ten thousand dollars per hectare), how much restoration will cost at a given site will depend not only on the environmental and social conditions of the restoration site and project objectives, but also on the selection of cost-effective restoration interventions (BENINI; ADEODATO, 2017; DE GROOT et al., 2013). We highlight that forest establishment without human intervention depends on several biotic and abiotic, local and landscape factors, and should not be used indiscrimately due to its low costs (ARROYO-RODRIGUEZ et al., 2015). Downsides of relying only in forest succession over plantings to increase native forest cover in restoration projects include its impredictability, monitoring for longer time frames, disagreement with legal and market/certification requirements and social perceptions of “bushy” areas as project failures (CHAZDON; GUARIGUATA, 2016; ZAHAWI; REID; HOLL, 2014). As all restoration actions, there is not a “one shoe fits it all” solution and careful diagnostics are needed to assess the better intervention needed at each site (HOLL; AIDE, 2011). 12

Sometimes the lines that divide production, spontaneous regeneration and active restoration can be blurred. Mono-specific forest plantings can be used as a first step to restore degraded areas by alleviating environmental conditions for spontaneously regenerating plants, attracting seed dispersers and generating income for landowners in the early stages of restoration (BRANCALION et al., 2012a; JOHNSTONE et al., 2016). If these plantings are managed with reduced impact techniques, they may favor the gradual recover of ecological memory by the gradual accumulation of a seed and seedling bank over time, which may develop quickly once (or even before) commercial plants are removed from plots (JOHNSTONE et al., 2016). Mono-specific plantings accumulate biomass faster than naturally regenerating forests (BONNER; SCHMIDT; SHOO, 2013)but house less (BARLOW et al., 2007)or similar (FONSECA et al., 2009) native woody plant species than naturally regenerating forests. Additionally, commercial forestry plantings may have a buffer effect around native forest remnants, reducind edge effect and disturbance when compared to other land uses, such as pastures (SOUZA et al., 2010). The role of silvicultural plantings to foment regeneration of native species depends on the biome where the planting is carried out, landscape context, species planted, previous land use and the silvicultural management techniques implemented (BROCKERHOFF et al., 2013; LAMB, 1998). Given the wide array of local and landscape, biotic and abiotic and human-mediated and natural drivers that influence the outcomes of restoration initiatives by either active seedling planting or natural regeneration without human intervention, identifying the the effect of these variables on the outcomes of these different approaches is a promising initiative to support biodiversity and restoration projects in agricultural landscapes. In this context, our study aims to compare high-diversity tree plantings with naturally established forests without human facilitation and to identify the drivers of the tree community attributes of the latter in agricultural landscapes of the Atlantic Forest in São Paulo, Brazil. We expect that forests established through tree seedling planting to have higher species richness, more animal-dispersed trees and biomass than forests spontaneously established through natural regeneration. We expect the main drivers of naturally established forests attributes to be surrounding and previous human land use and forest age. The results from this study are particularly relevant in the Brazilian context, in order to monitor restoration projects under the environmental law 12.651/2012 (“Lei de Proteção da Vegetação Nativa”, in Portuguese. They also seize the momentum created by national large-scale restoration programs, such as the PLANAVEG and the planning and priorization for large scale forest landscape restoration in initiaves developed by the private and third sectors such as the Atlantic Forest Restoration Pact. In the global context, we hope to contribute to ambitious international restoration goals such as the Bonn Challenge and the 20x20 Initiative, which will only be achieavable if we make informed decisions on the potential for natural regeneration in restoration sites and increase restoration cost-efficiency. This study is divided in four parts: 1) this overall introduction to the subjects approached; 2) the first scientific manuscript comparing naturally established second-growth forests after different previous land uses and mixed tree plantations; 3) the second manuscript analyzing the factors that drive naturally established second-growth forest attributes in agricultural landscapes; 4) final conclusion.

REFERENCES

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2. Early ecological outcomes of natural regeneration and tree plantations for restoring agricultural landscapes

Copyright by the Ecological Society of America. Expanded version of the manuscript accepted for publication in the journal Ecological Applications at 10/19/2017. Copyright aggreements require that this manuscript must be cited as:

César, R. G.; Moreno, V. S.; Coletta, G. D.; Chazdon, R. L.; Ferraz, S. F. B.; Almeida, D. R. A.; Brancalion, P. H. S. (2018) Early ecological outcomes of natural reeneration and tree plantations for restoring agricultural landscapes. Ecological Applications, v 20, n 2, p 373-384.

ABSTRACT

Mixed tree plantings and natural regeneration are the main restoration approaches for recovering tropical forests worldwide. Despite substantial differences in implementation costs between these methods, little is known regarding how they differ in terms of ecological outcomes, which is key information for guiding decision-making and cost- effective restoration planning. Here, we compared the early ecological outcomes of natural regeneration and tree plantations for restoring the Brazilian Atlantic Forest in agricultural landscapes. We assessed and compared vegetation structure and composition in young (7-20 years old) mixed tree plantings (PL), second-growth tropical forests established on former pastures (SGp), on former Eucalyptus spp. plantations (SGe), and in old-growth reference forests (Ref). We sampled trees DBH 1-5 cm (saplings) and trees DBH>5 cm (trees) in a total of 32 20 x 45 m plots established in these landscapes. Overall, the ecological outcomes of natural regeneration and restoration plantations were markedly different. SGe forests showed higher abundance of large (DBH >20 cm) non-native species – of which 98% were re-sprouting Eucalyptus trees – than SGp and PL, and higher total aboveground biomass; however, aboveground biomass of native species was higher in PL than in SGe. PL forests had lower abundance of native saplings and lianas than both naturally established second-growth forests, and lower proportion of animal dispersed saplings than SGe, probably due to higher isolation from native forest remnants. Rarefied species richness curve was lower in SGp, intermediate in SGe and Ref and higher in PL, whereas rarefied species richness curves of saplings was higher in SG than in Ref. Species composition differed considerably among forest types. Although these forests are inevitably bound to specific landscape contexts and may present varying outcomes as they develop through longer time frames, the ecological particularities of forests established through different restoration approaches indicate that naturally established forests may not show similar outcomes to mixed tree plantings. The results of this study underscore the importance that restoration decisions need to be based on more robust expectations of outcomes that allow for a better analysis of the cost-effectiveness of different restoration approaches before scaling-up forest restoration in the tropics.

Keywords: Active restoration; Ecological monitoring; Forest biomass; Passive restoration; Restoration ecology, Second-growth forests; Forest succession; Tropical forest restoration

2.1. INTRODUCTION Selection of the best restoration approaches is largely defined by the potential for autogenic regeneration of the target site and by management objectives, both of which determine the level of intervention required to foster ecosystem recovery (MCDONALD; JONSON; DIZON, 2016). Because of the spatial variation of these two driving factors, a wide gradient from passive to active restoration is observed, ranging from land abandonment and site protection to highly costly interventions to reconstruct ecological communities in degraded sites (HOLL; AIDE, 2011). The scale of restoration initiatives also affects the degree of intervention. Lower levels of intervention are adopted when ecosystem recovery is planned for larger spatial and temporal scales in landscapes that offer appropriate conditions for natural regeneration (CHAZDON; URIARTE, 2016), while more intensive and costly interventions have been employed in restoration projects requiring faster results but at smaller spatial scales, as in the case of mandatory projects to comply with environmental laws (BRANCALION et al., 2016). For tropical forests, a number of science-based guidelines have been developed to guide the selection of the most appropriate restoration 18

approach by practitioners, with the goal of increasing restoration effectiveness and cost reduction (HOLL; AIDE, 2011; STANTURF; PALIK; DUMROESE, 2014). Natural regeneration without human assistance is the most widely used restoration approach for restoring tropical forests (CHAZDON; GUARIGUATA, 2016), while mixed species tree planting is preferred for active restoration (HOLL; AIDE, 2011; RODRIGUES et al., 2011). These contrasting approaches can potentially affect the structure and composition of restored forests, with implications for expected outcomes and rates of recovery. During natural regeneration, for instance, the species pool is limited by the interaction of natural local dispersal and colonization processes, vegetation cover in the surrounding landscape, and micro-site conditions for tree species establishment (HOLL et al., 2000; HOOPER; LEGENDRE; CONDIT, 2005). Human activities, such as seed collection and seedling production in nurseries, overcome dispersal limitations of species that will be introduced in mixed tree plantings in restoration sites, allowing the species composition of the restored forest to vary independently of the natural potential of tree species to recolonize the site (BRANCALION et al., 2012b). The contribution of landscape dynamics also differs between these restoration approaches. Whereas natural regeneration is favored in closer proximity and connectivity to forest remnants (ARROYO-RODRIGUEZ et al., 2015; BARNES; CHAPMAN, 2014; SLOAN; GOOSEM; LAURANCE, 2015), mixed tree plantings are commonly established in sites further from remnant forests or in landscapes with reduced forest cover with low levels of seed rain (HOLL; AIDE, 2011; RODRIGUES et al., 2009). Economics and ecology are integrated into decision making frameworks to selection restoration approaches in different socio-ecological contexts (HOLL, 2017). Thus, understanding the ecological outcomes of natural regeneration and mixed tree planting in contexts in which both are needed and viable is a first step towards the selection of restoration approaches with higher cost-effectiveness, an emerging challenge for up-scaling forest restoration efforts globally (BIRCH et al., 2010). Although natural regeneration has considerably lower costs (but see Zahawi, Reid, & Holl (2014)), few studies have compared the ecological outcomes between this approach and tree planting while controlling for other factors (such as forest age, soil type and prior land use), thus preventing more accurate cost-effectiveness analyses (but see Gilman et al., 2016). The few comparative publications to date have shown marked differences between natural regeneration and mixed-species restoration plantations. Overall, young (5-10 years old) mixed tree plantations show higher tree species richness and lower canopy openness than naturally established forests in Australian wet tropics uplands (SHOO et al., 2016), Andean Ecuador (WILSON; RHEMTULLA, 2016), humid forests of Costa Rica (HOLL et al., 2013; HOLL; ZAHAWI, 2014) and seasonal tropical forests in Brazil (BRANCALION et al., 2016). In contrast, Gilman et al. (2016) observed that species richness and composition of naturally recruiting individuals in the understory of mixed tree plantings vs spontaneously regenerating forests were similar in wet forests in Costa Rica, but mixed tree plantings had higher aboveground biomass due to the planted trees. These differences may emerge from the comparison of passive and active restoration in conditions where the potential for autogenic restoration was naturally low, thus limiting the full potential of natural regeneration to recover forest structure and diversity, which is known to take several decades or more (CHAZDON, 2014; MARTIN; NEWTON; BULLOCK, 2013). The ecological outcomes in single species tree plantings are influenced by the composition, management treatments, harvesting cycles and context (LAMB, 1998). As an alternative to mixed tree plantings, single species plantations consistently house less native plant biodiversity than second-growth forests in their understory, but such plantations accumulate biomass at faster rates (BARLOW et al., 2007; BONNER; SCHMIDT; SHOO, 2013). Evidently, the results of these comparisons are context dependent. 19

This study compares early outcomes of natural regeneration and mixed tree planting in scenarios where both restoration approaches succeeded in reestablishing a forest community. We compared structure, species richness and composition of saplings and trees in forests undergoing restoration established through mixed-species tree plantings with natural regeneration in pastures and Eucalyptus spp. plantations. We also compare these second- growth forests with old-growth "reference" forests near the study region. We expected that second-growth forests naturally established on pastures and abandoned Eucalyptus plantations will show, when compared to mixed tree plantings: i) higher abundance of tree saplings and climbers, because tree plantings are usually carried out in areas more isolated from seed sources (HOLL; AIDE, 2011); and because mixed tree plantings may require more time to support understory colonization by seeds produced by planted trees; ii) lower abundance of bigger trees and reduced aboveground biomass (AGB) of native species. Since management interventions (fertilization, weeding, control of leaf-cutter ants, isolation from fires and cattle grazing, and regular spacing) will favor tree growth in mixed tree plantings, in contrast with self- organizing, second-growth forests; iii) lower species richness. Due to the reduced species pool in agricultural landscapes and the high diversity of species introduced in mixed tree plantings in the region; iv) higher proportion of abiotic-dispersed trees and more pioneer species, due to restoration guideline in Brazil and because practitioners favor biotic-dispersed species (Pedro H. S. Brancalion, personal communication) and a balanced proportion of fast- and slow-growing species in mixed tree plantings in our study region (RODRIGUES et al., 2009); v) a more similar composition to old-growth forests, since composition of second-growth forests will be essentially determined by the same regional species pool, while plantations introduce species that are outside the regional species pool or are locally rare.

2.2. METHODS 2.2.1. Study site

We studied second-growth forests established without human assistance and mixed tree species plantings in the Corumbataí river basin of São Paulo State, southeast Brazil, in seasonal semi-deciduous tropical forests of the Atlantic Forest biome. Old-growth, reference forests were sampled as close as possible from the Corumbataí river basin, because no single conserved forest was found within it (23.8 ± 4.9 km from the basin, min: 17.2 km, max 28.5 km). The climate of the region is classified as Cwa according to the Köppen classification (ALVARES et al., 2013), with dry winters and wet summers, mean annual precipitation of 1,367 mm and mean temperatures of 20.5 oC (minimum and maximum monthly averages of 15.6 oC and 29.5 oC, respectively). Altitude varies from 470 to 1060 meters asl. The basin occupies an area of 1,700 km² and the main soil types are Acrisols (44%) and Ferralsols (22%). The basin is an ecotone between the Atlantic Forest (42% of the basin) and Cerrado (tropical savanna) (58%). The seasonal semi-deciduous forests characterize the “interior” biogeographical zone of the Atlantic Forest, the second most threatened of this biome, with only 7% native cover remaining (RIBEIRO et al., 2009). The recent increase of native forest cover in some regions in this biome (BAPTISTA; RUDEL, 2006; REZENDE et al., 2015) provide a unique opportunity to study the dynamics of tropical forest regeneration in highly deforested agricultural landscapes. Most of the deforestation occurred in the 19th century for coffee plantation, which subsided in the early 20th century when coffee was gradually replaced by pastures and intensive agriculture in flatter terrains, mostly 20

sugarcane plantations. During the 1970s, the region developed industrially, resulting in the migration of the rural population to urban centers and land abandonment followed by forest regeneration in some areas unsuitable for mechanized agriculture (DEAN, 1977). This contributed to a doubling of native forest cover in the six landscapes analyzed in this study (described in the next section) from 8 to 16% between 1962 and 2008 (FERRAZ et al., 2014). Currently, the main land uses in the Corumbataí watershed are pastures and sugarcane fields, occupying 43.7% and 29.4% of the basin, respectively. Native forest remnants, active Eucalyptus plantations for firewood and timber, and other land uses (buildings, roads, water bodies, other agricultural plantations, etc.) occupy 12.4%, 7.3% and 7.2% of the entire basin, respectively. Native forest cover in this region is represented by a mosaic of disturbed remnants of different sizes and levels of human-mediated disturbances, mostly resulting from cattle grazing and recurrent fires coming from sugarcane fields, which were burned historically before manual harvesting. More recently, burning of sugarcane fields was legally prohibited and mechanized harvesting has predominated, which favors forest regeneration in slopes that can no longer be used for mechanized production (MOLIN et al., 2017). 21

Figure 1: Top: location of the selected landscapes in the Corumbataí basin, São Paulo State, Brazil. Bottom: high- resolution satellite images showing overall landscape context of mixed tree plantings for forest restoration (A) and naturally established second-growth forests (B) sampled in this study. Each forest type is highlighted in red in the bottom figure. Note that second-growth forests are next to existing forest remnants while tree plantings are located along riparian zones within sugarcane plantations, more isolated from forest fragments.

2.2.2. Landscape selection for sampling forests undergoing restoration

To locate second-growth forest in the 1,700 km² Corumbataí River watershed, we selected six landscapes following the diversity variability analysis proposed by Pasher et al. (2013). First, we divided the basin into 1×1, 2×2, 3×3, 4×4 and 5×5 km square grid cells. For each grid size, we calculated the Shannon landscape diversity index 22

(MCGARIGAL; MARKS, 1994) based on a 30m-resolution land-use map from 2002. Finally, we plotted the mean landscape diversity index of each grid size against the cell size. This method identified the 4×4 km cell (16 km²) as the smallest cell grid that shows no variation when compared to landscape diversity index of larger sizes, thus representing the landscape diversity of the study area. Using the 4 km square grid cell, we submitted the study region to a moving window analysis and calculated, for each pixel, the proportion of sugarcane, pasture and native forest cover of a sampling window centered on it. Finally, we selected six of the 4 x 4 km landscapes that had, in 2008 (latest image available), at least 70% of agricultural matrix and at least 10% native forest cover. To distribute the six selected landscapes more evenly across the study region, three landscapes were chosen randomly in the southern part of the basin and three in the northern part of the basin. For more details, see Ferraz et al. (2014).

2.2.3. Experimental design

In the six chosen landscapes described previously, we located second-growth forests that established without human assistance on planted cattle pastures (hereafter “SGp”) and in Eucalyptus spp. plantations abandoned after harvesting (hereafter “SGe”), the two most common prior land uses of second-growth forests in this region. When we refer to these second growth forests at the same time we use the abbreviation “SG”. We identified SG forests that were present in the 2008 satellite imagery but not in the 1995 imagery; since data gathering occurred in 2015, we estimated that sampled SG forests were 7-20 years old. Before the establishment of SGp, pastures had low productivity (i.e. <2 animals per hectare, without regular fertilization or other inputs), harbored very few isolated native trees and were covered by non-native fodder grasses, mainly Urochloa spp. In SGe, Eucalyptus spp., a non- native species, was hand-planted and harvested using a chainsaw (which probably occurred 6-8 years after planting). Some of the harvested eucalyptus resprouted, resulting in a mixed community of resprouting Eucalyptus spp. and naturally regenerating native trees. Except for the resprouting Eucalyptus spp. in SGe, no seedlings were planted in SG forests and all individuals sampled originated from natural regeneration. High-diversity mixed species tree plantations (hereafter “PL”) of the same age range (7-20 years) were identified in the same study region (basin) but not in the same 16 km² landscapes where we sampled SG forests, due to the lack of mixed tree plantings for restoration plantings in these small landscapes. These tree plantings were typically established by sugarcane mills to comply with Brazilian environmental law (see details in Rodrigues et al. (2011)). Mixed tree plantings were established in sites used for decades for intensive sugarcane cultivation, where low potential for autogenic recovery limited the use of passive restoration approaches. Over 50 species of native tree seedlings were planted in a 3 × 2 m spacing (1,666 seedlings/ha) (RODRIGUES et al., 2011). Mixed tree plantings were protected from fire, fenced to prevent cattle invasion, and planted seedlings were fertilized and weeded for 2–3 years after planting. Given that natural regeneration is more likely to occur near existing forest remnants (RODRIGUES et al., 2011; SLOAN; GOOSEM; LAURANCE, 2015) and on slopes (MOLIN et al., 2017), mixed tree plantings for forest restoration usually occur in more isolated areas (RODRIGUES et al., 2011); accordingly, the landscape context of SG and PL forests differed (Figure 1). Among the 18 SG forests sampled, only one was not adjacent to a native forest remnant. On the other hand, the average distance of the seven PL forests sampled from the closest native forest remnant was 1,275 m (min: 10 m, max: 3300 m); all PL forests sampled were <100 m from rivers or water 23

reservoirs and all, except one, was surrounded by large sugarcane plantations. Forest stand area where each plot was installed was: SGp: 21 ± 4 ha, SGe 50 ± 10 ha, PL 48 ± 36 ha. Finally, we identified old-growth conserved forests (Ref) of the same vegetation type in other agricultural landscapes near the study region. We selected four forests with no history of large disturbances in the last 100 years, protected from human and cattle encroachment and belonging to relatively large forest areas for the regional context (50 to 250 hectares), where small remnants predominate. Examples of the forest types sampled are in Supplementary File S3). We installed a 20 × 45 m (900 m²) plot to gather vegetation data in seven SGp forests, 11 SGe, and ten PL (hereafter collectively referred as “forest types”), and in four Ref sites, totaling 32 plots. We installed a single, long and continuous plot because both SG and PL forests were distributed in narrow patches at the limits of expanding forest remnants or in buffers around water streams, respectively. To avoid pseudo-replication, we did not place plots in forests with the same previous land use located in the same continuous forest remnant - or in the same continuous planting area for PL. In each 900 m² plot, we measured the diameter at breast height (DBH) and identified to the highest taxonomic level possible all living rooted trees and shrubs DBH≥5 cm (hereafter “trees”). We carried out superficial excavations to confirm if stems of the same species next to one another belonged to the same individual tree and we measured all stems DBH>5 cm of the same tree. Additionally, we installed a 4 × 30 (120 m²) subplot at the center of each plot to count and identify all trees and shrubs with DBH 1-5 cm (hereafter “saplings”). All sampled individuals were classified according to successional group (pioneer or non-pioneer), dispersal syndrome (animal-dispersed, abiotic dispersal or not classified) and species origin (native or non-native to the study region) (CREES; TURVEY, 2015; SWAINE; WHITMORE, 1988). We evaluated whether individuals sampled in PL were introduced during planting or established naturally by observing the distribution of individuals in the planting lines. Additionally, we estimated liana abundance by walking two 45-m transects in each plot and counting the number of touches of lianas diameter > 1 cm in a 2-m high pole, a method adapted from Vidal et al. (1997). In two PL plots we were not able to sample lianas and saplings.

2.2.4. Data analyses

To analyze forest structure, we divided all sampled trees into three diameter classes based on their largest stem: 1-5 cm, 5-20 cm, and >20 cm; the first class was sampled in the 120 m² subplot, while the others were sampled in the larger 900 m² plot. We analyzed the abundance of trees in each diameter class per plot separately for native and non-native species. We estimated AGB of each stem based on the equation 7 developed in the work of Chave et al. (2014). This equation requires only tree DBH and wood density. Data on wood density of the sampled trees were obtained from several references, but mainly from Chave et al. (2009) and Zanne et al. (2009). When wood density data was not available for a given species, we would use the following wood density values, in this order: i) average of the species of the same genus sampled in this study, or ii) average of species of the same genus in Zanne et al. (2009), or iii) average of the species of the same family sampled in this study. For species identified only to the genus or family level, we followed the steps mentioned previously. For unidentified morphospecies, we considered wood density as the average density of all species sampled in the study site. We used a specific equation developed locally to estimate AGB of Eucalyptus spp. trees (CAMPOS; SILVA; VITAL, 1992). Aboveground biomass of trees, abundance of native and non-native species for each diameter class, species density of native species, liana abundance, and proportion of pioneers and animal-dispersed native trees and 24

saplings were compared among forest types using ANOVA and means were compared by the Tukey multiple comparison procedure (α = 0.05) when data were normally distributed. We used a general mixed model considering the logarithmical distribution when data did not have a Gaussian (normal) distribution. In both cases, means between forest types were compared by the Tukey multiple comparison procedure (α = 0.05). Rarefied species richness curves, indicator species and species composition analyses were carried out using the R package “vegan” (OKSANEN et al., 2016). We included Ref in these analyses. Rarefied species richness accumulation curves were developed for trees and spontaneously regenerating saplings of each forest type using the function “rarefy”. Given the low abundance of spontaneously regenerating saplings in PL, we were unable to develop a rarefaction curve for this forest type. We used the function “indval” to identify indicator tree species in each forest type with indicator value >0.5 and p<0.05. To compare species composition of saplings and trees among forest types, we calculated the Chao-Jaccard dissimilarity index (CHAO et al., 2004) between each plot and created a graph using non-metrical multidimensional analysis to visualize similarity among plots of different forest types using the “mds” function. We considered only native species to calculate rarefied species richness curves, and both native and non-native species for indicator species and non-metrical multidimensional analyses. Trees that could not be identified due to the absence or difficulty to sample vegetative or reproductive material were not considered for calculating species density, diversity, rarefied richness curves and composition, while unidentified morphospecies for which vegetative material was collected were included in these analyses. Given that non-native tree species and pioneer native species are well described and easily identifiable in the field, we classified morphospecies that were not identified and trees that we could not collect vegetative material – due to the absence of vegetative material or difficulty to reach the canopy - as native non-pioneer species in order to analyze abundance, AGB and proportion of pioneers. Given the low number of repetitions and the distance from other forest types, Ref forests were not included in the statistical comparison of structure, biomass, species density, liana abundance and proportion of biotic-dispersed trees and pioneers among forest types, but the data obtained in these forests are included in the results for reference and discussion purposes.

2.3. RESULTS 2.3.1. Forest structure

We sampled a total of 1025 saplings and 3167 trees, of which 68 (6.6%) and 410 (12.9%) belonged to non-native species, respectively. We could not identify to the species level 143 (13.9%) and 198 (6.0%) of all saplings and trees, respectively. Overall, abundance of saplings of native species differed among forest types, being higher in SG forests (Supplementary File S1). Most of the saplings (87.9%) in PL were planted, with low abundance of spontaneously regenerating individuals. Forests of different types had similar abundance of trees DBH 5-20 cm, but SG showed lower abundance of larger native trees (DBH >20 cm) when compared to PL (Figure 2). Liana abundance differed among forest types, being lower in PL, whereas SGp and SGe did not differ (Supplementary File S1). The abundance of saplings of non-native species was similar among forest types; no Eucalyptus saplings were found in any of the sites (Supplementary File S1, Figure 2). However, SGe had the highest abundance of non- native tree species among the largest trees (DBH > 20 cm), mostly large Eucalyptus spp. individuals. In SGe, resprouted Eucalyptus spp. represented 0%, 4.1 ± 2.2% and 64.0 ± 6.8% of trees with DBH 1-5 cm (saplings), 5-20 cm, and >20 cm, respectively.

Figure 2: Tree community structure in 7-20 years old second-growth tropical forests established over pastures (SGp) and Eucalyptus spp. plantations (SGe), mixed-species tree plantings (PL) and reference forests (Ref) in agricultural landscapes of southeastern Brazil. Abundance of native and non-native species per hectare is shown in the left and right graphs, respectively. Community was divided in the three diameter at breast height (DBH) classes listed in the vertical axis of each graph at the left. Most non-native trees in SGe are resprouting Eucalyptus spp. Abundance of native species DBH 1-5 cm was compared using ANOVA (α = 0.05), while the abundance of native species of other DBH classes and all non-native classes were non-parametrical and a generalized mixed linear model considering lognormal distribution of data was used (α = 0.05). In both cases, means were compared by the Tukey multiple comparison procedure (α = 0.05). Ref forests were not included in statistical analyses. Boxplots followed by the same letter do not differ statistically in each DBH class. N=4 for Ref forests and N=7, 11 and 10 for SGp, SGe and PL forests, respectively. Note that graphs have different scales.

Aboveground biomass (AGB) differed among forest types (Supplementary File S1). SGe showed similar biomass of native species to SGp, but less than PL, while total AGB (native + non-native species) was higher in SGe in the other forests (Supplementary File S1 and Figure 3). Virtually all AGB stocked in PL (97.3%) was from planted trees.

Figure 3: Tree community aboveground biomass per hectare in 7-20 years old second-growth forests established over pastures (SGp) and Eucalyptus spp. plantations (SGe), mixed tree species plantings (PL) and old-growth reference forests (Ref) in agricultural landscapes of southeastern Brazil. Gray and white bars represent aboveground biomass of native and non-native species, respectively. Error bars represent one standard error from total aboveground biomass. Forest types were compared using ANOVA (α = 0.05) and means were compared by the Tukey multiple comparison procedure (α = 0.05). Bars followed by the same letter do not differ statistically. Upper- and lowercase letters refer to total and native species aboveground biomass, respectively. N=4 for Ref forests and N=7, 11 and 10 for SGp, SGe and PL forests, respectively.

2.3.2. Species richness and composition

For trees, we sampled a total of 56 families, 161 genera and 241 species. For saplings, we have sampled a total of 39 families, 100 genera and 176 species. Similar values of species density of native trees were found among forests. Species density of saplings was higher in SG than in PL when we included for planted trees in PL (Supplementary File S1). When considering only naturally regenerating saplings, PL had much lower species density than SG forests, with only two non-planted species found in one plot. Rarefied species richness curvesof trees was lowest in SGp, intermediate in SGe and Ref and highest in PL (Figure 4). All the 12 spontaneously regenerating saplings in PL were represented by only seven species. 27

Additionally, all the 13 non-native (to Corumbataí Basin) species found in PL were introduced during planting. Rarefied species richness curvvesof saplings was similar between SG and lowest in Ref. The few saplings in PL prevent reliable inferences about rarefied species richness curves in these forests and are mostly represented by planted seedlings, with only twelve individuals of this size class belonging to seven species found spontaneously in these forests (Figure 4).

Figure 4: Native species accumulation curve of trees DBH>5 cm and DBH 1-5 cm in 7-20 years old second-growth (SG) forests established naturally over pastures and Eucalyptus spp. plantations, mixed tree species plantings and reference forests in agricultural landscapes of southeastern Brazil. Species accumulation curves for trees DBH 1-5 cm represent only naturally established trees, and only five regenerating individuals belonging to two species were found in Mixed Tree Plantings in this size class. Dotted lines represent one standard error from the mean number of species. Trees DBH 1-5 cm in mixed plantings are represented mostly by planted individuals, with only 12 trees belonging to seven species found spontaneously regenerating in this forest type.

Species composition varied widely among plots, with each forest type composed by a particular species pool. Composition of SG and PL differed among themselves and from Ref (Figure 5). We identified indicator 28

species of trees in each forest type (Table 1). Overall, indicator species of SGp are shade-intolerant and wind- dispersed, in SGe they are shade-tolerant and animal-dispersed, in PL they have a balanced distribution regarding shade tolerance, but abiotic-dispersed species predominate, and finally, indicator species in Ref forests are predominantly shade-tolerant, with both biotic and abiotic-dispersed species (Table 1). The proportion of trees with biotic-dispersal was similar among forest types, while the proportion of biotic-dispersed saplings was higher in SGe than in PL (Supplementary File S2 and Supplementary File S1). Proportion of pioneer trees was higher in PL than SGe and similar among saplings in all forest types (Supplementary File S1).

Figure 5: two-dimensional non-metric scaling plot of the Chao-Jaccard dissimilarity index for 7-20 years old forests established naturally over pastures (diamond) and Eucalyptus spp. plantations (triangle), mixed tree species planting (square) and reference forests (circle) in agricultural landscapes of southeastern Brazil. Data is show for trees DBH>5 cm and DBH 1-5 cm in the top and bottom graphs, respectively. We computed only non-planted trees DBH 1-5 cm for PL.

Table 1: Indicator species for forests that established naturally in abandoned pastures or Eucalyptus spp. plantations or by mixed tree species plantings. IndVal = indicator value of the species for that forest type. Shade tolerance: I = Intolerant to shading, T = Tolerant to shading. Dispersal syndrome: A = abiotic dispersal, B = biotic dispersal. *Non-native species.

Shade Dispersal Forest type IndVal P tolerance Syndrome

Abandoned pastures (SGp) Moquiniastrum polymorphum (Less.) G. Sancho I A 0.83 0.001 *Psidium guajava L. I A 0.72 0.007 Machaerium hirtum (Vell.) Stellfeld I A 0.61 0.006 Luehea candicans Mart. & Zucc. I A 0.56 0.014 Machaerium nyctitans (Vell.) Benth. I A 0.54 0.021 Platypodium elegans Vogel I A 0.51 0.017 Abandoned Eucalyptus spp. Plantations (SGe) Eucalyptus spp. I A 0.93 0.001 Casearia sylvestris Sw. T B 0.54 0.020 Mixed tree species plantings (PL)

Schinus terebinthifolius Raddi I B 0.70 0.002 Citharexylum myrianthum Cham. I B 0.69 0.016 Peltophorum dubium (Spreng.) Taub. T A 0.63 0.004 Ceiba speciosa (A.St.-Hil.) Ravenna T A 0.56 0.004 Senegalia polyphylla (DC.) Britton & Rose I A 0.51 0.025 Clitoria fairchildiana R.A.Howard I A 0.50 0.010 Genipa americana L. T B 0.50 0.008 Inga vera Willd. I B 0.50 0.009 Reference forest (Ref) Roupala montana Aubl. I A 0.72 0.002 Amaioua intermedia Mart. ex Schult. & Schult.f. T B 0.50 0.013 Calyptranthes clusiifolia O.Berg T B 0.50 0.019 Chrysophyllum gonocarpum (Mart. & Eichler ex Miq.) Engl. T B 0.50 0.008 Eugenia dodonaeifolia Cambess. T B 0.50 0.014 Galipea jasminiflora (A.St.-Hil.) Engl. T A 0.50 0.014 Ixora brevifolia Benth. T B 0.50 0.013 Maytenus gonoclada Mart. T B 0.50 0.007 Metrodorea nigra A.St.-Hil. T A 0.50 0.015 Savia dictyocarpa Müll.Arg. T A 0.50 0.012 Xylopia brasiliensis Spreng. T B 0.50 0.014

2.4. DISCUSSION Both SG and PL forests promoted a rapid recovery of forest structure and housed many native tree species, thus showing a potential contribution for stocking carbon and conserving biodiversity in the highly-modified agricultural landscapes included in this study. As expected, the ecological outcomes in SG and PL were markedly different in structure and composition, and differed from reference forests. These differences highlight that, at least 30

in this region, forests established through spontaneous regeneration and mixed tree plantings are complementary and fulfill different roles and conditions in the process of restoring agricultural landscapes. Surprisingly, resprouting Eucalyptus spp. did not affect native species density and increased total aboveground biomass without hindering biomass accumulation by native species when compared to second-growth forests without Eucalyptus spp. (SGp). Although Eucalyptus spp. trees in our plots were reproductively mature (as observed by the several fallen fruits in our plots), we have not found a single sapling of this species in SGe. Over time, this non-native species will gradually disappear from these forests. PL were established in more isolated areas after intensive agriculture, where natural regeneration is unlikely to occur for decades and practitioners could not rely on spontaneous regeneration. In the early successional stages sampled in our study, PL forests are providing habitat structure and housing dozens of native species in agricultural landscapes where it is highly unlikely that forest could spontaneously establish in the short- to mid-term. In spite of the restricted colonization of native trees and lianas in young PL, mixed tree plantings have shown positive successional development over time in other forests in the same biome, with a gradual increase in forest structure and diversity (BERTACCHI et al., 2016; GARCIA et al., 2016; SUGANUMA; DURIGAN, 2015). These studies highlight that the limited recolonization of mixed tree plantings understory by native species – as found in PL in our study - may not compromise the long term ecological sustainability of these plantings. As expected for the early successional stages of SG and PL, these forests differ in most attributes of structure and composition from Ref. While tree composition in SG and PL are distinct from Ref, saplings in SG forests tend to be more similar to Ref (Figure 5). However, the persistence of planted native tree species in restoration sites is not yet fully understood (SUGANUMA; DURIGAN, 2015).

2.4.1. The role of Eucalyptus in natural regeneration

There is much controversy regarding the use of non-native species to foment the establishment of native tropical forests (BREMER; FARLEY, 2010). However, recent studies are reporting the positive effect of the initial use of non-native species to create a forest structure and accelerate successional processes in degraded areas (BROCKERHOFF et al., 2013; CATTERALL, 2016). We observed higher species richness curves in SGe, when compared to SG forests without Eucalyptus (SGp), probably due to two distinct processes. First, the establishment of an open, undisturbed, shaded understory may have allowed the continuous accumulation of seeds in the soil and promoted seedling and sapling recruitment. Compared to pastures, commercial Eucalyptus spp. plantations have less intensive management, as some pastures are annually burned in our study region. After harvesting of canopy trees, natural regeneration could then advance faster and with higher diversity. Second, large Eucalyptus spp. individuals may mimic the ecological role of native emergent trees, establishing a more complex vertical forest structure that attracts seed dispersers and creates favorable micro-site conditions for spontaneously colonizing seedlings (JOHNSTONE et al., 2016). Lower sapling species richness in Ref may be due to more intense ecological filters in the shaded understory of old growth forests, as well as the reduced number of plots sampled in these forests (n=4) (Figure 4). Populations of Eucalyptus spp. were mostly composed by few large individuals that increased total biomass of SGe when compared to SGp, while biomass of native species remained similar between these forests (Figure 3). Indicating another benefit of a non-native species in accelerating carbon sequestration in second-growth forests, without hindering other native species, at least in the early stages observed (BONNER; SCHMIDT; SHOO, 2013).

