São Tomé Archachatina Giant Land Snails (Pulmonata: Achatinidade)

UNIVERSIDADE DE LISBOA FACULDADE DE CIÊNCIAS DEPARTAMENTO DE BIOLOGIA ANIMAL

Biological invasion and the conservation of endemic island species: São Tomé Archachatina giant land snails (Pulmonata: Achatinidade)

Martina Panisi

Mestrado em Biologia da Conservação

Dissertação orientada por: Doutor Ricardo Faustino de Lima Professor Doutor Jorge Manuel Mestre Marques Palmeirim

2017 DEDICÁTORIA E AGRADECIMENTOS

Dedico il piú desiderato, vissuto e arduo lavoro fatto finora alla mia famiglia, che mi ha sempre appoggiato, nonostante le difficoltá materiali ed emozionali. A chi fin dalla mia prima partenza ha saputo essermi vicino sebbene fisicamente lontano, e che non ha mai obiettato che fossi felice dove dovevo essere. Un abbraccio al babbo piú figo del mondo, alla mamma piú paziente e bella che esista, a un fratello campione (sei figo anche tu ma non sei Justin, quindi stai calmo che l’importante é partecipare), nonni, zii e cugini pazzi ma con un grande cuore. Ai pelosi, squamosi e chitinosi, comandati dalla regina Minou. Vi voglio bene.

Un bacio a un grande motociclista che lá dall’alto mi veglia e tiene a bada, e a una nonna super coraggiosa.

Em primeiro lugar agradeço às duas pessoas que principalmente permitiram que este trabalho inteiro se delineasse, cumprisse e, por fim, realizasse, que me ajudaram e aturaram constantemente entre grandes reuniões de suporte metodológico, psicológicos…e tantas anedotas de vida (até vermos ilhas a forma de peras e de búzios). Pela forte motivação transmitida, pelos preciosos ensinamentos, pelas ideias, pelas inspirações, pela vossa ajuda e cuidado em tudo e pela introdução aos trópicos na carta, na mente e no campo, obrigada de coração a Ricardo Lima e ao Professor Jorge Palmeirim. Não podia ter orientação melhor.

Este trabalho não teria sido possível sem os dados recolhidos no âmbito da tese de doutoramento “Land-use management and the conservation of endemic species in the island of São Tomé” de Ricardo Faustino de Lima, e da “BirdLife International São Tomé and Príncipe Initiative”. A tese de doutoramento foi financiada pela FCT - Fundação para a Ciência e Tecnologia, através de uma bolsa de doutoramento cedida pelo Governo Português (Ref.: SFRH/BD/36812/2007), e pela “Rufford Small Grant for Nature Conservation”, que forneceu financiamento adicional para o trabalho de campo (“The impact of changing agricultural practices on the endemic birds of Sao Tome” - Ref.: 50.04.09). A “BirdLife International São Tomé and Príncipe Initiative” foi financiada pela “BirdLife’s Preventing Extinctions Programme”, através da família Prentice no âmbito da “BirdLife’s Species Champion Programme”, pela “Royal Society for the Protection of Birds”, pela “Disney Worldwide Conservation Fund”, pela “U.S. Fish and Wildlife Service Critically Endangered Animals Conservation Fund” (AFR-1411 - F14AP00529), pela “Mohammed bin Zayed Species Conservation Fund” (Project number 13256311) e pela “Waterbird Society Kushlan Research Grant”. Gostaria também de agradecer a todos os que contribuíram para o “International Action Plan for the Conservation of Critically Endangered Birds on São Tomé”, especialmente à Direção Geral do Ambiente, ao Parque Natural do Obô de São Tomé, à Direção das Florestas, à Associação dos Biólogos Santomenses e à associação MARAPA. Ainda, queria agradecer ao Eng. Arlindo Carvalho, Diretor Geral do Ambiente por apoiar as nossas atividades em São Tomé.

Um agradecimento em particular a toda a equipa de trabalho de campo da Associação Monte Pico que esteve envolvida na recolha de dados, nomeadamente Gabriel Cabinda, Ricardo Fonseca, Gabriel Oquiongo, Joel Oquiongo, Sedney Samba, Aristides Santana, Estevão Soares, Nelson Solé e Leonel Viegas. O trabalho de campo não teria sido possível sem a ajuda de Silvino Dias, José Malé, Filipe Santiago, Lidiney e inúmeros outros santomenses. Uma dedicação especial para "Dakubala". Nem sem a coordenação do Hugo Sampaio, da Sociedade Portuguesa para o Estudo das Aves (SPEA), ou sem o apoio institucional e empenho pessoal de Luís Costa (SPEA) e de Alice Ward-Francis (“Royal Society for the Protection of Birds” - RSPB), a quem agradecemos igualmente a disponibilização de dados. Finalmente, um agradecimento a Graeme M. Buchanan, pelas orientações e pelo apoio no planeamento experimental deste trabalho. Agradeço à Associação Monte Pico, pelo alojamento durante a minha estadia em São Tomé.

Agradeço fortemente a Filipa Soares pela ajuda e suporte constante durante a inteira realização deste trabalho, e sobretudo pelos dados recolhidos e pelos dados fornecidos no âmbito do trabalho de modelação da distribuição de espécies a nível da ilha inteira.

Um agradecimento especial a Manuel Sampaio pela ajuda no trabalho de campo.

Agradeço Matthias Neumann, David Holyoak e Geraldine Holyoak, para os conselhos, ajuda e suporte relativamente ao planeamento e a realização do trabalho de campo e Ana Coelho pelos concelhos relativamente a estadia em São Tomé e a escrita da dissertação.

Obrigada à Ricardo, Filipa, Bárbara e Manuel pela ajuda, paciência, força, inspiração e pelas inesquecíveis memórias de São Tomé.

Obrigada à melhor turma de amigos de sempre, biólogos malucos, fomos e seremos os melhores.

Obrigada a quem me ajudou, suportou e encorajou na defesa da tese: Castle, Ana, Fernando, Filipa, Anis, Kenzi, Amelinha, Reem e todo o resto do pessoal que assistiu à defesa.

Thanks to those friends that are and will always be there, grazie schizzi miei: Lella, Ricky, Alle, Sabi, Nastia, Vic, Lucy, Lina, Belmy. Thanks “Lisbon Gang”, for every happy and crazy moment spent together. Obrigada, “Amigos Internacionais”, pelos sorrisos e positividade.

Obrigada a ti, que estás sempre ao meu lado e me aturas em tudo. Se fosse eu não conseguia. Só tu sabes quantas dormidas devo a ti e ao Roso por causa deste último ano.

Não sei bem como agradecer aos companheiros de uma tão grande e linda aventura, pois ficam no coração, bem marcados para sempre, a família São tomense: Lucy, Gegé, Juary, Kaná, Edi, Neya, Leny, Sá, Francisco, Leonel, Nity, Estevão, Tomé, Mito e Sonia, Dodot, Lito, Sr. Filipe e mulher, gente de Brigoma, Emolve e Monte Café.

Obrigada Francisco, Leonel e Sá por me terem ajudado nos momentos em que mais precisava de alguém, pelos pequenos-almoços no Jardim Botânico, as lindas conversas, as folhas safa-barriga, as Rosemas com búzio-de-mato e para o meu primeiro búzio-d’Obô, Filippo.

Obrigada a quem durante uma noite de janeiro, no meio da floresta, deu-me a força para começar a nossa procura e, a partir daí, nunca me deixou desistir. Dois meses depois, ainda com o sorriso do primeiro dia, mais do que quatrocentos búzios lindos depois e imensas aventuras no coração. Obrigada Gabi (Gabriolo).

Ao transeto de Trás-os-Montes/Nova Ceilão que era suposto ser um dos mais simples e revelou- se um pesadelo sem fim. Obrigada, agora em comparação tudo parece mais simples.

A todos os (vertebrados ou não) que nesta aventura simplesmente confiaram numa desconhecida que lhes apareceu à frente… E começamos a fazer parte uns da vida do outro.

Por fim, como é justo, obrigada à beleza da natureza e da diversidade da vida, que nunca para de encantar, surpreender e ensinar…

E que me fez confirmar, mais uma vez, que atrás de cada cara, cada gesto ou cada ser há uma historia que só espera de ser contada, escutada ou então vivida, agora.

And that made me confirm, once again, that behind every face, every gesture or every living being there is a story that only hopes to be told, heard or lived, now.

E mi ha fatto confermare, ancora una volta, che dietro ogni viso, gesto o essere vivente esiste una storia che aspetta solo di essere raccontata, ascoltata oppure vissuta, adesso.

E’ só preciso dar-lhe uma possibilidade. E de repente estás a vivê-la.

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“Búzio-d ´Obô vê um humano pela primeira vez e fica amuado”.

Ilha de São Tomé – 2 de fevereiro 2017

RESUMO ALARGADO

A perda global de biodiversidade é uma das maiores consequências das atividades humanas. As ilhas são hotspots mundiais de biodiversidade, com elevado grau de espécies endémicas, mas os seus ecossistemas são também dos mais suscetíveis às alterações antrópicas. A introdução de espécies exóticas é a principal causa de extinções em ilhas, agravada quando em sinergia com outros fatores, como a alteração do tipo de uso do solo. Os moluscos são um dos grupos animais com mais extinções, e os caracóis terrestres, em particular, sofreram o maior número de extinções devido às atividades humanas. Estes organismos são excelentes bioindicadores da qualidade do habitat, exatamente porque são muito vulneráveis às alterações ambientais. Por outro lado, também existem diversas espécies de caracóis terrestres com grande capacidade invasora, e que se adaptam muito bem a habitats humanizados. A introdução de várias espécies de caracóis gigantes africanos (géneros Achatina e Archachatina) fora do continente resultou em danos na agricultura, problemas sanitários e ameaças para os ecossistemas nativos. Estes animais têm hábitos noturnos, são polífagos e hermafroditas, produzindo grandes quantidades de ovos, sendo muito procurados para fins medicinais, ornamentais, como animais de estimação e, por fim, pelo considerável valor, sobretudo no território africano, como recurso alimentar. A ilha de São Tomé está situada a 255 km da costa Oeste africana, no Golfo da Guiné, e tem uma área de 857 km2. É caraterizada por uma elevada humidade e precipitações que podem chegar até aos 7000 mm anuais no Sudoeste. As temperaturas médias anuais variam entre os 22 e os 30°C, com mínimas de 10°C em elevada altitude. Originalmente dominada por floresta, intensas modificações da paisagem ocorreram desde a sua descoberta e colonização, no final do século XV. Podemos atualmente identificar um gradiente de degradação ambiental ao longo da ilha: áreas não florestadas sobretudo junto à costa, seguidas por plantações de sombra, onde se cultiva o cacau e o café, a floresta secundária, que resulta em grande parte do abandono de antigas plantações e onde plantas nativas e exóticas coexistem e, por fim, a floresta nativa, nas zonas mais inacessíveis do interior da ilha, que permanece quase intocada pelas atividades humanas e alberga uma elevada taxa de espécies endémicas. Apesar da sua reduzida extensão territorial, a ilha é reconhecida internacionalmente pelo elevado número de endemismos em diversos grupos taxonómicos, tais como aves, anfíbios, plantas superiores, morcegos, répteis, borboletas e moluscos. Destes últimos, São Tomé conta com 40 espécies de moluscos, 31 dos quais são endémicos.

O caracol gigante do Golfo da Guiné Archachatina bicarinata (Bruguière, 1792), ou búzio-d’Obô, é uma espécie endémica das ilhas de São Tomé e Príncipe e tem sofrido um declínio acentuado em ambas as ilhas nas últimas décadas. A introdução do caracol gigante do Oeste

v africano Archachatina marginata (Swainson, 1821), ou búzio-vermelho, está entre as prováveis causas desse declínio.

No primeiro capítulo desta tese avaliamos quais os fatores que explicam a distribuição do caracol gigante introduzido em São Tomé. O amplo gradiente de degradação ambiental que existe na ilha providencia condições excelentes para se compreender as ligações entre a distribuição desta espécie e a humanização da paisagem. Verificámos que este caracol existe em quase toda a ilha, preferindo plantações e florestas secundárias de baixa altitude, e evitando as zonas de floresta nativa. A sua presença está associada a plantas introduzidas, típicas de ecossistemas degradados, e a sua população encontra-se em expansão, com elevada proporção de indivíduos juvenis, em especial nas zonas mais degradadas. Este estudo é uma contribuição essencial para o planeamento de medidas de conservação que visem limitar a ação da espécie invasora nos ecossistemas mais suscetíveis da ilha e serve também como um alerta para a necessidade de proteger a sua floresta nativa e as espécies que nela habitam.

No segundo capítulo avaliamos as possíveis interações entre o caracol gigante nativo e o invasor. Recolhemos diversos relatos que associam 31o desaparecimento do endémico à expansão do invasor ao longo do tempo. Documentamos uma forte segregação entre as duas espécies em termos espaciais, sendo que o endémico se encontra restrito às florestas nativas mais remotas, enquanto que o invasor se encontra maioritariamente em áreas mais degradadas, ocupando uma proporção muito mais significativa da ilha. As duas espécies estão associadas a vegetações totalmente diferentes, estando a endémica associa13da a flora endémica, e a introduzida a flora exótica, por sua vez igualmente associada a habitats antrópicos. A população atual do búzio- vermelho é composta por uma elevada proporção de juvenis, em contraste com a do endémico, em que claramente predominam os adultos. Finalmente, registámos diferenças nos padrões de atividade diária de ambas as espécies, com o endémico a ser principalmente diurno e o invasor a preferir estar ativo durante a noite.

Os nossos resultados sugerem que o declínio acentuado do búzio-d’Obô pode estar relacionado com a introdução do búzio-vermelho, representando o primeiro estudo dedicado à ecologia e distribuição destas espécies em São Tomé. Este estudo sugere que o grau de ameaça do búzio-d’Obô deve ser aumentado, bem como a necessidade urgente de implementar medidas de ação de conservação que assegurem a sua sobrevivência.

Palavras-chave: modelação ecológica, Archachatina bicarinata, declínio, interações interespecíficas, degradação do habitat

ABSTRACT

The global loss of biodiversity is a major consequence of human activities. Habitat destruction and the introduction of non-native species are among the principal drivers of this loss. Knowing the ecology of invasive species, namely their habitat preferences, distribution and potential interactions with local biodiversity, is thus fundamental for ecosystem management and for minimizing negative impacts. São Tomé Island holds an endemic-rich land snail fauna, including the Vulnerable Gulf of Guinea Giant Land Snail Archachatina bicarinata (Bruguière, 1792). This species was relatively widespread and abundant in the island, but its population has suffered a steep decline since mid-twentieth century. The introduction of the West African Giant Land Snail A. marginata (Swainson, 1821) has been implied in this decline, but very little is known about its dispersal or about its effects on native species. This thesis aims to assess the links between the dispersal of the introduced giant snail and human-modified ecosystems, and if this species is displacing the endemic giant snail. We found that the introduced giant snail is widely distributed throughout most of the island, preferring lowland plantations and other modified ecosystems rich in introduced plants. There was a strong spatial segregation between the two species, the endemic being restricted to the most remote patches of native forest. The invasive appeared to be expanding, having a large proportion of juveniles in its population, while the endemic showed the opposite trend. We also observed a temporal displacement between the occurrence of the two species: the endemic being active mostly during the day and the invasive principally around dusk and dawn. This was the first study on the ecological interaction between these two species. The small overlapping area in their distributions and the perceptions of local inhabitants suggest that the introduced snail is displacing the endemic. Gain01ing a better understanding of the mechanisms underlying this invasion process is essential to prevent its spread into the native forest. Immediate conservation actions aimed to preserve the endemic snail are necessary to halt its dramatic population collapse, which may warrant an uplisting of its conservation status.