2.4.2. Naturally established forests and mixed species plantations

The effect of restoration approaches could not be decoupled from that of landscape context in our study (PL are usually far more distant from remnant forests), so the observed ecological outcomes result from both the restoration approach and the landscape context in which it is implemented, however, the latter was not analyzed in the current study. Although the influence of landscape and methods factors can be separated experimentally, the reality of restoration projects will always reflect this combination of factors (at least in our study region), since tree plantings are the preferred method of restoration when natural regeneration is not a viable approach. Thus, our findings reflect the typical condition in which different restoration approaches are employed, and the inherent features of these approaches should be considered when comparing their cost-effectiveness. The lack of regenerating trees in the understory of PL may compromise their future sustainability, as a consequence of the collapse of forest structure following the senescence of pioneer trees. At the same time, the absence of lianas in PL demonstrates that other plant growth forms may have problems to recolonize restoration sites and lead to a less biologically complex restoration community (CROUZEILLES; CURRAN; CLOUGH, 2016; GARCIA et al., 2016; MAYFIELD, 2016). However, one may consider the deleterious effect of liana infestation in young second-growth forests (FARAH et al., 2014). Although further time may be necessary to assess if recruitment will increase as these forests age, the limited spontaneous regeneration of trees and lianas in PL may be caused by a synergy of local and landscape factors. Local factors may include reduced seed bank, the lack of seed production from planted trees, and reduced habitat heterogeneity promoted by fast-growing planted species (HOLL et al., 2000), factors that may change as PL develop. Landscape factors may include restrictions in seed sources and dispersers, due to large-scale defaunation and deforestation in human-modified landscapes of the Atlantic Forest (CROUZEILLES et al., 2016; SANSEVERO et al., 2011), which are intensified by the higher level of isolation in the landscape of restoration plantations. If connectivity increases in the landscape, these patterns may change. Despite the benefits of silvicultural treatments like fertilization and weeding for enhancing tree growth applied during PL establishment (CAMPOE; STAPE; MENDES, 2010), these forests showed similar biomass to SGp, the latter regenerating without human intervention. Our results contrasts with other works that observed higher AGB in mixed tree plantings (HOLL; ZAHAWI, 2014; SHOO et al., 2016) and highlights the potential of naturally established second-growth forests to develop structurally and sequester carbon from the atmosphere in relatively short periods (CHAZDON et al., 2016). SG and PL differed greatly in species taxonomic and functional composition. As expected, PL had higher species richness than SG, due to the introduction of over 50 tree species per planting in the study region (RODRIGUES et al., 2011), but species density at the plot scale was similar among forest types for both saplings and trees. Abundance of non-native trees was low (12.9% of all individuals sampled), and is not a major source of divergence among SG or PL forests. SG and PL also showed striking divergences in species composition. As expected, the human-oriented selection of tree species for PL lead to higher levels of floristic dissimilarities between SG and PL than between naturally established forests with different previous land uses (SGp and SGe) for both trees and saplings (ATKINSON; MARÍN-SPIOTTA, 2015). Sapling composition in PL is mostly composed by planted individuals and is more distinct from Ref than SG forests. However, there is overall high variability among all forests sampled.

2.4.3. Implications for conservation

As climate mitigation policies become increasingly integrated in international environmental agendas, carbon sequestration through natural regeneration and mixed tree plantings becomes a promising strategy to remove carbon from the atmosphere (CHAZDON et al., 2016; LOCATELLI et al., 2015). At least in agricultural landscapes, higher investments for tree planting in PL may not compensate the additional gain in AGB considering the success of SG restoration. If we consider only the costs for establishing the studied second-growth forests ($0, as lands were simply abandoned) and the implementation and maintenance costs of high-density mixed species restoration plantations in the region (~US$8,000/ha over a three-year period), we believe that the cost-effectiveness of forest restoration for carbon stocking is far more favorable in natural regeneration (CHAZDON et al., 2016). Former Eucalyptus plantations can be repurposed for promoting natural regeneration at virtually no cost, contributing to both carbon stocking and biodiversity conservation. Nevertheless, larger mixed tree plantings may also provide in situ biodiversity conservation of planted tree species and habitat refugia in highly deforested landscapes. Given the low abundance of spontaneously regenerating individuals and the dissimilarity in species composition with Ref, enrichment plantings may be needed to safeguard the persistence of PL forests with limited understory regeneration (BERTACCHI et al., 2016; COLE et al., 2011), as well as to reintroduce climbers (BOURLEGAT et al., 2013). Complementary to introducing new species, the removal of non-native trees from the canopy may promote the development of natural regeneration in PL and SGe (SWINFIELD et al., 2016). Both approaches (SG and PL) can be strategically used to complement each other in agricultural landscapes. There is a considerable deficit for restoration in degraded and/or isolated areas that will probably not support natural regeneration (SOARES-FILHO et al., 2014), implementing mixed tree plantings in these areas will be important for carbon storage but may play a limited role for biodiversity conservation in the long term unless landscape connectivity is improved. Finally, in larger scales, differences in composition between SG and PL may increase beta- diversity in agricultural landscapes (Rother et al., in prep). In this context, mixed tree plantings provide an opportunity to conserve endangered or dispersal-limited species. Finally, decisions have to be made on scientifically-based expectations of ecological outcomes to allow a better analysis of the cost-effectiveness of restoration approaches. Simply comparing the costs of natural regeneration and mixed tree plantings is not sufficient to support decision making, since the outcomes resulting from these approaches are not the same. Our findings reinforce that both restoration approaches have lessons to provide to each other, since the adoption of silvicultural treatments to assist spontaneously regenerating seedlings may greatly contribute to the development of SG, while benchmarking the community assembly of SG by PL may increase their capacity to overcome the biotic and abiotic filters operating in degraded sites, reducing the need for human assistance.

ACKNOWLEDGEMENTS

Funding for this research was provided by the FAPESP grant 2014/14503-7. SFBF received funding from FAPESP projects 2011/19767-4 and 2013/22679-5. PHSB thanks the National Council for Scientific and Technological Development of Brazil (CNPq) for a productivity grant (#304817/2015-5). RC was supported by a fellowship from the Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES) for research grant (#88881.064976/2014-01). This work was also supported by the PARTNERS Research Coordination Network grant #DEB1313788 from the U.S. NSF Coupled Natural and Human Systems Program. The authors also 33

would like to thank the several volunteers during field work and the landowners that allowed forest sampling in their properties.

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3. SURROUNDING LAND USE AND FOREST COVER AS MAJOR DRIVERS OF BIOMASS AND TREE DIVERSITY RECOVERY BY SECOND-GROWTH TROPICAL FORESTS IN AGRICULTURAL LANDSCAPES

ABSTRACT

Increase in native forest cover through natural regeneration may be one of the most cost-effective strategies to mitigate climate change and prevent species extinctions in human-dominated tropical landscapes. Several studies have explored the major biophysical and anthropogenic drivers of forest cover increase, but little is known about the combined effects of these drivers on the development of second-growth forests (SGF). Here, we analyzed the influence of local (forest age, basal area of eucalypt (Eucalyptus spp.), soil sum of bases and slope) and landscape factors (surrounding land use type, distance from watercourses, surrounding native forest cover and native forest cover variation since forest establishment) on the biomass, species density and phylogenetic dispersion of native trees in SGF. We based our analysis in 44 second-growth forests ranging 11-46 years old distributed in agricultural landscapes of the Atlantic Forest of Southeast Brazil. Biomass of native species was reduced in steeper areas and in forests with higher dominance of remnant eucalypt. Older forests showed more biomass, except when near sugarcane plantations. Species density increased with surrounding forest cover and in areas with increasing surrounding forest cover, but was reduced by the presence of eucalypt. Phylogenetic dispersion was favored by surrounding forest cover and reduced nearby sugarcane plantations. Overall, human land use and surrounding native forest cover trumps over age in predicting the structure and conservation potential of second-growth forests, and should receive special consideration by international forest landscape restoration programs when prioritizing land for cost-effective restoration.

Keywords: Atlantic Forest; Carbon sequestration; Ecological restoration; Secondary forests; Forest succession; Forest landscape restoration; Landscape ecology; Land use change; Drivers of natural regeneration

3.1. INTRODUCTION Increase in native forest cover through natural regeneration has been recently observed and celebrated in several regions of the globe (AIDE et al., 2013; REZENDE et al., 2015). Currently, second-growth forests represent more than half of global forest cover (FAO, 2015), and this proportion is expected to increase as many regions are experiencing forest transitions (HANSEN et al., 2013). There is growing evidence that second-growth forests are essential to safeguard biodiversity conservation and ecosystem services provisioning in human-modified tropical landscapes (CHAZDON et al., 2009b; MUKUL et al., 2016), and may constitute one of the most cost-efficient solutions to mitigate climate change (GRISCOM et al., 2017). However, land abandonment may not always result in the establishment of well-developed second-growth forests with the expected benefits for climate change mitigation and biodiversity conservation, since local and landscape resilience may have been so reduced that native species recolonization is insufficient, or human disturbances may further prevent the successional development of regenerating forests (ARROYO-RODRIGUEZ et al., 2015). Even well-established second-growth forests may take several decades to recover plant richness and aboveground biomass’ values of old-growth forests (LIEBSCH; MARQUES; GOLDENBERG, 2008; MARTIN; NEWTON; BULLOCK, 2013; POORTER et al., 2016), and may 40

never recover their species composition (BARLOW et al., 2007; GIBSON et al., 2011). Although remote sensing studies have advanced in identifying the most influential drivers of forest cover increase in human-modified tropical landscapes (MOLIN et al., 2017; RUDEL et al., 2016; SLOAN; GOOSEM; LAURANCE, 2015), little is known about the influence of local and landscape drivers on the recovery of biomass and tree diversity in regenerating tropical forests. Forest age has largely been the main studied driver of forest change during succession. Historically, chronosequences were used as the main methodological approach to investigate how regenerating tropical forests change their diversity and structure over time (CHEUNG; LIEBSCH; MARQUES, 2010; DENT; DEWALT; DENSLOW, 2013; LIEBSCH; MARQUES; GOLDENBERG, 2008; NORDEN et al., 2009; VAN BREUGEL et al., 2013, and many others). However recent long-term monitorings of regenerating tropical forests have shown that each fragment may follow an unique successional trajectory, and the recovery of biomass and biodiversity by second- growth forests may depend on several context-dependent local and landscape factors that influence successional processes over time, beyond the effect of age (ARROYO-RODRIGUEZ et al., 2015; NORDEN et al., 2015). More recently, other factors such as soil properties (MARTINS et al., 2015; POWERS et al., 2009), surrounding forest cover (JAKOVAC et al., 2015), distance from patches (CHUA; RAMAGE; POTTS, 2016), and human use and disturbance (ARROYO-RODRIGUEZ et al., 2015; JAKOVAC et al., 2015; LOHBECK; MARTÍNEZ-RAMOS, 2015) have been included in successional studies. The relative importance of factors other than age in tropical forest succession is expected to increase as landscapes become more dynamic, and human influence on natural regeneration process may overwhelm processes resulted solely from space-for-time substitution (HOGAN et al., 2016; MESQUITA et al., 2015; WALKER et al., 2010). There is a growing expectation that natural regeneration will play a major role for achieving global commitments on forest landscape restoration in the tropics, due to its low cost and potential to recover biodiversity and ecosystem services (Chazdon and Guariguata, 2016, Holl, 2017, Crouzeilles et al., 2017). However, the “promise” of natural regeneration to achieve the aforementioned restoration outcomes relies on a sufficient development of forest structure and recovery of tree diversity, beyond the simple re-establishment of a forest phisiognomy. Models to define priority areas for restoration should not only consider where forest cover can increase more easily − like in marginal agricultural lands and areas closer to forest fragments (MOLIN et al., 2017; SLOAN; GOOSEM; LAURANCE, 2015) − but also where forest succession may develop more efficiently. Understanding the main local and landscape drivers of tropical forest regeneration in human-modified landscapes is a major step towards the development of predictive models of tropical forest regeneration and more efficient schemes of restoration prioritization. We sampled naturally established second-growth forests in different local and landscape contexts in a highly deforested agricultural landscape in the Atlantic Forest of Southeast Brazil. We analyzed the effect of local (forest age, basal area of Eucalyptus spp., slope and soil sum of bases) and landscape factors (surrounding land use, distance from water streams, surrounding native forest cover and difference in surrounding forest cover from forest establishment until the present) on the recovery of eaboveground biomass, species density and phylogenetic dispersion of native trees in second-growth forests. Based on recent research findings in the literature, we hypothesized that aboveground biomass, species density and phylogenetic dispersion of native trees will be favored, in order of importance, by age (ANDERSON-TEIXEIRA et al., 2016; LETCHER; CHAZDON, 2009; LIEBSCH; MARQUES; GOLDENBERG, 2008; POORTER et al., 2016); higher surrounding forest cover in ethe landscape (GOOSEM et al., 2016; HOLL et al., 2016; SUGANUMA; TOREZAN; DURIGAN, 2017; ZERMEÑO- 41

HERNÁNDEZ et al., 2015); less intense surrounding land use (LAURANCE et al., 2012; MARTINEZ-RAMOS et al., 2016); and higher soil nutrient content (JAKOVAC et al., 2015; JOHN et al., 2007; MARTINS et al., 2015; ZERMEÑO-HERNÁNDEZ et al., 2015). These local and landscape factors are expected to favor a gradual recolonization of native tree species and their growth in regeneration sites, while avoiding setbacks in the successional trajectory of second-growth forests.

3.2. METHODS

3.2.1. Study region

Our study was carried out in the Corumbataí river basin, a highly deforested (12.4% forest cover remaining), 1,700 km² basin in southeast Brazil. According to the Köeppen-Geiger classification system, the climate of the region is Cwa, with dry winters and wet summers. Mean annual precipitation is 1,367 mm, most of it (80%) falling in the rainy season from October to March. Mean monthly temperature is 20.5 oC (minimum and maximum monthly averages of 15.6 oC and 29.5 oC, respectively) and the main soil types are Acrisols (44%) and Ferralsols (22%). Our study site is an ecotone between the Atlantic Forest and the Cerrado biomes, which occupy 42% and 58% of the basin, respectively. Forest sampling included only seasonal semideciduous forests, and did not include non-forest ecosystems, savanna woodlands, and riparian forests (Figure 1).

Figure 1: Location of the study region. The five landscapes in white where we sampled second growth forests were selected according to Ferraz et al. (2014). Detailed images of each landscape can be found in Supplementary File 9.

Most large-scale deforestation occurred in the early 19th century in our study region, until it gradually subsided in the early 20th century. Deforestation was fomented initially by coffee production for exportation, which was late replaced by cattle ranching and sugarcane plantations (DEAN, 1977, 1997). Industrial development in the 1970s led to migration to urban areas and favored the consolidation of sugarcane plantations in flat terrains and of extensive pastures and regenerating native forests in marginal agricultural lands (MOLIN et al., 2017). Native forest 42

cover doubled in the landscapes analyzed in this study between 1962 and 2008 (FERRAZ et al., 2014). Currently, the Corumbataí basin is predominantly covered by pastures and sugarcane fields, which occupy 43.7% and 29.4% of the basin, respectively. Native forest patches, eucalypt (Eucalyptus spp.) plantations and other land uses (buildings, water bodies, roads, etc.) occupy 12.4%, 7.3% and 7.2% of the basin, respectively. The major human disturbances to forest patches have been cattle grazing in forest understory and recurrent fires and drift of agrochemicals coming mainly from sugarcane fields. The frequency of fires from sugarcane burning has lowered in the study region in the last 10 years due to legal restrictions, until it was prohibited at the time of data gathering.

3.2.2. Experimental design

3.2.2.1. Landscape sampling

In order to define the location and size of the landscapes where second-growth forests (SGF) would be sampled, we used the diversity variability analysis approach proposed by Pasher et al. (2013) to I) divide the study region in square grids of five different scales: 1, 2, 3, 4 and 5 km square grid cells (1, 4, 9, 16 and 25 km2, respectively), II) calculate the Shannon landscape diversity index for each grid size based on a 30 m-resolution land map from 2002, III) plot mean landscape diversity of each grid size against the cell size. Following these steps, we selected the 4 km (16 km²) square grid size as the smallest sample size that shows no variation when compared to the landscape diversity index of larger sample sizes - i.e. it is the smallest landscape size that represents the study region. More details can be found in Ferraz et al. (2014). Since we aim to study SGF in agricultural landscapes, we selected landscapes that had at least 10% of native forest cover and at least 70% of agricultural land use in 2008. We carried out a moving window analysis using the 4-km side landscape size to find landscapes that fulfill these criteria. Among the potential landscapes, three were chosen randomly in the southern part of the study region and three in the northern part, where the matrix is represented by sugarcane fields and pastures, respectively. There was no overlapping among landscapes.

3.2.2.2. Second-growth forest sampling

We selected SGF patches within the aforementioned landscapes based exclusively on information on previous land use and forest age. The other local and landscape variables described below were obtained a posteriori, after the establishment of sampling plots based on these two first criteria. We classified land use of sampled landscapes based on panchromatic aerial photographs (scale of 1:25000) from the years of 1962, 1978 and 1995. We also used panchromatic images using a High-Resolution Panchromatic Camera from CBERS (spatial resolution of 2.7 m) for 2000, 2008 and 15m TerraColor imagery for 2015. We estimated age and previous land use of existing native forest cover by overlapping land-use classification maps of different ages (i.e. 1962, 1978, 1995, 2000 and 2008). We further refined forest age estimates by visually interpreting topographic maps from 1969, 1975 and 1979, and LANDSAT 5 and 8 images from the years 1984 to 2015. When the exact age of SGF establishment was not clear, we considered the average of the possible dates of establishment to calculate forest age. Sampled SGF age estimates ranged from 11 to 46.5 years old. 43

The main previous land uses of SGF selected for this study were cattle pastures and abandoned eucalypt plantations. We visually identified the latter as forest canopies with emergent eucalypt trees (Figure 2). Sugarcane plantation was not selected because the area of SGF over this previous land use was negligible.

Figure 2: Forest classification according to its age and previous land use. A) We obtained high resolution images for the studied landscapes for the years 1962, 1978, 1995, 2000 and 2008. B) Classification of land use based on the images. C) Overlapping the images of different dates obtained in B, we classified existing forests in a mosaic based on age and land use before forest establishment. Areas in red in C) represent forests that established over land uses other than pastures and eucalypt plantations.

Pastures in this region are mostly extensive (<1 animal unit per hectare) and planted with African fodder grasses, mainly Urochloa decumbens Stapf., in steep slopes where mechanized harvesting of sugarcane is not possible. Eucalypt plantations were represented by former small-scale woodlots abandoned after timber harvesting, where eucalypt trees resprouted and grow together with naturally colonizing woody species. In order to avoid pseudoreplication, we did not place plots in SGF with the same previous land use or age located in the same forest patch. Sampled SGF altitude ranged from 491 to 657 m asl. Due to access limitations and availability of SGF of all ages and previous land uses, SGF sampling distribution was not identical among forest age, previous land use or landscapes (Supplementary File 8). We could not sample SGF in one of the landscapes defined at the southern region of the study area due to its lack of SGF forests and steepy slopes, therefore we considered only five landscapes for this study (Figure 1). Supplementary File 9 contains plot location and maps of the study region. 44

We installed a total of 44 plots of 900 m² (20 × 45 m) to sample SGF tree community. We measured the diameter at breast height (DBH), and identified to the highest taxonomic level possible all the living rooted trees and shrubs DBH≥5 cm (hereafter “trees”) within each 900 m² plot. All sampled individuals were classified according to species origin (native or non-native to the study region)(CREES; TURVEY, 2015).

3.2.3. Drivers of second-growth forest regeneration

3.2.3.1. Local factors We employed forest age, basal area of Eucalypt spp., slope and soil sum of bases as local predictor variables. We obtained information on forest age as described above. Basal area of eucalypts (Eucalyptus spp.): since eucalypt plantation was one of the main previous land use in the SGF found in our study region, we considered using basal area of Eucalyptus spp. as a quantitative proxy of the legacy of previous land use of forest plantations. Slope: estimated based on the altitude contour lines of the study region and calculated it using the ArcGIS 10.1 tools “Topo to Raster” and “Slope” to generate a raster dataset of the slope, in degrees, of our study region. We then proceeded to extract the slope value of the raster pixel at the center of each SGF sampling plot. Soil sum of bases: we obtained one composite soil sample of three sub-samples at depth 0-10 cm per plot and determined soil pH, H+Al, cation exchange capacity, base saturation, organic matter, and P, K, Ca, Na and Mg content, which was further used to calculate sum of bases. We determined soil pH and H+AL by potentiometry, P and organic matter by colorimetry; and P, K, Ca, Na, and Mg using an ion exchange resin. Soil sum of bases was correlated in a Principal Component Analysis (Supplementary File 2) with most of the main nutrients found in the soils of our study site (P, Ca, Mg and K) and with other soil attributes (pH, organic matter content, cation exchange capacity and base saturation), so we considered only SB as the predictor variable from soil attributes (Supplementary File 2).

3.2.3.2. Landscape factors We employed surrounding land use, distance from water streams, average surrounding native forest cover and difference in surrounding native forest cover as landscape explanatory variables. We used the nearest neighbor analysis to identify if either pastures or sugarcane plantations (the two most common land uses in the study region) were closer to the centroid of SGF sampling plots for the years 2000, 2008 and 2015. Both pasture or sugarcane were the dominant land uses near sampled forests in the last 15 years before data gathering (Supplementary File 10). In only two plots (46 and 48) sugarcane plantation was the closest human land use only after 2008; in these cases we considered sugarcane as the dominant surrounding human land use because SGF showed clear signs of past burning caused by sugarcane harvesting practices. Distance from water streams: in order to estimate distance from water streams, we obtained the images with the location of all water bodies in our study site from Ferraz et al. (2014) and calculated the distance from the centroid of each SGF plot to the nearest water stream using nearest neighbor analysis. Surrounding native forest cover: second-growth forests are not only influenced by current local and landscape conditions, but also carry a legacy from past contexts that may have shaped these communities. Therefore, we initially considered four landscape factors related to past and present surrounding native vegetation cover in a 1 km radius around plots: i) forest cover at the estimated time of SGF establishment, ii) forest cover at the time of data gathering, 45

iii) average forest cover from SGF establishment to data gathering and iv) difference in forest cover from the time of SGF establishment to data gathering. Using the function corrplot in R software, we correlated these factors and grouped them in two hierarchical clusters in order to choose one factor from each cluster. For the first factor, we chose the difference in surrounding native forest cover since SGF establishment, as it can be employed as a proxy of landscape land use change dynamics. The second factor was the average surrounding native forest cover from forest establishment until data gathering, since current SGF may reflect not only present amount of forest cover, but may also carry legacies of past native forest configuration. We also believe that the latter factor is a good mid-term that can represent the overall effect of forest cover along time. Also, when considered in the model selection analyses, these four forest cover metrics had high variance inflation factors (VIF>10), which also indicates that they should not be all included in the data analyses. Further details of the selection process are detailed in Supplementary File 6 and in item 3.5. Given the overall small size of forest patches in our study site (only one remnant had > 100 ha, average ± standard deviation: 31.4 ± 26.5 ha, min: 0.8 ha, max: 101.0 ha) and the resulting proximity of all SGF sampled to remnant edge (only one SGF was >100 m from edge, average ± standard deviation: 48 ± 29 m, min: 10 m, max: 174 m), we did not consider these common landscape factors in our data analysis. These values reflect the human-dominated context of the agricultural landscapes considered in our study, and such low and narrow range of values will not allow us to detect the effect of these landscape factors in SGF attributes. Finally, such decision reduces the number of explanatory factors and increase statistical power. We carried out all landscape data generation using ArcGIS 10.1. Patch size and surrounding native forest cover for each plot are graphically represented in Supplementary File 5.

3.2.4. Attributes of second-growth forests

Forest attributes used as response variables were i) aboveground biomass of native trees (AGB), ii) native tree species density (hereafter “tree species density”), and iii) phylogenetic dispersal of native trees. We also analyzed species composition as a descriptive component of our study site. To calculate species density and phylogenetic dispersal, we discarded non-identified individuals. However, we included unidentified morphospecies to calculate biomass. Given that the relatively few non-native trees are well described and easily identifiable in the field, we considered morphospecies that were not identified and trees which we could not collect vegetative material as native species.

3.2.4.1. Biomass estimates

We calculated tree AGB based on equation (7) developed by Chave et al. (2014). We obtained tree wood density from several references, but mainly from Chave et al. (2009) and Zanne et al. (2009). Wood density values and references are in Supplementary File 11. When wood density data was not available for a given species, we used the following sequential decision approach to infer wood density values: i) average of the species of the same genus sampled in this study (3.3% of all trees sampled fell in this category), or ii) average of species of the same genus in tropical forests in the literature (0.5%), or iii) average of the species of the same family sampled in this study (2.5%), or iv) average wood density weighted by the basal area of all trees sampled in the same plot (3.2%). For species identified only to the genus or family level, we followed the steps mentioned previously. For unidentified morphospecies, we followed the method “iv)” described above. 46

3.2.4.2. Phylogenetic dispersion

To estimate phylogenetic dispersion, we employed as species pool the floristic inventory of all SGF forests sampled in the present study. We considered this species pool because our objective was to compare differences in the phylogenetic structure among forests in different local and landscape contexts. We used Phylocom (WEBB;

ACKERLY; KEMBEL, 2008) to construct a phylogeny from the species pool based on the most recent angiosperm tree available online (R20160415, GASTAUER & MEIRA NETO, 2017).

We employed the bladj algorithm implemented in the Phylocom software and evolutionary ages published by

Wikstrom, Savolainen & Chase (2001) to estimate the ages of the interior nodes of the evolutionary tree and evenly space the nodes between them. Before ageing the file we checked for internal node inconsistencies as recommended by (GASTAUER; MEIRA-NETO, 2013).

We estimated the evolutionary distance matrix between all species pairs sampled using Phylomatic (WEBB;

DONOGHUE, 2005). We estimated the Net Relatedness Index (Webb, Naturalist, & Aug, 2000) for each of the sampled forests. The NRI is a standardized metric for estimating the degree of phylogenetic clustering (higher presence of close relatives than expected by chance) or phylogenetic overdispersion (higher presence of far relatives than expected by chance) of a community present at a site comparing the mean pairwise evolutionary distance

(MPD) among the species observed compared to a random draw of species from the species pool (WEBB et al.,

2002).

We compared values to a null estimate generated by randomly sampling 9999 times from the local species pool, keeping observed plot abundance. Abundance-weighted phylogenetic indices are better than occurrence-based indices at capturing assembly processes such as environmental filtering or competition acting on a given community

(FREILICH; CONNOLLY, 2015). We employed the randomization routine called ‘taxa labels’, which shuffles the labels of a phylogenetic distance matrix across all taxa, as null model to estimate the random communities. This metric is considered as a conservative method for avoiding Type-I errors (HARDY, 2008). A positive NRI value indicates a higher presence of close relatives (phylogenetic clustering), whereas a negative value indicates a higher than expected presence of far relatives (phylogenetic overdispersion) in the plant community.

3.2.5. Data analyses

To compare the relative contribution of local and landscape factors on SGF attributes we used generalized mixed linear models, considering as the random factor the landscape where the SGF was located (MOSCATELLI;

MEZZETTI; LACQUANITI, 2012). We considered four local factors: forest age (hereafter “AGE”), basal area of 47

Eucalyptus spp. (AB_EUC), soil sum of bases (SB) and slope (SL); and four landscape factors: nearest human land use

(NU), distance from the nearest watercourse (RIVER), average proportion of native forest cover surrounding the

SGF plot since forest establishment until data gathering (FC) and difference in surrounding native forest cover from forest establishment until data gathering (ΔFC). We checked for autocorrelation by calculating the variance-inflation factor (VIF) and excluding any factor with VIF values >4, for details of the factors excluded, see Supplementary File

6, Table S1.

Additionally, we considered two interactions among factors for data analyses : i) AGE × NU: we hypothesize that more intensively agricultural activities surrounding SGF (such as sugarcane plantations) will have continuous deleterious effects that may gradually collapse SGF structure over time, while SGF near pastures may develop with less disturbances as they age; ii) FC × NU: surrounding land use and, therefore, its permeability, may mediate the influence of forest cover in seed dispersal to SGF. Thus, we analyzed the effect of 10 factors on SGF attributes:

AGE, AB_EUC, SB, SL, NU, RIVER, FC, ΔFC, AGE × NU and FC × NU.

For each SGF attribute, we considered all combinations of models using the factors above plus a null model. All forest attributes showed Gaussian distribution of data and we developed models considered this distribution and link

= identity in the function glmer in R 3.0. For each candidate model, we calculated the Akaike Information Criteria corrected for small samples (AICc), the Akaike weight (wi) and the marginal (R²m) and conditional (R²c) sum of squares, which represent the sum of squares without and with the random factor, respectively. We ranked models according to the ∆AICci (AICci – minimum AiCc). We proceeded to generate the average model for each forest attribute using the subset of all models ∆AICci ≤ 4 and presenting the results as the coefficients estimates and standard error and the relative importance of the factors in the average model. When the null model was within the models ∆i ≤ 4 for a given SGF attribute, we considered that all factors are uninformative to explain that attribute.

Local and landscape factors had unique effects on SGF attributes. Although the best models to estimate SGF attributes encompassed several factors, the direction of their effect (i.e. positive or negative) was context dependent

(i.e. standard deviation was greater than coefficient values). Therefore, we will mainly present in this section factors that showed a clear positive or negative effect on SGF attributes (i.e. factors which coefficient ± Std. Error range was ≥95% positive or negative).

To compare composition similarity of trees among SGF, we calculated the Chao-Jaccard dissimilarity index (CHAO et al., 2004) between each plot and created a graph using non-metrical multidimensional analysis to visualize similarity among SGF using the “mds” function. We considered both native and non-native species to calculate composition similarity. 48

Values are shown as mean ± one standard deviation. All analyses were carried out in the R 3.0 environment (R

DEVELOPMENT CORE TEAM, 2013), using the packages “MuMIn” (BARTON, 2016), “lme4” (BATES et al.,

2014), “piecewiseSEM” (LEFCHECK, 2016) and “ggplot2” (WICKHAM, 2009).

3.3. RESULTS

We sampled a total of 4,661 trees, composed by 189 identified species and 32 unidentified morphospecies, from 133 genera and 50 families; 148 (3.1%) trees could not be identified to any taxonomic level. Tree composition varied widely among plots and was not influenced by any of the factors approached in this study (Supplementary File 4). The most abundant tree species sampled were Casearia sylvestris Sw. (6.3% of all trees sampled), Eucalyptus spp. (4.4%), Luehea candicans Mart. & Zucc. (3.9%) and Piptadenia gonoacantha (Mart.) J.F.Macb (3.4%). Resprouted eucalypt trees composed 48 ± 24% of the basal area of SGF established in abandoned eucalypt plantations. The complete list of species sampled can be found in Supplementary File 3. Forest cover and human-related factors were the main drivers of SGF attributes in tropical agricultural landscapes of the southeast Atlantic Forest. Surrounding forest cover was positively related to species density, phylogenetic dispersion and AGB, for the latter, forest cover had a more positive effect when SGF were near sugarcane plantations. The presence of remnant eucalypt trees reduced AGB and species density in SGF, while SGF nearby sugarcane plantations showed reduced AGB along time and phylogenetic dispersion. Forest age had a secondary, but positive, effect on AGB and species density. Slope and difference in forest cover where attribute- specific factors, the first having a negative effect on AGB and the latter a positive effect on species density. Distance from water streams did not affect SGF attributes. Detailed information about the relative importance and coefficients for each SGF attribute are in Table 1, while visual summary of these results can be found in Figure 3. The complete list of models generated can be found in Supplementary File 1.

Table 1: Relative Importance and coefficient ± one standard error of the average model developed for native species biomass, native species density and phylogenetic dispersion of second-growth forests in tropical agricultural landscapes of Southeast Atlantic Forest, Brazil. Relative importance is the weighted proportion of the models ΔAICc≤4 that contain a given factor. Coefficient and standard error refer to the values of a given factor in the average model developed considering all models ΔAICc≤4 for a given forest attribute. AGE: forest age; AGE x NU: effect of age when forest is near sugarcane plantations, EUC: basal area of eucalypt; FC: proportion of native forest cover in a 1-km radius; ΔFC: current surrounding native forest cover in a 1-km buffer minus forest cover at the time of forest establishment; FC x NU: as FC but when forests are near sugarcane plantations; NU: nearby sugarcane plantation; SL: slope.

Relative Attribute and Factors Coefficient ± Standard Error Importance Biomass AGE (years) 0.79 1.27 ± 0.70 AGE x NU (years) 0.52 -2.74 ± 2.15 EUC (m²/ha) 1.00 -2.18 ± 0.73 FC (%) 1.00 -85.81 ± 127.13 ΔFC (%) 1.00 -51.50 ± 136.66 FC x NU (%) 1.00 222.51 ± 245.58 NU (qualitative) 1.00 -57.01 ± 69.13 SL (degrees) 0.68 -2.33 ± 1.62 Species density AGE (years) 0.11 0.14 ± 0.09 EUC (m²/ha) 0.16 -0.16 ± 0.10 FC (%) 1.00 23.87 ± 18.66 ΔFC (%) 1.00 19.06 ± 17.34 FC x NU (%) 1.00 -4.61 ± 33.54 NU (qualitative) 1.00 -0.16 ± 7.39 Phylogenetic dispersion FC (%) 1.00 3.00 ± 2.83 ΔFC (%) 0.67 -0.28 ± 2.87 FC x NU (%) 0.70 2.41 ± 5.51 NU (qualitative) 0.89 -1.08 ± 1.09

3.3.1. Aboveground Biomass

Basal area of remnant eucalypt, presence ofsugarcane plantations nearby and surrounding forest cover where the most important drivers of AGB in SGF. Proximity to sugarcane plantations defined the effect of other factors in AGB: older SGF showed more biomass except when surrounded by sugarcane plantations, in this case older forests showed lower biomass, and forest cover increased AGB specially when SGF was near sugarcane plantations. Basal area of remnant Eucalyptus spp. and slope reduced AGB of SGF. Difference in forest cover was an important factor influencing AGB, but its effect was highly variable (Figure 3A).For each year that SGF regenerate near sugarcane plantations, their estimated AGB is changes by -2.7 ± 2.2 Mg.ha-1, on the other hand, an increase of 10% in surrounding forest cover in this context could increase AGB in 22.2 ± 24.6 Mg.ha-1. Each additional m².ha-1 of Eucalyptus spp. basal area reduced native species AGB in -2.1 ± 0.7 Mg.ha-1. Forest slope and age change AGB estimates in -2.3 ± 1.6 Mg.ha-1 per degree and 1.3 ± 0.7 Mg.ha-1 per year, respectively. However, these two factors were not included in all the best models that encompassed the average model (Table 1).

3.3.2. Native Species Density

Species density in SGF was governed by factors related to surrounding native forest cover. Historical gains in native forest cover and higher average native forest cover increased species density estimates in SGF. Basal area of Eucalyptus spp. and forest age showed negative and positive coefficients to species density, respectively. However, these two factors were rarely considered among the best models (importance <0.2). The presence of nearby sugarcane plantations was an important driver of species density, but its effect was context dependent (Figure 3B). For each 10% increase in forest cover since SGF establishment, 1.9 ± 1.7 more species are estimated to be found in each plot, and an additional 2.3 ± 1.9 for each 10% of surrounding forest cover. Basal area of Eucalyptus spp. and SGF age changed species density estimates in -0.2 ± 0.1 and 0.1 ± 0.1. However these factors where included in just a few of the best models (Table 1).

3.3.3. Phylogenetic Dispersion

Phylogenetic dispersion estimates were negatively related to the presence of sugarcane plantations nearby plots, and positively to surrounding forest cover, evidencing that higher forest cover enhanced the colonization of SGF by species from more distance clades of the regional phylogenetic tree. Change in surrounding forest cover was included in most of the best models (importance <0.7), but its coefficient was highly variable (Figure 3C).Phylogenetic dispersion estimates in SGF increased by 0.3 ± 0.3 units for each 10% surrounding forest cover, and was reduced in -1.1 ± 1.1 when sugarcane plantations were the nearby land use (Table 1). 51

Figure 3: Graphical representation of landscape and local factors influencing tropical second-growth forest (SGF) native aboveground biomass (A), species density (B) and phylogenetic dispersion (C) in landscapes of the Atlantic Forest in Southeast Brazil. Left graph contains only drivers which the range of coefficient ± standard error was ≥95% within positive or negative values (i.e. drivers with a clear positive or negative effect on the SGF attribute). The right graph contains drivers that did not fit the requisites mentioned before (i.e. factor effect is variable and/or context-dependent). Results reflect the average model developed by merging all models ∆AICc ≤ 4. Arrow color represent how much of the range of coefficient ± standard error falls within positive and negative values. Relative importance: weighted proportion of the models ∆AICc ≤ 4 that contain the factor

3.4. DISCUSSION

Despite the dynamic nature of human-modified landscapes and the high spatial environmental variability of tropical forest regions, we could identify the major local and landscape drivers impacting the structure and diversity attributes of naturally established forests in our study region. Even in a context of very low forest cover and small forest patches, surrounding native forest cover played a major influence in regeneration effectiveness, as would play for restoration success. Previous and surrounding human land use were determinant to structure and biodiversity recovery in SGF. However, none of these factors influenced SGF composition (Supplementary File 4). Finally, older SGF had higher AGB but forest age had little and no importance to define species density and phylogenetic dispersion, respectively.