Keywords: ecological modelling, endemism, interspecific interactions, segregation, land-use

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TABLE OF CONTENTS

GENERAL INTRODUCTION...... 1 CHAPTER 1: Habitat degradation facilitates invasion: the West African Giant Land Snail Archachatina marginata, in São Tomé Island (Gulf of Guinea)...... 5 INTRODUCTION...... 5 METHODS...... 7 Study Area ...... 7 Field Methods...... 8 Species distribution modelling...... 8 Habitat associations...... 8 Data Analysis ...... 11 Species distribution modelling...... 11 Habitat associations...... 11 Population age structure...... 12 RESULTS ...... 12 Species distribution modelling...... 12 Local habitat associations...... 13 Population age structure...... 14 DISCUSSION………………………………………………………………………….. 15 Distribution in São Tomé and its determinants...... 15 Local habitat associations...... 16 Population age structure...... 16 Is habitat degradation facilitating African giant snail invasion?...... 17 Implications for native biodiversity...... 18 CHAPTER 2: Is the invasive West African Giant Land Snail Archachatina marginata displacing the Gulf of Guinea endemic Archachatina bicarinata?...... 19 INTRODUCTION ...... 19 METHODS ...... 21 Study Species and Area ...... 21 Field Methods ...... 22 Local perceptions about the changes in giant land snail distribution...... 22 Species distribution modelling...... 22 Transect sampling: habitat associations, daily activity patterns and populations age structure...... 22 Data Analysis...... 23 Species distribution modelling...... 23

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Habitat associations...... 23 RESULTS………………………………………………………………………………. 25 Local perceptions about the changes in giant land snail distribution……………….. 25 Species distribution modelling...... 26 Habitat associations at the transect level...... 29 Daily activity patterns………………………………………………………………. 32 Populations age structure……………………………………………………………. 32 DISCUSSION…………………………………………………………………………... 32 Local perceptions about the changes in giant land snail distribution...... 32 Island-wide species distribution modelling...... 33 Habitat associations at the transect level...... 35 Daily activity patterns...... 36 Population age structure ...... 36 Is the invasive West African Giant Land Snail displacing the endemic Gulf of Guinea Giant Land Snail?…………………………………………………………..………. 36 Conservation implications…………………………………………..…..…………. 38 FINAL CONSIDERATIONS ...... 39 REFERENCES ...... 41 SUPPLEMENTARY MATERIALS ...... 50 TABLES...... 50 Models outputs………………………………………………………………………… FIGURES…………………………………………………………………………….... 56 RSCRIPT…………………………………………………………………………...….. 62

LIST OF TABLES

Table 1.1 – List of the 17 environmental variables used to assess habitat associations of the West African Giant Land Snail………………………………………………………………...10 Table 1.2 – Relative variable importance (RVI) obtained from the island-wide distribution model for the invasive snail…………………………………………………………………….12 Table 1.3 – Relative Variable Importance (RVI) obtained from the habitat association analysis………………………………………………………………………………………… 14 Table 2.1 – Relative Variable Importance (RVI) obtained from the island-wide model for the distribution of both study species……………………………………………………………… 27 Table 2.2 – ANOVA results exploring the contribution of the invasive species to explain the occurrence of the endemic species………………………………………………………….…. 28 Table 2.3 – Relative variable importance (RVI) obtained from the habitat association analysis for both species……………………………………………………………………………….... 31 Table S1. (Supp. Materials) – Description of the predictor variables used to model the species distribution at island scale……………………………………………………………………... 50 Table S2. (Supp. Materials) – Habitat associations, plant species list………………………. 51 Table S3. (Supp. Materials) – Differences between population classes along the gradient of forest degradation……………………………………………………………………………… 53 Table S4. (Supp. Materials) – Localities and their map code with the associated number of interviewed performed………………………………………………………………………… 53 Table S4b (Supp. Materials) – Structure of the interview……………………………..…..….53 Table S5. (Supp. Materials) – Spatio-temporal dynamics in the distributions changes (year of decline – year of appearance)…………………………………………………………………. 54 Table S6. (Supp. Materials) – Relative Variable Importance (RVI) calculated by Model Averaging from the ONP buffer zone model for the distribution of both study species…………………………………………………………………………………………. 54 Table S7. (Supp. Materials) – ANOVA results exploring the contribution of the invasive species to explain the occurrence of the endemic species inside the limits of the ONP buffer zone….. 54 Table S8. (Supp. Materials) – Tests for the homogeneity of group dispersion in the vegetation composition ordination………………………………………………………………………... 54

Models outputs

Table S9a (Supp. Materials) – Chapter 1, island-wide analysis, introduced species…………55 Table S9b (Supp. Materials) – Chapter 1, island-wide analysis, introduced species………....55 Table S10a (Supp. Materials) – Chapter 1, habitat associations, introduced species………...55

Table S10b (Supp. Materials) – Chapter 1, habitat associations, introduced species………...56 Table S11a (Supp. Materials) – Chapter 2, island-wide, endemic species……………………56 Table S11b (Supp. Materials) – Chapter 2, island-wide, endemic species…………………….56 Table S12a (Supp. Materials) – Chapter 2, island - wide, endemic species (introduced species as a predictor)…………………………………………………………………………………....57 Table S12b (Supp. Materials) – Chapter 2, island - wide, endemic species (introduced species as a predictor)………………………………………………………………………………..…..57 Table S13a (Supp. Materials) – Chapter 2, ONP buffer area, invasive species…………...... 58 Table S13b (Supp. Materials) – Chapter 2, ONP buffer area, invasive species……………….58 Table S14a (Supp. Materials) – Chapter 2, ONP buffer area, endemic species………………..58 Table S14b (Supp. Materials) – Chapter 2, ONP buffer area, endemic species………………..59 Table S15a (Supp. Materials) – Chapter 2, ONP buffer area, endemic species (invasive species as a predictor)………………………………………………………………………………...... 59 Table S15b (Supp. Materials) – Chapter 2, ONP buffer area, endemic species (invasive species as a predictor)…………………………………………………………………………………....59 Table S16a (Supp. Materials) – Chapter 2, transects, invasive species …………………...... 60 Table S16b (Supp. Materials) – Chapter 2, transects, invasive species ………………………60 Table S17a (Supp. Materials) – Chapter 2, transects, endemic species ………………………60 Table S17b (Supp. Materials) – Chapter 2, transects, endemic species…………..……………61

LIST OF FIGURES

Figure 1.0 – Study species……………………….……………………………………………..4 Figure 1.1 – Maps of São Tomé showing the West African Giant Land Snail sampling locations………………………………………………………………………………………...9 Figure 1.2 – Maps of São Tomé showing the a) observations and b) modelled potential distribution of the West African Giant Land Snail in São Tomé………………………………13 Figure 1.3 – First two axes of the 20m vegetation composition NMDS (stress= 0.18)……….14 Figure 1.4 – Population age structure of the West African Giant Land Snail, based on shell length………………………………………………………………………………………….. 15 Figure 2.1 – Maps of São Tomé showing the sampling locations for both species………….. 23 Figure 2.2 – Spatio-temporal dynamics of the introduction of the West African Giant Land Snail and the decline of the Gulf of Guinea Giant Land Snail in São Tomé……………………….. 26 Figure 2.3 – Maps of São Tomé showing the potential distribution of both species………… 28 Figure 2.4 – NMDS analysis and the association of the plant and snail species to the axes of ordination …………………………………………………………………………………….. 30 Figure 2.5 – Distribution and abundance of the giant land snail species along the transects... 32 Figure 2.6 – Age structure histograms, based on shell length distribution for the invasive species (a) and the endemic species (b) ...…………………………………………………………….. 33 Figure 2.7 – Comparison of populations stru0000cture between species using a density plot….… 33 Figure S1. (Supp. Materials) – Proportion of observed presences of West African Giant Land Snail, depending on a) Land-use type and b) Topographic Position Index (TPI)……………….62 Figure S2. (Supp. Materials) – Observed and predicted presence of the West African Giant Land Snail depending on Elevation and Rainfall…………………………………………………… 62 Figure S3. (Supp. Materials) – Population shell width distribution of the West African Giant Land Snail………………………………………………………………………………….….. 63 Figure S4. (Supp. Materials) – Association between the correct identification of the endemic species and the age of the interviewed…………………………………………………….…... 63 Figure S5. (Supp. Materials) – Causes associated to the demise of the endemic species from the locals’ perceptions……………………………………………………………………...... …. 63

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Figure S6. (Supp. Materials) – Proportion of observed presences of the endemic species, depending on a) Land-use type and b) Topographic Position Index (TPI)……………………. 64 Figure S7. (Supp. Materials) – Observed and predicted presence of the endemic species depending on Elevation and Rainfall…………………………………………………………… 64 Figure S8. (Supp. Materials) – Comparison of the performance of the models for the endemic species………………………………………………………………………………………..… 64 Figure S9. (Supp. Materials) – Maps of São Tomé showing the potential distribution of both species inside the limits of the ONP Buffer Area……………………………………………. 65

Figure S10. (Supp. Materials) – Substrate composition ordination plot (stress= 0.17)……… 65 Figure S11. (Supp. Materials) – Plant species association with NMDS axes………………… 66 Figure S12. (Supp. Materials) – Selection of the best models and the most important variables for the West African Giant Land Snail in the habitat association analysis…………………….. 66 Figure S13. (Supp. Materials) – Selection of the best models and the most important variables for the Gulf of Guinea Giant Land Snail in the habitat association analysis…………………… 67 Figure S14. (Supp. Materials) – Daily activity patterns of São Tomé giant land snails……… 67 Figure S15. (Supp. Materials) – Population shell width variation for both species………..… 67

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GENERAL INTRODUCTION

Changes in ecosystems and species extinctions have always occurred, but human activities have accelerated these processes, threatening ecosystem functioning and biodiversity (Dirzo et al., 2014; Ceballos et al., 2015). Land-use change has had one of the largest impacts on global biodiversity, especially in areas with high species richness and endemism (Sala et al 2000). In many parts of Africa, for example, the rainforest is being cleared to grow cocoa, oil palm, rubber and timber, within global biodiversity hotspots (Oke et al., 2008). In addition, the last half century has witnesses an unprecedented acceleration in the importance of worldwide trade (Hulme, 2009). Increased trade in commodities has resulted in a legacy of recent biological invasions, often with catastrophic consequences on native biodiversity (Hulme, 2009; Spatz et al., 2017). Land-use changes and biological invasion can act in synergy with severe implications. Some species, such as terrestrial snails, are particularly sensitive to their impacts (Oke et al., 2008; Chiba and Cowie, 2016).

Terrestrial molluscs are one of the most diverse groups of animals, including more than 30,000 species described. However, they also have the highest number of documented extinctions of any major taxonomic group (Lydeard et al., 2004). Habitat loss coupled with the introduction of alien species have caused most of the current global wave of terrestrial mollusc extinctions (Lydeard et al. 2004, Chiba and Roy, 2011).

Land snails represent one of the most important groups of invertebrates in terrestrial ecosystems (Idohou et al., 2013). In forests, they contribute to soil production, calcium concentration in the soil, and are involved in the process of plant litter decomposition, as many species consume of decaying vegetal material. Terrestrial snails are useful indicators of environmental conditions, such as environmental health, and soil structure and texture (Dedov and Penev, 2004; Idohou et al., 2013, Nicolai et al., 2017). Land snails have particularly low abilities for active dispersal (Nicolai et al., 2017) and, in the humid tropics, where land snail diversity is highest, this results in spectacular radiations, with large numbers of locally endemic species and genera (Schilthuizen et al., 2002). Land snails that inhabit oceanic islands are more susceptible to extinction, because of their restricted distribution and because they have evolved in the absence of high predation pressure. Many snail extinctions have been attributed to introduced species (Cameron et al., 2013). The introduction of Euglandina rosea, a carnivorous land snail, on several Pacific islands has been one of the most catastrophic, and resulted from an attempt to control a previous introduction, of the agricultural pest Achatina (Lissachatina) fulica. E. rosea is likely to have contributed to the extinction of 134 land snail species, and did not control the invasive A. fulica (Lowe et al., 2000; Chiba and Cowie, 2016). Giant African land snails are grouped in two genera of terrestrial snails: Achatina and Archachatina. These are among the largest land snails and belong to the family Achatinidae, which

1 includes 13 genera in total. Archachatina spp. are mainly distributed throughout west Africa, while Achatina spp. have a wider distribution across sub-Saharan Africa (Raut and Barker, 2002). In the last two centuries, giant African Land Snails have spread in every continent as invasive species, and are now globally recognized as a threat to biodiversity, as agricultural pests and as vector of diseases (Lowe et al., 2000; Thiengo et al., 2007; Meyer et al., 2008; Agongnikpo, 2010). Their invasion success is mainly due to human voluntary introductions, motivated by their use as food source, ornament, medicine or even as pets. Their invasion is also favoured by high breeding rates, since they are hermaphrodites and can lay several clutches of eggs per year (Raut and Barker, 2002; Vásquez et al., 2017;). Achatina (Lissachatina) fulica, one of the most invasive species, can easily adapt and spreads in human-modified ecosystems (Tomyiama, 2000). However, it is not clear if the expansion of other invasive giant African land snails is also facilitated by their preferential dispersal through human-modified ecosystems. Giant African land snails are thus commonly known for their negative ecological, economical and epidemiological impact in many countries around the world. However, among these species, only some have been largely dispersed and are considered invasive.

Many Achatinidae are currently threatened in their native range of occurrence, but few studies have focused on understanding their decline, and few policies are implemented to ensure their conservation (Hodasi, 1984; Oke et al., 2008). In recent years, large areas of tropical lowland African rainforest have been cleared for agriculture and converted to plantations (e.g. oil palm, cocoa). The introduction of exotic tree species in many parts of equatorial Africa has altered the composition of the forest, some are being now dominated by a high abundance of monoculture tree species and other fast- growing exotics (Oke et al., 2008). The deep transformations of natural ecosystems are a main cause for the demise of many giant snail species, including several native and endemic taxa (Hodasi, 1984; Idohou et al., 2013). Moreover, many Achatinidae are edible and common in Africa, being an important food source and having a cultural value for medicinal and religious purposes in many countries (Adeola, 1992; Raut and Barker, 2002). Land-use changes, combined with an intense snail harvesting led to the decline of several Acatinidae in Africa (Osemeobo, 1992; Idohou et al., 2013). Forest-restricted species, such as Archachatina knorrii, are particularly vulnerable to habitat loss (Raut and Barker, 2002). Some are largely diffused as invasive, but are threatened in their native range because, regardless of being adapted to human disturbed habitat, are susceptible to intense harvest, such as the West African Giant Land Snail, Archachatina marginata (Swainson, 1821) (Idohou et al., 2013).

The Democratic Republic of São Tomé and Príncipe is the second smallest African country, but it is internationally recognized for its remarkable endemic species richness in several flora and fauna taxa (Jones, 1994). It is incorporated in the global biodiversity hotspot of the “Guinean forests of West Africa”, and it has been targeted by several ecology and conservation studies (e.g. de Lima et al., 2016). São Tomé is an 857 km2 oceanic island, located about 255 km west of mainland Africa. The volcanic origin of the island determines its rugged topography, marked by deep valleys and high ridges, up to

2024 meters above sea level (Salgueiro & Carvalho 2001). The high mountains in the centre and south of the island promote a variety of climates. The south-west is characterized by frequent rains and almost permanent cloud cover, while the north-east is fairly dry and sunny (Tenreiro, 1961). The island was almost entirely covered by forests when first discovered by a Portuguese expedition in the late 15th century. The colonization of the island has largely modified its habitat composition, mostly due to the conversion of native forest into plantations. Sugar-cane was the first wide-scale plantation in São Tomé, still in the 16th century. Three centuries afterwards, great part of the lowland forests was already replaced by cocoa and, to a lesser extent, coffee shade plantations (Tenreiro, 1961). Four main land-use types are currently recognized: native forest, secondary forest, shade plantation and non-forested areas (Jones et al., 1991). The native forest still covers most of the centre and the southwest of the island. It is characterized by dense canopy cover and by having few introduced plant species. Native forest is usually located in steep inaccessible terrains, ranging from sea level to the highest altitudes (Diniz et al., 2002). Most of these forests are inside the Obô Natural Park (ONP), which covers around one third of the island. The ONP was created in 2006, under the European Commission “Écosystèmes Forestiers en Afrique Centrale” (ECOFAC) program, which aimed to promote the conservation and sustainable use of forests in Central Africa (Direcção Geral do Ambiente, 2006). Native forest is surrounded by areas of secondary forest, most of which resulted from forest regeneration of abandoned plantations, and usually composed by smaller trees and a higher proportion of introduced species. Shade plantations are an agroforestry system dedicated to growing of coffee and cacao, shaded by large tree species, such as the coral tree Erythrina poeppigiana. Other crops, such as banana Musa spp., taro Xanthosoma saggitifolium, oil palm Elaeis guineensis and avocado Persea americana are also commonly found in shade plantations (Jones, 1994; Diniz et al., 2002; Salgueiro and Carvalho, 2002). Finally, non-forested areas are mainly represented by agricultural areas, small-holder horticulture and by coconut and oil palm productions that area characterized by lacking a continuous tree canopy cover (Diniz et al., 2002).

São Tomé holds 40 species of land snails, 31 of which are endemic to the island (CBD, 2015). In the Achatinidae family, São Tomé has one endemic genus, the monotypic Atopocochlis (Cross and Fisher, 1888), and shares with Príncipe Island, the endemic Gulf of Guinea Giant Land Snail Archachatina bicarinata (Bruguière, 1792), (Raut and Barker, 2002). Land-use changes, overexploitation and introduced species are main threats in São Tomé, and their consequences on the avifauna have been fairly well assessed (de Lima et al. 2016). However, almost no investigation has assessed the consequences on the terrestrial snail fauna of the island (Gascoigne, 1994a, 1994b). In these, the endemic Gulf of Guinea Giant Land Snail is said to have been widely distributed in São Tomé island, before having suffered a steep population decline. The decline has been linked to the introduction of the mainland West African Giant Land Snail, during the second half of the past century (Gascoigne, 1994a). Nevertheless, no systematic study has evaluated the dispersal of the introduced giant snail in São Tomé Island, or its interactions with the endemic species.

Between August 2013 and February 2015, the “BirdLife International São Tomé and Príncipe Initiative”, conducted a systematic survey of the two species of giant land snails, focusing on the main forest block of the island (de Lima et al., 2016). These data were complemented with survey data collected between January and March 2017, covering under-sampled portions of the island, and used to analyse the distribution and interaction between the two species. In the first chapter, we describe the distribution and habitat associations of the introduced West African Giant Land Snail in São Tomé Island. In the second chapter, we evaluate the interactions between the endemic Gulf of Guinea Giant Land Snail and the invasive West African Giant Land Snail in São Tomé Island. This is the first study on the ecology and distribution of these two species in São Tomé Island and its contribute is essential for any future action toward the protection of the endemic species or the control of the introduced snail.

Fig. 0.1 – Study species. Study species. The São Tomé and Príncipe endemic Archachatina bicarinata (b,d) and the introduced West African Giant Land Snail Archachatina marginata (a,c). The two species have shells with opposite coiling directions: the introduced snail is right-handed, while the endemic is left-handed. This allows identifying both adult (a,b) and juvenile (c,d) individuals. The photos show adults (a,b), juveniles and eggs (c,d) in proportion, to highlight differences in size.