3.4.1. Human land use and SGF in agricultural landscapes

Competition with re-sprouting eucalypt trees reduced AGB and species density estimates of SGF. In our study site, the hindering effect of remnant eucalypt trees may be due to the advanced age range of the SGF sampled in this study (11-46.5 years) and, consequently, the larger biomass they achieved; when we considered only forests

11-20 years old, eucalypt does not show a negative influence on the structure and diversity of SGF in the region

(CÉSAR et al., 2018). Eucalypt trees accumulated almost four times more AGB than what they reduced from native species AGB estimates: each square meter of eucalypt basal area accumulated 8.5 ± 3.7 Mg.ha-1, while reducing biomass of native species estimates in -2.2 ± 0.7 Mg.ha-1. These estimates may indicate the potential of this non- native species for quick accumulation of biomass in the early stages of forest succession and restoration, and possibly being harvested in later stages to release native species from competition (AMAZONAS et al., 2018). These negative impacts of eucalypts may not be, however, associated to allelopathy or other deleterious effects associated to species of this genus, it may be a simple effect of competition for resources: fewer large trees are able to regenerate in SGF dominated by eucalypts, so the AGB and species density will naturally decline due to the limited opportunities for native tree species colonization and development (AMAZONAS et al., 2018).

Reduced estimates of AGB and phylogenetic dispersion in SGF surrounded by sugarcane plantations may be due to past and current agricultural practices related to the management of this crop. Until approximately ten years before data gathering, sugarcane plantations were annually burned to facilitate harvesting and, currently, herbicide is sprayed by airplanes to enhance sugarcane maturation and has inevitably reached nearby SGF. Chronic disturbances caused by these surrounding agricultural practices could gradually collapse SGF structure and select disturbance-adapted species the longer they are exposed to it (BERENGUER et al., 2014; BONNER; SCHMIDT;

SHOO, 2013), thus explaining why SGF near sugarcane plantations showed lower AGB and phylogenetic dispersion. 53

3.4.2. Forest cover and conservation potential of SGF

As we expected, surrounding native forest cover showed an important and positive effect on the estimates of AGB, species density and phylogenetic dispersion of SGF. The amount of forest cover surrounding SGF is related to patch connectivity (LIRA et al., 2012) and, therefore, the species pool that can be dispersed to SGF (CHARLES; DWYER; MAYFIELD, 2016). Increase in forest biodiversity with increasing surrounding native forest cover was observed in other tropical forests in less deforested areas (CROUZEILLES et al., 2016; JAKOVAC et al., 2015; MARTÍNEZ-RAMOS et al., 2016); we found only one study that analyzed the effect of forest cover in forest phylogenetic diversity of trees, which showed a positive effect (MATOS et al., 2016). Our results are corroborated by the findings of the meta-analyses developed by Bonner et al. (2013) and Crouzeilles et al. (2016) pointed that forest cover played an utmost influence on AGB. Controversially, Letcher and Chazdon (2009), and Holl et al. (2016) observed that previous land use, and not surrounding forest cover, was determinant to biodiversity and biomass recovery of naturally regenerating forests and restoration plantings, respectively, in Costa Rica. However, both studies were carried out in landscapes with higher forest cover (25-90% for regenerating forests and 11-89%, for restoration plantings), in which the influence of neighboring forest cover may be lower as consequence of the maintenance of high levels of connectivity across the landscape (FAHRIG, 2003).

3.4.3. Age and SGF attributes in agricultural landscapes

Age is the core factor used to estimate forest attributes along succession, being adequate for a myriad of attributes in many contexts (Cheung et al., 2010; Dent & Joseph Wright, 2009; Garcia et al., 2016; Liebsch et al., 2008; Natalia Norden et al., 2009; Powers et al., 2009, and many others). However, for the tropical SGF embedded in highly deforested agricultural landscapes sampled in this study, forest age had a secondary – or even null – role in estimating SGF diversity attributes (i.e. species density and phylogenetic dispersion). Although the interaction of age and nearby land use was included in the best models to estimate AGB, the model that contained only forest age as the predictor variable had ΔAICc<6 from the null model for AGB; and the null model was better than the age-only model to estimate species density and phylogenetic dispersion. This weak relationship between SGF age and diversity-related attributes may have been caused by a myriad of methodological and context-dependent factors: i) forest age was estimated between >5 years intervals of high-resolution aereal photographs and low-resolution satellite images, therefore forest age estimates are not exact, mainly for SGF that may have been regenerating under Eucalyptus spp. plantations canopy for years before harvesting of this species; ii) diversity-related attributes may take longer to recover and respond to local context, disturbance regime and stochastic factors that are amplified in human-modified landscapes (LIEBSCH; MARQUES; GOLDENBERG, 2008; VAN BREUGEL et al., 2013); iii) SGF sampled are all >10 yo and were subjected for many years to unique disturbance regimes and landscape dynamics. The accumulated effect of these drivers along time may have driven SGF to somewhat unique successional trajectories that can be better estimated by disturbance- related factors than by forest age alone (ARROYO-RODRIGUEZ et al., 2015; MARTINEZ-RAMOS et al., 2016). We highlight that we are not advocating that forest age should be set aside in successional studies of historically disturbed forests. We argue that, in highly dynamic human-modified landscapes, successional studies should consider a myriad of factors (including forest age) that may interact with each other to estimate the regeneration potential of SGF. 54

3.4.4. Implications for climate change mitigation and biodiversity conservation

Natural regeneration is one of the most cost-effective solutions to mitigate climate change (GRISCOM et al., 2017) and tropical forests – the terrestrial ecosystems with the highest potential for accumulating biomass per area (BEER et al., 2010) – have concentrated carbon sequestration projects globally (LOCATELLI et al., 2015). Continental-scale models to predict the carbon sequestration potential of SGF rely primarily on forest age (CHAZDON et al., 2016; POORTER et al., 2016), a factor that is indeed related to AGB accumulation (Letcher & Chazdon 2009, Anderson-Teixeira et al. 2013, this study). However, when SGF are embedded in human-modified landscapes, previous and surrounding human activities, as well as surrounding forest cover, can reduce or even trump the positive influence of forest age on the structure and conservation potential of SGF. Human-mediated disturbances can stagnate, diverge or even revert succession (ARROYO-RODRIGUEZ et al., 2015), as we found for SGF surrounded by sugarcane plantations or established in abandoned eucalypt plantations. If the impact of anthropogenic activities is not included in the conceptual models and decision-making processes regarding tropical forest succession, the long-term potential of these forests to mitigate climate change may be overestimated in human-modified landscapes. Conserving existing forests and fomenting SGF establishment should approach not only the increase of native forest cover, but also the potential impacts of human activities around these forests. Human-mediated degradation can encroach deep in forests patches (BARLOW et al., 2016) and alter forest dynamics even in old- growth protected areas (MARTINEZ-RAMOS et al., 2016). Agricultural practices not only define matrix permeability, but also the type, frequency, and intensity of disturbance that native forests will receive over time. The positive effect of increasing forest cover for species richness and phylogenetic dispersion of SGF calls for public policies regulating land use in agricultural landscapes of the Atlantic Forest to i) conserve existing forest patches, even if young, small or disturbed (BANKS-LEITE et al., 2014); ii) increase landscape connectivity through the establishment of ecological corridors through forest restoration (NEWMARK et al., 2017); iii) restore degraded forest patches (CÉSAR et al., 2016) and iv) promote agricultural practices with minimum impacto to forest patches (MELO et al., 2013b). The results and methodological approach presented here may also be valuable to prioritize areas for cost-effective restoration in tropical forest regions, by selecting sites with higher potential to sequester carbon or conserve biodiversity, responding to the call for developing tropical reforestation approaches that better match the scale of transformation required to face the global commitments on forest landscape restoration (HOLL, 2017).

ACKNOWLEDGEMENTS

Funding for this research was provided by the FAPESP grants 2014/14503-7 and 2017/05662-2. SFBF received funding from FAPESP projects 2011/19767-4 and 2013/22679-5. PHSB thanks the National Council for Scientific and Technological Development of Brazil (CNPq) (grant #304817/2015-5). RC was supported by a fellowship from the Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES) for research grant (#88881.064976/2014-01). This work was also supported by the PARTNERS Research Coordination Network grant #DEB1313788 from the U.S. NSF Coupled Natural and Human Systems Program. The authors also would like to thank the several volunteers during field work and the landowners that allowed forest sampling in their properties.

AUTHOR CONTRIBUTIONS

RGC, PHSB and RLC conceived the research idea and designed the methodology; RC, VM and GDC collected the data; RGC, VM and GDC identified morphospecies; RGC and SFBF generated landscape attributes; RGC, DS, RC and JB analyzed the data; RGC, PHSB, JB and RLC lead the writing of the manuscript, with additional help of the other authors for interpretation of the results. All authors contributed to the previous versions of this manuscript and gave final approval for publication.

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4. Final considerations

Forests naturally established without human intervention and seedling plantings for forest restoration differ in structure, diversity and species composition, probably due by the contexts where these approaches are employed and the restoration techniques used in the field. The main drivers of structure and diversity of forests naturally established are previous and surrounding human land use and native forest cover in the landscape. In isolated and/or degraded areas with low potential for natural regeneration, seedling plantings play a crucial role in providing environmental services and conserving biodiversity in situ and acting as species refugia in highly deforested landscapes (BENAYAS et al., 2009). However, dispersal and local limitations may limit seedling recruitment of native trees and lianas. Our study was initially designed to study only the effect of previous land use and age of second-growth forests. However, as the study progressed, we observed that human-mediated drivers also played a crucial role in defining the structure and richness of these forests. Thus, we changed broadened our data gathering and analysis to include soil attributes and landscape variables. Like many ecological studies, our experimental design was bounded to the land use characteristics of the landscapes sampled in this project, therefore plot distribution was not homogenous among forest patches or landscapes, nor were the variables used to estimate forest attributes (i.e. sugarcane was the main land use in the southern part of the basin, while pastures were dominant in the northern part; there was only one plot of pasture as the previous land use surrounded by sugarcane plantation). In many cases forests simply were not accessible or steep slopes made plot implementation counter-productive. Finally, forest age estimative was based on widely spaced high-resolution images and annual LANDSAT images, in some cases this made forest age estimaive vary for a range >5 years, and made somewhat diffucult to estimate age of second-growth forests under eucalyptus plantations. Despite these methodological difficulties, we were able to identify successional patters of biomass and species diversity in second-growth forests in agricultural landscapes. Our results can be corrobotared by the existing literature and contribute to it by pointing that human-related factors and surrounding native forest cover were important drivers of second-growth forest structure and diversity. Given the secondary role of forest age by itself in defining second-growth forests attributes in our study, successional studies in human-modified landscapes should consider the significant role of landscape and human-mediated drivers in these forests (ARROYO-RODRIGUEZ et al., 2015; MARTINEZ-RAMOS et al., 2016). For example, if this study had stick to its original design of analyzing the effect of age and previous land use only, we would have found little evidence of drivers of second-growth forest attributes in agricultural landscapes, missing an opportunity for greater scientific insight, as discussed by MESQUITA et al. (2015). Of course, depending on the scale and the attributes studied in tropical second-growth forests, there may be unpredictable patterns (NORDEN et al., 2015). Accurate estimates of second-growth forest attributes will allow us to unfold the potential of using succession to expand natural habitat in agricultural landscapes, the areas which most need ecological restoration. Forest patches in agricultural landscapes are subjected to different disturbance regimes and biotic and abiotic regimes from the same forest formations in more forested landscapes. Therefore, the context of successional studies of tropical forest can have significant implications to the conclusions of the drivers of forest attributes (MESQUITA et al., 2015). Our study was carried out in landscapes with <30% native forest cover, a range where native habitat amount can significantly contribute to conservation potential of remnant habitat (BANKS-LEITE et al., 2014), thus the critical importance of surrounding forest cover to increase species diversity in our study sites. On the other hand, studies in more forested landscapes found no effect of the amount of surrounding native forest 62

cover on second-growth forests (CROUZEILLES et al., 2016; HOLL et al., 2016). Human-related drivers, either for ecosystem degradation or recovery, such as previous land use, surrounding land use and restoration interventions seem to have determinant role in the fate of second growth forests (HOLL et al., 2016; JAKOVAC et al., 2016; MARTINEZ-RAMOS et al., 2016; ZERMEÑO-HERNÁNDEZ; PINGARRONI; MARTÍNEZ-RAMOS, 2016). Thus, the critical role of the (scarce) surrounding forest cover in agricultural landscapes and past and present human activities encompass the drivers that should be considered besides forest age in the successional studies in these landscapes, which differ from second-growth forests in less degraded landscapes. Adaptative management in both actively and passively restored forests could contribute to remediate processes that may compromise the long-term sustainability of these forests, accelerate recovery and mitigate costs of ecological restoration. In restoration plantings with low abundance and richness of spontaneously regenerating trees, experimenting on enrichment planting of shade tolerant species and the removal of canopy pioneers to reduce competition with the understory could contribute to the long term sustainability of these communities (BERTACCHI et al., 2016; DURIGAN; RAMOS, 2013; SWINFIELD et al., 2016). Resprouting remnant eucalypt individuals in forests naturally established in abandoned plantations could be harvested using minimal impact logging techniques to generate income to landowners and reduce competition of this exotic species with spontaneous native regeneration (BRANCALION et al., 2012a). Alternatively, eucalypt and other exotic, non-invasive commercial trees and crops could be cultivated in the early stages of restoration plantings to supress exotic grasses and generate income (VIEIRA; HOLL; PENEIREIRO, 2009). Disturbance from agricultural practices surrounding native forest remnants may degrade the structure and reduce the conservation potential of forests established without human intervention. Human practices may encroach in remnant forests and even double degradation from deforestation per se (BARLOW et al., 2016). Even relatively well protected conservation units with old growth forests may gradually lose structure and change species dynamics due to surrounding human activities (FARAH et al., 2014; MARTINEZ-RAMOS et al., 2016). These evidences urge for less harmful agricultural practices surrounding native forest patches and for the maintenance of a minimum native habitat cover in agricultural landscapes to maintain and improve the conservation potential of forest patches (BANKS-LEITE et al., 2014; MELO et al., 2013b). In this context, legislation, market standards and public policies may play a crucial role in safeguarding biodiversity in agricultural landscapes. Based on the research findings of this thesis, we recommend restoration practitioners and decision makers aiming to increase native forest cover in order to sequester carbon and conserve biodiversity to foment restoration intiatives: i) buffered from disturbances from intensive agricultural practices, such as herbicides and burning; ii) in areas with more surrounding forest cover and with historical increases in native forest cover; and iii) gradually remove remnant exotic species. Finally, our results show that both SF and PL are complementary and valuable restoration approaches for the provision of environmental services and conservation of biodiversity. Not only the choice of the best restoration approach is context-dependent, but also the outcomes of these decisions are highly influenced by the surrounding context.

REFERENCES

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APPENDIXES

APPENDIX A. Information of forests sampled throughout this study. Plot location and data presented here was used for both manuscripts presented in Appendix B and C.

Appendix A.1. Location of plots installed to gather data on the three forest types sampled in this study: naturally-established second-growth forests, mixed tree plantings and old-growth reference forests.

Figure A.1.1: Location of the five landscapes established by (Ferraz et al., 2014) were second-growth forests that established without human assistance (SGF) were sampled. At the early drafts of this study, we had landscape number “5”, but no SGF could be sampled in this landscape and it was removed from our study.

Figure A.1.2: Location of the plots installed to sample SGF in agricultural landscapes of the Corumbataí watershed, Southeast Brazil. Forests are classified based on their estimated age range since establishment. Plot numbers are not continuous. Data collected in these forests were used in both manuscripts (Appendix B and C).

Figure A.1.3: Location of the plots installed to sample SGF in agricultural landscapes of the Corumbataí watershed, Southeast Brazil. Forests are classified based on their estimated age range since establishment. Plot numbers are not continuous. Data collected in these forests were used in both manuscripts (Appendix B and C).

Figure A.1.4: Location of the plots installed to sample SGF in agricultural landscapes of the Corumbataí watershed, Southeast Brazil. Forests are classified based on their estimated age range since establishment. Plot numbers are not continuous. Data collected in these forests were used in both manuscripts (Appendix B and C).

Figure A.1.5: Location of the plots installed to sample SGF in agricultural landscapes of the Corumbataí watershed, Southeast Brazil. Forests are classified based on their estimated age range since establishment. Plot numbers are not continuous. Data collected in these forests were used in both manuscripts (Appendix B and C).

Figure A.1.6: Location of the plots installed to sample SGF in agricultural landscapes of the Corumbataí watershed, Southeast Brazil. Forests are classified based on their estimated age range since establishment. Plot numbers are not continuous. Data collected in these forests were used in both manuscripts (Appendix B and C). 71

Figure A.1.7: Location of the plots installed to sample mixed tree plantings in the state of São Paulo, Southeast Brazil. Data collected in these forests were included in the manuscript in Appendix B.

Figure A.1.8: Location of the plots installed to sample reference forests in the state of São Paulo, Southeast Brazil. Data collected in these forests were included in the manuscript in Appendix B.

Appendix A.2. Plot dataset used for data analyses. Local and landscape drivers (i.e. soil sum of bases, slope, nearby use) were not generated for mixed tree plantings and reference forests because these were not part of the chapter that analyzed drivers (Appendix C). For details of each variable see manuscripts in Appendix B and C. Plot: plot code; Bio_total: biomass of all trees DBH>5 cm sampled in the plot; Bio_nat: same but only for native species; Sp_nat: number of native species in the plot; AB_Euc: basal area of Eucalyptus spp.; Phylo: phylogenetic dispersion of the species in the plot; Age: estimated forest age; Type: forest type: pasture: second-growth forests (SGF) established in abandoned pastures, silviculture: second-growth forests established in abandoned eucalypt plantations after harvesting, reference: old-growth conserved forests, planting: mixed tree plantings for forest restoration; SB: soil sum of bases; Slope: slope at the plot; Nearby: nearby human land use; River: distance from the nearest river. Forest_cover: average forest cover since forest establishment until data gathering; Delta_cover: difference in forest cover from forest establishment until data gathering; Landscape: landscape where the forest is located (See Appendix A.1.).

SB Bio_Total Bio_Nat AB_Euc Age Slope River Forest_cover Delta_cover Altitude Latitude Longitude Plot Sp_nat Phylo Type (mmolc/ Nearby Landscape (ton/ha) (ton/ha) (m² / ha) (years) (%) (m) (%) (%) (m asl) (degrees) (degrees) dm³) 12 70.18 69.39 21 0.00 -0.18 35.5 Pasture 179.32 15.8 Pasture 130.09 13.90 18.95 1 657 -22.2369 -47.5940 18 172.76 172.76 23 0.00 -2.39 35.5 Pasture 134.27 6.9 Pasture 52.18 20.95 9.09 1 620 -22.2186 -47.5952 19 201.88 201.88 30 0.00 -1.49 46.5 Pasture 133.17 4.0 Pasture 37.52 19.89 11.76 1 615 -22.2153 -47.5937 20 80.30 75.12 18 0.00 -1.46 24.0 Pasture 80.10 3.1 Pasture 39.71 19.20 6.89 1 621 -22.2150 -47.5920 22 190.74 190.32 21 0.00 -1.55 35.5 Pasture 203.17 8.1 Pasture 93.14 9.66 10.34 1 625 -22.2190 -47.5939 31 292.06 205.24 19 6.42 -1.08 31.0 Pasture 8.87 10.9 Pasture 56.41 22.10 0.12 2 629 -22.2931 -47.5956 32 353.86 93.01 16 20.38 -1.91 34.0 Silviculture 108.00 10.9 Pasture 55.34 12.04 1.09 2 639 -22.2928 -47.5963 33 331.83 98.92 18 16.37 -2.03 34.0 Silviculture 14.83 8.5 Pasture 126.47 13.74 0.98 2 648 -22.2922 -47.5966 46 353.61 25.88 18 24.16 -0.59 34.0 Silviculture 13.72 7.5 Sugarcane 270.72 29.36 16.07 3 636 -22.3398 -47.5769 48 69.52 69.52 16 0.00 -2.70 13.5 Pasture 11.30 2.6 Sugarcane 363.24 14.75 7.25 3 642 -22.3405 -47.5777 49 117.13 115.79 31 0.00 -2.53 46.5 Pasture 25.35 13.5 Pasture 214.46 16.03 19.01 3 626 -22.3408 -47.5763 52 170.60 170.16 19 0.00 -0.28 34.0 Silviculture 36.27 6.2 Pasture 75.49 9.96 16.94 3 574 -22.3413 -47.5720 53 68.02 40.48 14 0.00 -0.02 12.0 Pasture 53.74 10.4 Pasture 22.31 10.01 6.26 3 579 -22.3420 -47.5734 54 73.58 73.53 36 0.00 -0.36 30.0 Pasture 71.58 13.3 Pasture 27.29 22.63 14.68 3 588 -22.3420 -47.5741 56 114.59 114.59 25 0.00 -1.71 45.0 Pasture 97.93 8.7 Pasture 62.21 25.13 17.82 3 588 -22.3390 -47.5730 58 214.00 87.11 19 29.18 -2.85 17.5 Silviculture 10.47 8.9 Pasture 194.55 12.41 9.51 3 638 -22.3447 -47.5800 62 101.95 101.67 28 0.00 -1.55 30.0 Pasture 105.20 13.8 Pasture 45.13 11.57 15.73 3 583 -22.3442 -47.5742

63 122.67 105.53 24 0.00 0.68 30.0 Pasture 15.49 12.9 Pasture 73.29 13.63 14.30 3 626 -22.3431 -47.5770 64 335.53 335.53 29 0.00 -0.93 30.0 Pasture 51.25 13.3 Pasture 163.44 9.97 15.84 3 585 -22.3460 -47.5749 70 164.34 82.05 17 22.47 -0.88 48.5 Silviculture 51.00 1.2 Pasture 38.84 25.63 13.14 3 563 -22.3316 -47.5612 71 123.37 122.71 31 0.00 1.61 46.5 Pasture 31.62 0.9 Pasture 36.26 24.75 11.41 3 564 -22.3318 -47.5628 73 171.42 152.46 34 0.00 -0.35 30.0 Silviculture 27.87 6.3 Pasture 98.14 22.76 8.74 3 567 -22.3338 -47.5605 78 352.61 27.32 15 38.81 0.51 17.5 Silviculture 46.23 2.2 Pasture 41.72 23.60 10.27 3 563 -22.3438 -47.5704 83 181.62 95.02 33 11.54 -1.82 45.0 Silviculture 44.07 6.4 Pasture 26.35 23.10 10.55 3 582 -22.3525 -47.5637 84 123.42 90.34 24 4.52 -0.78 15.0 Pasture 24.15 9.5 Pasture 68.79 24.52 2.37 3 609 -22.3552 -47.5591 85 163.27 162.60 27 0.00 -1.28 34.0 Pasture 42.40 7.9 Pasture 25.99 8.87 7.76 3 587 -22.3539 -47.5622 87 154.71 152.77 30 0.00 -0.18 34.0 Pasture 32.07 5.0 Pasture 39.55 18.75 7.57 3 580 -22.3574 -47.5647 88 111.80 107.44 30 0.52 0.34 20.0 Pasture 19.28 5.0 Pasture 82.05 13.83 5.18 3 579 -22.3567 -47.5647 89 222.76 89.49 30 16.95 -1.49 46.5 Silviculture 35.07 6.1 Pasture 42.94 19.96 9.00 3 589 -22.3577 -47.5636 90 63.33 47.24 19 1.85 -0.58 11.0 Pasture 8.22 7.5 Pasture 105.64 19.22 1.66 3 594 -22.3573 -47.5631 108 336.13 65.42 40 25.96 -0.57 13.5 Silviculture 16.97 2.4 Sugarcane 50.78 29.42 4.33 4 537 -22.5003 -47.6742 134 268.90 55.22 30 21.71 -3.20 14.5 Silviculture 46.67 2.3 Sugarcane 37.94 11.78 17.57 6 519 -22.6050 -47.7321 144 52.08 20.46 26 5.22 -0.60 28.5 Silviculture 85.98 9.0 Sugarcane 153.82 10.76 10.83 3 569 -22.3383 -47.5701 150 161.32 89.92 28 13.97 -0.81 20.0 Silviculture 30.62 10.3 Pasture 114.47 29.58 2.87 3 601 -22.3290 -47.5495 154 136.26 44.05 22 13.01 0.32 19.5 Silviculture 12.00 7.5 Pasture 224.63 26.82 1.48 3 640 -22.3604 -47.5578 156 163.60 150.87 24 0.00 -1.46 45.0 Silviculture 38.02 12.1 Pasture 42.78 18.44 4.08 2 628 -22.2976 -47.5996 163 249.38 115.49 20 8.39 -0.93 12.0 Silviculture 53.83 18.4 Sugarcane 203.12 27.13 4.72 4 601 -22.5090 -47.6966 201 245.10 169.41 20 6.70 -0.27 34.0 Silviculture 140.97 2.8 Pasture 80.02 18.03 18.30 6 490 -22.6071 -47.7129 202 84.83 42.09 21 10.62 -1.49 14.5 Silviculture 16.33 19.2 Sugarcane 28.54 28.05 5.03 4 563 -22.5087 -47.6939 203 121.66 83.91 28 0.00 -2.28 14.5 Silviculture 68.10 6.6 Sugarcane 151.13 28.20 21.52 6 508 -22.6079 -47.7253 205 208.42 208.42 28 0.00 0.36 29.0 Pasture 12.83 5.8 Pasture 196.82 18.76 5.35 3 651 -22.3408 -47.5478 206 173.01 170.09 23 0.00 -1.24 16.0 Pasture 21.93 10.7 Pasture 204.51 31.38 0.78 2 581 -22.2889 -47.6230 207 121.77 121.77 29 0.00 -2.47 47.5 Pasture 36.00 16.0 Pasture 66.15 16.99 1.79 3 634 -22.3301 -47.5570 208 229.78 78.63 18 16.15 -1.64 14.5 Silviculture 27.67 1.9 Sugarcane 86.61 13.71 17.83 6 499 -22.5956 -47.7217 C1 522.44 522.44 33 0.00 NA >100 Reference NA NA NA NA NA NA NA NA -22.1381 -47.8476 C2 233.44 233.44 14 0.00 NA >100 Reference NA NA NA NA NA NA NA NA -22.9236 -47.6617 75

C3 433.48 433.48 35 0.00 NA >100 Reference NA NA NA NA NA NA NA NA -21.9614 -47.8951 C4 297.87 297.87 44 0.00 NA >100 Reference NA NA NA NA NA NA NA NA -22.4405 -48.1144 C5 352.23 352.23 22 0.00 NA >100 Reference NA NA NA NA NA NA NA NA -22.7839 -47.8224 C6 515.56 515.56 27 0.00 NA >100 Reference NA NA NA NA NA NA NA NA -22.3776 -47.3212 RR1 192.19 185.45 22 0.00 NA 12.0 Planting NA NA NA NA NA NA NA NA -22.6991 -47.6389 RR2 144.05 144.05 10 0.00 NA 7-15 Planting NA NA NA NA NA NA NA NA -22.5551 -47.6781 RR3 266.72 150.41 12 0.00 NA 7-15 Planting NA NA NA NA NA NA NA NA -22.5993 -47.6836 RR4 99.65 88.23 27 0.00 NA 7-15 Planting NA NA NA NA NA NA NA NA -22.5637 -47.5322 RR5 73.67 72.65 28 0.00 NA 7-15 Planting NA NA NA NA NA NA NA NA -22.5613 -47.5366 RR7 185.57 172.46 32 0.00 NA 14.0 Planting NA NA NA NA NA NA NA NA -22.7039 -47.6414 RR8 226.72 171.19 29 0.00 NA 15.0 Planting NA NA NA NA NA NA NA NA -21.9290 -47.7291 RR9 154.82 113.73 46 0.00 NA 10.0 Planting NA NA NA NA NA NA NA NA -22.7057 -47.6282

Appendix A.3. Species list found in this study. *Non-native species.

SGF - SGF - Mixed tree Species Reference Total Pasture Silviculture plantings Anacardiaceae Astronium fraxinifolium Schott 1 1 Astronium graveolens Jacq. 2 5 4 5 16 Lithrea molleoides (Vell.) Engl. 58 7 2 67 *Mangifera indica L 11 11 Myracrodruon urundeuva Allemão 70 4 74 Schinus terebinthifolius Raddi 68 68 Tapirira guianensis Aubl. 13 8 2 3 26 Tapirira obtusa (Benth.) J.D.Mitch. 1 1 Annonaceae Annona montana Macfad. 4 4 Annona sylvatica A.St.-Hil. 6 3 9 Xylopia aromatica (Lam.) Mart. 1 1 Xylopia brasiliensis Spreng. 3 3 Apocynaceae Aspidosperma cylindrocarpon Müll.Arg. 10 1 14 1 26 Aspidosperma olivaceum Müll.Arg. 1 1 Aspidosperma parvifolium A. DC. 2 2 Aspidosperma polyneuron Müll.Arg. 1 3 4 Aspidosperma ramiflorum Müll.Arg. 10 15 25 Tabernaemontana catharinensis A.DC. 4 2 6 Aquifoliaceae Ilex paraguariensis A.St.-Hil. 1 1 Araliaceae Dendropanax cuneatus (DC.) Decne. & Planch. 2 2 4

Arecaceae Syagrus romanzoffiana (Cham.) Glassman 17 7 3 26 53 Asteraceae Asteraceae sp. 1 1 Moquiniastrum polymorphum (Less.) G. Sancho 117 6 123 Vernonanthura divaricata (Spreng.) H.Rob. 1 1 Bignoniaceae Handroanthus cf. impetiginosus (Mart. ex DC.) Mattos 1 1 Handroanthus heptaphyllus (Vell.) Mattos 4 4 23 31 Handroanthus ochraceus (Cham.) Mattos 61 3 64 Handroanthus sp. 2 1 1 Handroanthus spp. 26 1 27 Jacaranda cuspidifolia Mart. 2 2 Jacaranda macrantha Cham. 1 1 2 4 Spathodea campanulata P. Beauv. 3 3 Tabebuia roseoalba (Ridl.) Sandwith 2 2 *Tecoma stans (L.) Juss. ex Kunth 43 6 49 Zeyheria tuberculosa (Vell.) Bureau ex Verl. 1 1 Boraginaceae Cordia africana Lam. 30 30 Cordia americana (L.) Gottschling & J.S.Mill. 3 12 6 1 22 Cordia ecalyculata Vell. 3 2 5 Cordia sellowiana Cham. 1 1 Cordia superba Cham. 5 5 Cordia trichotoma (Vell.) Arráb. ex Steud. 11 12 3 26 Cannabaceae Cannabaceae spp 2 1 3 Celtis ehrenbergiana (Klotzsch) Liebm. 14 14 Celtis fluminensis Carauta 18 12 2 32

Celtis iguanaea (Jacq.) Sarg. 7 1 8 Celtis spp. 2 1 3 Trema micrantha (L.) Blume 1 1 2 Cardiopteridaceae Citronella paniculata (Mart.) R.A.Howard 1 1 Caricaceae Jacaranda mimosifolia D. Don 1 1 Jacaratia spinosa (Aubl.) A.DC. 1 1 Celastraceae Maytenus aquifolia Mart. 2 2 Maytenus evonymoides Reissek 1 1 2 Maytenus gonoclada Mart. 2 5 7 Maytenus robusta Reissek 1 1 Maytenus sp. 1 1 1 Maytenus sp. 2 1 1 Maytenus spp. 5 3 8 Chrysobalanaceae *Licania tomentosa (Benth.) Fritsch 5 5 Combretaceae Terminalia argentea Mart. 1 1 Terminalia glabrescens Mart. 1 1 Terminalia triflora (Griseb.) Lillo 3 3 Cunoniaceae Lamanonia ternata Vell. 1 1 Cupressaceae Cupressus spp. 2 2 Ebenaceae Diospyros inconstans Jacq. 16 2 1 19 Elaeocarpaceae

Sloanea cf. guianensis (Aubl.) Benth 2 2 Sloanea lasiocoma K.Schum. 1 1 Erythroxylaceae Erythroxylum deciduum A.St.-Hil. 1 1 Erythroxylum pelleterianum A.St.-Hil. 3 3 Euphorbiaceae Actinostemon communis (Müll.Arg.) Pax 10 10 Actinostemon concepcionis (Chodat & Hassl.) Hochr. 2 27 29 Actinostemon concolor (Spreng.) Müll.Arg. 10 10 Actinostemon klotzschii (Didr.) Pax 6 7 13 Alchornea glandulosa Poepp. & Endl. 16 28 5 49 Alchornea triplinervia (Spreng.) Müll.Arg. 1 1 2 Croton floribundus Spreng. 79 42 12 4 137 Croton spp. 3 3 Croton urucurana Baill. 25 1 25 51 Gymnanthes klotzschiana Müll.Arg. 6 12 18 Hura crepitans L. 2 2 Joannesia princeps Vell. 3 3 Mabea fistulifera Mart. 5 5 Maprounea guianensis Aubl. 1 1 Sapium glandulosum (L.) Morong 1 1 Sebastiania brasiliensis Spreng. 36 5 41 Sebastiania spp. 2 28 4 4 38 Fabaceae Albizia niopoides (Spruce ex Benth.) Burkart 7 12 19 Anadenanthera cf. colubrina (Vell.) Brenan 6 6 Anadenanthera peregrina (L.) Speg. 9 9 Andira fraxinifolia Benth. 22 8 30 Andira spp. 1 1

Bauhinia forficata Link 32 13 45 Bauhinia longifolia (Bong.) Steud. 7 4 11 Bauhinia spp. 1 1 Bauhinia variegata L. 1 1 Calliandra tweedii Benth. 5 4 9 Cassia ferruginea (Schrad.) Schrad. ex DC. 1 4 4 9 Cassia fistula L. 2 2 Centrolobium tomentosum Guillem. ex Benth. 9 12 1 6 28 Clitoria fairchildiana R.A.Howard 35 35 Copaifera langsdorffii Desf. 8 63 3 2 76 Cyclolobium brasiliense Benth. 1 1 Dahlstedtia muehlbergiana (Hassl.) M.J.Silva & A.M.G. Azevedo 44 12 56 Dalbergia spp. 1 1 Enterolobium contortisiliquum (Vell.) Morong 22 12 10 44 Erythrina cristagalli L. 1 1 Erythrina falcata Benth. 3 3 Erythrina speciosa Andrews 1 1 Fabaceae spp 1 1 Holocalyx balansae Micheli 1 1 Hymenaea courbaril L. 4 9 1 14 Inga marginata Willd. 3 3 Inga vera Willd. 9 16 1 26 *Leucaena leucocephala (Lam.) de Wit 8 1 10 19 Leucochloron incuriale (Vell.) Barneby & J.W.Grimes 1 1 Libidibia ferrea (Mart. ex Tul.) L.P.Queiroz 4 4 Lonchocarpus cultratus (Vell.) A.M.G.Azevedo & H.C.Lima 30 58 5 93 Lonchocarpus spp. 2 2 4 Machaerium brasiliense Vogel 17 34 15 66 Machaerium hirtum (Vell.) Stellfeld 25 23 48

Machaerium nyctitans (Vell.) Benth. 77 34 6 1 118 Machaerium scleroxylon Tul. 1 1 Machaerium spp. 4 4 Machaerium stipitatum Vogel 36 38 3 1 78 Machaerium villosum Vogel 21 60 1 82 Mimosa caesalpiniifolia Benth. 6 6 Myroxylon peruiferum L.f. 3 13 16 Ormosia spp. 4 1 5 Parapiptadenia rigida (Benth.) Brenan 8 5 13 Peltophorum dubium (Spreng.) Taub. 3 17 38 58 Piptadenia gonoacantha (Mart.) J.F.Macb 70 87 2 159 Platymiscium floribundum Vogel 3 3 Platypodium elegans Vogel 83 14 4 101 Poecilanthe parviflora Benth. 6 6 Poincianella pluviosa (DC.) L.P.Queiroz 3 3 Pterocarpus rohrii Vahl 6 6 Pterogyne nitens Tul. 3 11 14 Schinus molle L. 1 1 *Schizolobium parahyba (Vell.)Blake 10 10 Senegalia polyphylla (DC.) Britton & Rose 2 3 32 2 39 Senna multijuga (Rich.) H.S.Irwin & Barneby 1 1 2 Sesbania virgata (Cav.) Pers. 1 1 *Tipuana tipu (Benth.) Kuntze 5 5 Lacistemataceae Lacistema hasslerianum Chodat 1 1 1 3 Lamiaceae Aegiphila integrifolia (Jacq.) Moldenke 3 2 1 6 Vitex megapotamica (Spreng.) Moldenke 3 3 Lauraceae

Endlicheria paniculata (Spreng.) J.F.Macbr. 3 3 Lauraceae spp 1 5 6 Nectandra lanceolata Nees 8 8 Nectandra megapotamica (Spreng.) Mez 4 4 9 17 Nectandra oppositifolia Nees 1 1 Ocotea cf. indecora (Schott) Mez 1 1 Ocotea corymbosa (Meisn.) Mez 1 1 2 Ocotea indecora (Schott) Mez 7 4 11 Ocotea odorifera (Vell.) Rohwer 11 1 5 17 Ocotea puberula (Rich.) Nees 1 1 Ocotea pulchella (Nees & Mart.) Mez 5 5 Ocotea spp. 3 1 4 Ocotea velutina (Nees) Rohwer 4 4 Persea willdenovii Kosterm. 2 2 Lecythidaceae Cariniana estrellensis (Raddi) Kuntze 13 5 10 6 34 Cariniana legalis (Mart.) Kuntze 6 3 9 Lithraceae Lafoensia pacari A.St.-Hil. 4 4 Lythraceae Lafoensia pacari A.St.-Hil. 9 3 9 21 Malpighiaceae Byrsonima spp. 2 2 Malvaceae Apeiba tiborbou Aubl. 1 1 Basiloxylon brasiliensis (All.) K.Schum. 3 3 Bastardiopsis densiflora (Hook. & Arn.) Hassl. 5 5 Ceiba speciosa (A.St.-Hil.) Ravenna 5 1 35 3 44 Eriotheca spp. 3 3

Guazuma ulmifolia Lam. 67 47 11 125 Heliocarpus popayanensis Kunth 6 6 Luehea candicans Mart. & Zucc. 131 49 5 185 Luehea divaricata Mart. & Zucc. 1 1 Luehea grandiflora Mart. & Zucc. 27 27 Luehea paniculata Mart. & Zucc. 3 7 10 *Montezuma speciosissima DC. 1 1 Pachira glabra Pasq. 4 4 Pseudobombax spp. 1 1 Pseudobombax grandiflorum (Cav.) A.Robyns 2 3 4 1 10 Pseudobombax longiflorum (Mart. & Zucc.) A.Robyns 1 1 Melastomataceae Miconia albicans (Sw.) Triana 9 3 12 Miconia lepidota DC. 2 2 Miconia spp. 1 1 Pleroma granulosa (Desr.) D. Don 1 1 Meliaceae Cabralea canjerana (Vell.) Mart. 1 2 2 2 7 Cedrela fissilis Vell. 4 13 15 1 33 Guarea guidonia (L.) Sleumer 2 1 3 Guarea kunthiana A.Juss. 4 4 Guarea macrophylla Vahl 44 74 118 Guarea spp. 2 2 *Melia azedarach L. 30 3 33 Meliaceae spp 2 2 Trichilia casaretti C.DC. 1 31 32 Trichilia catigua A.Juss. 7 4 1 8 20 Trichilia clausseni C.DC. 129 1 1 4 135 Trichilia elegans A.Juss. 1 2 3

Trichilia pallida Sw. 63 38 1 102 Monimiaceae Mollinedia widgrenii A.DC. 3 3 Moraceae Artocarpus heterophyllus Lam. 1 1 Ficus guaranitica Chodat 6 13 3 1 23 Ficus obtusifolia Kunth 3 3 Ficus sp. 1 1 1 Ficus sp.2 1 1 Ficus spp. 1 1 Maclura tinctoria (L.) D.Don ex Steud. 3 6 1 10 Myrtaceae Calyptranthes clusiifolia O.Berg 5 5 Calyptranthes grandifolia O.Berg 1 1 Calyptranthes spp. 2 2 Campomanesia neriiflora (O.Berg) Nied. 1 1 2 Campomanesia spp. 9 4 13 Campomanesia xanthocarpa (Mart.) O.Berg 1 1 *Eucalyptus spp. 9 271 280 Eugenia cf pluriflora DC. 1 1 Eugenia cf. repanda O.Berg 2 2 Eugenia dodonaeifolia Cambess. 4 4 8 Eugenia florida DC. 67 76 4 2 149 Eugenia ligustrina (Sw.) Willd. 1 1 Eugenia paracatuana O.Berg 20 2 22 Eugenia pluriflora DC. 8 8 Eugenia speciosa Cambess. 4 4 Eugenia spp. 2 30 9 41 Eugenia uniflora L. 2 2

Myrcia hebepetala DC. 2 2 4 Myrcia multiflora (Lam.) DC. 2 3 5 Myrcia sp.4 1 1 Myrcia splendens (Sw.) DC. 13 2 3 18 Myrcia spp. 3 3 6 Myrcia tomentosa (Aubl.) DC. 5 13 18 Myrcia venulosa DC. 1 3 4 Myrcianthes pungens (O.Berg) D.Legrand 1 1 Myrciaria floribunda (H.West ex Willd.) O.Berg 1 12 13 Myrtaceae spp 49 61 110 Pimenta pseudocaryophyllus (Gomes) Landrum 13 13 Plinia peruviana (Poir.) Govaerts 1 1 Plinia spp. 1 2 2 5 Psidium cattleianum Sabine 1 1 *Psidium guajava L. 82 11 15 108 Psidium sartorianum (O.Berg) Nied. 8 8 Psidium sp. 1 1 *Syzygium cumini (L.) Skeels 1 36 37 Syzygium spp. 2 2 Not classified Unclassified sp. 27 1 1 Nyctaginaceae Guapira cf. opposita (Vell.) Reitz 2 2 Guapira hirsuta (Choisy) Lundell 1 1 Guapira opposita (Vell.) Reitz 2 1 2 5 Guapira spp. 1 1 Ochnaceae Ouratea castaneifolia (DC.) Engl. 2 1 3 Opiliaceae

Agonandra excelsa Griseb. 2 2 Peraceae Pera glabrata (Schott) Poepp. ex Baill. 2 8 1 1 12 Phyllanthaceae Savia dictyocarpa Müll.Arg. 23 23 Phytolaccaceae Gallesia integrifolia (Spreng.) Harms 4 8 6 18 Phytolacca dioica L. 1 1 Seguieria langsdorffii Moq. 1 2 3 Picramnaceae Picramnia sellowii Planch. 5 5 Polygonaceae Coccoloba cordata Cham. 2 1 1 4 Coccoloba mollis Casar. 1 1 Ruprechtia laurifolia (Cham. & Schltdl.) A.C.Meyer 1 1 2 Ruprechtia laxiflora Meisn. 5 4 6 15 Triplaris americana L. 5 5 Primulaceae Myrsine coriacea (Sw.) R.Br. ex Roem. & Schult. 12 8 20 Myrsine parvifolia A. DC. 1 1 Myrsine sp. 4 4 Myrsine umbellata Mart. 5 17 2 1 25 Proteaceae Roupala montana Aubl. 7 1 9 17 Rhamnaceae Colubrina glandulosa Perkins 5 8 13 Rhamnidium elaeocarpum Reissek 23 38 2 63 Rosaceae *Eriobotrya japonica (Thunb.) Lind 1 1

Rubiaceae Amaioua intermedia Mart. ex Schult. & Schult.f. 35 35 Chomelia bella (Standl.) Steyerm. 1 4 1 6 Chomelia pohliana Müll.Arg. 1 1 Coutarea hexandra (Jacq.) K.Schum. 3 3 Faramea latifolia (Cham. & Schltdl.) DC. 1 1 2 Genipa americana L. 8 8 Ixora brevifolia Benth. 2 10 12 Psychotria vellosiana Benth. 4 4 Rutaceae Balfourodendron riedelianum (Engl.) Engl. 6 2 8 *Citrus spp. 5 4 9 Conchocarpus pentandrus (A. St.-Hil.) Kallunki & Pirani 2 2 Esenbeckia febrifuga (A.St.-Hil.) A. Juss. ex Mart. 21 6 1 28 Esenbeckia grandiflora Mart. 1 1 Esenbeckia leiocarpa Engl. 5 19 24 Galipea jasminiflora (A.St.-Hil.) Engl. 69 69 Metrodorea nigra A.St.-Hil. 62 62 Metrodorea stipularis Mart. 1 1 Zanthoxylum caribaeum Lam. 1 9 10 Zanthoxylum fagara (L.) Sarg. 1 1 Zanthoxylum monogynum A.St.-Hil. 24 27 51 Zanthoxylum petiolare A.St.-Hil. & Tul. 3 1 4 Zanthoxylum rhoifolium Lam. 26 18 2 4 50 Zanthoxylum riedelianum Engl. 3 6 9 Zanthoxylum spp. 1 1 2 Zeyheria tuberculosa (Vell.) Bureau ex Verl. 1 1 Salicaceae Casearia decandra Jacq. 12 10 22

Casearia gossypiosperma Briq. 27 25 1 2 55 Casearia lasiophylla Eichler 1 1 Casearia sylvestris Sw. 100 192 4 296 Prockia crucis P.Browne ex L. 1 2 1 4 Salicaceae spp 1 1 Xylosma ciliatifolia (Clos) Eichler 1 1 Xylosma pseudosalzmanii Sleumer 1 1 Sapindaceae Allophylus edulis (A.St.-Hil. et al.) Hieron. ex Niederl. 34 24 58 Allophylus racemosus Sw. 3 1 4 Cupania vernalis Cambess. 6 2 1 9 Diatenopteryx sorbifolia Radlk. 1 1 Matayba elaeagnoides Radlk. 2 15 3 2 22 Sapindus saponaria L. 3 3 Sapotaceae Chrysophyllum gonocarpum (Mart. & Eichler ex Miq.) Engl. 4 1 10 15 Chrysophyllum marginatum (Hook. & Arn.) Radlk. 30 43 73 Sapotaceae spp 1 1 Siparunaceae Siparuna guianensis Aubl. 1 7 1 9 Solanaceae Acnistus arborescens (L.) Schltdl. 2 21 23 Cestrum intermedium Sendtn. 1 1 Cestrum sp. 1 1 Solanaceae spp 1 1 Solanum granulosoleprosum Dunal 1 1 Solanum mauritianum Scop. 1 1 Solanum pseudoquina A.St.-Hil. 1 1 Solanum spp. 3 3

Styracaceae Styrax pohlii A.DC. 2 2 Symplocaceae Symplocos cf. laxiflora Benth. 1 1 Unclassified Unclassified 67 81 18 27 193 Urticaceae Cecropia pachystachya Trécul 4 1 12 17 Urera caracasana (Jacq.) Griseb. 1 2 3 Verbenaceae Aloysia virgata (Ruiz & Pav.) Juss. 46 14 2 62 Citharexylum myrianthum Cham. 10 10 34 54 Violaceae Pombalia spp. 1 1 Vochysiaceae Callisthene minor Mart. 1 2 3 Vochysia tucanorum Mart. 2 2

APPENDIX B. Supplementary files of the second chapter of this thesis: “Early ecological outcomes of natural regeneration and tree plantations for restoring agricultural landscapes”

SUPPLEMENTARY FILE S1 Table S1: Summary of the comparison of 7-20 years old second-growth forests established over pastures (SGp) and Eucalyptus spp. plantations (SGe), mixed tree species plantings (PL) and old-growth reference forests (Ref) in agricultural landscapes of southeastern Brazil. Numbers followed by the same letter do not differ statistically. Data is shown as average ± one standard error. N=4 for Ref forests and N= 7, 11 and 10 in SGp, SGe and PL, respectively. SGp SGe PL Ref F p Abundance (hectare) Native DBH>20 cm 89 ± 36b 48 ± 25b 141 ± 56a 286 ± 101 8.2 0.003 DBH 5-20 cm 919 ± 395a 826 ± 303a 690 ± 298a 1094 ± 320 1.1 0.357 DBH 1-5 cm 3059 ± 1766a 3212 ± 1218a 718 ± 688b* 4312 ± 1089 10.3 <0.0001 Non-native DBH>20 cm 8 ± 11b 106 ± 60a** 37 ± 47b 0 ± 0 10.4 <0.001 DBH 5-20 cm 136 ± 174a 90 ± 129a 103 ± 76a 0 ± 0 0.7 0.696

DBH 1-5 cm 261 ± 227 a 189 ± 337a 156 ± 113a* 125 ± 250† 2.3 0.322 Liana abundance (hectare) 220 ± 210a 338 ± 166a 10 ± 23b 64 ± 14 17.1 <0.0001 Aboveground Biomass (ton/ha) Native species 91.3 ± 45.5ab 63.7 ± 27.2b 132.2 ± 48.4a 317.2 ± 120.5 7.5 <0.01 Non-native species 15.4 ± 17.3b 188.4 ± 104.8a** 33.9 ± 37.5b 0 ± 0 17.0 <0.0001 Total 106.8 ± 45.1b 252.2 ± 94.9a 166.3 ± 69.7b* 317.2 ± 120.5 8.21 <0.01 Species density (species per plot) DBH>5 cm 20.3 ± 5.3a 22.8 ± 7.7a 26.2 ± 13.0a 31.7 ± 13.4 0.8 0.454 DBH 1-5 cm 12.6 ± 5.5a 12.6 ± 3.9a 6.0 ± 4.4b* 11.7 ± 4.6 5.9 0.008 Biotic Dispersed (%) DBH>5 cm 49.5 ± 15.1a 55.5 ± 13.6a 43.4 ± 6.6b 41.2 ± 21.8 5.4 0.067 91

DBH 1-5 cm 54.8 ± 14.4ab 74.7 ± 13.1a 47.8 ± 29.7b 57.9 ± 20.2 4.7 <0.05 Pioneers (%) DBH>5 cm 35.9 ± 27.3ab 24.6 ± 11.9b 47.6 ± 20.2a 3.4 ± 1.3 3.6 <0.05 DBH 1-5 cm 33.6 ± 20.7a 41.7 ± 12.2a 32.2 ± 21.2a 31.0 ± 5.5 0.8 0.455 Biodiversity DBH>5 cm Family 28 36 31 36 . . Genus 50 78 88 65 . . Species 68 116 130 102 . . DBH1-5 cm Family 27 28 19 16 . . Genus 48 51 36 30 . . Species 61 77 42 43 . . *Mostly (97.3%) represented by planted individuals. **Mostly (98.5%) represented by resprouting Eucalyptus spp. trees. †Due to six Coffea arabica L. individuals found in one of the plots.

SUPPLEMENTARY FILE S2

Figure S1: Proportion of biotic-dispersed trees DBH>5 cm and DBH 1-5 cm in 7-20 years old second-growth forests established naturally over pastures (SGp) and Eucalyptus spp. plantations (SGe), mixed tree species plantings (PL) and old-growth reference forests (Ref) in agricultural landscapes of southeastern Brazil. For trees DBH>5 cm, regeneration types were compared using ANOVA (α = 0.05) and means were compared by the Tukey multiple comparison procedure (α = 0.05). For trees DBH 1-5 cm regeneration types were compared using generalized linear mixed models. Bars followed by the same letter do not differ statistically. N=4 for Ref forests and N=7, 11 and ten for SGp, SGe and PL, respectively.

SUPPLEMENTARY FILE S5 Photographs of the likely initial conditions of the forest types sampled and their outcome after 7-20 years.

Figure S1: A) pasture with early successional spontaneous regeneration at the study site; B) Second-growth forest established in pastures after 7-20 years.

Figure S2: A) Resprouting eucalypt after harvesting with spontaneous naturl regeneration; B) Second-growth forest established in abandoned eucalypt plantations after 7-20 years.

Figure S3: A) one-year-old high diversity tree planting for forest restoration in the same forest type as our study site; B) Overall aspect of tree plantings after 7-20 years.

Figure S4: Example of reference forest. These forests are located in relatively large (>40 hectares) remnants with no sign of large disturbances in the last 100 years at least. 97

APPENDIX C. Supplementary files of the second chapter of this thesis: “Surrounding land use and forest cover as major drivers of biomass and tree diversity recovery by second-growth tropical forests in agricultural landscapes”

: SUPPLEMENTARY FILE S1 All models generated for the Native species biomass, species density and phylogenetic dispersion of SGF in agricultural landscapes of the Atlantic Forest in Southeast Brazil. Models included in the average model (ΔAICc<4) are bolded.

Forest Attribute Attribute Driver Description Native Biomass Biomass of native species DBH>5 cm based on data of the inventory plot (ton.ha-1) Species Density Number of native species DBH>5 cm in the inventory plot. Phyogenetic Dispersion Calculated based on the species of the inventory plot and the study species pool. See methods of the main manuscript for details

Drivers Code Driver Description AGE Forest age (estimate years since forest establishment) AB_EUC Basal area of eucalypt trees DBH>5 cm per plot (m²) SB Soil sum of bases (mmolc.dm-3) SLOPE Slope of the plot (degrees) NU (Sug.) Sugarcarcane as the nearby land use RIVER Distance from the nearest watercourse (m) FC Average native forest cover in a 1 km buffer around inventory plot from forest establishment to data gathering (relative number from 0-1) Relative native forest cover in a 1 km buffer around inventory plot from data gathering minus relative native forest cover at the time of forest Δ FC establishment (relative number from 0-1) FC X NU (Sug.) Interaction of average native forest cover and sugarcane as the nearby land use AGE X NU (Sug.) Interaction of forest age and sugarcane as the nearby land use

Biomass of Native Species

AGE AB_ NU FC X NU X NU R² R² Model Intercept AGE EUC SB SLOPE (Sug.) RIVER FC Δ FC (Sug.) (Sug.) Marg. Cond. ΔAICc weight 1 142.81 1.35 -2.20 NA -2.23 -37.13 NA -103.14 -78.57 268.34 -2.65 0.43 0.51 0.00 0.2677 2 111.43 1.40 -1.87 NA NA -9.08 NA -64.41 -64.67 155.37 -2.92 0.41 0.48 1.16 0.1497 3 158.13 1.09 -2.37 NA -2.41 -90.14 NA -100.96 -69.93 251.37 NA 0.43 0.55 1.18 0.1485 4 181.94 NA -2.43 NA -2.50 -104.40 NA -85.41 21.50 246.90 NA 0.39 0.51 1.55 0.1234 5 125.66 1.12 -2.02 NA NA -66.85 NA -59.72 -62.83 138.49 NA 0.40 0.51 2.88 0.0634 6 149.03 NA -2.07 NA NA -80.94 NA -42.93 27.94 132.04 NA 0.36 0.46 3.52 0.0461 7 132.61 1.53 -2.11 NA -2.46 -42.43 0.13 -114.35 -73.69 272.08 -3.02 0.45 0.56 4.71 0.0254 8 146.74 1.35 -2.23 -0.05 -2.21 -41.92 NA -106.19 -60.77 261.89 -2.45 0.42 0.52 5.28 0.0191 9 152.77 1.27 -2.33 NA -2.73 -101.47 0.13 -112.45 -63.69 225.00 NA 0.45 0.65 5.94 0.0137 10 162.89 1.13 -2.39 -0.09 -2.34 -90.74 NA -107.03 -43.33 236.90 NA 0.42 0.57 6.00 0.0133 11 99.60 1.60 NA NA NA 0.16 NA -112.48 -50.58 165.33 -3.91 0.31 0.35 6.08 0.0128 12 99.22 1.57 -1.75 NA NA -10.87 0.12 -71.43 -63.80 156.04 -3.34 0.43 0.52 6.10 0.0127 13 117.64 1.41 -1.92 -0.08 NA -16.58 NA -70.50 -44.42 153.61 -2.67 0.40 0.50 6.15 0.0123 14 184.54 NA -2.45 -0.04 -2.50 -104.70 NA -87.53 38.23 238.68 NA 0.38 0.53 6.31 0.0114 15 107.79 1.60 NA NA -0.63 -7.01 NA -126.30 -56.25 199.92 -3.91 0.30 0.35 6.49 0.0104 16 179.40 NA -2.40 NA -2.75 -113.34 0.09 -91.17 40.17 235.44 NA 0.40 0.56 6.96 0.0083 17 133.99 1.19 -2.06 -0.13 NA -69.68 NA -70.87 -32.07 129.34 NA 0.40 0.54 7.32 0.0069 18 117.99 1.27 -1.94 NA NA -75.63 0.11 -66.11 -67.32 123.41 NA 0.42 0.58 8.03 0.0048 19 153.91 NA -2.10 -0.07 NA -82.59 NA -47.56 51.00 125.39 NA 0.35 0.48 8.04 0.0048 20 137.74 1.19 -2.18 NA -2.17 -39.35 NA -85.58 NA 251.60 -2.60 0.43 0.51 8.30 0.0042 21 144.89 NA -2.02 NA NA -86.96 0.07 -44.89 37.16 124.64 NA 0.37 0.49 9.12 0.0028 22 117.07 1.21 NA NA NA -77.79 NA -109.57 -45.81 150.51 NA 0.28 0.37 9.17 0.0027 23 125.47 1.21 NA NA -0.66 -84.89 NA -123.55 -49.49 184.80 NA 0.28 0.37 9.34 0.0025 24 107.22 1.26 -1.86 NA NA -10.72 NA -50.71 NA 146.77 -2.93 0.41 0.47 9.55 0.0023 25 153.77 0.94 -2.36 NA -2.39 -90.75 NA -86.14 NA 235.00 NA 0.43 0.55 9.67 0.0021 99

26 183.91 NA -2.44 NA -2.49 -104.84 NA -89.73 NA 254.84 NA 0.40 0.51 9.81 0.0020 27 83.96 1.80 NA NA NA -0.47 0.15 -116.47 -48.93 167.94 -4.46 0.34 0.41 9.96 0.0018 28 151.79 NA NA NA -0.66 -101.72 NA -107.63 46.00 178.70 NA 0.23 0.33 10.04 0.0018 29 143.24 NA NA NA NA -94.52 NA -93.62 49.75 144.47 NA 0.23 0.33 10.09 0.0017 30 135.27 1.53 -2.13 -0.03 -2.47 -46.19 0.13 -115.94 -64.50 264.82 -2.86 0.44 0.57 10.23 0.0016 31 95.55 1.81 NA NA -0.96 -11.16 0.16 -137.38 -55.25 218.91 -4.48 0.34 0.41 10.40 0.0015 32 123.69 1.33 -2.14 NA -1.64 13.79 NA -25.47 -60.49 NA -2.55 0.42 0.51 10.48 0.0014 33 99.06 1.58 NA 0.02 NA -0.47 NA -109.35 -55.58 164.61 -3.87 0.30 0.36 10.91 0.0011 34 103.58 1.39 -1.88 NA NA 21.27 NA -20.00 -57.86 NA -2.88 0.41 0.48 10.91 0.0011 35 156.77 1.31 -2.34 -0.08 -2.65 -101.40 0.13 -117.67 -42.64 209.49 NA 0.44 0.66 10.97 0.0011 36 104.56 1.59 -1.79 -0.06 NA -17.55 0.12 -76.33 -50.21 151.83 -3.09 0.42 0.54 11.32 0.0009 37 106.90 1.58 NA 0.04 -0.67 -7.24 NA -122.54 -64.63 200.45 -3.88 0.30 0.35 11.48 0.0009 38 121.53 0.98 -2.00 NA NA -67.88 NA -46.64 NA 128.45 NA 0.40 0.50 11.51 0.0008 39 141.74 1.08 -2.31 NA -1.93 -42.16 NA -31.54 -48.58 NA NA 0.42 0.57 11.68 0.0008 40 181.07 NA -2.41 -0.02 -2.75 -113.40 0.09 -92.39 50.90 227.97 NA 0.39 0.57 11.89 0.0007 41 152.09 NA -2.08 NA NA -80.98 NA -49.05 NA 138.87 NA 0.37 0.46 11.98 0.0007 42 165.43 NA -2.38 NA -2.05 -56.87 NA -17.17 44.46 NA NA 0.39 0.54 12.20 0.0006 43 126.33 1.36 -1.97 -0.13 NA -78.00 0.11 -77.65 -38.86 109.50 NA 0.42 0.61 12.61 0.0005 44 119.00 1.11 -2.02 NA NA -39.73 NA -21.32 -53.99 NA NA 0.41 0.52 12.74 0.0005 45 127.97 1.38 -2.09 NA -2.43 -43.58 0.13 -98.46 NA 255.30 -3.00 0.45 0.56 12.75 0.0005 46 144.92 1.25 -2.23 -0.08 -2.15 -44.07 NA -96.89 NA 247.57 -2.39 0.42 0.53 13.35 0.0003 47 142.45 NA -2.08 NA NA -54.91 NA -6.15 35.86 NA NA 0.37 0.47 13.55 0.0003 48 107.34 1.37 NA NA NA -87.60 0.13 -113.42 -50.24 135.64 NA 0.32 0.47 13.58 0.0003 49 120.53 1.24 NA -0.05 NA -79.15 NA -113.58 -34.34 145.79 NA 0.28 0.40 13.64 0.0003 50 118.71 1.38 NA NA -0.95 -97.90 0.14 -133.24 -52.11 180.38 NA 0.32 0.47 13.77 0.0003 51 149.28 NA -2.04 -0.06 NA -88.28 0.07 -48.95 57.11 117.18 NA 0.36 0.51 13.81 0.0003 52 127.52 1.23 NA -0.03 -0.61 -85.36 NA -125.21 -40.17 177.65 NA 0.27 0.39 13.98 0.0002 53 149.33 1.13 -2.32 NA -2.75 -101.23 0.13 -99.79 NA 209.60 NA 0.45 0.65 14.22 0.0002 54 161.33 1.06 -2.39 -0.11 -2.31 -91.03 NA -100.42 NA 226.28 NA 0.42 0.57 14.26 0.0002

100

55 94.87 1.44 -1.73 NA NA -11.12 0.12 -58.11 NA 146.62 -3.38 0.43 0.51 14.29 0.0002 56 116.07 1.34 -1.91 -0.09 NA -17.70 NA -63.93 NA 146.71 -2.67 0.40 0.50 14.37 0.0002 57 142.21 NA NA 0.02 NA -93.91 NA -91.22 45.17 142.78 NA 0.23 0.33 14.40 0.0002 58 150.44 NA NA 0.03 -0.70 -101.14 NA -104.97 37.77 179.17 NA 0.23 0.33 14.47 0.0002 59 186.11 NA -2.45 -0.01 -2.50 -105.15 NA -92.10 NA 252.57 NA 0.39 0.51 14.65 0.0002 60 96.20 1.50 NA NA NA -1.49 NA -101.39 NA 160.50 -3.93 0.31 0.35 14.66 0.0002 61 81.83 1.79 NA 0.04 NA 0.34 0.15 -112.32 -58.92 167.66 -4.48 0.33 0.41 14.93 0.0002 62 103.32 1.49 NA NA -0.56 -8.41 NA -112.36 NA 190.46 -3.91 0.31 0.35 14.93 0.0002 63 116.48 1.50 -2.06 NA -1.97 2.99 0.14 -39.49 -50.92 NA -2.72 0.45 0.58 14.98 0.0001 64 148.33 NA NA NA -0.91 -112.44 0.10 -113.03 58.63 179.39 NA 0.25 0.38 14.99 0.0001 65 137.24 NA NA NA NA -102.25 0.10 -94.08 61.18 135.50 NA 0.25 0.37 15.00 0.0001 66 183.15 NA -2.41 NA -2.71 -113.92 0.09 -98.50 NA 252.40 NA 0.41 0.55 15.13 0.0001 67 130.49 1.33 -2.18 -0.07 -1.66 4.57 NA -33.77 -35.61 NA -2.26 0.41 0.53 15.45 0.0001 68 92.71 1.79 NA 0.06 -1.04 -9.64 0.16 -132.60 -71.45 222.79 -4.55 0.33 0.41 15.51 0.0001 69 91.87 1.56 -1.76 NA NA 18.28 0.12 -27.61 -54.97 NA -3.25 0.43 0.52 15.65 0.0001 70 110.47 1.39 -1.93 -0.08 NA 12.21 NA -27.65 -34.91 NA -2.59 0.40 0.50 15.71 0.0001 71 132.61 1.13 -2.06 -0.14 NA -70.04 NA -65.95 NA 124.01 NA 0.40 0.54 15.76 0.0001 72 140.93 1.29 -2.30 NA -2.47 -62.62 0.14 -58.63 -39.63 NA NA 0.45 0.69 15.95 0.0001 73 149.14 1.14 -2.34 -0.11 -1.89 -46.45 NA -44.90 -16.64 NA NA 0.42 0.59 16.16 0.0001 74 90.92 1.59 NA NA NA 33.21 NA -64.59 -45.24 NA -3.89 0.31 0.35 16.17 0.0001 75 113.77 1.12 -1.92 NA NA -75.57 0.10 -52.13 NA 112.73 NA 0.42 0.56 16.55 0.0001 76 155.80 NA -2.10 -0.03 NA -81.99 NA -53.24 NA 137.70 NA 0.36 0.47 16.64 0.0001 77 92.98 1.59 NA NA -0.19 32.79 NA -65.68 -46.31 NA -3.88 0.30 0.35 16.65 0.0001 78 170.21 NA -2.40 -0.06 -2.06 -59.63 NA -24.25 67.71 NA NA 0.38 0.55 16.70 0.0001 79 128.36 1.18 -2.07 -0.13 NA -44.83 NA -36.41 -20.72 NA NA 0.40 0.55 16.97 0.0001 80 165.52 NA -2.37 NA -2.43 -70.49 0.10 -30.62 69.03 NA NA 0.40 0.61 17.29 0.0000 81 149.23 NA -2.03 NA NA -86.87 0.06 -52.70 NA 135.01 NA 0.37 0.48 17.49 0.0000 82 112.32 1.27 -1.94 NA NA -52.59 0.11 -33.84 -57.77 NA NA 0.43 0.59 17.64 0.0000 83 148.15 NA -2.10 -0.07 NA -58.21 NA -13.67 61.23 NA NA 0.36 0.49 17.89 0.0000 101

84 113.93 1.11 NA NA NA -78.92 NA -99.85 NA 145.30 NA 0.28 0.37 18.00 0.0000 85 121.70 1.10 NA NA -0.62 -85.75 NA -112.14 NA 176.77 NA 0.28 0.37 18.03 0.0000 86 133.73 1.42 -2.13 -0.06 -2.42 -48.02 0.13 -106.12 NA 247.96 -2.78 0.44 0.58 18.08 0.0000 87 111.44 1.43 NA -0.06 NA -89.21 0.14 -119.28 -38.18 126.49 NA 0.32 0.50 18.13 0.0000 88 118.69 1.32 -2.16 NA -1.62 13.03 NA NA -53.69 NA -2.55 0.43 0.51 18.22 0.0000 89 80.56 1.70 NA NA NA -0.96 0.15 -106.03 NA 162.33 -4.50 0.34 0.41 18.34 0.0000 90 120.91 1.41 NA -0.04 -0.90 -98.40 0.14 -135.97 -43.25 169.38 NA 0.32 0.49 18.53 0.0000 91 91.30 1.70 NA NA -0.91 -11.76 0.16 -124.30 NA 209.48 -4.50 0.34 0.41 18.63 0.0000 92 157.66 NA NA NA -0.70 -102.33 NA -119.38 NA 190.74 NA 0.23 0.32 18.76 0.0000 93 99.94 1.37 -1.90 NA NA 20.26 NA NA -52.35 NA -2.86 0.42 0.48 18.84 0.0000 94 120.93 1.20 -2.12 NA -1.63 9.37 NA -15.99 NA NA -2.51 0.42 0.51 18.84 0.0000 95 138.75 NA -2.02 NA NA -62.94 0.07 -10.93 46.02 NA NA 0.37 0.50 18.96 0.0000 96 149.11 NA NA NA NA -94.57 NA -105.43 NA 154.43 NA 0.23 0.32 18.97 0.0000 97 155.53 1.24 -2.33 -0.10 -2.64 -101.12 0.13 -111.24 NA 198.98 NA 0.45 0.66 19.03 0.0000 98 102.88 1.50 -1.78 -0.08 NA -18.12 0.12 -68.72 NA 143.45 -3.10 0.42 0.53 19.35 0.0000 99 109.64 1.20 NA NA NA -48.04 NA -67.12 -38.11 NA NA 0.28 0.38 19.39 0.0000 100 135.72 NA NA 0.02 NA -101.76 0.10 -91.51 55.04 133.97 NA 0.25 0.37 19.42 0.0000 101 100.33 1.26 -1.87 NA NA 17.98 NA -9.85 NA NA -2.88 0.41 0.48 19.43 0.0000 102 97.41 1.49 NA 0.00 NA -3.34 NA -101.31 NA 159.42 -3.84 0.30 0.36 19.47 0.0000 103 146.31 NA NA 0.04 -0.96 -112.16 0.11 -110.21 47.31 181.46 NA 0.25 0.38 19.53 0.0000 104 112.62 1.20 NA NA -0.28 -48.35 NA -69.06 -38.66 NA NA 0.28 0.38 19.63 0.0000 105 135.23 1.06 -2.34 NA -1.90 -42.70 NA NA -41.29 NA NA 0.43 0.57 19.64 0.0000 - 106 123.87 1.62 -2.46 NA -1.77 NA NA -51.17 147.01 NA NA 0.37 0.43 19.86 0.0000 107 75.65 1.79 NA NA NA 31.91 0.15 -68.63 -41.36 NA -4.41 0.34 0.41 19.86 0.0000 108 104.33 1.47 NA 0.00 -0.57 -10.10 NA -111.80 NA 189.16 -3.83 0.30 0.36 19.90 0.0000 109 183.24 NA -2.41 0.01 -2.74 -114.01 0.09 -98.19 NA 249.54 NA 0.40 0.55 20.10 0.0000 110 122.93 1.50 -2.11 -0.06 -2.00 -6.55 0.14 -48.22 -33.19 NA -2.42 0.44 0.61 20.14 0.0000 111 139.54 0.97 -2.31 NA -1.95 -44.72 NA -24.53 NA NA NA 0.43 0.57 20.23 0.0000

102

112 161.75 NA -2.39 NA -2.03 -57.15 NA NA 47.97 NA NA 0.39 0.54 20.31 0.0000 113 80.62 1.79 NA NA -0.50 30.59 0.16 -72.05 -43.00 NA -4.38 0.34 0.41 20.46 0.0000 114 135.91 NA NA NA NA -65.82 NA -52.81 57.06 NA NA 0.24 0.33 20.47 0.0000 115 139.09 NA NA NA -0.30 -66.14 NA -54.88 56.82 NA NA 0.23 0.34 20.50 0.0000 116 98.25 1.57 -1.80 -0.07 NA 9.22 0.12 -35.22 -38.42 NA -2.96 0.42 0.54 20.63 0.0000 117 146.64 1.34 -2.30 -0.10 -2.37 -65.34 0.14 -69.12 -16.11 NA NA 0.44 0.70 20.64 0.0000 118 168.39 NA -2.38 NA -1.98 -54.57 NA -20.96 NA NA NA 0.39 0.52 20.73 0.0000 119 90.13 1.57 NA 0.03 NA 32.73 NA -61.52 -51.11 NA -3.86 0.30 0.35 20.82 0.0000 120 114.96 1.10 -2.04 NA NA -40.34 NA NA -48.44 NA NA 0.41 0.52 20.85 0.0000 - 121 104.41 1.64 -2.20 NA NA NA NA -46.44 137.45 NA NA 0.35 0.41 20.87 0.0000 122 124.95 1.28 -1.97 -0.14 NA -77.82 0.10 -71.54 NA 103.16 NA 0.42 0.60 20.91 0.0000 123 92.46 1.57 NA 0.03 -0.22 32.26 NA -62.66 -52.52 NA -3.84 0.29 0.35 21.45 0.0000 124 115.89 0.99 -2.01 NA NA -42.37 NA -12.39 NA NA NA 0.41 0.52 21.47 0.0000 125 141.25 NA -2.08 NA NA -55.13 NA NA 37.57 NA NA 0.37 0.48 21.82 0.0000 126 168.97 NA -2.38 -0.04 -2.43 -72.45 0.10 -35.83 84.58 NA NA 0.40 0.61 21.96 0.0000 127 121.87 1.36 -1.98 -0.14 NA -58.00 0.11 -50.41 -27.78 NA NA 0.43 0.63 21.97 0.0000 128 145.98 NA -2.09 NA NA -53.23 NA -11.30 NA NA NA 0.37 0.47 22.19 0.0000 129 151.71 NA -2.04 -0.02 NA -87.64 0.06 -55.33 NA 133.17 NA 0.37 0.49 22.27 0.0000 130 104.11 1.26 NA NA NA -87.87 0.13 -102.94 NA 130.20 NA 0.32 0.45 22.30 0.0000 131 115.15 1.26 NA NA -0.93 -98.12 0.14 -121.83 NA 173.28 NA 0.32 0.45 22.33 0.0000 132 118.96 1.17 NA -0.06 NA -79.94 NA -108.16 NA 141.49 NA 0.28 0.39 22.42 0.0000 133 108.07 1.48 -2.09 NA -1.90 4.37 0.13 NA -42.47 NA -2.79 0.45 0.58 22.57 0.0000 134 125.60 1.16 NA -0.05 -0.58 -86.01 NA -118.49 NA 170.19 NA 0.28 0.39 22.61 0.0000 135 122.86 1.31 -2.20 -0.06 -1.63 5.41 NA NA -31.55 NA -2.31 0.42 0.53 23.07 0.0000 136 114.02 1.39 -2.06 NA -1.98 0.61 0.13 -31.85 NA NA -2.73 0.45 0.58 23.10 0.0000 137 151.77 NA NA 0.06 -0.75 -100.91 NA -109.62 NA 190.44 NA 0.23 0.31 23.18 0.0000 138 143.18 NA NA 0.06 NA -92.79 NA -95.70 NA 152.06 NA 0.23 0.31 23.27 0.0000 139 80.08 1.69 NA 0.01 NA -1.91 0.15 -103.58 NA 160.52 -4.46 0.33 0.41 23.30 0.0000 103