CHAPTER 1. Habitat degradation facilitates invasion: the West African Giant Land Snail Archachatina marginata, in São Tomé Island (Gulf of Guinea)

Abstract: Habitat loss and invasive alien species are major causes for biodiversity loss worldwide and anthropogenic habitat modification might have a role in facilitating invasive species’ expansion. Oceanic islands have been particularly susceptible to invasions; however, few studies have assessed island’s land-use modification as an important factor for an invasive species success. In this study, we modelled the current distribution of the introduced West African Giant Land Snail Archachatina marginata (Swainson, 1821) on São Tomé Island and we predicted its habitat preferences and population structure across a gradient of forest degradation. We found that this species is widely distributed on most of the island, preferring lowland plantations and modified forests, while avoiding well-preserved areas. The species’ presence was also associated with introduced plants, typical of human modified ecosystems, and its population outnumbers of juveniles occurring primarily in more degraded habitats. This is the first systematic study ever on the distribution and ecology of the invasive West African Giant Land Snail on São Tomé after its introduction on the island. Its contribute is essential for strategic ecological management actions aimed to limit the invasive species in those more susceptible areas and as a call for the protection of the island’s native forest and its vulnerable flora and fauna. Keywords: ecological modelling, land-use, Achatinidae, species distribution, conservation

INTRODUCTION

Invasive alien species are one of the major drivers of biodiversity loss (IUCN, 2016). The accidental or deliberated introduction of species worldwide is contributing to global changes, through the gradual replacement of native biotas, resulting in taxonomic, functional and genetic homogenization (Olden et al., 2004). However, the overall impact of invasive species on ecosystems often co-occurs with other anthropogenic impacts (Gutiérrez et al., 2014). Land-use change is usually considered to be having the largest effect on biodiversity in terrestrial ecosystems (Sala et al., 2000). Habitat loss and modification have been implied in facilitating invasions, so these two processes might be acting synergistically in the ongoing extinction crisis (Brook et al., 2008). Thanks to their discrete geographical boundaries, islands have often been used as case studies to better understand the impact of invasive species on native diversity (Sax et al., 2002). The low levels of genetic diversity found in island species may limit their ability to adapt to changing environments, thus making them more susceptible to the impacts of biological invasions (Hofman and Rick, 2017). The ability to adapt to new environments, the suitability of the environment and the ease of human mediated dispersal are all factors that may influence the success of an invasion (Colautti et al., 2006;

Anderson, 2009). Many cases of invasions on islands by non-native birds or mammals have been investigated, but invertebrate introductions have arguably been less studied, even though they can also have large, sometimes irreparable, ecological impact. The giant East African land snail Achatina (Lissachatina) fulica, for instance, has been listed as one of worst invasive alien species (Lowe et al., 2000). Widely introduced in the tropics and subtropics since 1800, it soon exhibited wide environmental tolerances and high reproductive capacity, and it is now considered a pest, a vector of several diseases, an aggressive competitor for native mollusc fauna and a threat to native flora (Craze and Mauremootoo, 2002; Raut and Barker, 2002; Thiengo et al., 2007). In many cases of land-snail introductions on island, the species behave invasively, becoming widespread in a few decades, including secondary and primary forests. That is the case of A. fulica in many islands of the Pacific and Indian oceans ( Griffiths et al., 1993; Agongnikpo et al., 2010). However, in other cases, such as A. fulica’s introduction on Christmas Island and of Euglandina rosea in Mauritius, the snails avoided well-preserved forest, probably due to the scarcity of suitable food plants or to the presence of native predators and/or competitors (Lake and O’Dowd, 1991; Griffiths et al., 1993). Islands resulted particularly susceptible to invasion by giant African snails, and the African giant land snail family Achatinidae, has been largely and deliberately introduced in and outside Africa, for medicinal, ornamental and food purposes (Cowie, 2001; Raut and Barker, 2002; Thiengo et al., 2007). The West African Giant Land Snail Archachatina marginata (Swainson, 1821), has been introduced in the islands of São Tomé and Príncipe probably for its value as a source of protein in mainland Africa (Gascoigne, 1994a; Raut and Barker, 2002; Babalola and Akinsoyinu, 2009). The snail’s introduction on Príncipe island was followed by a fast expansion throughout human-modified ecosystems over the past 20 years. However, the species was never encountered inside the native forest (Dallimer and Melo, 2010). Its introduction in São Tomé is dated around 50 or 70 years ago, anticipating the Príncipe one (Gascoigne, 1994a). In 1994, it was restricted to the north and east of the island, found mostly in cocoa and coffee plantations, and could not be found in forest or at higher altitudes (Gascoigne, 1994a). The species rapidly started spreading on the island, probably facilitated by a deliberated diffusion as a food source. Rural populations in São Tomé rely on introduced wild species for protein, and the introduced snail certainly has an important, since a preliminary study found that it accounted for 45.7% of all protein intake consumed in a community (Carvalho et al., 2015). This species has certainly a remarkable importance as food source on the island, but its rapid spread may result in secondary consequences concerning agriculture damages, health issues and threats to native flora and fauna. This work intends to quantify the success of this introduced species as an invader on the highly human-modified landscape, while assessing which factors might explain its distribution. São Tomé Island holds a strong gradient of environmental degradation, from the densely populated coast to the centre still widely covered by native forest, which represents a good experimental setting to assess the links between the distribution of the introduced snail and land-use human modification. In this context, our specific objectives are to: (1) model the current distribution of the species to identify important

6 island-wide explanatory factors; (2) assess local habitat preferences to understand which variables facilitate invasion along the plantation-forest transition; and (3) study population structure across the land-use intensification gradient.

METHODS

Study area

São Tomé is a volcanic island situated in the Gulf of Guinea, just north of the Equator and 255km west of the African continent. It has a well-marked seasonality: the rainy season extends from September to May, and the dry season, called the gravana, which extends from June to August. A smaller and less intensive dry season, the gravanito, occurs during some weeks sometime between December and February. The steep mountains and altitudinal differences promote a variety of climates. The annual rainfall varies from less than 600 mm in the northeast to over 7,000 mm in the southwest (Tenreiro, 1961). Humidity is high and constant in most the island (Carvalho et al., 2015). The temperature at sea level is fairly constant, varying between 22 and 30º C. In altitude, temperature is more variable, reaching similar maxima, but dropping below 10º C (Silva, 1958; Bredero et al., 1977). São Tomé is internationally recognized as an important biodiversity hotspot, in particular due to its richness in endemic plants and birds, as well as its remarkable mollusc diversity (Jones, 1994). Most of its biodiversity lies within the São Tomé Obô Natural Park (ONP), which includes great part of the remaining native forest. Despite being a protected area, overexploitation, land-use intensification and the spread of exotic species represent major threats (De Lima et al, 2016). Complex landscape modifications have occurred in the island since it was first discovered in 1471, totally covered by forest. Nowadays, a gradient of forest degradation can be identified, from the mountainous areas in the centre and southwest of the island, where well-preserved forest prevails, to the surrounding secondary forest, resulting from abandoned cultivations and to the plantations. These extend to the coast, and are mostly composed by cocoa and coffee shade plantations intermixed with non-forest land-use types, such as oil palm monocultures, horticultural fields, urban areas and open savanna. (Jones et al., 1991; Salgueiro and Carvalho, 2001; Diniz et al., 2002; de Lima et al., 2014). A. marginata is said to have been introduced in São Tomé as a food source, between 1950 and 1970.

Field Methods

This study took place across São Tomé, mostly during the gravanito and rainy seasons, when the species is known to be active (Raut and Barker, 2002).

Species distribution modelling

To map the distribution of the snail in the island, we first compiled occasional and systematic observations registered by the BirdLife International São Tomé and Príncipe Initiative (BISTPI) between August 2013 and February 2015 (de Lima et al., 2016). These were later supplemented by additional occasional and systematic observations, collected between January and March 2017 (Soares, 2017). Both of these sources divided São Tomé in 4 km2 quadrats (Fig. 1.1a). One-hundred-seventy-two of these quadrats were sampled by performing five 10-minutes point counts, separated by at least 200m, in one of the four randomly selected 1 km2 tetrads (de Lima et al., 2013). Additional records were also made, especially when the species was found in interesting locations. For both type of records, the presence of the snail, location and altitude were registered using a GPS.

Habitat associations

To assess habitat preferences along the gradient of forest degradation, we created seven transects of variable length, totalling 16.8 km (Fig. 1.1b). The transects were chosen to have an overall representation of the forest degradation gradient throughout the island. The shade plantations and non- forested areas were combined in a unique class, representing mostly the cultivated areas surrounding the forest. Each transect was divided in 50 m long sectors that were characterized by recording coordinates, elevation and habitat type. All transects were sampled three times between mid-January and mid-March 2017 by two observers actively searching giant snails in a 4m wide band, while walking the transects at a constant and slow pace. Every egg, dead or live individual was recorded, taking note of the length and width of the shell.

Figure 1.1 – Maps of São Tomé showing the West African Giant Land Snail sampling locations. a) São Tomé divided in the 1-km2 tetrads, which were part of the 172 4-km2 quadrats, sampled to perform the island-wide species distribution modelling. Each dot represents a systematic point count. The 100-m elevation isolines are shown in the background. b) Location of the seven transects (thick black lines) used to assess habitat associations. Background colours indicate land-use categories: dark green for native forest, intermediate green for secondary forest, light green for shade plantation and yellow for non- forested areas (R. F. de Lima, unpublished data). The dotted line represents the boundaries of the ONP.

We chose a wide variety of vegetation and substrate characteristics, with the total measurement of 17 environmental variables (Table 1.1) in 150 sampling points, corresponding to 52 live snail presences and 98 absences. The sampling points where the presence of live snails was confirmed were selected at random, making sure that all selected points were at least 40 m from each other, to guarantee independence. The pseudo-absences were randomly computed in the sampling area, making sure their number was proportional to transect length and that they did not overlap with areas where snails had been recorded.

Table 1.1 – List of the 17 environmental variables used to assess habitat associations of the West African Giant Land Snail. Variables measured in the 150 presence and absence sampling points located along the transects. Variables were measured at two scales: within a 20 m or 2 m radius around the sampling points.

Variable Description Units Scale (Meters) Elevation Recorded by GPS m - Number of trees Counts of trees with diameter at breast N 20 height (dhb) > 30 cm and height > 20 m Canopy density Canopy cover measured with a convex % - spherical densiometer Distance to tree Distance to the closest tree with a dbh > m - 30cm Slope Five classes; - 2 1 - None or very soft 2 - Soft 3 - Medium 4 - Steep 5 - Very steep Habitat Three classes; - 20 1 - Native forest 2 - Secondary forest 3 – Plantations Understory density Five classes; - 2 1 - None or very sparse 2 - Sparse 3 - Medium 4 - Dense 5 - Very dense Substrate composition Litter weight Weight of a 20 x 20 cm sample of g 2 ground litter Wood Dead wood, fallen trunks and roots % 2 Litter Litter mostly decomposed % 2 Stones Stones and rock material % 2 Grass Herbaceous coverage % 2 Fresh litter Litter recently fallen to the ground % 2 Lichens Lichens presence/absence 0/1 2 Bryophytes Moss presence/absence 0/1 2 Vegetation composition Vegetation composition 20m Presence or absence of a predefined list 0/1 20 of 102 plant species Vegetation composition 2m Presence or absence of a predefined list 0/1 2 of 102 plant species

Data Analysis

Statistical analyses were made in R, version 3.3.3 (R core team, 2017) and in QGIS, version 2.18.4 (Quantum GIS Development Team, 2017).

Species distribution modelling

We performed Generalized Linear Models (GLM, McCullagh and Nelder, 1989) with binomial errors to model the species distribution in São Tomé, using 70% of the presence/absence records. As explanatory variables, we used Land-use type, Rainfall, Topographical Positioning Index (TPI), Elevation, Slope, Distance to rivers, Distance to the coast, Ruggedness and Remoteness (Table S1). Multicollinearity was assessed by calculating variance inflation factors (VIFs). We ranked all possible GLMs, without interactions, based on Akaike information criteria corrected for small sample size (AICc, Burnham and Anderson, 2002), using the function “dredge” of the “MuMIn” package (Barton, 2016). The contribution of each environmental variable was quantified calculating the relative variable importance (RVI), using the “model averaging” function of the same package. We validated the model with the remaining 30% of the presence/absence records. To assess model goodness of fit we used the curve (AUC) of the receiver operating characteristic curve (ROC), from the “pROC” package (Robin et al., 2011), and the McFadden’s Index, from the “pscl” package (Jackman et al., 2015). Finally, we used the “predict” function of the “stats” package (R core team 2017) to fit the best model to raster data and obtain the species potential distribution map.

Habitat associations

We started by doing a non-metric multidimensional scaling (NMDS) ordination using the function “metaMDS” of the “vegan” package (Oksanen et al., 2017) to compile information on substrate composition variables (litter weight, wood, litter, grass, stones, fresh litter, lichens, bryophytes) and another one on vegetation composition variables (vegetation composition assessed in a 20 m and in a 2 m radius), based on a Bray-Curtis distance matrix (Minchin 1987; Chechina & Hamann 2015). Subsequently, we performed a GLM with binomial errors to identify habitat associations. As explanatory variables, we used elevation, trees number, canopy density, tree distance, slope, habitat, understory density and the first two axes of the NMDSs. We calculated the VIFs to assess multicollinearity and the GLMs were ranked using the function “dredge” of the “MuMIn” package (Barton, 2016) based on AICc. We calculated RVI to assess the overall contribution of each environmental variable to explain the occurrence of the snail. Finally, to test differences in the species abundance calculated for every 50 m of transect sampled, a Kruskal-Wallis test was used with the three different habitats as a grouping factor.

Population age structure

The shell length and width were used to estimate the population age structure. The individuals of this species reach sexual maturity at the age of 9 months, when the average shell length is around 8.5cm (Plummer, 1975). We used this parameter to distinguish juveniles from adults, and divided the population in eggs, juveniles, adults and dead shells, to analyse population age structure across land- uses.

RESULTS

Species distribution modelling

Across the island, we recorded 957 presences and 891 absences. To avoid multicollinearity between variables; Distance to coast, Remoteness and Rugosity were excluded from the modelling. The GLM showed no multicollinearity (VIF < 2.69) and a good model fit (AUC = 0.83 and McFadden pseudo R-Square = 0.25). Land-use type was identified as the most important variable to explain the presence of the snail (Table 1.2), which clearly avoided native forest. The species was also associated with lower altitudes, lower rainfall, valleys, and middle and upper slope areas (Table 1.2, Fig. S2, Fig. S3, Table S9a, Table S9b). The potential distribution map shows that the snail avoids some coastal areas and the centre of the island, only marginally entering the ONP, especially in the south. It also shows that the species is well established in the ONP buffer area (Fig. 1.2).

Table 1.2 – Relative variable importance (RVI) obtained from the island-wide distribution model for the invasive snail. The most important predictor variables are highlighted in bold. Predictor variable RVI Land-use type 1.00 Rainfall 1.00 TPI 1.00 Elevation 1.00 Slope 0.89 Distance to Rivers 0.62

Figure 1.2 – Maps of São Tomé showing the a) observations and b) modelled potential distribution of the West African Giant Land Snail in São Tomé.

Local habitat associations

More than half of the length of the transects comprised native forest (55%), followed by secondary forest (28%) and plantation (17%). We recorded 293 live snails and 41 shells. We excluded the first axis of the 20m radius vegetation NMDS because it was highly correlated with other environmental variables and we used the second axis instead. The GLM showed no multicollinearity (VIF < 5.92). Land-use type was the best variable to explain the presence of the giant West African Giant Land Snail (Table 1.3, Table S10a, Table S10b) along the transects. It was mostly found in plantations (32.9 snails per km), followed by secondary forest (31.4 snails per km). This species was less abundant in the native forest (5.5 snails per km, Kruskal-Wallis test, p < 0.001). This preference is also associated with the presence of introduced plants, such as taro Xanthosoma saggitifolium, banana Musa sp., coral tree Erythrina poeppigiana, Cinchona sp., sweet potato Ipomea batatas, avocado tree Persea americana, jackfruit tree Artocarpus heterophylla and chayote Sechium edule (Fig. 1.3b). The snail tends to occur at lower altitudes, having been found only up to 1,330 m a.s.l., despite the transects elevation varying between 350 and 1,480 m a.s.l.. Finally, it also showed a positive association with the second axis of the vegetation NMDS (Fig. 1.3a), and particularly with the presence of grass and shrubs such as Pauridiantha floribunda, Costus giganteus, Psychotria peduncularis and Leea tinctoria and introduced trees, such as oil palm tree Elaeis guineensis, and spiny tree fern Alsophila manniana.

Table 1.3 –Relative Variable Importance (RVI) of predictor variables obtained from the habitat association analysis. RVI are calculated by model averaging of the Binomial GLM used to explain the presence of the West African Giant Land Snail along the sampling transects. Predictor variable RVI Elevation 1.00 Trees number 0.41 Canopy density 0.24 Tree distance 0.28 Substrate (NMDS 1 axis) 0.37 Substrate (NMDS 2 axis) 0.26 Slope 0.05 Land-use type 1.00 Understory density 0.53 Vegetation (NMDS 2 axis) 1.00

Figure 1.3 – First two axes of the 20m vegetation composition NMDS (stress= 0.18). a) West African Giant Land Snail abundance in each sampling location. The triangles represent presences, and points are absences. Symbol size is proportional to species abundance. The colours indicate land-use types: black is native forest, red is secondary forest and green is non- forested. b) Association between vegetal species (Table S2) and NMDS axes. The length of the arrows is proportional to the species association with each axis.