140 86.81 1.54 -1.79 NA NA 17.43 0.12 NA -47.78 NA -3.24 0.43 0.52 23.39 0.0000 141 144.02 NA -2.04 -0.07 NA -66.05 0.07 -18.06 68.04 NA NA 0.37 0.52 23.44 0.0000 142 104.67 1.38 -1.95 -0.07 NA 12.23 NA NA -30.58 NA -2.61 0.41 0.50 23.51 0.0000 143 100.93 1.37 NA NA NA -61.79 0.13 -76.94 -41.36 NA NA 0.33 0.48 23.56 0.0000 144 129.90 1.27 -2.18 -0.09 -1.65 1.94 NA -30.65 NA NA -2.23 0.42 0.54 23.63 0.0000 145 155.68 NA NA NA -0.93 -112.91 0.10 -126.94 NA 196.11 NA 0.25 0.36 23.63 0.0000 146 113.78 1.23 NA -0.05 NA -50.68 NA -73.58 -24.31 NA NA 0.28 0.41 23.68 0.0000 147 90.28 1.67 NA 0.02 -0.94 -12.47 0.16 -120.80 NA 208.42 -4.47 0.33 0.41 23.74 0.0000 148 144.74 NA NA NA NA -102.26 0.09 -108.11 NA 149.96 NA 0.25 0.36 23.82 0.0000 149 107.34 1.38 NA NA -0.63 -63.69 0.14 -83.04 -40.35 NA NA 0.33 0.49 23.84 0.0000 150 128.51 1.23 -2.33 NA -2.32 -60.86 0.13 NA -28.89 NA NA 0.45 0.67 23.91 0.0000 151 88.54 1.44 -1.75 NA NA 16.35 0.12 -18.23 NA NA -3.29 0.43 0.52 23.97 0.0000 152 116.34 1.23 NA -0.05 -0.25 -50.95 NA -75.19 -24.88 NA NA 0.28 0.41 24.05 0.0000 153 139.14 1.10 -2.37 -0.10 -1.85 -46.49 NA NA -11.53 NA NA 0.42 0.58 24.06 0.0000 154 109.53 1.34 -1.92 -0.09 NA 10.22 NA -23.95 NA NA -2.59 0.41 0.50 24.07 0.0000 155 139.43 1.20 -2.30 NA -2.51 -63.97 0.14 -53.13 NA NA NA 0.46 0.69 24.32 0.0000 156 148.46 1.11 -2.34 -0.12 -1.88 -47.11 NA -42.91 NA NA NA 0.43 0.59 24.55 0.0000 - 157 124.17 1.62 -2.46 0.00 -1.79 NA NA -51.25 146.72 NA NA 0.36 0.43 24.58 0.0000 158 135.35 NA NA 0.01 NA -65.79 NA -51.61 54.44 NA NA 0.23 0.34 24.63 0.0000 159 73.52 1.77 NA 0.04 NA 32.78 0.15 -64.69 -51.05 NA -4.42 0.33 0.41 24.63 0.0000 160 164.55 NA -2.42 -0.05 -2.03 -59.61 NA NA 69.27 NA NA 0.38 0.55 24.70 0.0000 161 163.51 NA -2.76 NA -1.90 NA NA -49.67 -60.78 NA NA 0.25 0.32 24.71 0.0000 162 78.32 1.56 NA NA NA 30.40 NA NA -26.65 NA -3.89 0.30 0.35 24.73 0.0000 163 138.70 NA NA 0.02 -0.33 -66.07 NA -53.74 53.78 NA NA 0.23 0.34 24.77 0.0000 164 88.16 1.50 NA NA NA 30.72 NA -55.79 NA NA -3.90 0.31 0.35 24.87 0.0000 165 120.68 1.16 -2.10 -0.12 NA -45.12 NA NA -15.33 NA NA 0.41 0.55 25.01 0.0000 166 79.48 1.56 NA NA -0.12 30.10 NA NA -27.09 NA -3.88 0.30 0.35 25.08 0.0000 167 130.48 NA NA NA NA -75.89 0.10 -56.63 69.57 NA NA 0.26 0.39 25.19 0.0000

104

168 89.83 1.49 NA NA -0.15 30.15 NA -56.50 NA NA -3.88 0.30 0.35 25.22 0.0000 169 158.70 NA -2.39 NA -2.36 -70.08 0.10 NA 72.43 NA NA 0.41 0.60 25.27 0.0000 170 136.25 NA NA NA -0.57 -77.37 0.10 -61.42 71.16 NA NA 0.26 0.40 25.27 0.0000 171 171.18 NA -2.39 -0.02 -2.00 -55.52 NA -24.71 NA NA NA 0.39 0.52 25.37 0.0000 172 78.49 1.77 NA 0.05 -0.55 31.83 0.16 -67.75 -54.57 NA -4.41 0.33 0.41 25.38 0.0000 - 173 105.26 1.65 -2.21 -0.01 NA NA NA -47.74 135.57 NA NA 0.35 0.41 25.44 0.0000 174 127.51 1.15 -2.07 -0.14 NA -45.65 NA -33.92 NA NA NA 0.41 0.55 25.54 0.0000 175 106.00 1.25 -1.97 NA NA -52.73 0.11 NA -48.92 NA NA 0.43 0.59 25.62 0.0000 176 169.39 NA -2.36 NA -2.26 -65.52 0.09 -33.36 NA NA NA 0.41 0.57 25.83 0.0000 - 177 120.43 1.70 -2.45 NA -1.83 NA 0.03 -55.39 151.99 NA NA 0.36 0.43 25.89 0.0000 178 143.88 NA -2.51 NA NA NA NA -46.81 -53.85 NA NA 0.23 0.28 26.02 0.0000 179 145.17 NA -2.12 -0.07 NA -58.35 NA NA 62.91 NA NA 0.37 0.50 26.05 0.0000 180 109.11 1.14 -1.93 NA NA -54.26 0.10 -24.06 NA NA NA 0.43 0.58 26.26 0.0000 181 117.74 1.20 -2.14 NA -1.62 9.40 NA NA NA NA -2.52 0.43 0.52 26.67 0.0000 182 149.68 NA -2.10 -0.03 NA -54.51 NA -15.98 NA NA NA 0.37 0.47 26.70 0.0000 183 109.85 1.35 NA -0.07 NA -89.33 0.13 -113.06 NA 121.85 NA 0.32 0.49 26.78 0.0000 - 184 101.64 1.69 -2.19 NA NA NA 0.02 -49.01 140.76 NA NA 0.35 0.41 26.80 0.0000 185 119.11 1.33 NA -0.06 -0.88 -98.47 0.14 -128.77 NA 161.93 NA 0.32 0.48 27.01 0.0000 186 136.58 NA -2.03 NA NA -63.20 0.07 NA 48.57 NA NA 0.38 0.51 27.08 0.0000 187 98.58 1.26 -1.88 NA NA 17.66 NA NA NA NA -2.87 0.42 0.48 27.43 0.0000 188 143.52 NA -2.04 NA NA -60.32 0.06 -16.77 NA NA NA 0.38 0.49 27.52 0.0000 189 111.17 1.48 -2.12 -0.04 -1.92 -2.01 0.13 NA -29.40 NA -2.59 0.44 0.59 27.61 0.0000 190 106.12 1.43 NA -0.07 NA -65.67 0.14 -86.89 -26.92 NA NA 0.33 0.51 27.90 0.0000 191 148.21 NA NA 0.08 -1.01 -111.87 0.10 -115.95 NA 197.56 NA 0.25 0.36 28.10 0.0000 192 122.30 1.45 -2.10 -0.07 -2.00 -8.12 0.13 -45.06 NA NA -2.42 0.44 0.61 28.11 0.0000 193 96.25 1.17 NA NA NA -50.31 NA NA -19.59 NA NA 0.28 0.38 28.17 0.0000 194 137.41 NA NA 0.07 NA -100.52 0.09 -97.21 NA 147.92 NA 0.25 0.35 28.19 0.0000 105

- 195 113.08 1.62 -2.51 NA -1.74 NA NA NA 136.87 NA NA 0.37 0.43 28.21 0.0000 196 90.72 1.54 -1.82 -0.05 NA 10.22 0.12 NA -33.27 NA -3.02 0.43 0.54 28.25 0.0000 197 134.44 0.97 -2.33 NA -1.91 -44.75 NA NA NA NA NA 0.43 0.57 28.27 0.0000 198 62.17 1.76 NA NA NA 29.84 0.15 NA -22.27 NA -4.42 0.34 0.41 28.27 0.0000 199 98.00 1.17 NA NA -0.19 -50.55 NA NA -19.65 NA NA 0.28 0.37 28.29 0.0000 200 107.22 1.12 NA NA NA -49.83 NA -60.11 NA NA NA 0.29 0.38 28.33 0.0000 201 111.68 1.43 NA -0.06 -0.60 -67.22 0.15 -91.95 -26.80 NA NA 0.33 0.52 28.34 0.0000 202 73.00 1.70 NA NA NA 30.55 0.15 -60.93 NA NA -4.44 0.34 0.41 28.38 0.0000 203 110.03 1.12 NA NA -0.26 -50.17 NA -61.86 NA NA NA 0.28 0.38 28.44 0.0000 204 131.30 1.27 -2.34 -0.08 -2.24 -63.09 0.13 NA -8.70 NA NA 0.44 0.67 28.55 0.0000 - 205 155.71 NA -2.68 0.14 -1.95 NA NA -35.43 102.77 NA NA 0.26 0.31 28.72 0.0000 206 65.59 1.76 NA NA -0.40 28.90 0.15 NA -22.95 NA -4.40 0.33 0.41 28.74 0.0000 - 207 91.25 1.94 NA NA NA NA NA -110.59 142.94 NA NA 0.20 0.24 28.75 0.0000 - 208 93.06 1.94 NA NA -0.18 NA NA -111.65 144.42 NA NA 0.19 0.24 28.78 0.0000 209 97.25 1.50 -1.80 -0.08 NA 7.63 0.12 -31.03 NA NA -2.97 0.43 0.54 28.81 0.0000 210 77.71 1.70 NA NA -0.47 29.00 0.15 -63.91 NA NA -4.40 0.34 0.41 28.83 0.0000 211 146.20 1.31 -2.30 -0.11 -2.37 -65.66 0.14 -67.13 NA NA NA 0.45 0.70 28.85 0.0000 212 164.14 NA -2.40 NA -1.96 -54.71 NA NA NA NA NA 0.40 0.52 28.96 0.0000 213 126.87 NA NA NA -0.23 -67.58 NA NA 69.86 NA NA 0.23 0.33 29.21 0.0000 214 76.92 1.54 NA 0.05 NA 32.02 NA NA -41.02 NA -3.90 0.30 0.35 29.21 0.0000 215 124.80 NA NA NA NA -67.32 NA NA 69.75 NA NA 0.23 0.33 29.29 0.0000 - 216 94.93 1.64 -2.26 NA NA NA NA NA 128.20 NA NA 0.35 0.41 29.35 0.0000 217 145.21 NA NA NA -0.31 -63.87 NA -64.63 NA NA NA 0.23 0.32 29.43 0.0000 218 129.58 NA NA 0.02 NA -76.01 0.10 -55.40 65.89 NA NA 0.25 0.39 29.45 0.0000 219 89.18 1.49 NA 0.00 NA 28.69 NA -55.81 NA NA -3.82 0.30 0.36 29.52 0.0000 220 142.01 NA NA NA NA -63.56 NA -62.75 NA NA NA 0.24 0.32 29.52 0.0000

106

221 135.39 NA NA 0.02 -0.61 -77.43 0.11 -60.11 66.28 NA NA 0.26 0.40 29.64 0.0000 222 113.62 0.99 -2.02 NA NA -42.56 NA NA NA NA NA 0.41 0.52 29.65 0.0000 223 78.56 1.54 NA 0.05 -0.17 31.64 NA NA -42.00 NA -3.89 0.29 0.35 29.69 0.0000 224 111.50 1.43 -2.47 NA -1.70 NA NA -30.34 NA NA NA 0.35 0.43 29.74 0.0000 225 160.47 NA -2.40 -0.03 -2.36 -71.65 0.10 NA 85.05 NA NA 0.40 0.61 29.82 0.0000 226 111.44 1.31 -2.02 -0.12 NA -57.27 0.11 NA -19.73 NA NA 0.43 0.62 29.92 0.0000 227 135.65 NA -2.41 0.13 NA NA NA -33.53 -94.82 NA NA 0.24 0.27 29.92 0.0000 228 91.17 1.48 NA 0.00 -0.16 27.93 NA -56.86 NA NA -3.79 0.30 0.36 30.01 0.0000 229 121.04 1.31 -1.97 -0.15 NA -58.65 0.11 -47.01 NA NA NA 0.43 0.62 30.40 0.0000 230 165.75 NA -2.76 NA -1.81 NA -0.04 -45.41 -62.51 NA NA 0.25 0.31 30.45 0.0000 231 143.93 NA -2.10 NA NA -53.42 NA NA NA NA NA 0.38 0.47 30.56 0.0000 232 170.05 NA -2.37 0.01 -2.30 -66.37 0.09 -34.58 NA NA NA 0.40 0.58 30.58 0.0000 - 233 119.92 1.69 -2.45 0.02 -1.86 NA 0.04 -54.32 155.12 NA NA 0.36 0.43 30.73 0.0000 234 92.65 1.46 -2.22 NA NA NA NA -25.42 NA NA NA 0.33 0.39 30.76 0.0000 235 107.09 1.39 -2.08 NA -1.91 2.60 0.13 NA NA NA -2.80 0.45 0.58 30.78 0.0000 236 122.80 1.26 -2.20 -0.07 -1.62 3.24 NA NA NA NA -2.30 0.42 0.53 31.40 0.0000 237 140.07 NA -2.06 -0.06 NA -66.06 0.07 NA 69.83 NA NA 0.37 0.52 31.45 0.0000 238 147.86 NA -2.52 NA NA NA -0.05 -41.82 -55.11 NA NA 0.23 0.28 31.46 0.0000 - 239 101.94 1.70 -2.20 0.00 NA NA 0.02 -49.55 140.85 NA NA 0.34 0.41 31.49 0.0000 240 85.30 1.44 -1.77 NA NA 16.10 0.12 NA NA NA -3.29 0.44 0.52 31.80 0.0000 241 104.40 1.33 -1.94 -0.08 NA 10.68 NA NA NA NA -2.62 0.41 0.50 32.01 0.0000 242 146.04 NA -2.05 -0.02 NA -61.50 0.07 -20.07 NA NA NA 0.37 0.50 32.14 0.0000 243 85.73 1.32 NA NA NA -63.02 0.13 NA -19.93 NA NA 0.32 0.46 32.27 0.0000 244 128.03 1.17 -2.32 NA -2.34 -61.68 0.13 NA NA NA NA 0.46 0.67 32.37 0.0000 245 98.44 1.28 NA NA NA -62.78 0.13 -69.25 NA NA NA 0.33 0.47 32.39 0.0000 246 97.59 1.18 NA -0.02 NA -51.67 NA NA -13.76 NA NA 0.28 0.39 32.39 0.0000 247 89.66 1.32 NA NA -0.47 -64.27 0.13 NA -18.37 NA NA 0.32 0.47 32.46 0.0000 248 104.82 1.28 NA NA -0.62 -64.60 0.14 -75.33 NA NA NA 0.33 0.48 32.53 0.0000 107

249 112.69 1.19 NA -0.06 NA -51.76 NA -70.32 NA NA NA 0.29 0.40 32.59 0.0000 250 138.81 1.08 -2.37 -0.10 -1.84 -46.82 NA NA NA NA NA 0.43 0.58 32.59 0.0000 251 99.19 1.17 NA -0.02 -0.18 -51.88 NA NA -14.08 NA NA 0.27 0.39 32.63 0.0000 - 252 85.91 1.83 NA 0.12 NA NA NA -95.58 168.12 NA NA 0.20 0.23 32.74 0.0000 - 253 112.95 1.60 -2.50 0.02 -1.76 NA NA NA 141.55 NA NA 0.36 0.43 32.80 0.0000 254 115.20 1.18 NA -0.06 -0.23 -52.09 NA -71.93 NA NA NA 0.28 0.40 32.82 0.0000 255 59.22 1.74 NA 0.07 NA 33.43 0.15 NA -41.41 NA -4.51 0.33 0.40 32.83 0.0000 - 256 88.75 1.82 NA 0.12 -0.30 NA NA -96.76 171.79 NA NA 0.20 0.23 32.89 0.0000 257 72.55 1.69 NA 0.01 NA 29.23 0.15 -59.09 NA NA -4.40 0.34 0.41 33.15 0.0000 258 123.46 NA NA 0.03 NA -66.32 NA NA 59.41 NA NA 0.23 0.33 33.33 0.0000 259 153.11 NA -2.82 NA -1.88 NA NA NA -49.00 NA NA 0.25 0.32 33.34 0.0000 260 125.82 NA NA 0.04 -0.27 -66.55 NA NA 58.83 NA NA 0.23 0.33 33.35 0.0000 261 62.96 1.74 NA 0.07 -0.49 32.90 0.16 NA -44.78 NA -4.51 0.33 0.40 33.42 0.0000 262 77.38 1.51 NA NA NA 29.64 NA NA NA NA -3.91 0.31 0.35 33.46 0.0000 263 165.47 NA -2.41 -0.01 -1.98 -55.43 NA NA NA NA NA 0.39 0.52 33.53 0.0000 264 156.21 NA -2.75 NA -1.88 NA NA -40.15 NA NA NA 0.25 0.33 33.56 0.0000 265 78.38 1.50 NA NA -0.10 29.26 NA NA NA NA -3.90 0.30 0.35 33.67 0.0000 266 135.95 NA NA 0.06 NA -62.16 NA -53.35 NA NA NA 0.23 0.32 33.68 0.0000 267 139.59 NA NA 0.06 -0.36 -62.55 NA -55.43 NA NA NA 0.23 0.32 33.70 0.0000 268 120.32 1.13 -2.09 -0.12 NA -45.61 NA NA NA NA NA 0.41 0.55 33.71 0.0000 269 77.44 1.68 NA 0.02 -0.50 27.52 0.16 -62.11 NA NA -4.36 0.33 0.42 33.74 0.0000 - 270 95.00 1.63 -2.26 0.01 NA NA NA NA 130.12 NA NA 0.35 0.41 33.81 0.0000 271 122.48 NA NA NA -0.47 -78.25 0.10 NA 84.09 NA NA 0.26 0.39 33.87 0.0000 272 118.58 NA NA NA NA -77.09 0.10 NA 82.35 NA NA 0.26 0.38 33.89 0.0000 273 162.32 NA -2.38 NA -2.19 -64.95 0.08 NA NA NA NA 0.41 0.56 33.97 0.0000 - 274 109.34 1.68 -2.51 NA -1.79 NA 0.03 NA 140.43 NA NA 0.36 0.43 34.13 0.0000

108

275 143.57 NA NA NA -0.54 -73.76 0.10 -71.69 NA NA NA 0.26 0.38 34.16 0.0000 276 116.71 1.50 -2.50 -0.07 -1.71 NA NA -41.44 NA NA NA 0.34 0.44 34.20 0.0000 277 138.14 NA NA NA NA -72.57 0.09 -67.56 NA NA NA 0.26 0.37 34.21 0.0000 - 278 86.58 2.02 NA NA NA NA 0.04 -114.12 148.05 NA NA 0.20 0.25 34.21 0.0000 279 104.75 1.13 -1.95 NA NA -54.13 0.10 NA NA NA NA 0.44 0.58 34.30 0.0000 - 280 88.91 2.03 NA NA -0.25 NA 0.04 -115.80 150.29 NA NA 0.19 0.24 34.36 0.0000 - 281 157.88 NA -2.68 0.12 -1.89 NA -0.03 -33.84 100.33 NA NA 0.26 0.30 34.67 0.0000 282 134.35 NA -2.56 NA NA NA NA NA -42.98 NA NA 0.23 0.29 34.78 0.0000 283 137.20 NA -2.50 NA NA NA NA -37.54 NA NA NA 0.23 0.29 34.96 0.0000 284 146.22 NA -2.12 -0.02 NA -54.55 NA NA NA NA NA 0.37 0.48 35.01 0.0000 285 98.29 1.54 -2.26 -0.08 NA NA NA -36.94 NA NA NA 0.33 0.41 35.09 0.0000 286 139.45 NA NA NA -0.09 NA NA -122.23 -41.00 NA NA 0.02 0.09 35.12 0.0000 - 287 92.18 1.68 -2.25 NA NA NA 0.02 NA 130.50 NA NA 0.35 0.41 35.17 0.0000 288 138.61 NA NA NA NA NA NA -121.97 -40.88 NA NA 0.02 0.09 35.41 0.0000 289 139.75 NA -2.43 0.12 NA NA -0.04 -31.40 -90.20 NA NA 0.24 0.27 35.60 0.0000 290 108.86 1.47 -2.46 NA -1.74 NA 0.02 -32.58 NA NA NA 0.34 0.42 35.68 0.0000 291 111.14 1.43 -2.12 -0.05 -1.92 -3.42 0.13 NA NA NA -2.59 0.45 0.59 35.74 0.0000 292 140.47 NA -2.06 NA NA -60.43 0.06 NA NA NA NA 0.38 0.50 35.77 0.0000 293 87.07 1.34 NA -0.03 NA -65.01 0.13 NA -13.71 NA NA 0.32 0.48 36.58 0.0000 294 91.00 1.48 -2.21 NA NA NA 0.01 -26.46 NA NA NA 0.33 0.39 36.58 0.0000 295 90.51 1.49 -1.82 -0.07 NA 8.96 0.11 NA NA NA -3.03 0.43 0.54 36.58 0.0000 296 105.05 1.37 NA -0.08 NA -66.23 0.14 -83.03 NA NA NA 0.33 0.51 36.69 0.0000 297 61.26 1.71 NA NA NA 29.92 0.15 NA NA NA -4.46 0.34 0.41 36.83 0.0000 298 90.59 1.34 NA -0.02 -0.47 -66.00 0.14 NA -13.85 NA NA 0.31 0.48 36.90 0.0000 299 131.15 1.25 -2.34 -0.08 -2.23 -62.96 0.13 NA NA NA NA 0.45 0.67 36.92 0.0000 300 110.61 1.37 NA -0.07 -0.59 -67.80 0.14 -88.11 NA NA NA 0.33 0.52 36.96 0.0000 301 147.97 NA -2.71 0.15 -1.94 NA NA NA -98.31 NA NA 0.26 0.31 37.13 0.0000 109

302 97.17 1.12 NA NA -0.18 -51.17 NA NA NA NA NA 0.28 0.37 37.14 0.0000 303 95.48 1.12 NA NA NA -50.93 NA NA NA NA NA 0.28 0.37 37.14 0.0000 304 64.60 1.71 NA NA -0.39 28.87 0.15 NA NA NA -4.44 0.34 0.41 37.16 0.0000 - 305 127.38 NA NA 0.28 -0.32 NA NA -96.34 137.50 NA NA 0.07 0.07 37.66 0.0000 306 149.49 NA -2.71 0.08 -1.95 NA NA -28.67 NA NA NA 0.25 0.34 37.81 0.0000 - 307 124.25 NA NA 0.27 NA NA NA -95.20 132.83 NA NA 0.07 0.07 37.82 0.0000 308 76.84 1.47 NA 0.03 NA 29.26 NA NA NA NA -3.89 0.30 0.35 38.00 0.0000 309 116.74 NA NA 0.04 NA -76.12 0.10 NA 70.33 NA NA 0.25 0.38 38.01 0.0000 310 105.35 1.43 -2.50 NA -1.68 NA NA NA NA NA NA 0.35 0.43 38.08 0.0000 311 120.81 NA NA 0.05 -0.53 -77.21 0.10 NA 70.24 NA NA 0.25 0.39 38.09 0.0000 - 312 67.47 1.96 NA NA -0.05 NA NA NA 121.00 NA NA 0.18 0.22 38.21 0.0000 - 313 79.61 1.91 NA 0.13 NA NA 0.05 -98.09 176.25 NA NA 0.20 0.23 38.25 0.0000 - 314 67.04 1.96 NA NA NA NA NA NA 120.67 NA NA 0.18 0.23 38.31 0.0000 315 132.16 NA NA NA -0.24 -65.16 NA NA NA NA NA 0.23 0.32 38.33 0.0000 316 78.22 1.47 NA 0.03 -0.13 28.65 NA NA NA NA -3.87 0.30 0.35 38.34 0.0000 317 130.81 NA NA 0.07 NA -71.16 0.10 -57.06 NA NA NA 0.25 0.36 38.42 0.0000 318 136.60 NA NA 0.07 -0.61 -72.53 0.10 -61.24 NA NA NA 0.25 0.37 38.48 0.0000 319 111.28 1.27 -2.01 -0.12 NA -57.60 0.10 NA NA NA NA 0.43 0.61 38.49 0.0000 320 128.49 NA -2.45 0.14 NA NA NA NA -90.36 NA NA 0.25 0.28 38.49 0.0000 - 321 83.00 1.91 NA 0.14 -0.40 NA 0.05 -99.82 181.80 NA NA 0.20 0.23 38.51 0.0000 322 130.04 NA NA NA NA -64.92 NA NA NA NA NA 0.23 0.32 38.53 0.0000 323 161.91 NA -2.39 0.02 -2.25 -65.62 0.09 NA NA NA NA 0.40 0.57 38.61 0.0000 324 78.59 1.76 NA NA 0.00 NA NA -88.22 NA NA NA 0.17 0.22 38.74 0.0000 - 325 108.46 1.66 -2.49 0.04 -1.83 NA 0.03 NA 148.91 NA NA 0.36 0.43 38.81 0.0000 326 78.52 1.76 NA NA NA NA NA -88.20 NA NA NA 0.17 0.22 38.84 0.0000

110

327 156.51 NA -2.81 NA -1.79 NA -0.04 NA -51.77 NA NA 0.25 0.31 38.91 0.0000 328 129.86 NA -2.44 0.08 NA NA NA -25.70 NA NA NA 0.23 0.29 39.11 0.0000 329 158.19 NA -2.74 NA -1.79 NA -0.04 -35.48 NA NA NA 0.25 0.33 39.16 0.0000 330 87.74 1.46 -2.25 NA NA NA NA NA NA NA NA 0.34 0.40 39.23 0.0000 - 331 91.71 1.67 -2.24 0.02 NA NA 0.02 NA 134.61 NA NA 0.34 0.41 39.73 0.0000 332 139.60 NA -2.57 NA NA NA -0.06 NA -45.42 NA NA 0.24 0.28 40.06 0.0000 333 141.04 NA -2.51 NA NA NA -0.05 -32.22 NA NA NA 0.23 0.28 40.27 0.0000 334 114.01 1.54 -2.50 -0.06 -1.74 NA 0.02 -42.64 NA NA NA 0.33 0.43 40.31 0.0000 335 141.68 NA -2.06 -0.01 NA -61.40 0.06 NA NA NA NA 0.38 0.50 40.31 0.0000 336 142.43 NA NA NA 0.00 NA -0.05 -118.62 -42.59 NA NA 0.03 0.08 40.42 0.0000 337 142.46 NA NA NA NA NA -0.05 -118.81 -42.96 NA NA 0.03 0.08 40.58 0.0000 338 96.79 1.56 -2.25 -0.07 NA NA 0.01 -37.29 NA NA NA 0.32 0.40 41.05 0.0000 339 85.12 1.27 NA NA NA -63.05 0.13 NA NA NA NA 0.32 0.45 41.13 0.0000 340 89.04 1.27 NA NA -0.47 -64.18 0.13 NA NA NA NA 0.32 0.46 41.19 0.0000 341 97.05 1.15 NA -0.02 NA -52.02 NA NA NA NA NA 0.28 0.39 41.40 0.0000 342 59.19 1.67 NA 0.05 NA 30.93 0.15 NA NA NA -4.50 0.34 0.40 41.46 0.0000 343 98.65 1.15 NA -0.02 -0.17 -52.28 NA NA NA NA NA 0.28 0.38 41.51 0.0000 344 62.88 1.66 NA 0.05 -0.45 29.72 0.16 NA NA NA -4.48 0.33 0.40 41.90 0.0000 - 345 63.96 1.81 NA 0.16 NA NA NA NA 159.71 NA NA 0.19 0.21 41.92 0.0000 - 346 66.05 1.81 NA 0.17 -0.24 NA NA NA 162.87 NA NA 0.19 0.21 41.93 0.0000 347 148.54 NA -2.80 NA -1.86 NA NA NA NA NA NA 0.25 0.33 42.22 0.0000 348 126.47 NA NA 0.09 -0.31 -62.77 NA NA NA NA NA 0.23 0.31 42.42 0.0000 349 123.74 NA NA 0.09 NA -62.45 NA NA NA NA NA 0.23 0.31 42.52 0.0000 350 107.70 1.49 -2.54 -0.05 -1.68 NA NA NA NA NA NA 0.34 0.44 42.52 0.0000 351 150.63 NA -2.71 0.13 -1.88 NA -0.03 NA -95.83 NA NA 0.26 0.31 42.92 0.0000 352 128.92 NA NA NA -0.43 -74.39 0.09 NA NA NA NA 0.25 0.36 43.00 0.0000 353 77.43 1.73 NA 0.02 NA NA NA -84.35 NA NA NA 0.17 0.22 43.03 0.0000 111

354 77.93 1.73 NA 0.02 -0.03 NA NA -84.82 NA NA NA 0.17 0.22 43.05 0.0000 355 125.28 NA NA NA NA -73.53 0.09 NA NA NA NA 0.25 0.36 43.13 0.0000 - 356 128.84 NA NA 0.27 -0.29 NA -0.02 -95.52 134.67 NA NA 0.07 0.07 43.22 0.0000 - 357 126.29 NA NA 0.26 NA NA -0.02 -94.43 130.25 NA NA 0.07 0.07 43.25 0.0000 358 90.49 1.53 -2.29 -0.06 NA NA NA NA NA NA NA 0.33 0.41 43.54 0.0000 359 152.35 NA -2.71 0.07 -1.86 NA -0.03 -26.84 NA NA NA 0.25 0.34 43.58 0.0000 - 360 62.63 2.03 NA NA NA NA 0.03 NA 124.18 NA NA 0.18 0.23 43.69 0.0000 - 361 63.42 2.03 NA NA -0.10 NA 0.03 NA 124.93 NA NA 0.18 0.23 43.70 0.0000 362 130.30 NA -2.55 NA NA NA NA NA NA NA NA 0.23 0.29 43.75 0.0000 363 102.57 1.47 -2.49 NA -1.71 NA 0.02 NA NA NA NA 0.35 0.42 43.90 0.0000 364 133.19 NA -2.46 0.12 NA NA -0.04 NA -85.82 NA NA 0.25 0.28 44.02 0.0000 365 74.77 1.81 NA NA NA NA 0.03 -90.28 NA NA NA 0.17 0.22 44.23 0.0000 366 133.84 NA NA NA -0.04 NA NA -114.56 NA NA NA 0.02 0.09 44.23 0.0000 367 75.27 1.81 NA NA -0.05 NA 0.03 -90.59 NA NA NA 0.17 0.22 44.25 0.0000 368 134.93 NA -2.46 0.06 NA NA -0.05 -23.67 NA NA NA 0.23 0.29 44.60 0.0000 369 133.41 NA NA NA NA NA NA -114.63 NA NA NA 0.02 0.09 44.63 0.0000 370 111.74 NA NA NA 0.06 NA NA NA -6.69 NA NA 0.00 0.11 44.84 0.0000 371 86.15 1.48 -2.24 NA NA NA 0.01 NA NA NA NA 0.33 0.39 44.93 0.0000 372 112.24 NA NA NA NA NA NA NA -7.22 NA NA 0.00 0.11 45.26 0.0000 373 86.73 1.31 NA -0.03 NA -64.89 0.13 NA NA NA NA 0.32 0.47 45.47 0.0000 374 90.24 1.31 NA -0.02 -0.46 -65.91 0.13 NA NA NA NA 0.32 0.47 45.64 0.0000 375 143.29 NA -2.73 0.09 -1.94 NA NA NA NA NA NA 0.25 0.35 46.32 0.0000 - 376 105.62 NA NA 0.30 -0.26 NA NA NA 124.03 NA NA 0.06 0.06 46.93 0.0000 377 121.92 NA NA 0.10 -0.54 -72.14 0.10 NA NA NA NA 0.25 0.35 47.08 0.0000 378 117.68 NA NA 0.10 NA -71.05 0.09 NA NA NA NA 0.25 0.35 47.14 0.0000 - 379 103.31 NA NA 0.30 NA NA NA NA 120.40 NA NA 0.06 0.06 47.21 0.0000

112

- 380 57.62 1.88 NA 0.17 NA NA 0.04 NA 166.78 NA NA 0.19 0.21 47.31 0.0000 - 381 60.13 1.88 NA 0.18 -0.34 NA 0.05 NA 171.66 NA NA 0.19 0.21 47.43 0.0000 382 115.95 NA NA 0.19 -0.11 NA NA -83.15 NA NA NA 0.05 0.08 47.44 0.0000 383 151.64 NA -2.79 NA -1.77 NA -0.04 NA NA NA NA 0.25 0.33 47.66 0.0000 384 114.65 NA NA 0.20 NA NA NA -82.71 NA NA NA 0.05 0.08 47.70 0.0000 385 124.46 NA -2.47 0.09 NA NA NA NA NA NA NA 0.24 0.29 47.77 0.0000 386 59.45 1.80 NA NA 0.09 NA NA NA NA NA NA 0.16 0.21 47.96 0.0000 387 60.13 1.80 NA NA NA NA NA NA NA NA NA 0.16 0.21 48.19 0.0000 388 105.02 1.52 -2.53 -0.05 -1.71 NA 0.02 NA NA NA NA 0.34 0.43 48.47 0.0000 389 72.78 1.78 NA 0.03 NA NA 0.03 -85.42 NA NA NA 0.17 0.22 48.52 0.0000 390 73.73 1.78 NA 0.03 -0.09 NA 0.03 -86.18 NA NA NA 0.16 0.22 48.66 0.0000 391 135.33 NA -2.55 NA NA NA -0.06 NA NA NA NA 0.24 0.29 48.91 0.0000 392 89.17 1.54 -2.29 -0.06 NA NA 0.01 NA NA NA NA 0.32 0.40 49.36 0.0000 393 136.59 NA NA NA 0.05 NA -0.05 -110.43 NA NA NA 0.02 0.09 49.40 0.0000 394 137.00 NA NA NA NA NA -0.05 -110.89 NA NA NA 0.02 0.08 49.68 0.0000 395 116.21 NA NA NA 0.14 NA -0.05 NA -9.24 NA NA 0.01 0.10 49.95 0.0000 396 117.41 NA NA NA NA NA -0.05 NA -10.12 NA NA 0.01 0.10 50.26 0.0000 397 146.65 NA -2.73 0.08 -1.85 NA -0.03 NA NA NA NA 0.25 0.34 51.95 0.0000 398 58.19 1.72 NA 0.06 0.03 NA NA NA NA NA NA 0.16 0.21 52.10 0.0000 399 58.31 1.72 NA 0.07 NA NA NA NA NA NA NA 0.16 0.21 52.22 0.0000 - 400 107.56 NA NA 0.29 -0.22 NA -0.02 NA 120.84 NA NA 0.06 0.06 52.33 0.0000 - 401 105.81 NA NA 0.29 NA NA -0.02 NA 117.60 NA NA 0.06 0.06 52.50 0.0000 402 118.29 NA NA 0.18 -0.05 NA -0.03 -82.03 NA NA NA 0.05 0.08 52.83 0.0000 403 117.61 NA NA 0.19 NA NA -0.03 -81.75 NA NA NA 0.05 0.07 52.97 0.0000 404 130.05 NA -2.48 0.07 NA NA -0.05 NA NA NA NA 0.24 0.29 53.13 0.0000 405 56.33 1.85 NA NA 0.05 NA 0.02 NA NA NA NA 0.16 0.21 53.36 0.0000 406 56.65 1.85 NA NA NA NA 0.02 NA NA NA NA 0.16 0.21 53.48 0.0000 113