Population age structure

The snails found in the transects ranged between 1.8 and 11.9 cm, with a median of 7.6 cm (Fig. 1.4a). We didn’t analyse in detail the shell width because it resulted strongly correlated with shell length (Spearman’s rank correlation rho=0.95, p < 0.001, Fig. S3). We estimate that 65% of the snails were juveniles because their size was below than the threshold of 8.5 cm of shell length. The juveniles which predominated in secondary forest and plantations, while adults and dead shells prevailed in native forest (Pearson’s Chi-squared test, p<0.001, Fig. 1.4b, Table S3).

Figure 1.4 – Population age structure of the West African Giant Land Snail, based on shell length. a) Overall shell length distribution. The bold vertical line separates juveniles from adults (x = 8.5 cm). b) Age class percentage across land-use types.

DISCUSSION

This first systematic study on the ecology and distribution of the West African Giant Land Snail in São Tomé has shown that land-use type and altitude are key factors to explain its presence, both at island-wide and at local scale.

Distribution in São Tomé and its determinants

The species is currently well established across most of the island, avoiding large extents of native forest and some non-forested areas, such as the savannahs in the dry northeast and the oil palm monocultures in the south. Only marginally does it occur within the boundaries of the ONP. The invasive snail is strongly associated to drier secondary forest and shade plantations. It also prefers lower altitudes and avoids mountain ridges. These results are in line with habitat preferences described for species of the same family in continental Africa. Achatinids are often well adapted to human environmental modifications, prevailing in forest-margin habitats and abounding in plantations (Raut and Barker, 2002). Comparing to others achatinids, Archachatina spp. is not as dependent on humid areas (Hodasi, 1984). The species became widespread in just over half a century after being introduced on São Tomé Island. It seems to prefer São Tomé’s mosaic of human-modified ecosystems, which suggests that these modifications promoted its spreading. The anthropogenic modification of ecosystems is known to drive

15 increases in the local abundance or regional distribution of invaders (Marvier et al., 2004; Didham et al., 2007). Moreover, dispersion was probably actively facilitated by people, since this snail is a key source of protein. Significantly higher amounts of wild snails are consumed in more remote areas inhabited by poorer families (Carvalho et al., 2015). Thus, it seems likely that its spread in rural areas might have contributed to the current widespread distribution of the species, which includes forests in protected areas. The species association to lower altitudes is very likely also linked to more intensive human disturbance in the lowlands. Its scarcity on the coast and in the palm-oil monoculture may be due to the higher temperature and lower humidity, coupled with the oversimplified vegetation structure, which provide overall lower habitat suitability (Osemeobo, 1992).

Local habitat associations

At local scale, the species maintains the preference for lowland secondary forest and plantations. The vegetation analysis revealed that the species is associated with introduced plants, typical of human- disturbed ecosystems and with shrubs and grasses typical of riparian forest and sub forest, normally found at the edge of the native forest (Diniz et al., 2002). These results reinforce that elevation and land- use type are key factors to explain the presence of this snail in São Tomé.

Population age structure

The shell length measurements suggest that most individuals were juveniles. Even though the West African Giant Land Snail can reach up to 16 cm in shell length, the individuals we measured never surpassed 11.9 cm. This rather small size suggests that the life span is no longer than 2 years (Plummer, 1975), which is probably linked to intensive local harvesting. Nevertheless, since published measurements refer to captive individuals, we must consider that, in the wild, growth rate and life span could be influenced by other factors, such as aestivation, food shortages and competition. In proportion, juveniles prevail in secondary forest and plantations, but not in native forest. Knowing that locals tend to harvest bigger individuals (Pers. Observ.), a higher anthropogenic pressure in ecosystems closer to local villages could lead to a prevalence of smaller individuals. The poorness of juveniles and the prevalence of adults and dead shells in native forest could indicate that this is not a highly suitable ecosystem for this species. We observed several egg clutches, often just laid on the soil surface. Some of these hatched between February and March, suggesting that this species’ first hatch of the year occurs at the end of the small dry season and the juveniles are only three months old when the main dry season starts. This timing partially matches the cycle of Nigerian conspecifics, which hatch when the wet season begins, so that the young snails are able to feed during the wettest months. The growth of the snails is faster

16 during the first 3 to 5 months of life, to ensure provision and increased chances of survival during the following dry season (Plummer, 1975). Our conclusions are mainly based on observations in the wild, and provide a first glimpse into the life cycle adaptations of this species in São Tomé. However, whole year observations complemented with captivity experiments performed in standard conditions are needed to gain a better knowledge of this key aspect of the species biology, which will be key for future management plans to control this invasive species.

Is habitat degradation facilitating African giant snail invasion?

In São Tomé, we have found adult snails moving and feeding inside native forest (n=37), up to 1.5 kilometres away from other ecosystems. These observations indicate that the species might migrate and survive inside well-preserved forests, despite occurring at lower densities (5.5 snails per km compared to 31.4 in secondary forest). This low density and adult-dominated population inside the native forest may be the sign of a recent expansion, and that the species is currently only marginally able to use this ecosystem. It is known that the success of an invasion depends on the capacity of a species to adapt to new conditions, or on the invasibility of the recipient ecosystem, and that plants and animals dispersed by humans may cause radical disturbances in the environment that encourage invasions (Vitousék et al., 1997; Marvier et al., 2004). Thus, the snail’s preference for feeding on cultivated plant species may have favoured its expansion in human-altered environments (Imevbore, 1992; Raut et al., 2002). Lettuce, taro, banana, sweet potato, avocado, chayote, jackfruit tree, and other species with succulent leaves, tubers and fruits, commonly found in plantations and forested areas around plantations, are examples of edible plants associated to the occurrence of the exotic snail in São Tomé. Other introduced plants occur in more preserved land-use types, including native forests, thus functioning as a dispersion pathway for the snail to reach well-preserved forest patches. Such plant species include the oil palm tree E. guineensis and the coral tree, whose flowers were confirmed as food items for the snail in the study area. Whether the current restricted distribution of the species inside the native forest is due to limited food availability, biological control by predation or parasitism, or by others factors it is not known. A better understanding of the factors constraining the species invasion of the ONP is essential to ensure that conservation strategies are in place to avoid or minimize this invasion, as it is most likely due to a lag time rather than to an ecological impossibility. Species management may attain its broadest success by simply identifying and protecting large stands of minimally disturbed and relatively unfragmented ecosystems (Marvier et al., 2004). The species distribution map and the most important predictors of presence are, for this purpose, a useful tool for future management plans involving those well-preserved areas with a current higher risk of invasion by the West African Giant Land Snail.

Implications for native biodiversity

There are anecdotal indications that anthropogenic snail gathering pressure may already be forcing the species to adapt and survive in many of the secondary forests that compose the ONP buffer zone. The continuation of such pressure might help promoting the species invasion of native forests, which are mostly found inside the ONP. Since this invasive species is known to feed on a great variety of plants species, it can be a threat to the native plants (Agongnikpo et al., 2010). It has already been documented that invasive Achatinidae can also feed on other snails (Meyer et al., 2008), and indirect ecosystem disruption might threaten the endemic-rich native ecosystems and their species (Peterson et al., 1998; Orwig, 2002; Dukes & Mooney, 2004). The endemic São Tomé and Príncipe giant land snail, Archachatina bicarinata was common throughout the islands, including at low altitudes, until the introduction of the invasive West African Giant Land Snail (Gascoigne, 1994a). In Príncipe island this species is now restricted to the native forests, at higher altitude or in less accessible areas, mostly outside the distribution range of the invasive species, while no systematic survey has been carried in São Tomé (Dallimer and Melo, 2010). The introduced snail has been implied in the rapid decline of the endemic snail (Gascoigne, 1994b), but no specific process linking the two species has been identified. To identify effective conservation measures to protect the endemic species it is key to clarify how these two species interact. A broader evaluation of the ecological repercussions of introduced snail on the ecosystems and species of these islands is also urgent to ensure negative impacts are avoided. Finally, we concluded that anthropogenic ecosystem degradation facilitated the spread of the invasive giant land snail up until the marginal portions of the native forest. Thus, future conservation actions must consider the management of the West African Giant Land Snail inside the ONP and in its buffer zone. This species has already spread throughout the island, occurring in high densities, therefore eradication measures will not be very feasible. Future research should focus on identifying which factors are associated with the pervasiveness of the invasive species in the native forest. On a wider context this study shows how anthropogenic ecosystem changes can facilitate the spreading of invasive species. In particular, how the introduction of exotic species, creates favourable conditions for the survival, growth and reproduction of invasives.

Chapter 2. Is the invasive West African Giant Land Snail Archachatina marginata displacing the Gulf of Guinea endemic Archachatina bicarinata?

Abstract: The biodiversity loss crisis is severely affecting invertebrates worldwide. Island terrestrial molluscs are among the most vulnerable taxa, being particularly affected by habitat destruction and introduced species. The Gulf of Guinea Giant Land Snail Archachatina bicarinata, endemic to the islands of São Tomé and Príncipe, has suffered a severe population decline in the last decades. However, knowledge of its distribution, ecology and major threats remains very scarce. One of the most likely causes for the demise of this endemic species is the introduction of the West African Giant Land Snail Archachatina marginata, which in just half a century spread across much of the island. This study aims to understand possible interactions between the exotic and native giant land snails in São Tomé Island. We found a strong temporal and spatial segregation between the two species. The reports of local inhabitants seem to match written accounts in that the contraction of the endemic giant snail’s distribution coincided and is linked to the expansion of the introduced. Nowadays, the distribution of the two species in São Tomé is almost complementary, and they use very distinct habitats: the endemic is restricted to the most remote patches of native forest, while the invasive prefers degraded habitats, only marginally occurring in native forest. The current population of the invasive snail includes a high proportion of juveniles, which contrasts with the worrisome adult-dominated situation of the endemic. Finally, we found a displacement in the daily activity patterns of the two snails, with the endemic being active mostly during the day and the invasive during the night. Our results represent the first systematic report on the distribution and habitat preferences of the Gulf of Guinea Giant Land Snail in São Tomé Island, providing further indications that the introduced West African Giant Land Snail is behaving as an invasive and seems to be linked to its dramatic decline. The situation of this endemic species requires immediate conservation action and that its conservation status on the IUCN Red List is upgraded.

Keywords: species distribution modelling, biological invasion, habitat degradation, interspecific competition, São Tomé and Príncipe

Introduction

Over the past 500 years, human activities have led to habitat modifications, overexploitation of species and introduction of exotic species, which are the key drivers of the current biodiversity crisis (Briggs, 2015). These are often interlinked, namely because the invasibility of introduced species is increased by habitat disturbance, which opens ecological space for the penetration of recently arrived species (Gillespie, 2007). Human colonization and subsequent biological invasions have been particularly damaging to island ecosystems (Sax and Gaines, 2008; Ceballos et al., 2015). These have evolved in isolation, have high rates of endemism with naturally small population sizes and ranges, and

19 simple ecological networks marked by reduced competition, thus being particularly susceptible to invasion (Gillespie, 2007). Oceanic islands have high extinctions rates among terrestrial vertebrates and invertebrates (Briggs, 2015). However, invertebrates are highly underrepresented in conservation research, in favour to more charismatic vertebrate taxa (Clarke and May, 2002; Lydeard et al., 2004). Land snails are useful ecosystem health indicators, as they are very sensitive to habitat degradation. This sensitivity is linked to their low mobility and restricted geographic distributions, as well as to being closely associated to soil properties (Dedov and Penev, 2004; Horsák et al., 2009; Oke and Omoregie, 2015; Nicolai et al., 2017). Among all animal groups, land snails have suffered the largest number of species extinctions due to human activities, with the great majority of extinctions taking place in oceanic islands (Lydeard et al. 2004; Chiba and Cowie, 2016). In addition to the direct impacts of anthropogenic activities, intentionally or unintentionally introduced species on islands also affect native land snail faunas (Chiba and Cowie, 2016).

São Tomé is an oceanic island in the Gulf of Guinea, off the west coast of Africa and is one of the 25 global biodiversity hotspots, due to its diverse range of unique and threatened species (Myers at al., 2000; Jones, 1994). The island harbours seven endemic genera, at least one endemic family of terrestrial molluscs, and endemic species account for 77% of its land snail fauna (Jones, 1994; CBD, 2015). Despite some taxonomic studies on the unique land snail fauna of São Tomé, few studies have explored their distribution, ecology and conservation status.

The Gulf of Guinea Giant Land Snail Archachatina bicarinata (Bruguière, 1792), is one of the most iconic terrestrial molluscs of São Tomé Island. Endemic to the islands of São Tomé and Príncipe, this giant snail used to be relatively common, especially inside the forest (Moller, 1894; Gascoigne, 1994a). From the 19th century, a reduction of its range has been documented, at an alarming rate in recent decades (Gascoigne, 1994b; Dallimer and Melo, 2010). The causes of this decline are not entirely clear, but have been attributed to the introduction of the West African Giant Land Snail Archachatina marginata (Swainson, 1821) (Gascoigne, 1994a). The exotic snail, probably introduced as a food source, has rapidly spread through human-modified habitat, being currently present in great part of São Tomé Island, including the native forest (Panisi, 2017). The rapid decline of the endemic snail in Príncipe Island (Dallimer and Melo, 2010) underlines the importance of addressing its conservation. This requires an understanding of the major threats affecting the species, namely habitat degradation and the spread of the introduced congener.

This study focuses on the ecology of the endemic Gulf of Guinea Giant Land Snail and of the invasive West African Giant Land Snail, to understand how these species might be interacting in São Tomé Island. More specifically, we aim: (1) reconstructing the historic changes in the distribution of both species using knowledge of local people; (2) modelling the current island-wide distribution of both species, identifying important ecological determinants and; (3) assessing habitat preferences, activity

20 patterns and population age structure along a gradient of forest degradation, to understand how the introduced snail might be linked to the demise of the endemic.

METHODS

Study species and area

São Tomé is an 857 km2 island, located 255km west of the African mainland, in the Gulf of Guinea. It was discovered in 1471 and since then its territory has been widely modified, mostly by agriculture (de Lima et al., 2014). The human population is currently estimated at 201.025, corresponding to three times more than half a century ago (CIA, 2017). Most of its population is located along the coast, mostly dominated by savannah in the north and other non-forested ecosystems in the centre and south. Inland, towards higher elevation, different types of ecosystems can be found, including plantations and vast secondary forests, most of which derived from abandoned plantations (Jones et al., 1991; Diniz et al., 2002). Finally, the remaining native forest can be found in remote areas, most of which in the steep mountains extending through the west-centre and south of the island. Annual rainfall varies across the island from less than 600 mm in the northeast to over 7,000 mm in the southwest (Bredero et al. 1997). Rain is concentrated during the wet season, from September to May. The dry season, the gravana, extends from June to August, with a less demarcated dry period, the gravanito, from December to March. Humidity is high throughout the year in most of the island (de Lima et al., 2016). Altitude creates a temperature gradient, with annual averages ranging from 23 to 30 ºC at sea level to less than 13.5 °C above 1500 m (Silva, 1958). São Tomé is an important biodiversity hotspot, holding a remarkable richness of endemic flora and fauna, such as birds, orchids and terrestrial molluscs (Jones, 1994). The Ôbo Natural Park (ONP), covering around one third of the island, was established, together with its buffer zone, in 2006 and includes most of the island’s remaining native forest. Despite being a protected area, hunting, logging and harvesting of several other forest products persist (de Lima et al., 2016). Most of the numerous endemics of São Tomé are concentrated in this protected area, including the Gulf of Guinea Giant Land Snail. This species was described as common throughout São Tomé, namely at low altitudes (Gascoigne 1994a). However, it seems to have been subject to a rapid decline in the last decades, presumably associated to the introduction of the invasive West African Giant Land Snail between decades 50s-70s (Girard, 1893; Gascoigne, 1994a, 1994b). A recent systematic survey on Príncipe Island revealed a dramatic population decrease, describing that the endemic species currently occurs exclusively in the less accessible areas of the primary rainforest (Dallimer and Melo, 2010). In 1994, the known distribution of the invasive West African Giant Land Snail in São Tomé was limited to cocoa and coffee plantations in the north and east of the island, while it was absent from primary and secondary forest, and higher altitudes (Gascoigne, 1994a, 1994b). In the last couple of decades, the species spread rapidly

21 and nowadays it is distributed across most of the island, being associated to more degraded ecosystems, even though marginally it can appear within the native forest (Panisi, 2017). Our work took place mostly during the short dry season and the rainy season, and across São Tomé Island, focusing on the ONP and its buffer zone.

Field methods

Local perceptions about the changes in giant land snail distribution

To understand the perceptions of local rural inhabitants on the distribution changes of the giant land snails of São Tomé, we performed 86 interviews in 21 villages (Table S4, Table S4b). These villages are located across São Tomé, but most are within the ONP buffer zone. Each interviewee was asked to identify photos of both study species, to assess if they could identify them correctly. Then, we asked when and how the invasive species arrived in São Tomé, to reconstruct a spatio-temporal gradient of expansion in the island. Furthermore, we enquired about the spatio-temporal changes in the distribution of the endemic species. Finally, we questioned about the anthropogenic uses of both species.

Island-wide species distribution modelling

To model the distribution of the two species island-wide, we jointed occasional and systematic observations registered by the BirdLife International São Tomé and Príncipe Initiative (BISTPI) between August 2013 and February 2015 (de Lima et al. 2016). Additionally, we collected supplementary systematic records between January and March 2017 to ensure that the entire island was sampled adequately (Soares, 2017). This sampling included five 10-minutes point counts in 174 1-km2 quadrats, spread across the island (de Lima et al. 2013; Panisi, 2017; Fig. 2.1a). Additional records were also made, registering the location and altitude with GPS whenever the endemic species was found, but only in unusual locations for the invasive.