407 110.89 NA NA NA 0.06 NA NA NA NA NA NA 0.00 0.09 54.00 0.0000 408 111.40 NA NA NA NA NA NA NA NA NA NA 0.00 0.09 54.52 0.0000 409 97.37 NA NA 0.23 -0.06 NA NA NA NA NA NA 0.04 0.07 56.71 0.0000 410 96.62 NA NA 0.23 NA NA NA NA NA NA NA 0.04 0.07 57.08 0.0000 411 54.06 1.77 NA 0.07 -0.02 NA 0.03 NA NA NA NA 0.16 0.20 57.58 0.0000 412 53.78 1.77 NA 0.07 NA NA 0.03 NA NA NA NA 0.16 0.21 57.58 0.0000 413 115.15 NA NA NA 0.16 NA -0.06 NA NA NA NA 0.01 0.09 58.97 0.0000 414 116.36 NA NA NA NA NA -0.05 NA NA NA NA 0.01 0.08 59.39 0.0000 415 100.17 NA NA 0.22 0.01 NA -0.03 NA NA NA NA 0.04 0.07 61.95 0.0000 416 99.97 NA NA 0.22 NA NA -0.03 NA NA NA NA 0.04 0.06 62.21 0.0000

114

Species Density

AB NU FC X NU AGE X R² R² Model Intercept AGE EUC SB SLOPE (Sug.) RIVER FC Δ FC (Sug.) NU (Sug.) Marg. Cond. ΔAICc weight 1 18.13 NA NA NA NA -0.61 NA 23.48 20.65 -4.43 NA 0.07 0.07 0.00 0.5088 2 18.47 NA -0.16 NA NA 0.66 NA 27.34 18.00 -5.48 NA 0.12 0.14 3.04 0.1110 3 14.83 0.14 NA NA NA 1.66 NA 21.45 9.93 -4.59 NA 0.10 0.14 3.82 0.0753 4 17.53 NA NA NA 0.04 -0.09 NA 24.48 21.19 -6.96 NA 0.06 0.07 4.11 0.0653 5 13.08 0.19 NA NA NA 10.42 NA 20.99 8.27 -4.43 -0.44 0.14 0.21 5.48 0.0329 6 18.38 NA NA NA NA -1.52 NA 22.20 20.65 NA NA 0.07 0.07 5.97 0.0257 7 20.75 NA NA NA NA -0.17 NA 18.41 NA -3.14 NA 0.03 0.06 6.14 0.0237 8 15.37 0.14 -0.16 NA NA 2.72 NA 25.13 7.49 -5.74 NA 0.15 0.21 7.16 0.0142 9 19.54 NA -0.18 NA -0.08 -0.17 NA 25.85 17.00 -1.14 NA 0.12 0.15 7.20 0.0139 10 18.66 NA NA NA NA NA NA 20.11 18.30 NA NA 0.06 0.07 7.28 0.0134 11 19.67 NA NA NA NA 1.20 -0.02 23.64 18.32 -5.88 NA 0.11 0.15 7.58 0.0115 12 19.13 NA NA -0.02 NA -1.38 NA 21.36 25.99 -2.86 NA 0.08 0.08 8.14 0.0087 13 14.34 0.14 NA NA 0.04 2.09 NA 22.28 10.34 -6.68 NA 0.10 0.14 8.14 0.0087 14 20.80 NA -0.17 NA NA 1.02 NA 23.23 NA -4.15 NA 0.10 0.13 8.67 0.0067 15 15.40 0.16 NA NA NA 2.08 NA 19.17 NA -3.90 NA 0.10 0.15 8.71 0.0065 16 18.77 NA -0.16 NA NA -0.46 NA 25.76 17.95 NA NA 0.12 0.14 8.82 0.0062 17 13.80 0.18 -0.14 NA NA 10.13 NA 24.44 6.73 -5.15 -0.38 0.18 0.26 9.50 0.0044 18 15.08 0.14 NA NA NA 0.72 NA 20.18 10.00 NA NA 0.10 0.14 9.61 0.0042 19 18.87 NA -0.17 NA NA NA NA 25.38 17.35 NA NA 0.12 0.14 9.66 0.0041 20 20.30 NA -0.19 NA NA 2.87 -0.02 28.07 15.31 -6.95 NA 0.18 0.24 9.89 0.0036 21 12.51 0.19 NA NA 0.04 10.92 NA 21.92 8.61 -6.76 -0.44 0.14 0.21 10.02 0.0034 22 18.09 NA NA NA 0.03 -1.52 NA 22.34 20.94 NA NA 0.07 0.07 10.10 0.0033 23 13.56 0.21 NA NA NA 10.76 NA 19.15 NA -3.43 -0.44 0.14 0.22 10.13 0.0032 24 20.68 NA NA NA 0.01 -0.11 NA 18.52 NA -3.46 NA 0.03 0.06 10.13 0.0032 25 15.32 0.13 NA NA NA NA NA 21.07 11.93 NA NA 0.11 0.14 10.78 0.0023 26 19.87 NA -0.18 -0.02 NA -0.26 NA 25.08 25.15 -3.48 NA 0.14 0.14 10.98 0.0021 115

27 13.32 0.19 NA NA NA 9.51 NA 19.74 8.21 NA -0.44 0.14 0.21 11.08 0.0020 28 18.36 NA NA NA 0.03 NA NA 20.27 18.61 NA NA 0.06 0.07 11.28 0.0018 29 16.48 0.14 -0.17 NA -0.08 1.87 NA 23.60 6.62 -1.37 NA 0.15 0.22 11.52 0.0016 30 15.83 0.15 -0.16 NA NA 3.00 NA 23.50 NA -5.01 NA 0.15 0.22 11.74 0.0014 31 18.56 NA NA NA 0.09 2.27 -0.02 25.58 19.10 -10.81 NA 0.11 0.15 11.77 0.0014 32 15.84 0.15 NA -0.02 NA 0.92 NA 19.13 16.60 -2.88 NA 0.12 0.13 11.89 0.0013 33 16.61 0.13 NA NA NA 2.98 -0.02 21.88 8.94 -5.55 NA 0.14 0.19 11.93 0.0013 34 20.92 NA NA NA NA -0.81 NA 17.50 NA NA NA 0.03 0.06 12.27 0.0011 35 18.34 NA NA -0.02 0.06 -0.68 NA 22.63 26.88 -6.33 NA 0.08 0.08 12.37 0.0010 36 22.23 NA -0.19 NA -0.12 -0.25 NA 21.37 NA 2.23 NA 0.10 0.14 12.52 0.0010 37 22.94 NA NA NA NA -0.74 NA NA 14.68 NA NA 0.02 0.04 12.67 0.0009 38 15.67 0.13 -0.16 NA NA 1.57 NA 23.57 7.54 NA NA 0.15 0.21 12.75 0.0009 39 15.08 0.16 NA NA 0.03 2.37 NA 19.67 NA -5.29 NA 0.10 0.15 12.91 0.0008 40 19.63 NA -0.18 NA -0.08 -0.40 NA 25.52 16.96 NA NA 0.12 0.15 12.94 0.0008 41 22.09 NA NA NA NA 1.54 -0.02 19.32 NA -4.12 NA 0.08 0.14 13.24 0.0007 42 19.99 NA NA NA NA -0.01 -0.02 21.95 18.22 NA NA 0.11 0.15 13.38 0.0006 43 20.95 NA NA NA NA NA NA 16.68 NA NA NA 0.03 0.05 13.42 0.0006 44 19.72 NA -0.18 NA -0.08 NA NA 25.23 16.42 NA NA 0.12 0.14 13.64 0.0006 45 22.92 NA NA NA NA NA NA NA 13.92 NA NA 0.02 0.03 13.81 0.0005 46 14.07 0.20 NA -0.02 NA 9.64 NA 19.10 13.66 -3.96 -0.43 0.15 0.20 13.87 0.0005 47 14.22 0.19 -0.14 NA NA 10.37 NA 23.00 NA -4.29 -0.38 0.18 0.26 13.87 0.0005 48 14.85 0.14 NA NA 0.02 0.73 NA 20.28 10.14 NA NA 0.10 0.14 13.94 0.0005 49 19.30 NA NA -0.02 NA -1.98 NA 20.49 26.00 NA NA 0.08 0.08 13.95 0.0005 50 16.13 0.11 -0.15 NA NA NA NA 24.84 10.97 NA NA 0.15 0.21 13.99 0.0005 51 14.71 0.18 -0.15 NA -0.07 9.35 NA 23.25 6.15 -1.70 -0.38 0.17 0.26 14.13 0.0004 52 20.02 NA NA NA NA NA -0.02 21.98 18.31 NA NA 0.11 0.14 14.30 0.0004 53 20.88 NA -0.19 NA -0.04 2.39 -0.02 27.24 14.88 -4.55 NA 0.17 0.24 14.36 0.0004 54 14.77 0.17 NA NA NA 10.16 -0.01 21.42 7.87 -4.80 -0.37 0.16 0.25 14.39 0.0004 55 13.11 0.21 NA NA 0.03 11.18 NA 19.83 NA -5.25 -0.45 0.14 0.22 14.51 0.0004

116

56 21.02 NA -0.17 NA NA 0.18 NA 22.04 NA NA NA 0.10 0.14 14.60 0.0003 57 15.61 0.16 NA NA NA 1.30 NA 18.08 NA NA NA 0.10 0.15 14.65 0.0003 58 17.52 0.12 -0.18 NA NA 4.35 -0.02 26.21 6.61 -6.53 NA 0.20 0.27 14.66 0.0003 59 20.75 NA NA 0.00 NA -0.24 NA 18.23 NA -3.08 NA 0.03 0.08 14.69 0.0003 60 14.07 0.18 -0.14 NA NA 9.10 NA 23.00 6.63 NA -0.38 0.18 0.26 14.89 0.0003 61 15.09 0.13 NA NA 0.02 NA NA 21.18 12.10 NA NA 0.10 0.13 14.97 0.0003 62 22.35 NA -0.19 NA NA 3.11 -0.02 24.74 NA -5.18 NA 0.16 0.23 15.00 0.0003 63 16.74 0.14 -0.17 -0.03 NA 1.82 NA 22.84 16.85 -3.20 NA 0.18 0.18 15.06 0.0003 64 20.70 NA -0.18 -0.02 -0.06 -0.92 NA 23.97 24.23 -0.03 NA 0.14 0.14 15.36 0.0002 65 19.50 NA NA -0.01 NA NA NA 18.27 21.93 NA NA 0.07 0.07 15.40 0.0002 66 20.67 NA -0.19 NA NA 1.45 -0.02 26.08 15.13 NA NA 0.18 0.24 15.46 0.0002 67 21.05 NA -0.17 NA NA NA NA 22.19 NA NA NA 0.10 0.13 15.53 0.0002 68 21.24 NA NA -0.02 NA 0.27 -0.02 21.26 25.54 -4.44 NA 0.13 0.14 15.58 0.0002 69 13.01 0.19 NA NA 0.03 9.56 NA 19.90 8.30 NA -0.44 0.14 0.21 15.59 0.0002 70 18.96 0.15 NA NA NA 1.46 NA NA 3.27 NA NA 0.07 0.14 15.81 0.0002 71 13.74 0.21 NA NA NA 10.07 NA 18.19 NA NA -0.44 0.14 0.22 15.87 0.0002 72 17.02 0.15 -0.17 NA -0.09 2.01 NA 22.02 NA -0.15 NA 0.15 0.22 15.91 0.0002 73 16.25 0.14 NA NA NA NA NA 18.89 NA NA NA 0.10 0.16 15.98 0.0002 74 23.92 NA -0.14 NA NA 0.21 NA NA 11.70 NA NA 0.06 0.10 16.15 0.0002 75 20.93 NA NA NA 0.00 -0.82 NA 17.49 NA NA NA 0.03 0.06 16.26 0.0001 76 15.11 0.15 NA -0.02 0.06 1.57 NA 20.31 17.35 -6.10 NA 0.12 0.13 16.35 0.0001 77 15.63 0.12 NA NA 0.08 3.93 -0.02 23.63 9.62 -9.94 NA 0.13 0.19 16.35 0.0001 78 20.34 NA -0.17 NA NA NA -0.02 27.18 17.30 NA NA 0.18 0.22 16.54 0.0001 79 20.07 NA -0.18 -0.02 NA -0.98 NA 24.02 25.17 NA NA 0.14 0.14 16.56 0.0001 80 17.16 0.14 NA NA NA 3.33 -0.02 19.87 NA -4.64 NA 0.13 0.20 16.60 0.0001 81 22.89 NA NA NA 0.01 -0.74 NA NA 14.72 NA NA 0.02 0.04 16.65 0.0001 82 16.59 0.14 -0.17 NA -0.09 1.59 NA 23.23 6.60 NA NA 0.15 0.22 17.05 0.0001 83 16.19 0.18 NA -0.01 NA 1.87 NA 16.98 NA -3.41 NA 0.10 0.17 17.06 0.0001 84 17.17 0.20 NA NA NA 10.26 NA NA 2.29 NA -0.44 0.11 0.20 17.09 0.0001 117

85 23.96 NA -0.14 NA NA NA NA NA 12.09 NA NA 0.06 0.10 17.15 0.0001 86 21.47 NA -0.18 -0.01 NA 0.82 NA 22.07 NA -3.72 NA 0.09 0.14 17.30 0.0001 87 20.95 NA NA NA 0.00 NA NA 16.68 NA NA NA 0.03 0.05 17.30 0.0001 88 19.70 0.13 NA NA NA NA NA NA 5.79 NA NA 0.07 0.15 17.32 0.0001 89 21.37 NA NA NA 0.06 2.28 -0.02 20.55 NA -7.42 NA 0.08 0.14 17.34 0.0001 90 20.20 NA -0.19 -0.02 NA NA NA 23.14 23.20 NA NA 0.14 0.14 17.36 0.0001 91 16.09 0.15 -0.16 NA NA 2.00 NA 22.11 NA NA NA 0.15 0.22 17.48 0.0001 92 16.03 0.15 NA -0.02 NA 0.31 NA 18.33 17.09 NA NA 0.12 0.13 17.49 0.0001 93 22.53 NA -0.20 -0.03 NA 1.75 -0.03 25.47 24.93 -5.26 NA 0.21 0.21 17.50 0.0001 94 16.91 0.13 NA NA NA 1.85 -0.02 20.31 8.87 NA NA 0.14 0.19 17.54 0.0001 95 19.38 NA NA NA 0.06 0.04 -0.02 22.29 18.67 NA NA 0.11 0.15 17.62 0.0001 96 22.84 NA NA NA 0.01 NA NA NA 14.00 NA NA 0.02 0.03 17.68 0.0001 97 15.16 0.18 -0.15 -0.02 NA 9.06 NA 22.41 14.07 -4.41 -0.36 0.19 0.23 17.82 0.0001 98 16.03 0.15 -0.17 NA NA 9.74 -0.02 25.52 6.16 -5.65 -0.29 0.21 0.30 17.90 0.0001 99 17.04 0.11 -0.16 NA -0.08 NA NA 24.34 9.86 NA NA 0.15 0.23 18.14 0.0001 100 18.85 NA NA -0.02 0.05 -1.98 NA 20.66 26.68 NA NA 0.08 0.08 18.19 0.0001 101 15.20 0.19 -0.16 NA -0.07 9.47 NA 21.81 NA -0.49 -0.38 0.18 0.26 18.29 0.0001 102 24.19 NA NA NA NA -0.34 NA NA NA NA NA 0.00 0.03 18.34 0.0001 103 19.40 NA NA NA 0.06 NA -0.02 22.38 18.88 NA NA 0.11 0.14 18.40 0.0001 104 16.15 0.14 NA -0.02 NA NA NA 18.70 18.28 NA NA 0.13 0.13 18.43 0.0001 105 22.06 NA -0.19 NA -0.11 0.21 NA 22.01 NA NA NA 0.10 0.14 18.43 0.0001 106 13.29 0.20 NA -0.02 0.06 10.35 NA 20.35 14.36 -7.32 -0.43 0.14 0.20 18.55 0.0000 107 14.30 0.22 NA -0.01 NA 10.49 NA 17.29 NA -2.94 -0.44 0.14 0.22 18.70 0.0000 108 20.61 NA NA 0.00 0.01 -0.12 NA 18.44 NA -3.66 NA 0.03 0.08 18.81 0.0000 109 15.24 0.19 NA NA NA 10.45 -0.02 19.68 NA -3.75 -0.38 0.16 0.25 18.83 0.0000 110 15.47 0.16 NA NA 0.01 1.29 NA 18.12 NA NA NA 0.10 0.15 18.84 0.0000 111 17.25 0.10 NA NA NA NA -0.02 22.19 13.21 NA NA 0.13 0.18 18.93 0.0000 112 16.97 0.13 -0.15 NA NA NA NA 22.69 NA NA NA 0.15 0.24 18.97 0.0000 113 17.94 0.13 -0.18 NA NA 4.58 -0.02 24.81 NA -5.70 NA 0.20 0.27 18.99 0.0000

118

114 13.83 0.17 NA NA 0.08 11.04 -0.02 23.09 8.38 -8.91 -0.37 0.16 0.25 19.05 0.0000 115 22.30 NA NA NA NA 0.69 -0.02 18.15 NA NA NA 0.09 0.14 19.20 0.0000 116 22.10 NA -0.19 NA -0.11 NA NA 22.22 NA NA NA 0.10 0.13 19.23 0.0000 117 23.18 NA -0.20 NA -0.07 2.33 -0.02 23.57 NA -1.44 NA 0.16 0.23 19.24 0.0000 118 14.31 0.20 NA -0.02 NA 8.79 NA 17.96 13.86 NA -0.43 0.15 0.20 19.28 0.0000 119 18.13 0.12 -0.19 NA -0.05 3.85 -0.02 25.33 6.19 -4.03 NA 0.19 0.27 19.35 0.0000 120 14.43 0.19 -0.14 NA NA 9.51 NA 21.80 NA NA -0.38 0.18 0.26 19.41 0.0000 121 14.84 0.18 -0.15 NA -0.07 9.01 NA 22.76 6.08 NA -0.38 0.18 0.26 19.44 0.0000 122 20.07 0.15 -0.14 NA NA 2.15 NA NA 0.07 NA NA 0.11 0.22 19.46 0.0000 123 19.07 NA NA -0.01 0.04 NA NA 18.42 22.56 NA NA 0.07 0.07 19.49 0.0000 124 24.18 NA NA NA NA NA NA NA NA NA NA 0.00 0.02 19.51 0.0000 125 17.57 0.14 -0.18 -0.03 -0.06 1.18 NA 21.71 15.74 0.21 NA 0.18 0.18 19.68 0.0000 126 15.02 0.17 NA NA NA 9.19 -0.01 20.06 7.74 NA -0.37 0.17 0.25 19.80 0.0000 127 19.87 NA NA -0.02 0.12 1.65 -0.02 23.65 27.32 -11.02 NA 0.13 0.14 19.82 0.0000 128 18.08 0.13 NA -0.03 NA 2.22 -0.02 19.23 16.83 -4.20 NA 0.16 0.18 19.87 0.0000 129 21.24 NA -0.19 NA -0.06 1.46 -0.02 25.89 14.66 NA NA 0.18 0.24 19.88 0.0000 130 24.53 NA NA NA NA 0.72 -0.02 NA 12.61 NA NA 0.07 0.12 19.95 0.0000 131 18.95 0.15 NA NA 0.00 1.46 NA NA 3.21 NA NA 0.07 0.14 19.96 0.0000 132 17.03 0.17 -0.17 -0.02 NA 2.78 NA 20.71 NA -4.09 NA 0.16 0.22 20.00 0.0000 133 24.90 NA -0.16 NA -0.10 0.21 NA NA 10.77 NA NA 0.07 0.12 20.01 0.0000 134 17.86 0.12 -0.18 NA NA 3.04 -0.02 24.37 6.44 NA NA 0.20 0.28 20.03 0.0000 135 16.14 0.15 NA NA 0.01 NA NA 18.89 NA NA NA 0.10 0.16 20.05 0.0000 136 23.72 NA NA -0.02 NA -1.37 NA NA 22.17 NA NA 0.04 0.04 20.18 0.0000 137 13.48 0.21 NA NA 0.02 10.12 NA 18.31 NA NA -0.45 0.14 0.22 20.24 0.0000 138 22.32 NA NA NA NA NA -0.02 18.65 NA NA NA 0.09 0.13 20.31 0.0000 139 16.94 0.14 -0.17 -0.03 NA 1.15 NA 21.90 17.10 NA NA 0.18 0.18 20.41 0.0000 140 20.94 NA NA 0.00 NA -0.87 NA 17.32 NA NA NA 0.03 0.08 20.70 0.0000 141 19.06 0.16 NA NA NA 1.69 NA NA NA NA NA 0.07 0.13 20.71 0.0000 142 22.60 NA -0.19 NA NA 2.05 -0.02 23.27 NA NA NA 0.17 0.23 20.73 0.0000 119

143 20.87 NA -0.18 NA -0.05 NA -0.02 27.01 16.80 NA NA 0.18 0.22 20.81 0.0000 144 16.33 0.15 NA NA 0.07 4.16 -0.02 21.25 NA -8.35 NA 0.13 0.20 20.89 0.0000 145 24.89 NA -0.15 NA -0.09 NA NA NA 11.12 NA NA 0.07 0.11 20.90 0.0000 146 20.70 NA -0.18 -0.02 -0.06 -0.93 NA 23.96 24.22 NA NA 0.14 0.14 20.92 0.0000 147 20.92 0.14 -0.13 NA NA NA NA NA 2.31 NA NA 0.11 0.29 21.07 0.0000 148 24.56 NA NA NA NA NA -0.02 NA 13.56 NA NA 0.07 0.10 21.09 0.0000 149 21.52 NA NA -0.02 NA -0.68 -0.02 19.93 25.75 NA NA 0.13 0.14 21.18 0.0000 150 17.26 0.12 -0.16 -0.03 NA NA NA 22.97 19.93 NA NA 0.18 0.18 21.30 0.0000 151 22.91 NA -0.19 -0.01 -0.12 -0.44 NA 20.19 NA 2.64 NA 0.10 0.14 21.31 0.0000 152 19.73 0.13 NA NA 0.00 NA NA NA 5.67 NA NA 0.07 0.15 21.34 0.0000 153 18.36 0.19 -0.12 NA NA 9.92 NA NA 0.11 NA -0.39 0.14 0.24 21.37 0.0000 154 24.99 NA -0.15 NA NA 0.58 NA NA NA NA NA 0.05 0.09 21.38 0.0000 155 15.72 0.18 NA -0.01 0.04 2.29 NA 17.70 NA -5.57 NA 0.10 0.17 21.39 0.0000 156 17.11 0.20 NA NA 0.01 10.27 NA NA 2.26 NA -0.44 0.11 0.20 21.43 0.0000 157 23.51 NA NA -0.02 NA NA NA NA 19.55 NA NA 0.04 0.04 21.44 0.0000 158 20.89 NA -0.19 -0.02 -0.07 NA NA 23.13 22.29 NA NA 0.14 0.14 21.55 0.0000 159 17.04 0.15 -0.17 NA -0.09 1.98 NA 21.98 NA NA NA 0.15 0.22 21.60 0.0000 160 20.91 NA NA 0.00 NA NA NA 16.59 NA NA NA 0.03 0.06 21.79 0.0000 161 17.25 0.20 NA NA NA 10.38 NA NA NA NA -0.44 0.11 0.19 21.83 0.0000 162 22.70 NA NA -0.01 NA 1.39 -0.02 18.27 NA -3.74 NA 0.08 0.14 21.85 0.0000 163 18.30 0.08 -0.16 NA NA NA -0.02 26.50 12.92 NA NA 0.19 0.25 21.91 0.0000 164 15.61 0.15 NA -0.02 0.04 0.30 NA 18.48 17.57 NA NA 0.12 0.13 21.95 0.0000 165 21.75 NA NA -0.02 NA NA -0.02 19.17 24.70 NA NA 0.13 0.13 21.97 0.0000 166 16.37 0.13 NA NA 0.06 1.90 -0.02 20.62 9.18 NA NA 0.14 0.20 21.98 0.0000 167 16.42 0.16 -0.17 NA NA 9.92 -0.02 24.22 NA -4.79 -0.29 0.21 0.30 22.03 0.0000 168 24.30 NA NA NA -0.01 -0.36 NA NA NA NA NA 0.00 0.03 22.19 0.0000 169 22.68 NA -0.20 -0.03 -0.01 1.62 -0.02 25.24 24.68 -4.55 NA 0.20 0.21 22.22 0.0000 170 19.54 0.12 -0.20 -0.03 NA 3.50 -0.02 23.47 17.02 -4.97 NA 0.23 0.25 22.23 0.0000 171 22.52 NA -0.18 NA NA NA -0.02 24.13 NA NA NA 0.16 0.21 22.26 0.0000

120

172 20.00 0.14 NA NA NA NA NA NA NA NA NA 0.07 0.16 22.33 0.0000 173 17.40 0.14 NA NA NA 2.38 -0.02 18.56 NA NA NA 0.14 0.20 22.36 0.0000 174 15.38 0.21 -0.15 -0.02 NA 9.93 NA 20.56 NA -3.63 -0.37 0.18 0.25 22.43 0.0000 175 25.11 NA -0.14 NA NA NA NA NA NA NA NA 0.05 0.09 22.52 0.0000 176 15.79 0.18 -0.16 -0.02 -0.05 8.52 NA 21.58 13.33 -1.85 -0.36 0.19 0.23 22.71 0.0000 177 15.73 0.14 NA -0.02 0.04 NA NA 18.86 18.90 NA NA 0.13 0.13 22.74 0.0000 178 16.17 0.17 NA -0.02 NA 9.08 -0.02 19.25 14.29 -4.53 -0.35 0.17 0.23 22.78 0.0000 179 22.86 NA -0.20 -0.03 NA 0.63 -0.02 23.88 25.16 NA NA 0.21 0.21 22.83 0.0000 180 16.54 0.15 -0.17 NA -0.04 9.28 -0.02 24.80 5.86 -3.61 -0.29 0.21 0.30 22.84 0.0000 181 16.38 0.18 NA -0.01 NA 1.20 NA 16.00 NA NA NA 0.10 0.16 22.85 0.0000 182 25.87 NA -0.16 NA NA 2.04 -0.02 NA 9.18 NA NA 0.12 0.19 22.86 0.0000 183 17.93 0.13 -0.17 NA -0.09 NA NA 22.38 NA NA NA 0.15 0.25 22.91 0.0000 184 15.44 0.18 -0.15 -0.02 NA 8.11 NA 21.14 14.34 NA -0.36 0.20 0.23 22.97 0.0000 185 16.31 0.15 -0.16 NA NA 8.62 -0.02 23.93 5.97 NA -0.29 0.21 0.31 23.07 0.0000 186 21.70 NA -0.18 -0.01 NA 0.07 NA 20.96 NA NA NA 0.10 0.14 23.10 0.0000 187 19.65 0.16 NA -0.03 NA 0.97 NA NA 11.79 NA NA 0.09 0.12 23.21 0.0000 188 16.78 0.10 NA NA 0.05 NA -0.02 22.51 13.65 NA NA 0.13 0.18 23.23 0.0000 189 13.73 0.22 NA -0.01 0.04 11.02 NA 18.14 NA -5.28 -0.44 0.14 0.22 23.24 0.0000 190 24.29 NA NA NA -0.01 NA NA NA NA NA NA 0.00 0.02 23.26 0.0000 191 21.88 NA NA NA 0.05 0.75 -0.02 18.36 NA NA NA 0.09 0.14 23.32 0.0000 192 14.40 0.19 NA NA 0.07 11.27 -0.02 21.09 NA -7.36 -0.38 0.16 0.25 23.33 0.0000 193 25.07 NA -0.16 -0.03 NA -0.40 NA NA 20.85 NA NA 0.09 0.09 23.33 0.0000 194 22.70 NA -0.20 -0.03 NA NA -0.02 24.38 26.37 NA NA 0.22 0.22 23.39 0.0000 195 21.17 0.15 -0.16 NA -0.11 2.02 NA NA -0.77 NA NA 0.11 0.25 23.45 0.0000 196 18.63 0.13 -0.19 NA -0.06 3.97 -0.02 23.88 NA -2.81 NA 0.20 0.27 23.49 0.0000 197 23.87 NA -0.20 -0.01 NA 2.86 -0.03 22.64 NA -4.60 NA 0.16 0.22 23.55 0.0000 198 20.82 0.14 NA NA NA 2.61 -0.02 NA 2.71 NA NA 0.10 0.18 23.64 0.0000 199 15.24 0.19 -0.16 NA -0.08 9.37 NA 21.67 NA NA -0.38 0.18 0.26 23.78 0.0000 200 21.84 NA -0.18 -0.01 NA NA NA 20.91 NA NA NA 0.10 0.13 23.91 0.0000 121

201 13.86 0.20 NA -0.02 0.05 8.83 NA 18.15 14.19 NA -0.43 0.15 0.20 23.93 0.0000 202 16.98 0.16 NA -0.01 NA NA NA 16.63 NA NA NA 0.10 0.18 24.02 0.0000 203 24.21 NA NA NA 0.04 0.76 -0.02 NA 12.81 NA NA 0.07 0.12 24.05 0.0000 204 18.22 0.12 NA NA NA NA -0.02 19.88 NA NA NA 0.12 0.19 24.13 0.0000 205 25.05 NA -0.16 -0.03 NA NA NA NA 20.09 NA NA 0.10 0.10 24.17 0.0000 206 20.11 0.15 -0.14 NA NA 2.29 NA NA NA NA NA 0.11 0.19 24.19 0.0000 207 23.43 NA NA -0.02 0.03 -1.37 NA NA 22.62 NA NA 0.04 0.04 24.27 0.0000 208 14.47 0.22 NA -0.01 NA 9.89 NA 16.44 NA NA -0.44 0.14 0.22 24.29 0.0000 209 21.97 NA NA NA 0.04 NA -0.02 18.83 NA NA NA 0.09 0.13 24.32 0.0000 210 18.14 0.17 -0.18 -0.02 -0.09 1.85 NA 19.35 NA 0.61 NA 0.16 0.22 24.37 0.0000 211 16.81 0.13 NA -0.03 0.11 3.51 -0.02 21.48 18.32 -10.33 NA 0.16 0.18 24.38 0.0000 212 15.43 0.19 NA NA NA 9.69 -0.01 18.64 NA NA -0.38 0.17 0.25 24.40 0.0000 213 20.28 0.14 NA -0.03 NA NA NA NA 15.62 NA NA 0.10 0.10 24.42 0.0000 214 25.68 NA -0.14 NA NA NA -0.02 NA 11.75 NA NA 0.11 0.18 24.42 0.0000 215 14.46 0.17 NA NA 0.06 9.28 -0.02 20.42 7.90 NA -0.37 0.16 0.25 24.44 0.0000 216 18.23 0.13 -0.18 NA NA 3.43 -0.02 23.21 NA NA NA 0.20 0.28 24.52 0.0000 217 18.44 0.12 -0.19 NA -0.06 3.04 -0.02 24.15 5.98 NA NA 0.20 0.28 24.64 0.0000 218 19.06 0.16 NA NA 0.00 1.68 NA NA NA NA NA 0.07 0.13 24.74 0.0000 219 17.98 0.21 NA -0.02 NA 9.35 NA NA 10.08 NA -0.42 0.12 0.18 24.79 0.0000 220 20.89 NA NA 0.00 0.00 -0.87 NA 17.35 NA NA NA 0.03 0.08 24.81 0.0000 221 22.09 0.14 -0.15 NA -0.12 NA NA NA 1.45 NA NA 0.12 0.34 24.83 0.0000 222 18.38 0.16 NA -0.02 NA 3.14 -0.02 17.02 NA -3.78 NA 0.14 0.20 24.91 0.0000 223 23.28 NA -0.20 NA -0.07 2.04 -0.02 23.16 NA NA NA 0.16 0.23 24.92 0.0000 224 17.56 0.14 -0.18 -0.03 -0.06 1.22 NA 21.80 15.91 NA NA 0.18 0.18 24.99 0.0000 225 26.03 NA -0.16 NA -0.11 0.59 NA NA NA NA NA 0.06 0.11 25.03 0.0000 226 24.26 NA NA NA 0.04 NA -0.02 NA 13.83 NA NA 0.07 0.10 25.09 0.0000 227 25.68 NA NA NA NA 1.12 -0.02 NA NA NA NA 0.06 0.11 25.26 0.0000 228 18.33 0.13 NA -0.03 NA 1.33 -0.02 18.00 17.10 NA NA 0.16 0.18 25.27 0.0000 229 23.23 NA NA -0.02 0.03 NA NA NA 19.99 NA NA 0.04 0.04 25.40 0.0000

122

230 20.74 NA NA -0.02 0.09 -0.65 -0.02 20.25 27.07 NA NA 0.13 0.14 25.49 0.0000 231 17.26 0.17 -0.17 -0.02 NA 1.99 NA 19.55 NA NA NA 0.16 0.22 25.57 0.0000 232 21.75 0.10 NA NA NA NA -0.01 NA 7.57 NA NA 0.09 0.17 25.59 0.0000 233 19.25 0.19 -0.13 NA -0.09 9.74 NA NA -0.40 NA -0.38 0.14 0.24 25.64 0.0000 234 18.91 0.18 NA NA NA 9.91 -0.01 NA 1.94 NA -0.37 0.13 0.23 25.71 0.0000 235 17.84 0.12 -0.17 -0.03 -0.06 NA NA 22.96 19.21 NA NA 0.18 0.18 25.74 0.0000 236 20.86 NA NA 0.00 0.00 NA NA 16.64 NA NA NA 0.03 0.06 25.78 0.0000 237 18.41 0.19 -0.12 NA NA 9.95 NA NA NA NA -0.39 0.14 0.23 25.93 0.0000 238 21.10 0.14 -0.13 NA NA NA NA NA NA NA NA 0.11 0.27 25.93 0.0000 239 17.20 0.20 NA NA 0.00 10.39 NA NA NA NA -0.44 0.11 0.19 26.03 0.0000 240 18.08 0.15 -0.18 -0.03 NA 8.15 -0.02 23.17 15.04 -5.29 -0.24 0.23 0.27 26.04 0.0000 241 26.14 NA -0.16 NA -0.11 NA NA NA NA NA NA 0.06 0.10 26.06 0.0000 242 21.93 NA NA -0.01 0.07 2.20 -0.02 19.57 NA -7.36 NA 0.08 0.15 26.08 0.0000 243 20.95 NA NA -0.02 0.09 NA -0.02 19.51 26.16 NA NA 0.13 0.13 26.11 0.0000 244 18.68 0.11 NA -0.03 NA NA -0.02 19.33 21.37 NA NA 0.17 0.17 26.23 0.0000 245 20.05 0.14 NA NA -0.01 NA NA NA NA NA NA 0.07 0.16 26.24 0.0000 246 23.22 NA -0.19 NA -0.08 NA -0.02 24.02 NA NA NA 0.16 0.21 26.29 0.0000 247 18.87 0.08 -0.17 NA -0.06 NA -0.02 26.18 12.30 NA NA 0.18 0.26 26.33 0.0000 248 24.65 NA NA -0.01 NA -0.53 NA NA NA NA NA 0.00 0.04 26.57 0.0000 249 21.08 0.16 -0.15 -0.03 NA 1.81 NA NA 10.78 NA NA 0.14 0.17 26.60 0.0000 250 25.81 NA NA NA NA NA -0.02 NA NA NA NA 0.05 0.09 26.62 0.0000 251 16.92 0.14 NA NA 0.05 2.44 -0.02 18.80 NA NA NA 0.13 0.20 26.66 0.0000 252 17.00 0.16 -0.17 NA -0.05 9.37 -0.02 23.46 NA -2.44 -0.28 0.21 0.30 26.75 0.0000 253 18.10 0.15 -0.16 -0.02 NA NA NA 19.97 NA NA NA 0.16 0.26 26.85 0.0000 254 22.32 0.13 -0.16 NA NA 3.70 -0.02 NA -0.34 NA NA 0.15 0.25 26.85 0.0000 255 19.19 0.10 -0.16 NA NA NA -0.02 24.14 NA NA NA 0.18 0.27 26.91 0.0000 256 26.56 NA -0.17 NA -0.07 2.03 -0.02 NA 8.72 NA NA 0.12 0.20 26.99 0.0000 257 19.78 0.15 -0.19 -0.02 NA 4.43 -0.02 21.27 NA -4.57 NA 0.21 0.26 27.04 0.0000 258 16.26 0.20 -0.16 -0.02 -0.07 9.11 NA 19.52 NA -0.10 -0.36 0.18 0.25 27.07 0.0000 123

259 22.71 NA -0.19 -0.01 -0.11 0.10 NA 20.97 NA NA NA 0.10 0.14 27.08 0.0000 260 16.14 0.18 NA -0.01 0.02 1.17 NA 16.07 NA NA NA 0.10 0.17 27.17 0.0000 261 25.87 NA NA -0.03 NA -0.09 -0.02 NA 22.12 NA NA 0.10 0.10 27.19 0.0000 262 19.74 0.12 -0.20 -0.03 -0.02 3.32 -0.02 23.17 16.70 -4.05 NA 0.23 0.24 27.21 0.0000 263 19.85 0.12 -0.20 -0.03 NA 2.45 -0.02 22.00 17.29 NA NA 0.24 0.25 27.34 0.0000 264 16.65 0.16 -0.17 NA NA 8.97 -0.02 22.89 NA NA -0.29 0.22 0.31 27.36 0.0000 265 16.35 0.20 NA -0.01 NA 10.02 -0.02 17.35 NA -3.22 -0.36 0.16 0.25 27.45 0.0000 266 25.74 NA -0.17 -0.03 -0.07 -0.34 NA NA 19.85 NA NA 0.09 0.09 27.46 0.0000 267 19.40 0.16 NA -0.03 0.03 0.96 NA NA 11.94 NA NA 0.09 0.12 27.50 0.0000 268 23.06 NA -0.21 -0.03 -0.02 0.67 -0.02 23.86 24.59 NA NA 0.21 0.21 27.52 0.0000 269 15.00 0.17 NA -0.02 0.10 10.22 -0.02 21.35 15.53 -10.18 -0.34 0.17 0.23 27.54 0.0000 270 22.92 NA NA -0.01 NA 0.62 -0.02 17.17 NA NA NA 0.09 0.14 27.67 0.0000 271 24.63 NA NA -0.01 NA NA NA NA NA NA NA 0.00 0.02 27.70 0.0000 272 26.76 NA -0.17 NA NA 2.37 -0.02 NA NA NA NA 0.12 0.19 27.73 0.0000 273 22.87 NA -0.19 -0.01 -0.11 NA NA 20.96 NA NA NA 0.10 0.13 27.75 0.0000 274 15.58 0.21 -0.15 -0.02 NA 9.20 NA 19.51 NA NA -0.37 0.18 0.25 27.78 0.0000 275 15.95 0.18 -0.16 -0.02 -0.05 8.12 NA 21.05 13.38 NA -0.35 0.19 0.23 27.79 0.0000 276 16.81 0.15 -0.17 NA -0.05 8.57 -0.02 23.75 5.66 NA -0.29 0.21 0.30 27.90 0.0000 277 22.90 NA -0.20 -0.03 -0.02 NA -0.02 24.37 26.07 NA NA 0.21 0.21 27.91 0.0000 278 21.84 0.14 -0.14 -0.03 NA NA NA NA 12.68 NA NA 0.14 0.22 27.92 0.0000 279 20.54 0.14 NA NA 0.03 2.64 -0.02 NA 2.81 NA NA 0.10 0.18 27.94 0.0000 280 16.44 0.17 NA -0.02 NA 8.11 -0.02 17.93 14.41 NA -0.34 0.17 0.23 27.97 0.0000 281 24.63 NA -0.21 -0.01 -0.07 2.12 -0.03 21.58 NA -1.03 NA 0.16 0.22 27.98 0.0000 282 25.93 NA NA -0.03 NA NA -0.02 NA 22.25 NA NA 0.10 0.10 28.04 0.0000 283 21.12 0.15 -0.15 NA -0.10 2.18 NA NA NA NA NA 0.12 0.22 28.07 0.0000 284 25.74 NA -0.17 -0.03 -0.07 NA NA NA 19.17 NA NA 0.10 0.10 28.17 0.0000 285 16.74 0.16 NA -0.01 0.02 NA NA 16.64 NA NA NA 0.10 0.18 28.20 0.0000 286 17.88 0.12 NA NA 0.04 NA -0.02 20.04 NA NA NA 0.12 0.19 28.31 0.0000 287 20.91 0.14 NA NA NA 2.78 -0.02 NA NA NA NA 0.11 0.17 28.37 0.0000

124

288 19.65 0.18 NA -0.02 NA 1.51 NA NA NA NA NA 0.08 0.15 28.41 0.0000 289 26.38 NA -0.15 NA -0.07 NA -0.02 NA 11.21 NA NA 0.11 0.19 28.41 0.0000 290 20.01 0.14 NA -0.03 0.03 NA NA NA 15.91 NA NA 0.09 0.10 28.57 0.0000 291 20.24 0.09 -0.18 -0.04 NA NA -0.02 24.09 23.60 NA NA 0.23 0.23 28.59 0.0000 292 23.10 NA NA -0.01 NA NA -0.02 17.34 NA NA NA 0.09 0.13 28.65 0.0000 293 14.11 0.22 NA -0.01 0.03 9.95 NA 16.60 NA NA -0.44 0.14 0.22 28.79 0.0000 294 19.52 0.20 -0.13 -0.03 NA 8.89 NA NA 9.72 NA -0.37 0.16 0.21 28.86 0.0000 295 14.90 0.19 NA NA 0.05 9.80 -0.02 18.96 NA NA -0.38 0.16 0.26 28.88 0.0000 296 18.83 0.13 -0.19 NA -0.06 3.40 -0.02 23.08 NA NA NA 0.20 0.28 28.95 0.0000 297 24.13 NA -0.20 -0.01 NA 1.92 -0.03 21.29 NA NA NA 0.17 0.22 29.11 0.0000 298 25.45 NA NA NA 0.03 1.15 -0.02 NA NA NA NA 0.05 0.11 29.25 0.0000 299 17.70 0.21 NA -0.02 0.03 9.38 NA NA 10.25 NA -0.43 0.12 0.18 29.27 0.0000 300 17.40 0.16 NA -0.02 0.08 4.14 -0.02 18.62 NA -8.33 NA 0.14 0.21 29.33 0.0000 301 23.16 0.10 -0.13 NA NA NA -0.02 NA 5.39 NA NA 0.13 0.28 29.34 0.0000 302 26.05 NA -0.16 -0.01 NA 0.39 NA NA NA NA NA 0.06 0.09 29.44 0.0000 303 27.87 NA -0.18 -0.04 NA 1.20 -0.03 NA 20.98 NA NA 0.17 0.17 29.51 0.0000 304 22.19 0.14 -0.15 NA -0.12 NA NA NA NA NA NA 0.12 0.31 29.59 0.0000 305 20.65 0.17 -0.14 NA NA 9.47 -0.02 NA -0.49 NA -0.30 0.17 0.27 29.61 0.0000 306 26.81 NA -0.15 NA NA NA -0.02 NA NA NA NA 0.10 0.17 29.63 0.0000 307 17.93 0.22 NA -0.02 NA 10.05 NA NA NA NA -0.43 0.12 0.19 29.75 0.0000 308 21.59 0.10 NA NA 0.02 NA -0.01 NA 7.67 NA NA 0.09 0.17 29.75 0.0000 309 17.60 0.13 NA -0.03 0.08 1.37 -0.02 18.32 18.13 NA NA 0.16 0.18 29.80 0.0000 310 20.49 0.17 NA -0.02 NA NA NA NA NA NA NA 0.09 0.19 29.81 0.0000 311 18.10 0.17 -0.18 -0.02 -0.09 1.98 NA 19.53 NA NA NA 0.16 0.22 29.88 0.0000 312 19.26 0.19 -0.13 NA -0.08 9.77 NA NA NA NA -0.38 0.14 0.23 30.06 0.0000 313 18.64 0.18 NA NA 0.03 9.96 -0.01 NA 1.97 NA -0.37 0.13 0.23 30.19 0.0000 314 18.99 0.18 NA NA NA 9.98 -0.01 NA NA NA -0.37 0.14 0.22 30.28 0.0000 315 24.23 NA -0.19 -0.02 NA NA -0.02 21.88 NA NA NA 0.16 0.20 30.39 0.0000 316 26.25 NA -0.16 -0.02 NA NA NA NA NA NA NA 0.06 0.08 30.41 0.0000 125

317 27.71 NA -0.17 -0.04 NA NA -0.02 NA 22.99 NA NA 0.17 0.17 30.43 0.0000 318 18.21 0.18 -0.18 -0.02 NA 9.20 -0.02 21.17 NA -4.16 -0.26 0.22 0.29 30.49 0.0000 319 18.59 0.16 NA -0.02 NA 2.37 -0.02 15.91 NA NA NA 0.14 0.20 30.50 0.0000 320 25.67 NA NA NA 0.02 NA -0.02 NA NA NA NA 0.05 0.09 30.52 0.0000 321 24.70 NA NA -0.01 -0.01 -0.54 NA NA NA NA NA 0.00 0.04 30.53 0.0000 322 22.06 0.11 NA NA NA NA -0.01 NA NA NA NA 0.09 0.18 30.55 0.0000 323 17.98 0.11 NA -0.03 0.08 NA -0.02 19.64 22.68 NA NA 0.17 0.17 30.60 0.0000 324 21.97 0.15 NA -0.03 NA 2.00 -0.02 NA 12.97 NA NA 0.14 0.16 30.78 0.0000 325 21.78 0.16 -0.16 -0.03 -0.07 1.74 NA NA 8.81 NA NA 0.14 0.19 30.91 0.0000 326 18.40 0.15 -0.18 -0.03 NA 7.01 -0.02 21.62 15.19 NA -0.24 0.24 0.27 30.93 0.0000 327 18.91 0.15 -0.17 -0.02 -0.08 NA NA 19.79 NA NA NA 0.16 0.27 30.98 0.0000 328 19.86 0.10 -0.17 NA -0.07 NA -0.02 23.89 NA NA NA 0.18 0.28 31.12 0.0000 329 23.03 0.13 -0.17 NA -0.08 3.65 -0.02 NA -0.78 NA NA 0.15 0.26 31.17 0.0000 330 18.23 0.15 -0.18 -0.03 -0.01 8.02 -0.02 22.94 14.79 -4.60 -0.24 0.22 0.27 31.25 0.0000 331 25.27 NA NA -0.03 0.08 -0.07 -0.02 NA 23.26 NA NA 0.10 0.10 31.35 0.0000 332 22.35 0.13 -0.15 NA NA 3.72 -0.02 NA NA NA NA 0.16 0.24 31.39 0.0000 333 21.09 0.18 -0.15 -0.03 NA 2.27 NA NA NA NA NA 0.13 0.19 31.55 0.0000 334 24.67 NA NA -0.01 -0.01 NA NA NA NA NA NA 0.00 0.02 31.55 0.0000 335 27.48 NA -0.18 NA -0.08 2.35 -0.02 NA NA NA NA 0.12 0.19 31.69 0.0000 336 20.33 0.15 -0.20 -0.02 -0.05 3.91 -0.02 20.54 NA -2.12 NA 0.21 0.26 31.77 0.0000 337 22.45 NA NA -0.01 0.05 0.68 -0.02 17.40 NA NA NA 0.09 0.15 31.90 0.0000 338 17.17 0.16 -0.17 NA -0.05 8.88 -0.02 22.77 NA NA -0.28 0.21 0.30 32.00 0.0000 339 22.56 0.15 -0.16 -0.03 -0.08 NA NA NA 10.28 NA NA 0.14 0.27 32.04 0.0000 340 25.31 NA NA -0.03 0.08 NA -0.02 NA 23.41 NA NA 0.10 0.10 32.06 0.0000 341 15.38 0.20 NA -0.02 0.08 11.01 -0.02 18.94 NA -7.55 -0.36 0.16 0.25 32.08 0.0000 342 19.38 0.14 NA -0.02 NA NA -0.02 17.22 NA NA NA 0.13 0.20 32.10 0.0000 343 22.91 0.11 NA -0.03 NA NA -0.02 NA 18.81 NA NA 0.14 0.14 32.19 0.0000 344 20.08 0.12 -0.20 -0.03 -0.03 2.48 -0.02 21.95 16.66 NA NA 0.23 0.25 32.25 0.0000 345 16.28 0.20 -0.16 -0.02 -0.07 9.09 NA 19.48 NA NA -0.36 0.18 0.25 32.35 0.0000

126

346 20.04 0.15 -0.19 -0.02 NA 3.50 -0.02 19.93 NA NA NA 0.22 0.27 32.36 0.0000 347 20.64 0.14 NA NA 0.03 2.81 -0.02 NA NA NA NA 0.10 0.17 32.54 0.0000 348 19.51 0.18 NA -0.02 0.02 1.47 NA NA NA NA NA 0.08 0.15 32.57 0.0000 349 15.74 0.17 NA -0.02 0.08 8.16 -0.02 18.28 15.09 NA -0.34 0.17 0.23 32.73 0.0000 350 22.70 NA NA -0.01 0.04 NA -0.02 17.56 NA NA NA 0.09 0.13 32.77 0.0000 351 16.53 0.20 NA -0.01 NA 9.36 -0.02 16.42 NA NA -0.36 0.17 0.25 32.84 0.0000 352 22.07 0.17 -0.14 -0.03 NA NA NA NA NA NA NA 0.14 0.29 33.04 0.0000 353 27.03 NA -0.18 -0.01 -0.11 0.41 NA NA NA NA NA 0.06 0.10 33.25 0.0000 354 20.14 0.19 NA -0.03 NA 8.61 -0.02 NA 10.90 NA -0.34 0.15 0.20 33.29 0.0000 355 20.42 0.09 -0.18 -0.04 -0.02 NA -0.02 24.08 23.34 NA NA 0.23 0.23 33.32 0.0000 356 26.66 NA NA -0.01 NA 0.91 -0.02 NA NA NA NA 0.06 0.11 33.38 0.0000 357 23.97 0.10 -0.15 NA -0.10 NA -0.01 NA 4.60 NA NA 0.14 0.32 33.40 0.0000 358 20.08 0.20 -0.14 -0.03 -0.06 8.86 NA NA 8.65 NA -0.36 0.16 0.21 33.41 0.0000 359 27.57 NA -0.16 NA -0.09 NA -0.02 NA NA NA NA 0.11 0.18 33.43 0.0000 360 24.06 0.14 -0.18 -0.04 NA 3.15 -0.02 NA 12.42 NA NA 0.20 0.21 33.44 0.0000 361 24.71 NA -0.21 -0.01 -0.07 1.91 -0.03 21.27 NA NA NA 0.17 0.22 33.48 0.0000 362 19.48 0.21 -0.14 -0.02 NA 9.52 NA NA NA NA -0.37 0.16 0.22 33.59 0.0000 363 20.34 0.17 NA -0.02 0.01 NA NA NA NA NA NA 0.09 0.19 33.83 0.0000 364 28.10 NA -0.19 -0.04 -0.03 1.24 -0.02 NA 20.35 NA NA 0.16 0.17 33.96 0.0000 365 20.69 0.16 -0.14 NA NA 9.42 -0.02 NA NA NA -0.30 0.17 0.27 33.99 0.0000 366 17.73 0.22 NA -0.02 0.02 10.07 NA NA NA NA -0.43 0.12 0.19 34.09 0.0000 367 27.25 NA -0.17 -0.02 -0.11 NA NA NA NA NA NA 0.07 0.08 34.10 0.0000 368 23.39 0.10 -0.13 NA NA NA -0.01 NA NA NA NA 0.14 0.28 34.12 0.0000 369 21.29 0.16 -0.15 NA -0.07 9.36 -0.02 NA -0.79 NA -0.29 0.17 0.28 34.15 0.0000 370 26.94 NA NA -0.02 NA NA -0.02 NA NA NA NA 0.06 0.09 34.54 0.0000 371 21.94 0.11 NA NA 0.01 NA -0.01 NA NA NA NA 0.09 0.18 34.59 0.0000 372 24.86 NA -0.20 -0.02 -0.07 NA -0.02 21.87 NA NA NA 0.16 0.20 34.59 0.0000 373 18.72 0.18 NA NA 0.03 10.03 -0.01 NA NA NA -0.37 0.13 0.22 34.62 0.0000 374 20.85 0.12 -0.17 -0.02 NA NA -0.02 21.10 NA NA NA 0.19 0.28 34.63 0.0000 127

375 27.93 NA -0.17 -0.04 -0.02 NA -0.02 NA 22.65 NA NA 0.16 0.16 34.73 0.0000 376 18.00 0.16 NA -0.02 0.07 2.43 -0.02 16.16 NA NA NA 0.14 0.21 34.92 0.0000 377 21.41 0.15 NA -0.03 0.07 2.03 -0.02 NA 13.80 NA NA 0.13 0.16 35.16 0.0000 378 25.12 0.09 -0.15 -0.04 NA NA -0.02 NA 20.17 NA NA 0.19 0.19 35.40 0.0000 379 28.62 NA -0.19 -0.02 NA 2.16 -0.03 NA NA NA NA 0.13 0.17 35.41 0.0000 380 18.65 0.18 -0.18 -0.02 -0.04 8.77 -0.02 20.61 NA -2.29 -0.25 0.21 0.28 35.45 0.0000 381 23.02 0.13 -0.17 NA -0.07 3.66 -0.02 NA NA NA NA 0.16 0.25 35.57 0.0000 382 18.44 0.18 -0.18 -0.02 NA 8.35 -0.02 19.96 NA NA -0.26 0.22 0.29 35.60 0.0000 383 21.93 0.17 -0.16 -0.02 -0.09 2.17 NA NA NA NA NA 0.13 0.21 35.66 0.0000 384 21.89 0.17 NA -0.02 NA 2.72 -0.02 NA NA NA NA 0.12 0.17 35.88 0.0000 385 18.60 0.15 -0.18 -0.03 -0.02 7.06 -0.02 21.57 14.66 NA -0.24 0.23 0.27 36.06 0.0000 386 18.90 0.14 NA -0.02 0.05 NA -0.02 17.36 NA NA NA 0.13 0.21 36.39 0.0000 387 22.38 0.11 NA -0.04 0.07 NA -0.02 NA 19.87 NA NA 0.13 0.13 36.42 0.0000 388 22.55 0.17 -0.16 -0.04 NA 7.80 -0.02 NA 10.85 NA -0.25 0.20 0.24 36.75 0.0000 389 22.89 0.17 -0.16 -0.03 -0.09 NA NA NA NA NA NA 0.14 0.31 36.95 0.0000 390 28.78 NA -0.17 -0.02 NA NA -0.02 NA NA NA NA 0.12 0.15 37.00 0.0000 391 20.50 0.15 -0.20 -0.02 -0.05 3.48 -0.02 19.93 NA NA NA 0.21 0.26 37.02 0.0000 392 15.90 0.20 NA -0.02 0.07 9.49 -0.02 16.75 NA NA -0.36 0.17 0.25 37.44 0.0000 393 26.35 NA NA -0.01 0.04 0.95 -0.02 NA NA NA NA 0.06 0.11 37.48 0.0000 394 23.00 0.14 NA -0.02 NA NA -0.02 NA NA NA NA 0.11 0.19 37.85 0.0000 395 19.64 0.19 NA -0.03 0.07 8.63 -0.02 NA 11.47 NA -0.34 0.15 0.21 37.88 0.0000 396 20.18 0.21 -0.15 -0.02 -0.07 9.38 NA NA NA NA -0.36 0.15 0.22 37.95 0.0000 397 24.18 0.11 -0.15 NA -0.09 NA -0.01 NA NA NA NA 0.14 0.30 38.03 0.0000 398 24.33 0.14 -0.18 -0.04 -0.03 3.18 -0.02 NA 11.66 NA NA 0.19 0.21 38.10 0.0000 399 20.03 0.20 NA -0.02 NA 9.45 -0.02 NA NA NA -0.35 0.15 0.21 38.12 0.0000 400 23.98 0.16 -0.18 -0.03 NA 3.84 -0.02 NA NA NA NA 0.19 0.23 38.28 0.0000 401 21.30 0.16 -0.15 NA -0.06 9.31 -0.02 NA NA NA -0.29 0.17 0.27 38.37 0.0000 402 26.68 NA NA -0.02 0.03 NA -0.02 NA NA NA NA 0.06 0.09 38.54 0.0000 403 21.33 0.12 -0.18 -0.02 -0.05 NA -0.02 21.01 NA NA NA 0.19 0.28 39.06 0.0000

128

404 29.20 NA -0.20 -0.02 -0.07 2.15 -0.02 NA NA NA NA 0.13 0.17 39.58 0.0000 405 25.32 0.09 -0.16 -0.04 -0.02 NA -0.02 NA 19.88 NA NA 0.18 0.18 39.88 0.0000 406 21.44 0.17 NA -0.02 0.05 2.76 -0.02 NA NA NA NA 0.12 0.18 40.16 0.0000 407 18.82 0.18 -0.18 -0.02 -0.04 8.30 -0.02 19.95 NA NA -0.25 0.22 0.29 40.48 0.0000 408 24.91 0.13 -0.15 -0.03 NA NA -0.02 NA NA NA NA 0.16 0.28 40.94 0.0000 409 29.39 NA -0.18 -0.02 -0.07 NA -0.02 NA NA NA NA 0.12 0.15 41.02 0.0000 410 22.44 0.19 -0.16 -0.03 NA 8.60 -0.02 NA NA NA -0.26 0.19 0.25 41.33 0.0000 411 22.80 0.17 -0.17 -0.03 -0.03 7.82 -0.02 NA 10.23 NA -0.25 0.19 0.24 41.59 0.0000 412 22.65 0.14 NA -0.02 0.04 NA -0.02 NA NA NA NA 0.11 0.20 41.99 0.0000 413 19.59 0.20 NA -0.02 0.05 9.52 -0.02 NA NA NA -0.35 0.14 0.22 42.57 0.0000 414 24.44 0.16 -0.18 -0.03 -0.05 3.81 -0.02 NA NA NA NA 0.19 0.23 42.73 0.0000 415 25.39 0.13 -0.16 -0.03 -0.06 NA -0.02 NA NA NA NA 0.16 0.29 45.13 0.0000 416 22.83 0.18 -0.17 -0.03 -0.05 8.55 -0.02 NA NA NA -0.25 0.19 0.25 45.97 0.0000

Phylogenetic Dispersion

AB NU FC X NU AGE X R² R² Model Intercept AGE EUC SB SLOPE (Sug.) RIVER FC Δ FC (Sug.) NU (Sug.) Marg. Cond. ΔAICc weight 1 -1.58 NA NA NA NA -1.19 NA 2.86 -0.16 2.42 NA 0.10 0.18 0.00 0.3555 2 -1.59 NA NA NA NA -1.19 NA 2.90 NA 2.41 NA 0.10 0.18 1.02 0.2131 3 -1.71 NA NA NA NA -0.70 NA 3.56 -0.06 NA NA 0.09 0.18 2.55 0.0992 129

4 -1.68 NA NA NA NA NA NA 2.85 -1.00 NA NA 0.04 0.14 2.70 0.0920 5 -1.71 NA NA NA NA -0.70 NA 3.58 NA NA NA 0.10 0.17 3.70 0.0560 6 -1.80 NA NA NA NA NA NA 3.01 NA NA NA 0.03 0.13 4.14 0.0450 7 -1.07 NA NA NA NA NA NA NA -1.60 NA NA 0.01 0.11 5.13 0.0273 8 -0.96 NA NA NA NA -0.60 NA NA -0.95 NA NA 0.06 0.16 5.59 0.0217 9 -1.04 NA NA NA -0.04 -1.64 NA 1.97 -0.46 4.61 NA 0.12 0.21 6.50 0.0138 10 -1.21 NA NA NA NA NA NA NA NA NA NA 0.00 0.10 6.88 0.0114 11 -1.04 NA NA NA NA -0.62 NA NA NA NA NA 0.06 0.15 6.99 0.0108 12 -1.11 NA NA NA -0.04 -1.64 NA 2.09 NA 4.53 NA 0.12 0.20 7.39 0.0088 13 -1.15 -0.02 NA NA NA -1.46 NA 3.11 1.30 2.34 NA 0.12 0.20 8.04 0.0064 14 -1.56 NA -0.01 NA NA -1.13 NA 3.06 -0.27 2.39 NA 0.10 0.19 9.15 0.0037 15 -1.07 -0.02 NA NA NA -1.42 NA 2.83 NA 2.50 NA 0.12 0.19 9.25 0.0035 16 -1.39 NA NA NA -0.03 -0.71 NA 3.33 -0.23 NA NA 0.11 0.21 9.47 0.0031 17 -1.39 NA NA NA -0.03 NA NA 2.65 -1.14 NA NA 0.05 0.16 9.54 0.0030 18 -1.59 NA -0.01 NA NA -1.13 NA 3.13 NA 2.36 NA 0.10 0.18 10.04 0.0024 19 -1.28 -0.02 NA NA NA -0.99 NA 3.79 1.39 NA NA 0.12 0.19 10.41 0.0020 20 -1.42 NA NA NA -0.03 -0.72 NA 3.39 NA NA NA 0.11 0.20 10.49 0.0019 21 -1.54 NA NA NA -0.03 NA NA 2.84 NA NA NA 0.05 0.16 10.89 0.0015 22 -1.65 NA -0.01 NA NA NA NA 3.25 -1.07 NA NA 0.05 0.15 11.08 0.0014 23 -1.68 NA -0.01 NA NA -0.65 NA 3.75 -0.18 NA NA 0.10 0.18 11.55 0.0011 24 -0.78 NA NA NA -0.03 NA NA NA -1.71 NA NA 0.03 0.14 11.65 0.0011 25 -1.20 -0.02 NA NA NA -0.91 NA 3.53 NA NA NA 0.12 0.19 11.79 0.0010 26 -1.52 NA NA 0.00 NA -1.22 NA 2.76 0.11 2.43 NA 0.10 0.19 12.01 0.0009 27 -1.62 0.00 NA NA NA NA NA 2.85 -0.82 NA NA 0.04 0.15 12.04 0.0009 28 -0.65 NA NA NA -0.04 -0.63 NA NA -1.00 NA NA 0.08 0.21 12.07 0.0009 29 -1.78 NA -0.01 NA NA NA NA 3.42 NA NA NA 0.05 0.14 12.39 0.0007 30 -0.82 -0.03 NA NA NA -3.15 NA 3.14 1.40 2.28 0.08 0.19 0.24 12.45 0.0007 31 -1.70 NA -0.01 NA NA -0.65 NA 3.80 NA NA NA 0.10 0.18 12.57 0.0007 32 -1.48 NA NA NA NA -1.08 0.00 2.88 -0.29 2.41 NA 0.10 0.19 13.08 0.0005

130

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132

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134

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136

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138

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140

323 -0.74 NA -0.01 0.00 NA NA 0.00 NA NA NA NA 0.04 0.13 38.97 0.0000 324 -0.68 NA -0.01 0.00 NA -0.50 0.00 NA -0.38 NA NA 0.07 0.16 39.16 0.0000 325 -0.56 -0.03 NA 0.00 NA -2.82 0.00 3.68 1.77 NA 0.10 0.20 0.27 39.22 0.0000 326 -0.66 -0.01 NA 0.00 NA NA 0.00 NA NA NA NA 0.04 0.17 39.23 0.0000 327 -0.65 -0.02 -0.02 NA -0.04 -0.79 0.00 3.90 1.17 NA NA 0.15 0.27 39.30 0.0000 328 -0.79 -0.02 -0.02 0.00 -0.05 -0.85 NA 3.73 NA NA NA 0.14 0.28 39.40 0.0000 329 -0.31 -0.02 NA 0.00 NA -0.73 0.00 NA NA NA NA 0.08 0.18 39.44 0.0000 330 0.27 -0.03 -0.01 NA NA -2.69 0.00 NA NA NA 0.10 0.17 0.27 39.53 0.0000 331 -0.81 -0.01 -0.02 NA -0.04 NA 0.00 3.52 NA NA NA 0.11 0.25 39.57 0.0000 332 -0.02 -0.02 -0.01 0.00 NA -2.68 NA NA NA NA 0.09 0.16 0.28 39.72 0.0000 333 -1.25 -0.01 -0.02 0.00 -0.05 NA NA 3.50 NA NA NA 0.08 0.26 39.80 0.0000 334 0.31 -0.03 -0.03 0.00 -0.08 -4.05 NA 2.43 1.59 2.06 0.12 0.23 0.62 39.96 0.0000 335 -0.59 -0.02 -0.02 NA -0.04 -0.73 0.00 3.74 NA NA NA 0.15 0.26 40.14 0.0000 336 0.33 -0.03 -0.03 NA -0.06 -3.83 0.00 2.61 0.93 4.43 0.11 0.24 0.37 40.17 0.0000 337 -0.68 NA -0.01 0.00 NA -0.52 0.00 NA NA NA NA 0.07 0.14 40.26 0.0000 338 -0.52 -0.03 NA 0.00 NA -2.69 0.00 3.47 NA NA 0.10 0.20 0.27 40.27 0.0000 339 0.37 -0.03 -0.03 NA -0.06 -3.83 0.00 2.46 NA 4.84 0.11 0.25 0.34 40.43 0.0000 340 0.16 -0.03 -0.02 0.00 -0.07 -3.85 NA 2.26 NA 4.01 0.10 0.23 0.46 40.60 0.0000 341 -0.58 -0.02 NA 0.00 -0.04 -1.74 0.00 2.36 0.88 4.30 NA 0.13 0.27 40.63 0.0000 342 -0.44 -0.01 -0.02 0.00 -0.05 NA NA NA -1.70 NA NA 0.05 0.22 40.95 0.0000 343 -0.02 -0.02 -0.01 0.00 -0.05 -0.84 NA NA 0.40 NA NA 0.11 0.30 41.03 0.0000 344 -0.77 NA -0.02 0.00 -0.05 -1.52 0.00 2.13 -0.69 4.98 NA 0.13 0.25 41.12 0.0000 345 -0.09 -0.01 -0.02 NA -0.04 NA 0.00 NA -1.06 NA NA 0.07 0.23 41.15 0.0000 346 -0.54 -0.02 NA 0.00 -0.04 -1.73 0.00 2.20 NA 4.50 NA 0.13 0.26 41.38 0.0000 347 0.34 -0.03 -0.03 0.00 -0.08 -3.95 NA 2.75 2.05 NA 0.13 0.24 0.68 41.52 0.0000 348 -0.81 NA -0.02 0.00 -0.05 -1.54 0.00 2.22 NA 4.86 NA 0.13 0.23 41.72 0.0000 349 0.19 -0.03 NA 0.00 NA -2.75 0.00 NA 1.03 NA 0.10 0.17 0.26 41.73 0.0000 350 -1.13 NA -0.02 0.00 -0.04 NA 0.00 3.41 -1.28 NA NA 0.09 0.24 41.82 0.0000 351 0.11 -0.02 -0.01 NA -0.05 -0.71 0.00 NA 0.30 NA NA 0.11 0.28 42.02 0.0000 141

352 0.24 -0.03 -0.03 NA -0.06 -3.33 0.00 3.42 1.83 NA 0.12 0.24 0.51 42.11 0.0000 353 -0.02 -0.02 -0.01 0.00 -0.05 -0.82 NA NA NA NA NA 0.11 0.27 42.13 0.0000 354 -0.14 -0.01 -0.02 NA -0.04 NA 0.00 NA NA NA NA 0.08 0.23 42.28 0.0000 355 -0.51 -0.01 -0.02 0.00 -0.05 NA NA NA NA NA NA 0.05 0.22 42.39 0.0000 356 0.24 -0.03 -0.03 0.00 -0.07 -3.66 NA 2.80 NA NA 0.13 0.24 0.63 42.57 0.0000 357 1.12 -0.03 -0.03 0.00 -0.09 -4.41 NA NA 2.00 NA 0.15 0.24 0.75 42.74 0.0000 358 -1.17 -0.01 NA 0.00 -0.03 NA 0.00 3.23 -0.67 NA NA 0.08 0.28 42.77 0.0000 359 0.20 -0.03 NA 0.00 NA -2.66 0.00 NA NA NA 0.10 0.17 0.25 42.78 0.0000 360 0.09 -0.03 -0.02 NA -0.05 -2.93 0.00 3.66 NA NA 0.11 0.24 0.38 42.81 0.0000 361 -0.90 -0.02 -0.01 0.00 NA -1.26 0.00 3.37 1.35 2.26 NA 0.13 0.23 42.88 0.0000 362 -1.22 NA -0.02 0.00 -0.04 NA 0.00 3.51 NA NA NA 0.09 0.22 42.89 0.0000 363 0.11 -0.02 -0.01 NA -0.04 -0.69 0.00 NA NA NA NA 0.11 0.25 42.94 0.0000 364 -0.87 -0.02 NA 0.00 -0.03 -0.88 0.00 3.61 1.19 NA NA 0.12 0.27 42.94 0.0000 365 -1.13 NA -0.02 0.00 -0.04 -0.53 0.00 3.56 -0.37 NA NA 0.12 0.24 43.63 0.0000 366 -0.86 -0.02 -0.01 0.00 NA -1.21 0.00 3.17 NA 2.43 NA 0.13 0.23 43.70 0.0000 367 1.20 -0.03 -0.03 NA -0.08 -4.10 0.00 NA 2.03 NA 0.15 0.24 0.70 43.79 0.0000 368 -1.19 -0.01 NA 0.00 -0.03 NA 0.00 3.28 NA NA NA 0.09 0.27 43.82 0.0000 369 -0.84 -0.02 NA 0.00 -0.03 -0.81 0.00 3.48 NA NA NA 0.13 0.26 43.94 0.0000 370 1.05 -0.03 -0.03 0.00 -0.08 -4.19 NA NA NA NA 0.14 0.25 0.73 44.00 0.0000 371 -1.24 -0.01 -0.02 0.00 NA NA 0.00 3.72 -0.35 NA NA 0.09 0.24 44.08 0.0000 372 -0.37 NA -0.02 0.00 -0.04 NA 0.00 NA -1.67 NA NA 0.06 0.20 44.14 0.0000 373 -1.15 NA -0.02 0.00 -0.04 -0.55 0.00 3.60 NA NA NA 0.12 0.23 44.39 0.0000 374 -1.02 -0.02 -0.01 0.00 NA -0.81 0.00 4.01 1.47 NA NA 0.13 0.23 44.71 0.0000 375 -0.44 -0.01 NA 0.00 -0.03 NA 0.00 NA -1.17 NA NA 0.05 0.23 44.88 0.0000 376 1.11 -0.03 -0.03 NA -0.07 -3.74 0.00 NA NA NA 0.14 0.24 0.63 44.93 0.0000 377 -1.24 -0.01 -0.02 0.00 NA NA 0.00 3.73 NA NA NA 0.09 0.23 45.05 0.0000 378 -0.07 -0.03 NA 0.00 -0.04 -3.57 0.00 2.35 1.20 3.87 0.10 0.20 0.31 45.10 0.0000 379 -0.10 -0.02 NA 0.00 -0.04 -0.80 0.00 NA 0.54 NA NA 0.09 0.25 45.29 0.0000 380 -0.46 NA -0.02 0.00 -0.04 NA 0.00 NA NA NA NA 0.06 0.18 45.50 0.0000