Transect sampling: habitat associations, daily activity patterns and population age structure

To assess the habitat preferences of both species, and compare the distribution of the two species, we sampled seven transects of variable length along the gradient of forest degradation, totalling 16.8 km (Fig. 2.1b). The transects were chosen to cover the transition between the distribution of both study species, and to represent the forest degradation gradient throughout the island. The shade plantations and non-forested areas were combined in the unique class “plantations”, representing the cultivated areas surrounding the forest, which was the focus of this sampling effort. Each transect was sampled three times between mid-January and mid-March 2017. The transects were divided in 50 m long sectors, each of which was characterized by recording GPS location, elevation and predominant habitat type.

The transects were sampled simultaneously by two observers walking at constant slow pace, while actively searching for both species of giant land snails in a 4 m wide band (about 1.5 km/h). We recorded exact location, species, time, elevation, habitat type, activity, and shell length and width for every dead or live specimen found during the transects. To assess daily activity patterns, we tried to keep an equal proportion of day and night sampling hours, and the activity (e.g. eating or crawling) or inactivity were assessed for each giant snail detected. All transects were sampled around sunrise (5.30 AM), between 4 AM and 11 AM, and around sunset (5.30 PM), between 13 PM and 21 PM.

Shell length and width were used to estimate the population age structure of each species. The West African Giant Land Snail reaches sexual maturity at the age of 9 months, when the average shell length is estimated at 8.5cm (Plummer, 1975). We used this parameter to distinguish juveniles from adults in both species, since there is no information about the exact size of the endemic species when it reaches sexual maturity. We built a density plot to compare the population structure of both species.

We measured 17 environmental variables (Panisi, 2017), which included a wide variety of vegetation and substrate measurements, to describe the sampled locations in detail. The variables were collected in 150 sampling points, including 52 places where the presence of live invasive snails was confirmed, 17 where the presence of live endemic snails was confirmed, and 84 pseudo-absences. The sampling points where the presence of live snails was confirmed were selected at random, making sure that all selected points were at least 40 m from each other, to guarantee independence. The pseudo- absences were computed randomly in the sampling area to ensure that their number was proportional to the length of each transect and that they did not overlap with areas where live snails had been recorded.

Figure 2.1 – Maps of São Tomé showing sampling locations for both species. a) São Tomé divided in the 1-km2 tetrads, which were part of the 172 4-km2 quadrats, sampled to perform the island-wide species distribution modelling. Each dot represents a systematic point count. The 100-m elevation isolines are shown in the background. b) Location of the villages where the interviews were performed (green points) and of the seven transects (thick red lines) used to assess smaller-scale habitat associations, daily activity patterns and population age structure. Background colours indicate land-use categories: dark green for native forest, intermediate green for secondary forest, light green for shade plantation and yellow for non-forested areas (Soares, 2017). The black line represents the boundaries of the ONP, the dotted line represents the limits of the ONP buffer area.

Data Analysis

To perform the data analysis, we used the software R, version 3.3.3 (R core team, 2017) and in QGIS, version 2.18.4 (Quantum GIS Development Team, 2017).

Species distribution modelling

We used 70% of the island-wide sampling records compiled to build models for the distribution of the two species of giant land snails occurring in São Tomé (McCullagh and Nelder, 1989). We used Generalized Linear Models (GLM) with binomial errors, having the presence of each species as the response variable and the following explanatory variables; Land-use type, Rainfall, Topographical Positioning Index (TPI), Elevation, Slope, Distance to rivers, Distance to the coast, Ruggedness and Remoteness (Table S1). We ranked all possible GLMs, without interactions, based on Akaike information criteria corrected for small sample size (AICc, Burnham and Anderson, 2002), using the function “dredge” of the “MuMIn” package (Barton, 2016). Then, we used the “predict” function of the “stats” package (R core team, 2017) to fit the best model for each species to raster data, and thus obtain the species potential distribution maps. To assess the influence of the invasive snail in the occurrence and distribution of the endemic snail, we created a new model for the endemic species, with the presence of the projected/effective invasive snail as predictor variable. We performed an ANOVA analysis to test for a significant reduction in the residual deviances after the inclusion of the invasive snail presence as a predictor (Ros et al., 2015). Finally, we assessed the distribution of both species in the native and secondary forest inside the ONP and corresponding buffer zone, to focus on the areas where the transition between the two species occurs. For each model we assessed multicollinearity calculating the relative variance inflation factors (VIFs), and ranked all possible GLMs, without interactions, based on Akaike information criteria corrected for small sample size (AICc, Burnham and Anderson, 2002), calculating the relative variable importance (RVI) of each variable. The remaining 30% of the presence/absence records was used to assess the goodness of fit of each model by calculating the curve (AUC) of the receiver operating characteristic curve (ROC), from the “pROC” package (Robin et al., 2011), and the McFadden’s Index, from the “pscl” package (Jackman et al., 2015).

Habitat associations

To assess habitat associations, we compiled substrate (litter weight, wood, litter, grass, stones, fresh litter, lichens, bryophytes) and vegetation variables (Panisi, 2017; Table 1.1), using a two- dimensions non-metric multidimensional scaling (NMDS) ordination through the function “metaMDS” of the “vegan” package (Oksanen et al., 2017). To assess the link between vegetation and the distribution of the giant land snails, we plotted the probability density function of each species on to the first axis of the NMDS ordination. Subsequently, we performed a GLM with binomial errors to identify associations between the presence of each study species and environmental characteristics. As explanatory variables we used elevation, number of trees, canopy density, distance to the closest tree, slope, habitat type,

24 understory density, the first two axes of the substrate and vegetation NMDSs, and presence of the other giant land snail species. We calculated the VIFs to assess multicollinearity and all possible models for each species were ranked using the function “dredge” of the “MuMIn” package (Barton, 2016), based on AICc. Finally, we calculated RVI to assess the overall contribution of each environmental variable to explain the occurrence of the snails. To test differences in the species abundance calculated for every 50 m of transect sampled, a Kruskal-Wallis test was used with the three different habitats as a grouping factor.

RESULTS

Local perceptions about the changes in giant land snail distribution

All the 86 interviewees recognized the invasive species, but only 65 recognized the endemic. We found a significant association between the correct identification of the endemic snail and the age of the interviewed (Spearman’s rank correlation rho= 0.58, p < 0.001, Fig. S4): most of the interviewees that did not recognize the endemic snail were younger than 15 years old. All the interviewees that recognized the endemic species referred its decline. According to them, in most of the localities the demise of the endemic occurred after the introduction of the invasive species, exception made for the localities in the south-east (Fig. 2.2). Most interviewees linked the decline of the endemic snail to the invasive species (48.75%, Fig. S5), but other causes of its demise were also cited, such as snail harvest (16.2%), predation by feral pigs Sus scrofa (15%), habitat destruction (10%), predation by black snake Naja peroescobari (7.5%) and diseases (2.5%). The endemic species disappeared from many localities inside and in the surrounding of the PNO buffer zone limits, but it has been also recently sighted in several localities inside and closer to the buffer zone (Fig. 2.2). The invasive species was said to have been first introduced in the north of the island, and then voluntarily introduced in many localities across the island. Some of the interviewees (n = 8) believe that the invasive species was introduced by Nigerian or Cameroonian expatriates, working in Bobo Forro, near the capital of São Tomé (nr. 10 in Fig. 2.2). The endemic species resulted in an important cultural and food value for all the interviews that knew the species (n = 65). Its importance as food source was highlighted in 53.9% of the answers (with 16.7% supporting that this species is a healthier food supply than the invasive one), its use for medicinal purposes in 38.2% of the answers and its biodiversity value in 7.9 % of the answers. All the interviewed cited the importance of the invasive species as a food source and for its trade, however, 46.5% of the total interviewees also highlights that the introduced species is a severe pest for horticulture and for plantations.

Figure 2.2 – Spatio-temporal dynamics of the introduction of the West African Giant Land Snail and the decline of the Gulf of Guinea Giant Land Snail in São Tomé. Black crosses represented the localities where the endemic species is said to have disappeared and white points where it is said to have been recently present. Red arrows represent the voluntary introduction of the introduced snail from a site to another. Black points represent the villages where the interviews were performed, and they are labelled with a number circled in black (localities codes and the number of interviews for every locality are listed in Table S4). For every village an estimation of the year of the invasive species’ arrival is indicated on the top of the flag (orange scale of colours) and an estimation of the year of the starting decline of the endemic species is indicated below (grey scale of colours). The lighter the colours of the years, the closer is the event to the present time (averages and standard deviations, Table S5).

Species distribution modelling

Across the island, we recorded 957 presences and 891 absences for the introduced snails, and 149 presences and 1699 absences for the endemic. The explanatory variables “Distance to coast”, “Remoteness” and “Rugosity” were excluded from the modelling of both species to avoid multicollinearity. The GLM of both species showed no multicollinearity (VIFs < 2.69 for the invasive and VIFs < 2.48 for the endemic) and good model fits (AUC = 0.83 and McFadden pseudo R-Square = 0.25 for the invasive, and AUC = 0.86 and McFadden pseudo R-Square = 0.26 for the endemic). Land-use type and Rainfall were the most important variables to explain the presence of both species (Table 2.1), but while the invasive species preferred plantations and drier secondary forests and avoided native forest, the endemic species showed the opposite tendency. The invasive species was also associated with lower altitudes, valleys, and middle and upper slopes (Table 2.1, Fig. S2, Fig. S3, Table S9a, Table S9b), while the endemic was associated to higher altitudes, and valleys and upper slope areas (Table 2.1, Fig. S6, Fig. S7, Table S11a, Table S11b). The potential distribution map shows that the invasive snail avoids the coast and the centre of the island, only marginally entering the ONP in the south, even though it is well established within the

ONP buffer area. On the other hand, the endemic snail appears to be restricted to remote areas of the ONP. The two species were found together in only 10 points on a total of 1848 (Fig. 2.3). When the occurrence of the invasive species was added as an explanatory variable to model the occurrence of the endemic, residual deviance reduced significantly (Table 2.2). Model performance was also improved (AUC = 0.89 and McFadden pseudo R-Square = 0.30, Fig. S8), maintaining no multicollinearity (VIFs <2.59). In fact, the presence of the invasive species became the most important variable to explain the endemic species’ occurrence, through a negative correlation (Table 2.1, Spearman’s rank correlation rho= - 0.27, p < 0.001, Table S12a, Table S12b).

Table 2.1 – Relative Variable Importance (RVI) calculated by Model Averaging obtained from the island-wide model for the distribution of both study species. The most important predictor variables for each model are highlighted in bold and numbered by order of importance. Regarding the endemic models; model (b) differs to model (a) for the presence of the invasive species as a predictor variable. RVI Predictor variable Invasive Endemic (a) Endemic (b) Land-use type 1.001 1.002 0.99 Rainfall 1.002 1.001 1.003 TPI 1.003 1.003 1.002 Elevation 1.004 1.004 0.98 Slope 0.89 0.40 0.43 Distance to Rivers 0.62 0.82 0.55 Invasive species presence - - 1.001

Figure 2.3 – Maps of São Tomé showing the potential distribution of both species. Map representing the probable distribution of a) West African Giant Land Snail and b) Gulf of Guinea Giant Land Snail ranged by colours. Orange points indicate the areas where both species were found together

Table 2.2 – ANOVA results exploring the contribution of the invasive species to explain the occurrence of the endemic species. The analysis shows an increase of the variability explained by the model which included the invasive, in comparison to the model without invasive as an explanatory variable.

Regression model Residual df Residual Change in p (endemic species) deviance deviance Best model without invasive 1280 577.77 Best model with the 1279 546.52 21.259 <0.001 invasive species occurrence

When we assess the distribution of the invasive snail only in the forests of the ONP and buffer zone, Rainfall was the best explanatory variable, followed by Land-use Type and Elevation (Table S6, Table S13a, Table S13b). The species preferred drier areas, covered by secondary forests and located at lower altitudes. Topography was the best variable to explain the occurrence of the endemic in this part of the island. The species was associated to valleys, preferably in native forest, at higher altitudes, and where the annual rainfall was higher (Table S6, Table S14a, Table S14b). The GLM of both species showed no multicollinearity (VIFs < 3.04 for the invasive and VIFs < 2.33 for the endemic) and reasonable model fits (AUC = 0.84 and McFadden pseudo R-Square = 0.3 for the invasive, and AUC = 0.79 and McFadden pseudo R-Square = 0.15 for the endemic). When the occurrence of the invasive species was added as a predictor variable, topography and the invasive species became the most

28 important variables to explain the occurrence of the endemic snail (Table S6), and model performance was improved (AUC = 0.81 and McFadden pseudo R-Square = 0.19, and still no multicollinearity - VIFs <2.5, Table S7, Table S15a, Table S15b). The potential distribution map inside the limits of the ONP buffer zone shows that the invasive snail enters the ONP mostly in the south and it is well established within the ONP buffer area. The endemic snail appears to be restricted to remote areas, in high altitudes restricted zones inside the ONP (Fig. S9).

Habitat associations at the transect level

More than half of the total length of the sampled transects were in native forest (55%), followed by secondary forest (28%) and plantations (17%). In total, we recorded 293 live and 41 dead invasive snails, and 56 live and 23 dead endemic snails.

The vegetation NMDS is strongly correlated with habitat types: native forest points can be found on the bottom-left of the plot, while disturbed forest is at the top, and non-forested area on the left (Fig. 2.4). The Gulf of Guinea Giant Land Snail is thus associated with the occurrence of native and endemic species, such as Dryptes glabra, Sterculia tragacanta, Santiria trimera and Begonia baccata. On the contrary, A. marginata prefers disturbed forest and plantations associated with the presence of introduced plants such as taro Xanthosoma saggitifolium, chayote Sechium edule, banana Musa sp. and Cestrum laevigatum. Significant differences were detected between the dispersion of the two species along the axes of the vegetation NMDS indicating that the species are associated to significantly different group of plants (permutation test, p<0,001, Table S8). The substrate ordination (stress=0,17, Fig. S10) indicates that the endemic species preferred substrates composed mostly by stones and with moss, and avoided naked soil or substrate densely covered by grass.

Figure 2.4 – NMDS analysis and the association of the plant and snail species to the axes of ordination a) NMDS plot for the vegetation measured at the 20m scale (stress= 0.18). Considering the NMDS stress value, the first two axes are a good representation of most of the variance between sampling points. Symbol size is proportional to species abundance. Triangles are the presences of the invasive snail, diamonds are the presences of the endemic snail and circled points highlight the presence of both species. b) Probability density functions show association of each species along the first axis of the vegetation NMDS, in which zero represents availability. Grey line represents the association of the endemic species and black line represent the association of the invasive species. In attachment the list of plant species’ codes (Table S2) and the plant species association with the NMDS axes (Fig. S11).

To avoid multicollinearity, we only used the second axis of the NMDS. VIF values calculated were smaller than 6.089 for the invasive species model and smaller than 2.28 for the endemic species model. The RVI based on data collected on the transects also showed that the presence of both species was best explained by land-use type. The invasive species was found mostly in plantations (32.9 snails per km), followed by secondary forest (31.4 snails per km) and significantly less abundant in the native forest (5.5 snails per km, Kruskal-Wallis test, p < 0.001), while the endemic was found mostly in native forest (4.2 snails per km) and in secondary forest (3.6 snails per km) and none endemic snail was found in plantations. The presence of the endemic was also positively associated with the second axis of the substrate ordination, while the presence of invasive was additionally linked to lower altitudes and positive values of the second axis of the vegetation ordination (Table 2.3, Fig. 2.5). The most important models were ranked including all the possible combinations of predictor variables. The model that fits the best to the explanatory variables is chosen and the variables are ranked in order of importance. Altitude, Land-use type and vegetation composition were the most important variables to explain the

30 occurrence of the invasive species, since they are included in all the best models (Fig. S12, Table S16a, Table S16b). Land-use type and substrate composition are the most important variable to explain the occurrence of the endemic species (Fig. S13, Table S17a, Table S17b).

Table 2.3- Relative variable importance (RVI) of each predictor variable, as calculated in the habitat association analysis for both species. The most important predictor variables for each species are highlighted in bold. RVI Predictor variable Invasive Endemic Elevation 1.00 0.31 Trees number 0.41 0.32 Canopy density 0.24 0.29 Tree distance 0.28 0.33 Substrate (NMDS 1 axis) 0.37 0.52 Substrate (NMDS 2 axis) 0.26 0.73 Slope 0.05 0.19 Habitat 1.00 0.79 Understory density 0.52 0.09 Vegetation (NMDS 2 axis) 1.00 0.41 Other species presence 0.25 0.31 (A.bicarinata/A.marginata)

Figure 2.5 – Distribution and abundance of the giant land snail species along the transects. Transects are ordered according to the distance to the forest limits. Each circular dot represents a 50 m portion of a transect. Symbol size is proportional to species abundance.

Daily activity patterns

Almost all the endemic snails we found were active during the day time (94%), while we found only 6% of the individuals during the night time and behaving active. The invasive species was observed to be active mostly during the beginning of the night time and during the morning (Fig. S14). The results of the interviews also confirm these pattern, being the endemic species cited as active during the day (82.3% of answers, n= 28) and the invasive during the night (83.3% of answers, n = 42).

Population age structure

Most of the sampled endemic individuals were adults, and reached larger sizes than the individuals belonging to the invasive species (Fig. 2.7). There is a significant different between the shell length of the two species (Mann-Whitney U test, p<0,001). The native species measured from 2.5 cm to 15.6 cm, with a median of 11.1 cm, while the West African Giant Land Snail varied between 1.5 and 11.9 cm, with a median of 7.6 cm. (Fig. 2.6). Most of the invasive individuals were juveniles (65%), compared to just 21% of the endemic snails. The shell width was not analysed in detail, since it was very strongly correlated with shell length (Spearman’s rank correlation rho=0.95, p < 0.001, Fig. S15).