142

381 -0.02 -0.03 NA 0.00 -0.04 -3.55 0.00 2.15 NA 4.18 0.10 0.21 0.30 45.59 0.0000 382 -0.98 -0.02 -0.01 0.00 NA -0.72 0.00 3.85 NA NA NA 0.13 0.22 45.74 0.0000 383 -0.36 NA -0.01 0.00 -0.04 -0.49 0.00 NA -0.87 NA NA 0.09 0.23 45.90 0.0000 384 -0.47 -0.01 NA 0.00 -0.03 NA 0.00 NA NA NA NA 0.05 0.22 46.19 0.0000 385 -0.09 -0.02 NA 0.00 -0.03 -0.77 0.00 NA NA NA NA 0.10 0.23 46.39 0.0000 386 -0.25 -0.03 -0.02 0.00 NA -3.34 0.00 3.45 1.77 2.11 0.11 0.22 0.28 46.49 0.0000 387 -0.47 -0.01 -0.01 0.00 NA NA 0.00 NA -0.86 NA NA 0.05 0.18 46.68 0.0000 388 -0.40 NA -0.01 0.00 -0.04 -0.53 0.00 NA NA NA NA 0.09 0.21 46.94 0.0000 389 -0.32 -0.03 NA 0.00 -0.03 -2.84 0.00 3.42 1.46 NA 0.10 0.20 0.34 47.02 0.0000 390 -0.21 -0.03 -0.02 0.00 NA -3.24 0.00 3.20 NA 2.22 0.11 0.22 0.28 47.08 0.0000 391 -0.22 -0.02 -0.01 0.00 NA -0.72 0.00 NA 0.75 NA NA 0.09 0.18 47.49 0.0000 392 -0.30 -0.03 NA 0.00 -0.03 -2.73 0.00 3.31 NA NA 0.10 0.20 0.32 47.81 0.0000 393 -0.48 -0.01 -0.01 0.00 NA NA 0.00 NA NA NA NA 0.05 0.16 47.87 0.0000 394 -0.37 -0.03 -0.02 0.00 NA -2.92 0.00 4.06 1.80 NA 0.11 0.22 0.28 47.96 0.0000 395 -0.21 -0.02 -0.01 0.00 NA -0.68 0.00 NA NA NA NA 0.09 0.18 48.57 0.0000 396 -0.33 -0.03 -0.02 0.00 NA -2.79 0.00 3.84 NA NA 0.11 0.22 0.28 48.79 0.0000 397 0.47 -0.03 NA 0.00 -0.04 -3.08 0.00 NA 1.12 NA 0.11 0.19 0.45 48.89 0.0000 398 -0.32 -0.02 -0.02 0.00 -0.05 -1.74 0.00 2.53 0.76 4.86 NA 0.15 0.30 49.56 0.0000 399 0.44 -0.03 NA 0.00 -0.04 -2.87 0.00 NA NA NA 0.10 0.18 0.38 49.87 0.0000 400 -0.28 -0.02 -0.02 0.00 -0.05 -1.74 0.00 2.40 NA 5.05 NA 0.15 0.29 50.07 0.0000 401 0.40 -0.03 -0.01 0.00 NA -2.82 0.00 NA 0.98 NA 0.11 0.18 0.28 50.71 0.0000 402 -0.87 -0.02 -0.02 0.00 -0.04 NA 0.00 3.67 -0.46 NA NA 0.11 0.31 50.85 0.0000 403 0.41 -0.03 -0.01 0.00 NA -2.73 0.00 NA NA NA 0.11 0.18 0.26 51.56 0.0000 404 -0.89 -0.02 -0.02 0.00 -0.04 NA 0.00 3.70 NA NA NA 0.11 0.30 51.69 0.0000 405 -0.67 -0.02 -0.02 0.00 -0.04 -0.78 0.00 3.90 1.14 NA NA 0.14 0.30 51.80 0.0000 406 -0.63 -0.02 -0.02 0.00 -0.04 -0.72 0.00 3.80 NA NA NA 0.14 0.29 52.58 0.0000 407 0.36 -0.03 -0.03 0.00 -0.06 -3.83 0.00 2.59 1.14 4.05 0.11 0.23 0.41 53.20 0.0000 408 -0.10 -0.01 -0.02 0.00 -0.05 NA 0.00 NA -1.05 NA NA 0.07 0.25 53.23 0.0000 409 0.40 -0.03 -0.03 0.00 -0.06 -3.82 0.00 2.41 NA 4.67 0.11 0.24 0.36 53.43 0.0000 143

410 0.13 -0.02 -0.01 0.00 -0.05 -0.72 0.00 NA 0.48 NA NA 0.11 0.29 54.27 0.0000 411 -0.12 -0.01 -0.02 0.00 -0.04 NA 0.00 NA NA NA NA 0.07 0.25 54.34 0.0000 412 0.31 -0.04 -0.03 0.00 -0.06 -3.47 0.00 3.30 1.94 NA 0.13 0.24 0.57 54.78 0.0000 413 0.13 -0.02 -0.01 0.00 -0.04 -0.69 0.00 NA NA NA NA 0.11 0.26 55.19 0.0000 414 0.17 -0.03 -0.03 0.00 -0.05 -3.09 0.00 3.45 NA NA 0.12 0.24 0.46 55.49 0.0000 415 1.20 -0.03 -0.03 0.00 -0.08 -4.10 0.00 NA 2.00 NA 0.15 0.24 0.70 56.21 0.0000 416 1.12 -0.03 -0.03 0.00 -0.07 -3.83 0.00 NA NA NA 0.14 0.24 0.66 57.26 0.0000

144

Supplementary File S2 Analyses of soil attributes of semideciduous seasonal second-growth tropical forests sampled in agricultural landscapes at southeast Brazil. In this case, soil sum of bases was selected as the main variable to represent soil attributes. SB: soil sum of bases; P: Phosphorus; Ca: calcium; Mg: Magnesium; K: potassium; pH: potential Hydrogen acidity; OM: organic matter content; CTC: cation exchange capacity; V%: base saturation; H+Al: potential acidity.

Table S1: Correlation of SB with other soil attributes. Sum of bases R² p P 0.45 0.002 Ca 0.82 <0.001 Mg 0.72 <0.001 K 0.23 0.13 pH 0.36 <0.017 OM 0.57 <0.001 CTC 0.73 <0.001 V% 0.56 <0.001 H+Al -0.12 0.40

145

Figure S1: Principal Component Analysis of soil attributes. Each black square represent a plot in second- growth forests where vegetation was surveyed. Labels in each axis display the explained variation.

146

Supplementary File 3 of the Appendix C is included in Appendix A3

147

Supplementary File S4

Figure S2: Non-metric multidimensional scaling (NMDS) for species composition of SGF in agricultural landscapes of southeast Brazil. Point color and shape represent previous and surrounding land uses, respectively. Solid and dashed ellipses represent 95% confidence intervals for species composition based on previous and surrounding land use, respectively. Brown: pasture; Blue: eucalypt plantations; Red: Sugarcane plantations.

148

Supplementary File 5 Landscape forest dynamics for sampled second-growth forests. Numbers on top of each graph represent plot code.Vertical and horizontal axes indicate remnant size in hectares and time (1962-2016), respectively. Green lines indicate remnant size, since forest establishment. Remnant size varies greatly due merging and fragmentation when remnants expand and are deforested, respectively. Black horizontal lines represent mean remnant size since forest establishmet. Grey shaded filled lines represent relative surrounding forest cover in a 1-km buffer around the forest sampled, these lines fill the graph proportionally (i.e. in the first graph (plot 12), the vertical axis goes to 100, therefore, when the grey shaded line is at 25, it represents 25% surrounding forest cover; in the next graph (plot 18), the vertical axis goes only to 50, therefore in this graph the grey line at 25 would represent 50% surrounding forest cover). Dark blue horizontal lines represent mean surrounding forest cover since forest establishment. 149

150

Supplementary File 6 Justification for the selection of difference in forest cover and average forest cover as the surrounding forest cover metrics in this study. These two factors encompass a myriad of landscape process that affect second- growth forests and sustained robust data interpretation. We did not consider variables related to patch size because patches were overall small in our study site (only one remnant had > 100 ha, average ± standard deviation: 31.4 ± 26.5 ha, min: 0.8 ha, max: 101.0 ha) and highly variable along time (Supplementary file 5). Similarly, we did not consider forest edge as a factor since all but one plot were <100 m from patch edge (average ± standard deviation: 48 ± 29 m, min: 10 m, max: 174 m). Using the function corrplot in R software, we correlated these factors and grouped them in two hierarchical clusters in order to choose one factor from each cluster (Figure S1).

Figure S1: correlation matrix of all landscape factors related to forest cover and remnant size. Circle size and color are proportional to the correlation coefficient. The two groups delimited by the black rectangles were established by hierarchical clustering using the function corrplot in R 3.2. From the four possible landscape factors, we aimed to choose one factor to represent each cluster. Therefore, we chose flor_med (average surrounding forest cover in a 1 km radius since forest establishment) in the first cluster of factors. For the second cluster, we avoided choosing factors directly related to surrounding forest cover in order to avoid redundancy in data interpretation with flor_med, therefore we chose delta (difference in surrounding forest 151 cover from forest establishment to the moment of data gathering) as a proxy for land use change around the sampled forest. Legend: flor: relative native forest cover in a 1 km radius around plots at the time of data gathering; flor_med: average value of the relative native forest cover in a 1 km radius around plots from the year of forest establishment until data gathering. Values were calculated considering native forest cover values in high resolution images of 1962, 1978, 1995, 2000, 2008 and 2015; flor_est: relative surrounding forest cover in a 1 km radius around plots at the estimated year of forest establishment; delta: difference in relative surrounding forest cover in a 1 km radius around plots from estimated year of forest establishment until data gathering. Additionally, the Variance Inflation Factor (VIF) of the four surrounding native forest cover metrics in the full model was very high, providing another justification for the removal of two metrics from this study. High VIF indicates high correlation among predictor variables in a regression, a trait that is not recommended among predictor variable sin model selection. Usually, VIF values >10 between two predictors indicate that one of them should be removed from the analysis (Table S1). Table S1: VIF values for the factors considered in this study before and after the selection of the surrounding forest cover metrics. High VIF values are bolded.

Factor VIF before VIF after Age 1.823429522 1.559379255 Basal area Eucalyptus spp. 1.351374361 1.261909014 Soil sum of bases 1.489912986 1.385351211 Slope 1.306514735 1.136026428 Nearby Use 1.825960763 1.718949563 Distance from waterstreams 1.232372551 1.221346612 Current Forest Cover 21.98902577 - Forest Cover at the time of forest establishment 10.15304342 - Average Forest Cover 1.224304372 1.155283967 Difference in Forest Cover 16.65284349 1.477700738

152

Supplementary File 7 Relative importance, coefficient estimates and standard error of the drivers included in the average model developed by compiling all models Δ≤4. Importance is related to the proportion of models that the driver is included, weighted by model weight. Estimate is the coefficient of the driver in the average model. Std. Error is the standard error of the driver in the average model. Since each driver and forest attribute has its own unit, driver effect should be interpreted by its importance (roughly interpreted as the relevancy of the driver for a given response variable), estimate sign (positive or negative effect on forest attribute) and the estimate/Std.Error ratio (variance in the driver effect due to context). Table S1: Driver details.

Driver Description AB Eucalyptus Basal area of eucalypt in the plot Age Forest age in years Age x Nearby Use Interaction of forest age and sugarcane as the nearby land use Difference of native forest cover in a 1 km buffer from the time of forest Delta Cover establishment until data gathering Forest Cover Native forest cover in a 1 km buffer around plot

Forest Cover x Nearby Use Interaction of native forest cover and sugarcane as the nearby land use

Nearby Use (Sug.) Sugarcarcane as the nearby land use River Distance from the nearest watercourse Slope Slope, in degrees, of the plot Sum of bases Soil sum of bases

Table S2: Average model coefficients for biomass of native species.

Driver Importance Coefficient Std. Error Forest Cover 1.00 -85.813 127.137 Nearby Use (Sug.) 1.00 -57.010 69.132 Delta Cover 1.00 -51.503 136.662 Age x Nearby Use 0.52 -2.743 2.156 Slope 0.68 -2.340 1.624 AB Eucalyptus 1.00 -2.186 0.734 Age 0.79 1.276 0.707 Forest Cover x Nearby Use 1.00 222.519 245.583

153

Table S3: Average model coefficients for density of native species.

Driver Importance Coefficient Std. Error Forest Cover x Nearby Use 1.00 -4.612 33.545 AB Eucalyptus 0.16 -0.164 0.101 Nearby Use (Sug.) 1.00 -0.163 7.393 Age 0.11 0.140 0.099 Delta Cover 1.00 19.067 17.342 Forest Cover 1.00 23.876 18.667

Table S4: Average model coefficients for phylogenetic dispersion.

Driver Importance Coefficient Std. Error Nearby Use (Sug.) 0.89 -1.083 1.094 Delta Cover 0.67 -0.282 2.875 Forest Cover x Nearby Use 0.7 2.417 5.510 Forest Cover 1 3.005 2.833

154

Supplementary File 8

Dataset used for Chapter 3

plot bio_nat sp_nat phylo age ab_euc sb slope nearby river native_cover delta landscape 12 69.39 21 -0.18 35.5 0.00 179.32 15.8 Pasture 130.09 0.14 0.19 1 18 172.76 23 -2.39 35.5 0.00 134.27 6.9 Pasture 52.18 0.21 0.09 1 19 201.88 30 -1.49 46.5 0.00 133.17 4.0 Pasture 37.52 0.20 0.12 1 20 75.12 18 -1.46 24.0 0.00 80.10 3.1 Pasture 39.71 0.19 0.07 1 22 190.32 21 -1.55 35.5 0.00 203.17 8.1 Pasture 93.14 0.10 0.10 1 31 205.24 19 -1.08 31.0 6.42 8.87 10.9 Pasture 56.41 0.22 0.00 2 32 93.01 16 -1.91 34.0 20.38 108.00 10.9 Pasture 55.34 0.12 0.01 2 33 98.92 18 -2.03 34.0 16.37 14.83 8.5 Pasture 126.47 0.14 0.01 2 46 25.88 18 -0.59 34.0 24.16 13.72 7.5 Sugarcane 270.72 0.29 0.16 3 48 69.52 16 -2.70 13.5 0.00 11.30 2.6 Sugarcane 363.24 0.15 0.07 3 49 115.79 31 -2.53 46.5 0.00 25.35 13.5 Pasture 214.46 0.16 0.19 3 52 170.16 19 -0.28 34.0 0.00 36.27 6.2 Pasture 75.49 0.10 0.17 3 53 40.48 14 -0.02 12.0 0.00 53.74 10.4 Pasture 22.31 0.10 0.06 3 54 73.53 36 -0.36 30.0 0.00 71.58 13.3 Pasture 27.29 0.23 0.15 3 56 114.59 25 -1.71 45.0 0.00 97.93 8.7 Pasture 62.21 0.25 0.18 3 58 87.11 19 -2.85 17.5 29.18 10.47 8.9 Pasture 194.55 0.12 0.10 3 62 101.67 28 -1.55 30.0 0.00 105.20 13.8 Pasture 45.13 0.12 0.16 3 63 105.53 24 0.68 30.0 0.00 15.49 12.9 Pasture 73.29 0.14 0.14 3 64 335.53 29 -0.93 30.0 0.00 51.25 13.3 Pasture 163.44 0.10 0.16 3 70 82.05 17 -0.88 48.5 22.47 51.00 1.2 Pasture 38.84 0.26 0.13 3 71 122.71 31 1.61 46.5 0.00 31.62 0.9 Pasture 36.26 0.25 0.11 3 73 152.46 34 -0.35 30.0 0.00 27.87 6.3 Pasture 98.14 0.23 0.09 3 78 27.32 15 0.51 17.5 38.81 46.23 2.2 Pasture 41.72 0.24 0.10 3 83 95.02 33 -1.82 45.0 11.54 44.07 6.4 Pasture 26.35 0.23 0.11 3 84 90.34 24 -0.78 15.0 4.52 24.15 9.5 Pasture 68.79 0.25 0.02 3 155

85 162.60 27 -1.28 34.0 0.00 42.40 7.9 Pasture 25.99 0.09 0.08 3 87 152.77 30 -0.18 34.0 0.00 32.07 5.0 Pasture 39.55 0.19 0.08 3 88 107.44 30 0.34 20.0 0.52 19.28 5.0 Pasture 82.05 0.14 0.05 3 89 89.49 30 -1.49 46.5 16.95 35.07 6.1 Pasture 42.94 0.20 0.09 3 90 47.24 19 -0.58 11.0 1.85 8.22 7.5 Pasture 105.64 0.19 0.02 3 108 65.42 40 -0.57 13.5 25.96 16.97 2.4 Sugarcane 50.78 0.29 0.04 4 134 55.22 30 -3.20 14.5 21.71 46.67 2.3 Sugarcane 37.94 0.12 0.18 6 144 20.46 26 -0.60 28.5 5.22 85.98 9.0 Sugarcane 153.82 0.11 0.11 3 150 89.92 28 -0.81 20.0 13.97 30.62 10.3 Pasture 114.47 0.30 0.03 3 154 44.05 22 0.32 19.5 13.01 12.00 7.5 Pasture 224.63 0.27 0.01 3 156 150.87 24 -1.46 45.0 0.00 38.02 12.1 Pasture 42.78 0.18 0.04 2 163 115.49 20 -0.93 12.0 8.39 53.83 18.4 Sugarcane 203.12 0.27 0.05 4 201 169.41 20 -0.27 34.0 6.70 140.97 2.8 Pasture 80.02 0.18 0.18 6 202 42.09 21 -1.49 14.5 10.62 16.33 19.2 Sugarcane 28.54 0.28 0.05 4 203 83.91 28 -2.28 14.5 0.00 68.10 6.6 Sugarcane 151.13 0.28 0.22 6 205 208.42 28 0.36 29.0 0.00 12.83 5.8 Pasture 196.82 0.19 0.05 3 206 170.09 23 -1.24 16.0 0.00 21.93 10.7 Pasture 204.51 0.31 0.01 2 207 121.77 29 -2.47 47.5 0.00 36.00 16.0 Pasture 66.15 0.17 0.02 3 208 78.63 18 -1.64 14.5 16.15 27.67 1.9 Sugarcane 86.61 0.14 0.18 6

156

Supplementary File 9 is included in Appendix A1.

157

Supplementary File 10 Nearest human land use for the years of 2000, 2008 and 2015 in second-growth forests sampled. Plot Near_2000 NEAR_2008 NEAR_2016 12 Pasture Pasture Pasture 18 Pasture Pasture Pasture 19 Pasture Pasture Pasture 20 Pasture Pasture Pasture 22 Pasture Pasture Pasture 29 Pasture Pasture Pasture 31 Pasture Pasture Pasture 32 Pasture Pasture Pasture 33 Pasture Pasture Pasture 46 Pasture Pasture Sugarcane 48 Pasture Pasture Sugarcane 49 Pasture Pasture Pasture 52 Pasture Pasture Pasture 53 Pasture Pasture Pasture 54 Pasture Pasture Pasture 56 Pasture Pasture Pasture 58 Pasture Pasture Pasture 62 Pasture Pasture Pasture 63 Pasture Pasture Pasture 64 Pasture Pasture Pasture 70 Pasture Pasture Pasture 71 Pasture Pasture Pasture 73 Pasture Pasture Pasture 78 Pasture Pasture Pasture 83 Pasture Pasture Pasture 84 Pasture Pasture Pasture 85 Pasture Pasture Pasture 87 Pasture Pasture Pasture 88 Pasture Pasture Pasture 89 Pasture Pasture Pasture 90 Pasture Pasture Pasture 108 Sugarcane Sugarcane Sugarcane 134 Sugarcane Sugarcane Sugarcane 144 Sugarcane Sugarcane Sugarcane 150 Pasture Pasture Pasture 154 Pasture Pasture Pasture 156 Pasture Pasture Pasture 163 Sugarcane Sugarcane Sugarcane 201 Pasture Pasture Pasture 202 Sugarcane Sugarcane Sugarcane 203 Sugarcane Sugarcane Sugarcane 205 Pasture Pasture Pasture 206 Pasture Pasture Pasture 207 Pasture Pasture Pasture 208 Sugarcane Sugarcane Sugarcane

158

Supplementary File S11

Wood density values for species sampled in this study. Genus local: wood density estimated as the average of the species of the same genus sampled in this study; Genus global: wood density estimated as the average of the same genus in Chave & Zanne 2014; Family local: estimated as the average of species of the same family sampled in this study. NA: wood density estiamted as the weighted average of the trees in the same plot. References are in the end of the list.

Family and Species Wood Density Reference Anacardiaceae Chave & Zanne Astronium graveolens Jacq. 0.818 2014 Lithrea molleoides (Vell.) Engl. 0.53 Andre 2012 Chave & Zanne Mangifera indica L 0.43 2014 Chave & Zanne Myracrodruon urundeuva Allemão 0.96 2014 Chave & Zanne Tapirira guianensis Aubl. 0.457 2014 Annonaceae Annona sylvatica A.St.-Hil. 0.471 Oliveira 2014 Apocynaceae Chave & Zanne Aspidosperma cylindrocarpon Müll.Arg. 0.637 2014 Chave & Zanne Tabernaemontana catharinensis A.DC. 0.34 2014 Araliaceae Dendropanax cuneatus (DC.) Decne. & Planch. 0.42 Junior 2016 Arecaceae Unpublished Syagrus romanzoffiana (Cham.) Glassman 0.354 database Asteraceae Moquiniastrum polymorphum (Less.) G. Sancho 0.685 Filho 2015 Bignoniaceae Handroanthus heptaphyllus (Vell.) Mattos 0.56 Andre 2012 Chave & Zanne Handroanthus ochraceus (Cham.) Mattos 0.82 2014 Jacaranda macrantha Cham. 0.546 Oliveira 2014 Chave & Zanne Tecoma stans (L.) Juss. ex Kunth 0.47 2014 Boraginaceae Cordia americana (L.) Gottschling & J.S.Mill. 0.44 Andre 2012 Cordia sellowiana Cham. 0.407 Oliveira 2014 Chave & Zanne Cordia trichotoma (Vell.) Arráb. ex Steud. 0.56 2014 Cannabaceae Cannabaceae spp 0.556 Fam study Unpublished Celtis fluminensis Carauta 0.63 database Chave & Zanne Celtis iguanaea (Jacq.) Sarg. 0.655 2014 Celtis spp. 0.642 Genus Study Chave & Zanne Trema micrantha (L.) Blume 0.25 2014 Celastraceae Maytenus aquifolia Mart. 0.59 Genus Study 159

Maytenus evonymoides Reissek 0.59 Genus Study Maytenus gonoclada Mart. 0.59 Genus Study Maytenus spp. 0.59 Genus Study Combretaceae Chave & Zanne Terminalia argentea Mart. 0.81 2014 Terminalia glabrescens Mart. 0.608 Oliveira 2014 Chave & Zanne Terminalia triflora (Griseb.) Lillo 0.748 2014 Cupressaceae Cupressus spp. 0.429 Genus global Ebenaceae Chave & Zanne Diospyros inconstans Jacq. 0.83 2014 Erythroxylaceae Chave & Zanne Erythroxylum deciduum A.St.-Hil. 0.81 2014 Erythroxylum pelleterianum A.St.-Hil. 0.81 Genus Study Euphorbiaceae Unpublished Actinostemon concepcionis (Chodat & Hassl.) Hochr. 0.704 database Actinostemon klotzschii (Didr.) Pax 0.907 Magnago 2015 Chave & Zanne Alchornea glandulosa Poepp. & Endl. 0.378 2014 Croton floribundus Spreng. 0.45 Andre 2012 Croton spp. 0.471 Genus Study Chave & Zanne Croton urucurana Baill. 0.41 2014 Gymnanthes klotzschiana Müll.Arg. 1.1 Genus global Chave & Zanne Sapium glandulosum (L.) Morong 0.415 2014 Chave & Zanne Sebastiania brasiliensis Spreng. 0.674 2014 Sebastiania spp. 0.632 Genus Study Fabaceae Chave & Zanne Albizia niopoides (Spruce ex Benth.) Burkart 0.63 2014 Chave & Zanne Andira fraxinifolia Benth. 0.92 2014 Bauhinia forficata Link 0.39 Andre 2012 Chave & Zanne Bauhinia longifolia (Bong.) Steud. 0.67 2014 Calliandra tweedii Benth. 0.85 Encyclopedia of Life Chave & Zanne Cassia ferruginea (Schrad.) Schrad. ex DC. 0.866 2014 Chave & Zanne Centrolobium tomentosum Guillem. ex Benth. 0.58 2014 Chave & Zanne Copaifera langsdorffii Desf. 0.6 2014 Cyclolobium brasiliense Benth. 0.9 Encyclopedia of Life Dahlstedtia muehlbergiana (Hassl.) M.J.Silva & A.M.G. Azevedo 0.72 Lorenzi Chave & Zanne Enterolobium contortisiliquum (Vell.) Morong 0.42 2014 Fabaceae spp 0.677 Fam study Chave & Zanne Hymenaea courbaril L. 0.787 2014

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Chave & Zanne Inga vera Willd. 0.575 2014 Chave & Zanne Leucaena leucocephala (Lam.) de Wit 0.45 2014 Leucochloron incuriale (Vell.) Barneby & J.W.Grimes 0.42 Andre 2012 Unpublished Lonchocarpus cultratus (Vell.) A.M.G.Azevedo & H.C.Lima 0.74 database Lonchocarpus spp. 0.74 Genus Study Machaerium brasiliense Vogel 0.49 Andre 2012 Chave & Zanne Machaerium hirtum (Vell.) Stellfeld 0.66 2014 Unpublished Machaerium nyctitans (Vell.) Benth. 0.95 database Chave & Zanne Machaerium scleroxylon Tul. 0.803 2014 Machaerium spp. 0.748 Genus Study Unpublished Machaerium stipitatum Vogel 0.84 database Chave & Zanne Machaerium villosum Vogel 0.745 2014 Chave & Zanne Myroxylon peruiferum L.f. 0.793 2014 Ormosia spp. 0.579 Genus global Parapiptadenia rigida (Benth.) Brenan 0.6 Andre 2012 Chave & Zanne Peltophorum dubium (Spreng.) Taub. 0.745 2014 Chave & Zanne Piptadenia gonoacantha (Mart.) J.F.Macb 0.68 2014 Chave & Zanne Platymiscium floribundum Vogel 0.89 2014 Chave & Zanne Platypodium elegans Vogel 0.75 2014 Chave & Zanne Pterogyne nitens Tul. 0.683 2014 Chave & Zanne Senegalia polyphylla (DC.) Britton & Rose 0.629 2014 Chave & Zanne Senna multijuga (Rich.) H.S.Irwin & Barneby 0.582 2014 Lacistemataceae Unpublished Lacistema hasslerianum Chodat 0.5185 database Lamiaceae Chave & Zanne Aegiphila integrifolia (Jacq.) Moldenke 0.86 2014 Chave & Zanne Vitex megapotamica (Spreng.) Moldenke 0.81 2014 Lauraceae Chave & Zanne Endlicheria paniculata (Spreng.) J.F.Macbr. 0.682 2014 Lauraceae spp 0.63 Fam study Unpublished Nectandra lanceolata Nees 0.7 database Unpublished Nectandra megapotamica (Spreng.) Mez 0.7 database Unpublished Nectandra oppositifolia Nees 0.54 database Ocotea corymbosa (Meisn.) Mez 0.574 Oliveira 2014 Unpublished Ocotea indecora (Schott) Mez 0.473 database 161

Chave & Zanne Ocotea odorifera (Vell.) Rohwer 0.76 2014 Chave & Zanne Ocotea puberula (Rich.) Nees 0.455 2014 Chave & Zanne Ocotea pulchella (Nees & Mart.) Mez 0.65 2014 Ocotea spp. 0.642 Genus Study Lecythidaceae Chave & Zanne Cariniana estrellensis (Raddi) Kuntze 0.565 2014 Lythraceae Lafoensia pacari A.St.-Hil. 0.52 Andre 2012 Malpighiaceae Byrsonima spp. 0.646 Genus Global Malvaceae Chave & Zanne Bastardiopsis densiflora (Hook. & Arn.) Hassl. 0.651 2014 Ceiba speciosa (A.St.-Hil.) Ravenna 0.25 Andre 2012 Chave & Zanne Guazuma ulmifolia Lam. 0.505 2014 Unpublished Luehea candicans Mart. & Zucc. 0.578 database Luehea divaricata Mart. & Zucc. 0.41 Andre 2012 Luehea grandiflora Mart. & Zucc. 0.498 Oliveira 2014 Chave & Zanne Luehea paniculata Mart. & Zucc. 0.54 2014 Pseudobombax spp. 0.357 Gen study Pseudobombax grandiflorum (Cav.) A.Robyns 0.24 Andre 2012 Pseudobombax longiflorum (Mart. & Zucc.) A.Robyns 0.474 Oliveira 2014 Melastomataceae Miconia albicans (Sw.) Triana 0.674 Pinho 2014 Miconia spp. 0.712 Genus Study Meliaceae Chave & Zanne Cabralea canjerana (Vell.) Mart. 0.478 2014 Chave & Zanne Cedrela fissilis Vell. 0.437 2014 Chave & Zanne Guarea guidonia (L.) Sleumer 0.548 2014 Chave & Zanne Guarea kunthiana A.Juss. 0.575 2014 Chave & Zanne Guarea macrophylla Vahl 0.52 2014 Guarea spp. 0.548 Genus Study Chave & Zanne Melia azedarach L. 0.438 2014 Meliaceae spp 0.573 Fam study Unpublished Trichilia casaretti C.DC. 0.69 database Trichilia catigua A.Juss. 0.69 Oliveira 2014 Unpublished Trichilia clausseni C.DC. 0.681 database Unpublished Trichilia elegans A.Juss. 0.69 database Chave & Zanne Trichilia pallida Sw. 0.67 2014

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Monimiaceae Unpublished Mollinedia widgrenii A.DC. 0.43 database Moraceae Unpublished Ficus guaranitica Chodat 0.36 database Ficus spp. 0.36 Genus Study Chave & Zanne Maclura tinctoria (L.) D.Don ex Steud. 0.733 2014 Myrtaceae Calyptranthes grandifolia O.Berg 0.53 Genus Study Calyptranthes spp. 0.53 Genus Study Chave & Zanne Campomanesia neriiflora (O.Berg) Nied. 0.84 2014 Campomanesia spp. 0.77 Genus Study Unpublished Campomanesia xanthocarpa (Mart.) O.Berg 0.86 database Unpublished Eucalyptus spp. 0.63167 database Eugenia cf pluriflora DC. 0.733 Genus Study Eugenia cf. repanda O.Berg 0.733 Genus Study Eugenia dodonaeifolia Cambess. 0.733 Genus Study Chave & Zanne Eugenia florida DC. 0.587 2014 Eugenia paracatuana O.Berg 0.733 Genus Study Unpublished Eugenia speciosa Cambess. 0.735 database Eugenia spp. 0.733 Genus Study Myrcia hebepetala DC. 0.304 Oliveira 2014 Unpublished Myrcia multiflora (Lam.) DC. 0.824 database Chave & Zanne Myrcia splendens (Sw.) DC. 0.8 2014 Myrcia spp. 0.624 Genus Study Myrcia tomentosa (Aubl.) DC. 0.53 Andre 2012 Myrcia venulosa DC. 0.66 Silva 2015 Chave & Zanne Myrcianthes pungens (O.Berg) D.Legrand 0.911 2014 Chave & Zanne Myrciaria floribunda (H.West ex Willd.) O.Berg 0.84 2014 Myrtaceae spp 0.723 Fam study Plinia spp. 0.52 Genus Study Chave & Zanne Psidium guajava L. 0.629 2014 Psidium sartorianum (O.Berg) Nied. 0.741 Oliveira 2014 Chave & Zanne Syzygium cumini (L.) Skeels 0.673 2014 Syzygium spp. 0.673 Genus Study Nyctaginaceae Chave & Zanne Guapira cf. opposita (Vell.) Reitz 0.83 2014 Guapira hirsuta (Choisy) Lundell 0.83 Genus Study Chave & Zanne Guapira opposita (Vell.) Reitz 0.83 2014 Guapira spp. 0.83 Genus Study 163

Ochnaceae Chave & Zanne Ouratea castaneifolia (DC.) Engl. 0.77 2014 Peraceae Chave & Zanne Pera glabrata (Schott) Poepp. ex Baill. 0.67 2014 Phytolaccaceae Chave & Zanne Gallesia integrifolia (Spreng.) Harms 0.48 2014 Chave & Zanne Seguieria langsdorffii Moq. 0.59 2014 Picramnaceae Picramnia sellowii Planch. 0.63 Poorter 2014 Polygonaceae Coccoloba cordata Cham. 0.52 Genus Study Ruprechtia laurifolia (Cham. & Schltdl.) A.C.Meyer 0.57 Genus Study Chave & Zanne Ruprechtia laxiflora Meisn. 0.57 2014 Primulaceae Chave & Zanne Myrsine coriacea (Sw.) R.Br. ex Roem. & Schult. 0.647 2014 Myrsine umbellata Mart. 0.62 Silva 2015 Proteaceae Chave & Zanne Roupala montana Aubl. 0.73 2014 Rhamnaceae Unpublished Rhamnidium elaeocarpum Reissek 0.714 database Rosaceae Chave & Zanne Eriobotrya japonica (Thunb.) Lind 0.88 2014 Rubiaceae Chomelia bella (Standl.) Steyerm. 0.53 Encyclopedia of Life Unpublished Chomelia pohliana Müll.Arg. 0.58 database Chave & Zanne Coutarea hexandra (Jacq.) K.Schum. 0.6 2014 Faramea latifolia (Cham. & Schltdl.) DC. 0.523 Oliveira 2014 Ixora brevifolia Benth. 0.88 Shimamoto 2012 Rutaceae Chave & Zanne Balfourodendron riedelianum (Engl.) Engl. 0.64 2014 Citrus spp. 0.74 Genus global Chave & Zanne Esenbeckia febrifuga (A.St.-Hil.) A. Juss. ex Mart. 0.866 2014 Chave & Zanne Zanthoxylum caribaeum Lam. 0.55 2014 Chave & Zanne Zanthoxylum fagara (L.) Sarg. 0.7 2014 Chave & Zanne Zanthoxylum monogynum A.St.-Hil. 0.9 2014 Unpublished Zanthoxylum petiolare A.St.-Hil. & Tul. 0.41 database Chave & Zanne Zanthoxylum rhoifolium Lam. 0.493 2014 Zanthoxylum riedelianum Engl. 0.504 Oliveira 2014

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Zanthoxylum spp. 0.593 Genus Study Salicaceae Chave & Zanne Casearia decandra Jacq. 0.647 2014 Unpublished Casearia gossypiosperma Briq. 0.88 database Chave & Zanne Casearia sylvestris Sw. 0.59 2014 Chave & Zanne Prockia crucis P.Browne ex L. 0.58 2014 Xylosma pseudosalzmanii Sleumer 0.661 Genus global Sapindaceae Chave & Zanne Allophylus edulis (A.St.-Hil. et al.) Hieron. ex Niederl. 0.651 2014 Allophylus racemosus Sw. 0.519 Oliveira 2014 Chave & Zanne Cupania vernalis Cambess. 0.663 2014 Unpublished Diatenopteryx sorbifolia Radlk. 0.74 database Matayba elaeagnoides Radlk. 0.83 Andre 2012 Sapotaceae Chrysophyllum gonocarpum (Mart. & Eichler ex Miq.) Engl. 0.47 Andre 2012 Chave & Zanne Chrysophyllum marginatum (Hook. & Arn.) Radlk. 0.704 2014 Sapotaceae spp 0.587 Fam study Siparunaceae Unpublished Siparuna guianensis Aubl. 0.57 database Solanaceae Acnistus arborescens (L.) Schltdl. 0.5037 Fonseca 2012 Cestrum intermedium Sendtn. 0.33 Oliveira 2016 Solanaceae spp 0.446 Fam study Unpublished Solanum granulosoleprosum Dunal 0.404 database Chave & Zanne Solanum pseudoquina A.St.-Hil. 0.53 2014 Solanum spp. 0.465 Genus Study Symplocaceae Symplocos cf. laxiflora Benth. 0.534 Genus global Unclassified Unclassified 0.635514741 NA Urticaceae Chave & Zanne Cecropia pachystachya Trécul 0.41 2014 Chave & Zanne Urera caracasana (Jacq.) Griseb. 0.18 2014 Verbenaceae Aloysia virgata (Ruiz & Pav.) Juss. 0.54 Andre 2012 Citharexylum myrianthum Cham. 0.39 Andre 2012 Vochysiaceae Callisthene minor Mart. 0.745 Genus Global Vochysia tucanorum Mart. 0.465 Oliveira 2014

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