Figure 2.6 - Age structure histograms, based on shell length distribution for the invasive species (a) and the endemic species (b). The bold vertical lines separate juveniles from adults in both species (x = 8.5 cm).

Figure 2.7 - Comparison of populations structure between species using a density plot. Dotted lines indicate the average shell lengths for both species.

DISCUSSION

The endemic Gulf of Guinea Giant Land Snail and the introduced West African Giant Land Snail are strongly segregated in São Tomé Island. The endemic snail is currently restricted to remote areas of native forest, while the introduced is behaving as an invasive, having spread throughout most human-modified ecosystems and into the margins of the native forest.

Local perceptions about the changes in giant land snail distribution

The results of our interviews show that there is, among the inhabitants of São Tomé, the widespread perception of the decline of the Gulf of Guinea Giant Snail in the island. These perceptions are supported by previous written accounts (Girard, 1893; Gascoigne, 1994a). Most of the younger locals interviewed did not recognize the endemic snail, but all the interviewees recognized the invasive snail. This might be linked to the invasive species being more abundant in humanized areas and the endemic species being difficult to find, mostly in the last decades. The changes in distribution between the two species suggest that the introduction of the invasive snail is linked to the demise of the endemic snail. This connection is widely recognized by the local inhabitants of most localities around the island.

However, in the south, this pattern is not so clear and local inhabitants refer overharvest, habitat destruction or diseases as the main causes for the regression of the endemic snail. It is possible that in the south, the land-use conversion to coffee and cocoa cultivation, or to more recently oil palm monocultures, had a strong impact on the population of the endemic species before the introduction of the invasive species (Gascoigne 1994b).

Interviewees consistently described that the West African Giant Land Snail had been voluntarily introduced in many localities, facilitating its spread. The species is now an important source of protein for the local population, being much more consumed than the endemic. However, the endemic is the preferred species for consumption, having also an important cultural value as, unlike the introduced, it is used in traditional medicine.

Species distribution modelling

Even though we sampled most of the island, the rarity of the endemic species associated with the remoteness of the locations where it persists, made it difficult to identify important environmental determinants of its distribution. We found this species in just 148 out of the 1848 overall sampling sites, a clear indication that it has become very rare and that it has a severely restricted distribution.

Land-use type and rainfall were the most important variables to explain the distribution of both species at island-scale. The invasive species is associated to drier lowlands and more degraded ecosystems, while the endemic is restricted to native rainforest in remote areas. The West African Giant Land Snail can inhabit from savannah to forested areas in the mainland, but its abundance is influenced by favourable humid conditions (Osemeobo, 1992; Idohou et al., 2013). In São Tomé, the snail spread in a variety of habitats, but its occurrence in native forest is still limited, since human-modified habitat might better fit their food preferences (Imevbore, 1993, Panisi, 2017). This species widely spread inside the buffer zone. However, its entrance inside the ONP appears to be restricted in the north, where it is probably difficulted due to higher altitudes. On the contrary, the endemic species clearly avoids human- modified landscapes, occurring in remoted areas at higher altitudes, probably limited to specific restricted valleys. A similar result has been described for the endemic species population in Príncipe Island (Dallimer and Melo, 2010).

When we included the occurrence of the invasive species as a predictor to model the distribution of the native species, this became the most important variable at island-wide scale and the second most important at the buffer zone scale. In fact, the species are strongly spatially segregated, with the invasive appearing just barely until the limits of the distribution of the endemic species, in native forest. Congener species frequently have parapatric distribution characterized by very narrow contact zones, which suggests that a competitive relationship may be occurring (Mooney and Cleland, 2001; Anderson et al., 2002). The displacement of the native species can occur because the introduced species uses the available food resources more efficiently (Byers, 2000). Thus, the West African Giant Snail might have

34 benefited from the anthropogenic habitat modification providing resources that confer it a competitive advantage over the endemic. The few contact zones that we identified were located at the limits of the native forest, both in montane secondary forest in the north and in lowland native rainforest in the south. The extremely limited number of sympatric locations (sampling points = 10) and their wide environmental variability made it impossible to identify the key determinants for the simultaneous occurrence of both species.

Habitat associations at the transect level

The vegetation analyses revealed that the two species are associated to totally different groups of plant species, which relate to the gradient of forest degradation. The invasive snail is associated to introduced plants, such as taro, chayote and banana, while the endemic snail is associated to native plants, some of which are endemic. At the local scale, as observed at island-scale, a few sympatric areas were recorded, corresponding to the edge between native and secondary forest. However, the scarce records (three sampling points) do not allow to define patterns in the vegetation composition for the areas of sympatry.

The substrate analysis revealed that the endemic species is associated to rock outcrops and mossy substrates. Rocky substrates are likely to be preferred by endemic giant land snails due to favourable microclimatic conditions and to the security that rock crevices provide against pests and predators (Osemeobo, 1992). In São Tomé this type of habitat is often found in the proximity of streams, creating moist conditions and an environment suitable for rich and abundant land snails’ assemblages (Martin and Sommer, 2004).

Elevation was an important factor explaining the island-wide distribution of both species, but at the local scale it was only important to explain the occurrence of the invasive, confirming its preference for lowlands. The endemic species was found both in the wettest lowland forests of the south and in the montane forests of the north.

Despite the lack of statistical significance in the correlation between the two species at local scale, they are almost totally segregated in space, sometimes separated by natural barriers such as streams. The contact areas were limited to higher elevations, above which none of the species occurred. Probably environmental conditions, such as lower temperatures and montane vegetation composition, do not favour the occurrence of either species.

Finally, the difference between the number of dead individuals for the two species is remarkable (14% of the invasive snails found were dead, in comparison to 41% of the endemic). We include the probable occurrence of a disease among the factor responsible for the decline of the endemic population, maybe spread by the invasive as previously cited (Gascoigne 1994).

Daily activity patterns

We observed an unusual activity pattern for the Gulf of Guinea Giant Snail, which was active mainly during day time. The giant African land snails are usually nocturnal (Raut and Barker, 2002). Daily activity of terrestrial snails is normally related to acceptable combinations of high humidity, low temperature conditions and food availability (Cook, 2001). This exception might be linked to a high availability of food and to favourable temperature and humidity conditions inside the native forest. Another nocturnal animal, the bat Hipposideros ruber, was also found to be active during daylight in São Tomé, a behaviour mostly associated to reduced predation risk during the light hours (Russo et al., 2011). A similar explanation may apply to the daily activity of the Gulf of Guinea Giant Land Snail, but data on its predators is insufficient to evaluate this hypothesis. Most of the endemic snails we found were active, but a large proportion of the invasive was inactive. When inactive, snails normally hide in refuges, and during adulthood settle for a specific refuge. Nevertheless, in large densities they may rest in exposed sites due to the scarcity of available hiding spots (Raut and Barker, 2002). This suggests that during sampling the invasive may have been more visible while inactive, because it occurs at larger densities.

Population age structure

The invasive species has a large proportion of juveniles, while the Gulf of Guinea Giant Snail had very few young individuals. The larger size of the endemic may indicate that it takes longer to reach sexual maturity. In various animal taxa, island lineages tend to lay larger eggs, in smaller numbers. Such island syndromes have been associated to decreased predation pressure and environmental factors, such as temperature and humidity (Chiba and Cowie, 2016). The endemic species lays fewer but bigger eggs than the West African Giant Land Snail (Pers. Observ.). Such a reproduction strategy may have increased fitness in the environment with little competition in which the endemic presumably evolved, but may now be a disadvantage for the competition with the exotic species.

Is the West African Giant Land Snail displacing the Gulf of Guinea Giant Land Snail?

This study provides several lines of evidence that suggest that there is interspecific competition between the two giant land snail species occurring in São Tomé. However, the specific dynamics of their interaction are not totally understood, and further research is needed, namely regarding sympatric areas. Nowadays, the introduced giant snail is widespread and extremely abundant in plantations. This is probably due to its high reproductive rate, together with multiple voluntary introductions across the island and with its preference for human-modified habitats. Its increase in degraded environments may have been the main driver for retraction of the endemic snail, which has become restricted to the most

36 remote patches of native forest. Very few studies have focused on the interactions between species of terrestrial molluscs (Miranda and Pecora, 2017). However, there is evidence that interference among terrestrial snails may be mediated through aggressive behaviour or through the production of mucus that inhibits the growth and behaviour of other conspecifics or other closely related species from the same genera, and may result in exclusion from food and home sites when the latest are scarce (Cameron and Carter 1979; Cook, 2001). The exact mechanisms underlying interspecific competition are difficult to fully understand and most likely are a combination of interconnected causes (Gutiérrez et al., 2014, Chiba and Cowie, 2016). In São Tomé, a combination of several factors may have been responsible for the observed decline of the native giant snail, in which the introduction and spread of the invasive congener might have been the final triggering factor for the accelerated contraction in recent times. However, habitat destruction, overharvesting, animal predation and diseases have almost surely also played an important role. Some giant land snail species are restricted to native forests, rapidly disappearing or declining in second-growth or plantations land-use changes (Raut and Barker, 2002). In São Tomé, habitat loss was probably one of the first factor contributing to the decline of the native snail, even before the introduction of the invasive West African Giant Land Snail, especially in the south of the island (Gascoigne, 1994b). However, the native snail often appeared to be resilient to land-use changes, since its distribution used to extend well outside the limits of the native forest and include some anthropogenic ecosystems. In recent decades, habitat degradation seems to have facilitated the spread of the invasive species (Panisi, 2017), which in turn has pushed the endemic towards the inaccessible native forest patches where it persists. The endemic snail has long been used as food and for traditional medicine (Girard 1893; Gascoigne 1994b; Carvalho et al., 2015). Nonetheless, even if snail harvest is involved in its population reduction, it is not likely that it is solely responsible for such a rapid decline. Since the endemic has disappeared from the proximity of villages and the invasive has become very abundant, people started feeding on the latter (Gascoigne 1994b). This does not mean that there is no longer pressure on the endemic, since these are still purchased for medicinal purposes. In fact, anecdotical observations suggest that the endemic snail has become much more valuable in the market, promoting harvest even in the most remote locations. Various species are suspected of predation on the native giant land snails, such as: feral pigs (Sus scrofa), São Tomé thrush (Turdus olivaceofuscus), malacophagous flatworms and beetles (Gascoigne, 1994b; Ogren, 1995; Krauss, 1964; Walker, 2003; Dallimer and Melo, 2010). However, these are not likely to cause such a steep and widespread decline, namely because there is no indication that their abundance has increased in both islands. Finally, it is worth mentioning that during our survey we found, in a very remote and restricted native forest location, near Cabumbé Peak, in the south of São Tomé, 22 freshly dead adult native snails. We did not find evidence of the presence of the invasive snail in the surroundings. Such a mass mortality

37 may have been caused by a disease, which could have contributed to the decline of the species. It has been suggested that such a disease could have been introduced in the island together with the West African Giant Snail (Gascoigne, 1994a).

Conservation implications

The Gulf of Guinea Giant Land Snail is classified as “Vulnerable” since 1994, due to a suspected population reduction during the previous ten years and to a reduced extent of its occurrence through a decline in its area of occupancy, potential levels of exploitations and introduced taxa (criteria A1cde and B1+2b, Clarke and Naggs, 1996; IUCN, 2017). Considering the result of our study and the severe decline of the species on Príncipe Island (Dallimer and Melo, 2010), we suggest that this species might be better qualified as “Endangered”, since it has an extent of occurrence estimated to be less than 5000 km2, limited to two locations (São Tomé and Príncipe), where a continuous decline in its extent of occurrence, area of occupancy, area and quality of habitat and number of subpopulations has been observed and estimated (criterion B1 and B2ab (i, ii, iii, iv)).

The Gulf of Guinea Giant Land Snail has been suggested as an indicator to assess the effectiveness of the protected areas for biodiversity conservation in São Tomé e Principe (Dallimer and Melo, 2010). A great part of its distribution is within the limits of the protected ONP, but human harvesting and the invasion of the West African Giant Land Snail penetrate these limits. The last remote areas where the species occurs must be specifically preserved and conservation measures need to be implemented. For example, the populations of both islands must be estimated and monitored, and harvesting inside the native forest must be forbidden. Specific conservation efforts must focus on the edge of the distribution of the species, where the invasive species also occurs. Also, there is still a lack of knowledge critical for addressing the conservation of the species, such as its breeding ecology, population genetic structure, and vulnerability to diseases. Finally, most locals recognize the decline of this species and conservation efforts will be most effective if they involve the Santomean people. The iconic Gulf of Guinea Giant Snail is the type species for the Archachatina genus, and we cannot risk that it becomes solely another legendary island giant. Conservation action is urgently needed, and this study contributes toward its safeguard.

FINAL CONSIDERATIONS

This study has shown how human activities may have multiple cascading effects on ecosystems and on native biodiversity. Land-use changes and the introduction of plant species lead to deep habitat modifications, reflected in distinct vegetation and substrate composition, and associated climatic variations (Peterson et al., 1998; Dukes and Mooney, 2004). Non-native species subsequently introduced in these habitats, may be less constrained by these environmental changes than native species and, thus, successfully spread throughout human-modified habitats (MacDougall and Turkington, 2005). The first chapter concludes that habitat disturbance may be a main factor involved in the success of an invasion. The introduction of the West African Giant Land Snail in São Tomé Island resulted in its wide dispersal across modified ecosystems in less than half a century, only marginally occupying native forest located inside the ONP. Future studies should evaluate how much human disturbance promotes invasion suitability. The second chapter shows how a combination of direct and indirect anthropogenic factors determined the rapid decline of the native Gulf of Guinea Giant Land Snail, which is currently restricted to remote areas of native forest. Namely, it provides multiple lines of evidence suggesting that the invasive giant snail is displacing the endemic, highlighting strong temporal and spatial segregations between these species and relating the historical changes in the two species’ distributions. The dynamics between an introduced and an endemic species described in this thesis may be interpreted in the light of taxon cycling. Taxon cycles are phases of expansion and contraction of species, associated with shifts in distribution and that can be framed within the theory of island biogeography (Wilson, 1959). Expanding widespread taxa, often originating from continental sources, first occupy marginal, lowland habitats at the edges of islands, while contracting native taxa exhibit reduced or fragmented ranges occupying interior and montane, forested habitats. Shifts between expanding and contracting phases are accompanied by complementary habitat shifts, until the recent arrival fully replaces the native species, thus balancing the number of species occurring in the island as a whole (Ricklefs and Bermingham, 2002). The current situation of giant snail species in São Tomé present strong analogies to what is described in taxon cycle. The speed at which the changes have occurred in this specific case, raises further concern about the persistence of the endemic species, since taxon cycling culminates with the extinction of the native species. As an island, São Tomé economy is particularly reliant on imports, and intense trade is often associated to biological invasions (Hulme, 2009). Human population density is increasing fast, as well as the extent of both urbanized and agricultural land cover. The rare endemic species are thus likely to be facing growing pressures in the nearby future, as the quality of the remaining forest will continue to

39 be negatively affected by introduced organisms and direct anthropogenic ecosystem degradation (Dallimer et al., 2009; Vásquez et et al, 2017). This study provides an important overview of the current situation regarding São Tomé giant snail species, assessing vulnerability to invasion and subsequent interspecific interactions, linked to the direct impact of human activities. Considering that resources available for conservation are limited and the importance of the invasive species to feed the human population in the island, we suggest that future research and conservation actions focus in the ONP. The best way to maintain native biodiversity is to reduce the spread of invasives inside the protected area, where most of the endemic and threatened species occur. Therefore, it is also key to focus research on the factors that explain the distribution of the introduced snail inside the Park. At the same time, local communities need to be made aware of the extraordinary malacofauna of the island, and the Gulf of Guinea Giant Land Snail can be used as a flag species to engage them in its protection.

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SUPPLEMENTARY MATERIALS

TABLES

Table S1 – Description of the predictor variables used to model the species distribution at island scale. All variables were built in Quantum GIS program, are in raster format and projected coordinate reference system, WGS 84 (EPSG 4326). Pixel size is 0.000833º x 0.000833º. Dimensions are 471 x 359 cells (rows x columns).

Variables Description Type Units Calculated from a Digital Elevation Elevation Model with 90 meters of resolution. URL Continuous Metres https://www2.jpl.nasa.gov/srtm/ Class 1- Flat Plain Areas Index representing the position of each Class 2 - Valleys Topographic Position pixel regarding the mean elevation of a Categorical Class 3 - Middle Slope Index neighbourhood within a 0.05º radius Class 4 - Upper Slope (Jenness, 2007; Soares, 2017) Class 5 - Ridges Ruggedness Index calculated from the Ruggedness Continuous - Digital Elevation Model Slope calculated from the Digital Slope Continuous Decimal Degrees Elevation Model Class 1 - Native Forest Land use map built from satellite images, Class 2 - Secondary Forest Land-use type field information, 1970 historical land use Categorical Class 3 - Shade Plantation map and military maps Class 4 - Non- Forested Areas Vectorised map obtained from a map with 30 years of mean annual precipitation Rainfall compiled data throughout the island and Continuous Millimetres later smoothed with a circular filter of 20 pixels radius Minimum linear distance between each Distance to Coast pixel and the nearest point in coast line Continuous Decimal Degrees (Soares, 2017) Cost accumulated surface created with a friction surface derived from slope and Remoteness Index Continuous - weighted by the population density (Soares, 2017) Minimum linear distance between each Distance to rivers Continuous Decimal Degrees pixel and the nearest river

Table S2 – Habitat associations, plant species list (Figueredo et al., 2011, and Diniz, 2002).

Code Common name Scientific name 1 Abacateiro Persea americana 2 Alho-d'Obô Psychotria peduncularis 3 - Anthocleista sp. 4 Avenca Adiantum raddianum 5 Avenca Adiantum lunulatum 6 Azeitona Manilkara obovata 7 Bambú Bambusa vulgaris 8 Bananeira Musa spp. 9 Batata doce Ipomea batatas 10 Fiá-bôba-d’Obô Begonia ampla 11 - Begonia subalpestris 12 Bobô-bobô Casearia barteri 13 Bordão-macaco Costus giganteus 14 Cacau-d'Obô Pseudogrostistachys africana 15 Café-arábica Coffea arabica 16 Café-d'Obô Oxyanthus speciosus 17 Cajamangueira Spondias cytherea 18 Camarões Impatiens buccinalis 19 Capim-de-água Commelina diffusa 20 Chapéu de Panamá Carludovica palmata 21 Cata d'Obô Tabernaemontana pachysiphon 22 Cata-grande Voacanga africana 23 Cata-pequena Rauvolfia vomitoria 24 Cedrela Cedrela odorata 25 Celê-alê Leea tinctoria 26 Coedano Cestrum laevigatum 27 Cola-macaco Trichilia grandifolia 28 Cubango Croton stellulifer 29 - Dicranolepis thomensis 30 Eritrineira-fêmea Erythrina poeppigiana 31 Feijão Phaseolus vulgaris 32 - Marattia fraxinea 33 - Platycerium stemaria 34 Feto-gigante endémico Alsophila welwitschii 35 Feto-gigante introduzido Alsophila manniana 36 Fiá-bôba Begonia baccata 37 Figo-porco Ficus mucuso 38 Figo-tordo Ficus sur 39 - Iresine herbstii 40 Fruteira Artocarpus altilis 41 Girassol Tithonia diversifolia

42 Gofe Cecropia peltata 43 Gofe-d'Obô Musanga cecropioides 44 Gogô Carapa gogo 45 Goiabeira Psidium guajava 46 Grigô Morinda lucida 47 Camarões Impatiens thomensis 48 Ingué-bobô Xylopia aethiopica 49 Jambo Syzygium jambos 50 Jaqueira Artocarpus eterophylla 51 Lemba-lemba Ficus thonningii 52 Macambrará Craterispermum montanus 53 Mamao d'obo Drypetes glabra 54 Mangue-d'Obô Uapaca guineensis 55 Marapião Zanthoxylum gilletii 56 Matabaleira Xanthosoma saggitifolium 57 Matias-jorge Syzygium guineense 58 Moindro Bridelia micrantha 59 Morango Rubus spp. 60 Mussandá Ficus kamerunensis 61 Mussinica Prunus africana 62 Nêspera-d'Obô Sterculia tragachanta 63 Nicolau Pauridiantha floribunda 64 Obata Ficus chlamydocarpa 65 Óleo-barão Symphonia globulifera 66 Ossame Aframomum sp. 67 Palmeira-dendém Elaeis guineensis 68 Pau-branco Tetrorchidium didymostemon 69 Pau-cabra Trema orientalis 70 Pau-cadela Funtumia africana 71 Pau-caixão Pycnanthus angolensis 72 Pau-chuva Maesopsis eminii 73 Pau-esteira Pandanus thomensis 74 Pau-fede Celtis gomphophylla 75 Pau-ferro Margaritaria discoidea 76 Pau-impé Olea capensis 77 Pau-lixa Ficus exasperata 78 Pau-maria Shirakopsis elliptica 79 Pau-óleo Santiria trimera 80 Pau-pimenta Piper guineense 81 Pau-purga Croton draconopsis 82 Pau-quimi Newboldia laevis 83 Pau-sabão Dracaena arborea 84 Pau-sangue Harungana madagascariensis 85 Pau-três Allophylus africanus 86 Pau-vermelho Staudtia pterocarpa

87 Perna-d'Ôbo Mapania ferruginea 88 Pimpinela Sechium edule 89 Pinheiro Afrocarpus mannii 90 Quaco-maguita Psychotria subobliqua 91 Quebra-machado Homalium henriquesii 92 Quina Cinchona sp. 93 Quina-nº2 Discoclaoxylon occidentale 94 - Renealmia grandiflora 95 Repolho Brassica oloracea 96 Safú-d'Obô Pseudospondias microcarpa 97 Safuzeiro Dacryodes edulis 98 - Schefflera barteri 99 - Schefflera mannii 100 Ucuête-macaco Palisota pedicellata 101 Untué Chrysophyllum albidum 102 Zamumo Chrysophyllum africanum

Table S3 – Differences between population classes along the gradient of forest degradation. Standardized Pearson residuals computed after Chi-squared test. Juveniles Adults Dead shells Eggs Primary forest -3.72 2.94 2.63 0.50 Secondary forest 1.34 -0.95 -0.70 -1.03 Plantations 1.34 -1.20 -1.26 0.90

Table S4 – Localities and their map code with the associated number of interviewed performed

Map Village Number of code interviews 1 Água Crioula 1 2 Água das Belas 1 3 Água Izé 5 4 Agulha 1 5 Alto Douro 1 6 Angolares 9 7 Anselmo Andrade 7 8 Bemposta 1 9 Bernardo Faro 7 10 Bobo Forro 7 11 Claudino Faro 5 12 Cruzeiro 2 13 Dona Augusta 5 14 Ilhéu das Rolas 5 15 Lembá 3 16 Manuel Caroça 3 17 Monte Café 4 18 Porto Alegre 5 19 Santa Catarina 8

20 São Miguel 2 21 Terra Batata 4 Total 86

Table S4b – Structure of the interviews. The four questions related to the photo tests (B1, B2, C1 and C2) involved showing the interviewee a separate photo for each question, to assess the ability to recognize eggs and adults of the two species.

Section A) Interviewees data 1. Name; 2. Locality; 3. Age; 4. Profession; 5. Number of years of professional experience Section B) Endemic giant snail (Archachatina bicarinata) 1. Eggs recognizance (photo) 2. Adult snail recognizance (photo) 3. Where can you currently find this snail? 4. Are there less or more snails now? 5. When did it start to disappear? 6. Why did it start to disappear? 7. Where was it possible to find this snail in the past? 8. What does it eat? 9. How many eggs does it lay? 10. Is it important for São Tomé Island and citizens? If yes, why? 11. Has it some negative effects on São Tomé Island and citizens? If yes, which? 12. Is this snail active during the day or the night hours? Section C) Invasive giant snail (Archachatina marginata) 1. Eggs recognizance (photo) 2. Adult snail recognizance (photo) 3. Where can you currently find this snail? 4. When and how did this snail arrive in São Tomé? 5. When and how did this snail3 arrive in the locality? 6. Are there less or more snails now? 7. What does it eat? 8. How many eggs does it lay? 9. Is it important for São Tomé Island and citizens? If yes, why? 10. Has it some negative effects on São Tomé Island and citizens? If yes, which? 11. Is this snail active during the day or the night hours?

Table S5 – Spatio-temporal dynamics in the distributions changes (year of decline – year of appearance). Means and standard deviations are presented for every village and for the two questions presented.

When does the endemic snail started to When does the invasive snail appeared in disappear in the proximity of the village? the proximity of the village?

Village Number of Number of Year of Year of Number of Year of Year of (Code) interviews answers decline decline answers appearance appearance (average) (standard (average) (standard deviation) deviation) 1 1 1 2012 0 1 2000 0 2 1 1 2001 0 1 1999 0 3 5 0 - - 5 1984 4,24 4 1 0 - - 1 2001 0 5 1 1 1999 0 1 1987 0 6 9 3 1998 4 2 1996 5 7 7 4 2000 1.5 5 1990 3,28 8 1 1 1997 0 1 1997 0 9 7 3 2000 0 1 1991 0

10 7 1 2000 0 5 1984 2,75 11 5 2 1991 1 3 2000 0,45 12 2 2 1999 2 1 1998 0 13 5 2 1995 2 2 1998 2 14 3 1 - 0 2 2000 0 15 5 1 2005 0 1 1990 0 16 3 2 1983 2 2 2000 3 17 4 1 1990 0 2 1987 3 18 5 2 1999 1 2 2003 3,5 19 8 3 1993 4.2 2 1991 1 20 2 0 - - 2 No presence No presence

21 4 3 1997 11.3 4 1993 6,63

Table S6 – Relative Variable Importance (RVI) calculated by Model Averaging from the ONP buffer zone model for the distribution of both study species. The most important predictor variables for each model are highlighted in bold and numbered by order of importance. Regarding the endemic models; model (b) differs to model (a) for the presence of the invasive species as a predictor variable.

RVI

Predictor variable Invasive Endemic (a) Endemic (b) Land-use type 1.002 1.003 0.98 Rainfall 1.001 1.002 0.98 TPI 0.99 1.001 1.001 Elevation 1.003 1.004 0.95 Slope 0.55 0.36 0.42 Distance to Rivers 0.45 0.61 0.49 Invasive species presence - - 1.002

Table S7 – ANOVA results exploring the contribution of the invasive species to explain the occurrence of the endemic species inside the limits of the ONP buffer zone. The analysis shows an increase of the variability explained by the model which included the invasive, in comparison to the model without invasive as an explanatory variable.

Regression model (endemic Residual df Residual Change in p species) deviance deviance Best model without invasive 947 529.68 Best model with the invasive 946 505.44 24.241 <0.001 species occurrence

Table S8 – Tests for the homogeneity of group dispersion in the vegetation composition ordination. The species resulted non-homogeneous with a significant difference between their dispersions in the ordination plot.

Endemic Invasive Anova (p-value) 1.597e-09 6.215e-16

Permutation test (p- 0,001 0,001 value)

Model outputs The following tables summarize, for each global model: a) the first 10 specific models, ranked by AICc, and b) the full model-averaged coefficients obtained by model averaging. The differences in AICc are expressed by Δ AICc and the weight for each model is expressed by ω. The significance levels are coded as: ‘***’ - < 0.001, ‘**’ - < 0.01, ‘*’ - < 0.05, and ‘.’ - < 0.1.

Table S9a – Chapter 1, island-wide analysis, introduced species.

Table S9b – Chapter 1, island - wide analysis, introduced species.

Table S10a – Chapter 1, habitat associations, introduced species.

Table S10b – Chapter 1, habitat associations, introduced species.

Table S11a – Chapter 2, island - wide, endemic species.

Table S11b – Chapter 2, island - wide, endemic species.

Table S12a – Chapter 2, island - wide, endemic species (introduced species as a predictor)

Table S12b– Chapter 2, island - wide, endemic species (introduced species as a predictor)

Table S13a– Chapter 2, ONP buffer area, invasive species

Table S13b– Chapter 2, ONP buffer area, invasive species

Table S14a– Chapter 2, ONP buffer area, endemic species

Table S14b– Chapter 2, ONP buffer area, endemic species

Table S15a– Chapter 2, ONP buffer area, endemic species (invasive species as a predictor)

Table S15b – Chapter 2, ONP buffer area, endemic species (invasive species as a predictor)

Table S16a – Chapter 2, transects, invasive species

Table S16b – Chapter 2, transects, invasive species

Table S17a – Chapter 2, transects, endemic species

Table S17b – Chapter 2, transects, endemic species

FIGURES

Figure S1 – Proportion of observed presences of West African Giant Land Snail, depending on a) Land-use type and b) Topographic Position Index (TPI).

Figure S2 – Observed and predicted presence of the West African Giant Land Snail depending on Elevation and Rainfall.

Figure S3 – Population shell width distribution of the West African Giant Land Snail. Min = 1.10 cm, max = 7.0 cm, mean = 4.41 cm.

Figure S4 – Association between the correct identification of the endemic species and the age of the interviewed.

Figure S5 – Causes associated to the demise of the endemic species from locals’ perceptions. The number of answers and their overall proportions are represented in the graph. The graph indicates exclusively the answers given from the interviewed that knew the species (n=65) and, inside this class, from those who knew the causes (n=52). Each interviewed answered one or more causes (N tot=80).

Figure S6 – Proportion of observed presences of the endemic species, depending on a) Land-use type and b) Topographic Position Index (TPI).

Figure S7 – Observed and predicted presence of the endemic species depending on Elevation and Rainfall.

Figure S8 - Comparison of the performance of the model for the endemic species, with and without the invasive species as a predictor variable represented through respective ROC curves.

Figure S9– Maps of São Tomé showing the potential distribution of both species inside the limits of the ONP Buffer Area. Map representing the probable distribution of a) West African Giant Land Snail and b) Gulf of Guinea Giant Land Snail ranged by colours inside the native and secondary forest.

Figure S10- Substrate composition ordination plot (stress = 0.17). The size of the shape is proportional to the abundance of the two species. Circled points represent the presence of both species.

Figure S11 – Plant species association with NMDS axes. The numbered species are fitted in the ordination plot to represent their association with the first two axes of the NMDS for the vegetation analysis.

Figure S12 – Selection of the best models and the most important variables for the West African Giant Land Snail in the habitat association analysis. Models are ranked by AICc and represented through rows, the thicker is the row, the best is the model. The most important variables for every model are highlighted by filled rows. Elevation, Land-use and Vegetation composition are the best variables, being highlighted in every model.

Figure S13 – Selection of the best models and the most important variables for the Gulf of Guinea Giant Land Snail in the habitat association analysis. Models are ranked by AICc and represented through rows, the thicker is the row, the best is the model. The most important variables for every model are highlighted by filled rows. Land-use and substrate composition are the best variables, being highlighted in every model.

Figure S14 – Daily activity patterns of São Tomé giant land snails. The histograms for the endemic and invasive species show the % of active snails calculated for every hour of sampling time and for both species.

Figure S15 – Population shell width variation for both species. Age structure histograms, based on shell length distribution for the invasive species, median = 4.5 cm (a) and the endemic species, median = 6.0 cm (b). Finally, the comparison of populations structure between the two species using a density plot (c). Dotted lines indicate the average shell widths for both species.

R SCRIPTS

* ”Ab” is the acronym for the endemic species (A.bicarinata), “Am” is the acronym for the invasive species (A.marginata).

#1) SPECIES DISTRIBUTION MODELLING ISLAND-WIDE #Import AB.csv for Ab analysis View(AB) #Import ModelsFinal9.csv for Am analysis View(ModelsFinal9) dadAB<- AB dadm<-ModelsFinal9 op <- par(mfrow = c(1, 1), mar = c(3, 3, 3, 1)) dotchart(dadm$Slope, main = "Slope", group = NULL) dotchart(dadm$Tobler, main = "Remoteness", group = NULL) dotchart(dadm$Rugosidade, main = "Rugosidade", group = NULL) dotchart(dadm$DistCosta, main = "CoastDistance", group = NULL) dotchart(dadm$Chuva, main = "Rain", group = NULL) dotchart(dadm$SRTM, main = "Elevation", group = NULL) dotchart(dadm$rivers, main="Distance to rivers", group=NULL) par(op) #Visualize categorical variables op <- par(mfrow = c(1, 2)) hist(dadm$LU2016) hist(dadm$cTPI_005) par(op) #Correlation between variables z<- cbind(dadm$Ab,dadm$Am,dadm$Slope,dadm$Tobler,dadm$Rugosidade,dadm$DistCosta,dadm$Chuva,dadm$ LU2016,dadm$cTPI_005,dadm$SRTM,dadm$rivers) colnames(z)<- c("Ab","Am","Slope","Remoteness","Rugosidade","CoastDist","Rain","Habitat","TPI","SRTM","Rivers") panel.smooth2<-function (x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, col.smooth = "red", span = 2/3, iter = 3, ...) { points(x, y, pch = pch, col = col, bg = bg, cex = cex) ok <- is.finite(x) & is.finite(y) if (any(ok)) lines(stats::lowess(x[ok], y[ok], f = span, iter = iter), col = 1, ...) } panel.cor<-function(x, y, digits=1, prefix="", cex.cor) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) r1=cor(x,y,use="pairwise.complete.obs") r <- abs(cor(x, y,use="pairwise.complete.obs")) txt <- format(c(r1, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep="") if(missing(cex.cor)) cex <- 0.9/strwidth(txt) text(0.5, 0.5, txt, cex = cex * r) } panel.hist<-function(x, ...) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) )

h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col="white", ...) } pairs(z,lower.panel=panel.smooth2,upper.panel=panel.cor,diag.panel=panel.hist) # I remove "Rugosity", "Coast Distance" and “Remoteness” because of multicollinearity #Set train and test data #Am library(caTools) set.seed(101) sample = sample.split(dadm$Code, SplitRatio = .70) trainA = subset(dadm, sample == TRUE) testA = subset(dadm, sample == FALSE) trainA #Same for Ab #Set categorical variables #Am trainA$LU2016<-as.factor(trainA$LU2016) trainA$cTPI_005<-as.factor(trainA$cTPI_005) testA$cTPI_005<-as.factor(testA$cTPI_005) testA$LU2016<-as.factor(testA$LU2016) #Same for Ab #Model_Am modam<-glm(Am~LU2016+Chuva+cTPI_005+SRTM+Slope+rivers,data=trainA, family=binomial(link='logit')) summary(modam) #Model_Ab modab<-glm(Ab~LU2016+Chuva+cTPI_005+SRTM+Slope+rivers,data=trainAB, family=binomial(link='logit')) summary(modab) #Model Ab + Am presence/absence modab2<-glm(Ab~Amc+Slope+Chuva+SRTM+LU2016+cTPI_005+rivers,data=trainAB, family=binomial(link='logit')) summary(modab2) # VIFs library(car) vif(modam) max(vif(modam)) #Same for Ab models # Dredge and Model Averaging library(MuMIn) options(na.action = "na.fail") #dredge Am ddam<-dredge(modam) ddam avgddam<-model.avg(ddam) summary(avgddam) op <- par(mfrow = c(1, 1)) plot(ddam) par(op) #Same for Ab #ROC curves #Am modam1t<-glm(Am~LU2016+Chuva+cTPI_005+SRTM+Slope+rivers,data=testA, family=binomial(link='logit')) prob=predict(modam1t, type=c("response")) prob library(pROC) g <- roc(testA$Am ~ prob) plot(g)

69 auc(g) #Same for Ab #Mc Fadden index library (pscl) pR2(modam1) #Same for Ab #or nullmodel<-glm(Am~1,data=testA, family=binomial(link='logit')) 1-logLik(modam1t)/logLik(nullmodel) #Comparing Ab models with and without Am as a predictor variable plotting ROC curves library(ROCR) #Obtain true positives and false positives for the first model pred1 <- prediction(fitted(modab), trainAB$Ab) stats1a <- performance(pred1, 'tpr', 'fpr') #Same for the second model pred2 <- prediction(fitted(modab2), trainAB$Ab) stats2 <- performance(pred2, 'tpr', 'fpr') #Plot mod1.lab <- expression('Model A - Endemic') mod2.lab <- expression('Model B - Endemic + invasive as a predictor') plot([email protected][[1]], [email protected][[1]], type='s', [email protected], [email protected], col=1, lwd=2, lty=1) lines([email protected][[1]], [email protected][[1]], type='s', col="grey70", lty=1, lwd=2) legend('right', c(mod1.lab, mod2.lab), col=c(1,'grey70',1), lwd=c(2,2,1), lty=1, cex=.9, bty='n')

#SPECIES DISTRIBUTION MAPS #Modelling and mapping the endemic species distribution with the potential occurrence of the invasive as a predictor variable is not possible because AM’s values of potential presence and habitat are highly correlated ##Import environmental variables library(raster) Chuva = raster("C:/Users/ASUS/Desktop/rasteraligned/Chuva.tif") NAvalue(Chuva) <- -3.4028234663852886e+38 par(mfrow=c(1,1),mar=c(2,4,2,4)) plot(Chuva) Slope = raster("C:/Users/ASUS/Desktop/rasteraligned/Slope.tif") NAvalue(Slope) <- 3.4028234663852886e+38 plot(Slope) cTPI_005 = raster("C:/Users/ASUS/Desktop/rasteraligned/cTPI_005.tif") NAvalue(cTPI_005) <- 255 plot(cTPI_005) LU2016 = raster("C:/Users/ASUS/Desktop/rasteraligned/LU2016.tif") NAvalue(LU2016) <- 255 plot(LU2016) rivers = raster("C:/Users/ASUS/Desktop/rasteraligned/rivers.tif") NAvalue(rivers) <- -999 plot(rivers) SRTM = raster("C:/Users/ASUS/Desktop/rasteraligned/SRTM.tif") NAvalue(SRTM) <- -32768 plot(SRTM) #Stack rasters rasters <- stack(SRTM, Slope, cTPI_005, LU2016, Chuva, rivers, bands=NULL) names(rasters) plot(rasters) ##Predict par() par(mfrow=c(1,2),mar=c(2,2,2,2)) #Am distribution map AMdis <- predict(rasters, modam , type="response") plot(AMdis, xaxt='n', yaxt='n', main = "Archachatina marginata") writeRaster(AMdis, 'AM6var.tif')

#Same for Ab #ANOVA between the two Ab models anova(modab, modab2, test="Chisq") #Represent categorical variables_Am library(ggplot2) y<-categorical_var_represent yhab<-y[1:8,6:8] yhab g <- ggplot(yhab, aes(x=Land_use, y=Frequency)) g + geom_bar(aes(fill=Class),stat="identity", width = 0.5) yTPI<-y[1:10,6:8] yTPI t <- ggplot(yTPI, aes(x=TPI, y=Frequency_1)) t + geom_bar(aes(fill=Class_TPI),stat="identity", width = 0.5) #Same for Ab library(ggplot2) y<-categorical_ab yhab<-y[1:8,7:9] yhab g <- ggplot(yhab, aes(x=Land_use, y=Frequency)) g + geom_bar(aes(fill=Class),stat="identity", width = 0.5) yTPI<-y[1:10,21:23] yTPI t <- ggplot(yTPI, aes(x=TPI_1, y=Frequency_1)) t + geom_bar(aes(fill=Class_TPI),stat="identity", width = 0.5) #Representing continuous variables_Am x<-ModelsFinal9 op<-par(mfrow=c(1,2),mar=c(4,4,1,1)) plot(x$SRTM,x$Am,xlab="Elevation (m)",ylab="Species presence") go=glm(Am~SRTM,family=binomial,x) curve(predict(go,data.frame(SRTM=x),type="resp"),add=TRUE) points(x$SRTM,fitted(go),pch=20) plot(x$Chuva,x$Am,xlab="Mean Annual Precipitation (mm)",ylab="Species presence") gol=glm(Am~Chuva,family=binomial,x) curve(predict(gol,data.frame(Chuva=x),type="resp"),add=TRUE) points(x$Chuva,fitted(gol),pch=20) # Same for AB #All the island-wide modelling analysis was repeated for the buffer area and considering only the forested areas inside this area. #2) HABITAT ASSOCIATIONS #VEGETATION COMPOSITION NMDS #Import X20m150_ABU.csv mdad<-X20m150_ABU names(mdad) str(mdad) library(vegan) library(FactoMineR) mdad1<-mdad[,7:108] names(mdad1) veg <- decostand(mdad1,"hell",diag=T,upper=T) dveg <- vegdist(veg,diag=T,upper=T,method="jaccard") vmds <- metaMDS(dveg,trymax=999) vmds str(vmds) #Graphs op<-par(mfrow=c(1,1)) plot(vmds,type="n",display="sites", xlab="NMDS Axis 1", ylab="NMDS Axis 2", xlim=c(-0.6,0.6)) points(vmds$points[,1],vmds$points[,2],pch=19,cex=mdad$Abs*0.2,col=adjustcolor("black")) points(vmds$points[,1],vmds$points[,2],pch=24,cex=mdad$Am*0.6,col="black", bg="white",lwd=1.5) points(vmds$points[,1],vmds$points[,2],pch=23,cex=mdad$Ab*0.6,col="black", bg="gray87",lwd=1.5)

71 points(vmds$points[,1],vmds$points[,2],pch=1,cex=mdad$Both*3,col="darkgrey",lwd=3) points(vmds$points[,1],vmds$points[,2],pch=24,cex=mdad$Both*0.6,col="black",bg="white",lwd=1.5) ##Variables NMDS op<-par(mfrow=c(1,1)) plot(vmds,display="sites",type="n",xlab="NMDS1",ylab="NMDS2", xlim=c(-0.6,0.6), ylim=c(-0.5,0.5)) ef1<-envfit(vmds,mdad1, permu = 999) ef1 plot(ef1, p.max = 0.001,col=grey(0.1)) ###To know vectors length factor vegan:::ordiArrowMul(scores(ef1, display="vectors")) ##Density curves graph ###Import vmds1 csv vmds1 <- read.csv("C:/Users/Ricardo/Desktop/Martina_20_06_17/vmds1.csv", sep=";") names(vmds1) vmds2<-vmds1[,10:12] names(vmds2) Am0x<-density(vmds2$Am.2[1:52],from=-0.6,to=0.8)$x Am0y<-(density(vmds2$Am.2[1:52],from=-0.6,to=0.8)$y-density(vmds2$Absence.2[1:84],from=- 0.6,to=0.8)$y) Ab0x<-(density(vmds2$Ab.2[1:17],from=-0.6,to=0.8)$x) Ab0y<-(density(vmds2$Ab.2[1:17],from=-0.6,to=0.8)$y-density(vmds2$Absence.2[1:84],from=-0.6,to=0.8)$y) ###Plot plot(Am0x,Am0y,ylim=c(-1.5,2),xlim=c(-0.6,0.6),type="n",lwd=1, xlab="NMDS Axis 1",ylab="Density") abline(h=0) lines(Am0x,Am0y,lwd=5) lines(Ab0x,Ab0y,lwd=5,col="grey") #Species presence in function of NMDS axis 1 and 2 op<-par(mfrow=c(1,2)) boxplot(vmds$points[,1]~mdad$AmPA) cor.test(vmds$points[,1],mdad$AmPA,method="spearman") boxplot(vmds$points[,2]~mdad$AmPA) cor.test(vmds$points[,2],mdad$AmPA,method="spearman") #Same for Ab #Tests for groups dispersion_presence/absence (PERMDISP2) #Am op<-par(mfrow=c(1,2)) vdispAM<-betadisper(dveg,AM) plot(vdispAM) boxplot(vdispAM) anova(vdispAM) permutest(vdispAM) #Same for Ab #Exportar coordenadas axis 1 and 2 mdad$nmds1<-as.numeric(vmds$points[,1]) mdad$nmds2<-as.numeric(vmds$points[,2]) list(mdad$nmds1) list(mdad$nmds2) library(WriteXLS) write.table(mdad$nmds1, file='vmds1.csv', sep=';', dec=',', row.names=FALSE) write.table(mdad$nmds2, file='vmds2.csv', sep=';', dec=',', row.names=FALSE)

#SUBSTRATE COMPOSITION NMDS ##Import NMDSsoil.csv mdada<-NMDSsoil[,8:16] names(mdada) mmdsS<-metaMDS(mdada,trymax=999) mmdsS str(mmdsS) #Graphs ##Points NMDS

72 op<-par(mfrow=c(1,1)) plot(mmdsS,type="n",display="sites", xlab="NMDS Axis 1", ylab="NMDS Axis 2", xlim=c(-0.6,0.6)) points(mmdsS$points[,1],mmdsS$points[,2],pch=19,cex=NMDSsoil$Abs*0.2,col=adjustcolor("black")) points(mmdsS$points[,1],mmdsS$points[,2],pch=24,cex=NMDSsoil$AmABU*0.6,col="black", bg="white",lwd=1.5) points(mmdsS$points[,1],mmdsS$points[,2],pch=23,cex=NMDSsoil$AbABU*0.6,col="black", bg="gray87",lwd=1.5) points(mmdsS$points[,1],mmdsS$points[,2],pch=1,cex=NMDSsoil$Both*3,col="darkgrey",lwd=3) points(mmdsS$points[,1],mmdsS$points[,2],pch=24,cex=NMDSsoil$Both*0.8,col="black",bg="white",lwd=1.5 ) ef2<-envfit(mmdsS,mdada, permu = 999) ef2 plot(ef2, p.max = 0.001,col=grey(0.4)) #Species presence in function of NMDS axis 1 and 2 #AM_Presence/absence op<-par(mfrow=c(1,2)) boxplot(mmdsS$points[,1]~NMDSsoil$Am) cor.test(mmdsS$points[,1],NMDSsoil$Am,method="spearman") boxplot(mmdsS$points[,2]~NMDSsoil$Am) cor.test(mmdsS$points[,2],NMDSsoil$Am,method="spearman") #Same for Ab #Exportar coordenadas MMDS axis 1 and 2_ Same as for the vegetation comp. analysis

# HABITAT ASSOCIATIONS MODELS ##Import matrix Final Model.csv mdata<-FinalModel names(mdata) str(mdata) library(vegan) library(FactoMineR) #Correlation between variables_ Same as the island-wide analysis #Veg 20m and Veg 2m are correlated, I remove Veg axis 1 2m, Veg axis 2m because the 20m measures is more complete, I remove Veg axis 1 because correlated with Habitat #Perform models names(mdata[,3:15]) #Categorical variables mdata$Slope<-as.factor(mdata$Slope) mdata$Understorydensity<-as.factor(mdata$Understorydensity) mdata$Habitat<-as.factor(mdata$Habitat) #TOTAL #GLM_AM paper 2 modAM<- glm(Am~Ab+Veg_2+Substr_1+Substr_2+Altitude+TreeDistance+CanopyCover+Ntrees+Understorydensity+Ha bitat+Slope,data=mdata, family=binomial) summary(modAM) #GLM_AM paper 1 (without Ab occurrence) modAM1<- glm(Am~Veg_2+Substr_1+Substr_2+Altitude+TreeDistance+CanopyCover+Ntrees+Understorydensity+Habitat +Slope,data=mdata, family=binomial) summary(modAM1) #GLM_AB modAB<- glm(Ab~Am+Veg_2+Substr_1+Substr_2+Altitude+TreeDistance+CanopyCover+Ntrees+Understorydensity+Ha bitat+Slope,data=mdata, family=binomial) summary(modAB) #VIFs, Dredge and Model Averaging_ Same as the island-wide analysis

#POPULATION AGE STRUCTURE ##Import Shell Length1.csv length<-Shell_length1

73 op<-par(mfrow=c(1,3)) #Differente in between species’ shell length and width library(car) shapiro.test(length$ABLength) leveneTest(length$ShellLength, length$Species) wilcox_test(length$ABLength, length$AMLength) wilcox.test(length$Abwidth, length$Amwidth) #Density plots library(plyr) library(ggplot2) t<-Shell_lengthFIN mu <- ddply(t, "Species_2", summarise, grp.mean=mean(StSW)) head(mu) legth<-ggplot(length, aes(x=StShellLength, fill=factor(Species))) + geom_density(alpha=0.4)+geom_vline(data=mu, aes(xintercept=grp.mean, color=Species), linetype="dashed")+ labs(title="",x="Shell Length / Maximum Shell Length", y = "Density") width<-ggplot(t, aes(x=StSW, fill=factor(Species_2))) + geom_density(alpha=0.4)+geom_vline(data=mu, aes(xintercept=grp.mean, color=Species_2), linetype="dashed")+ labs(title="",x="Shell Width / Maximum Shell Width", y = "Density") #Histograms op<-par(mfrow=c(1,2),mar=c(4,4,1,1), xlab="Shell length (cm)") hist(length$AMLength, breaks=20, main="", xlim=range(0:15), xlab="Invasive snail - Shell length (cm)", ylab="Frequency") abline(v=8.5,lwd=3) #Same for Ab hist(length$Amwidth, breaks=25, main="", xlab="Shell width (cm)", ylab="Frequency", xlim=range(0:8)) #Same for Ab #Correlation shell length and shell width cor.test(length$ABLength,length$Abwidth,method="spearman") cor.test(length$AMLength,length$Amwidth,method="spearman")

#AM_ POPULATION STRUCTURE vs HABITATS #Import x.csv library(ggplot2) theme_set(theme_classic()) x<-AMclassesPOPHab x1<-x[,8:11] names(x1) # Histogram g <- ggplot(x1, aes(x=Habitat, y=Frequence)) c<- g + geom_bar(aes(fill=Class),stat="identity", width = 0.5)+ ylab("Frequency(%)")+ xlab("") c + scale_fill_grey(start = 0, end = .9)+ theme_bw() #Test for significance between groups and habitats #Import M.csv (Xsq <- chisq.test(M)) # Prints test summary Xsq$observed # observed counts (same as M) Xsq$expected # expected counts under the null Xsq$residuals # Pearson residuals Xsq$stdres # standardized residuals

#ABUNDANCE IN FUNCTION OF ALTITUDE AND HABITAT library(ggplot2) #Import TUDO.csv #Starting point separation between forest and non- forested areas ggplot(GraphTR,aes(x=Transects,y=Elevation))+labs(x="Distance from forest limits (m)",y="Elevation (m)")+geom_point(aes(size=ABUND,shape=factor(SPECIES),color=factor(SPECIES)))+geom_point(aes(color =factor(Habitat)))+scale_color_manual(values=c("palegreen3","palegreen4","palegreen3","lightyellow3","steelb lue3","black","black","black"),labels=c("HABITAT","Native forest","Secondary forest","Non- forestal","Rivers","","",""),name="")+scale_shape_manual(values=c(1,3,24,23,1),name="SPECIES",labels=c("", "Rivers","A.marginata","A.bicarinata","Both species"))+ geom_vline(xintercept = 0)

#ACTIVITY PATTERNS #Import act.csv names(act) par(mfrow=c(2,1),mar=c(2,4,1,6)) barplot(height=act$ABActivo,xlim=c(0,18),ylim=c(0,1),ylab="Active A. bicarinata (%)") barplot(height=act$AMActivo, names.arg=act$Hour,xlim=c(0,18),ylim=c(0,1),ylab="Active A. marginata (%)") hist(x=act$ABActivo, width=act$AB.h,xlim=c(0,18),ylim=c(0,1)) barplot(height=act$ABActivo, xlim=c(0,18),ylim=c(0,1)) barplot(height=act$AMActivo, xlim=c(0,18),ylim=c(0,1)) barplot(height=act$ABActivo) barplot(height=act$AMActivo,ylim=c(0,1)) library(ggplot2) ggplot(act[,4:5])

#KW TEST SPECIES ABUNDANCE/HABITAT #AM KWtest1$Habitat <- as.factor(KWtest1$Habitat) kruskal.test(KWtest1$AmAbu,KWtest1$Habitat) dunn.test.control(KWtest1$AmAbu, KWtest1$Habitat) #Same for Ab

#3) INTERVIEWS #Import BP.csv #Age_Rec Photos dat<-InterviewANalys hist(dat$Idade, xlab="Age of the interwieved", breaks=90) op<-par(mfrow=c(1,2),mar=c(4,4,1,1)) #Ab plot(dat$Idade_1,dat$BP,xlab="Age of the interviewed",ylab="Endemic recognizance", main="") gi=glm(BP~Idade_1,family=binomial,dat) curve(predict(gi,data.frame(Idade_1=x),type="resp"),add=TRUE) points(dat$Idade_1,fitted(gi),pch=20) cor.test(dat$Idade_1,dat$BP, method="spearman")