biological invasion and the conservation of endemic island

2017

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

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

ii

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.

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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.

iv

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

Ilha de São Tomé – 2 de fevereiro 2017

v

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

vi

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

vii

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

viii

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

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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

xi

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

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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

1

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

2

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

3

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.

4

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.

5

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;

6

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

7

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.

8

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.

9

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.

10

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

height (dhb) > 30 cm and height > 20 m

N 20

Canopy density Canopy cover measured with a convex

spherical densiometer

% -

Distance to tree Distance to the closest tree with a dbh >

30cm

m -

Slope Five classes;

1 - None or very soft

2 - Soft

3 - Medium

4 - Steep

5 - Very steep

- 2

Habitat Three classes;

1 - Native forest

2 - Secondary forest

3 – Plantations

- 20

Understory density Five classes;

1 - None or very sparse

2 - Sparse

3 - Medium

4 - Dense

5 - Very dense

- 2

Substrate composition

Litter weight Weight of a 20 x 20 cm sample of

ground litter

g 2

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

of 102 plant species

0/1 20

Vegetation composition 2m Presence or absence of a predefined list

of 102 plant species

0/1 2

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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).


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.


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.

12

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


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

13

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.

14

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.


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).

15

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

16

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.


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

17

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.

18

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.

19

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

20

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

21

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

22

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.

23

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.

24

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).


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).


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,

25

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


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.

26

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).


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

27

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

28

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

(endemic species)

Residual df Residual

deviance

Change in

deviance

p

Best model without invasive 1280 577.77

Best model with the

invasive species occurrence

1279 546.52 21.259 <0.001

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

29

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.

30

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

31

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

(A.bicarinata/A.marginata)

0.25 0.31

32

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).


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).

33

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.


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.

34

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.


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

35

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).

36

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.


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

37

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

38

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.

39

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

40

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.

41

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49

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

Elevation

Calculated from a Digital Elevation

Model with 90 meters of resolution. URL

https://www2.jpl.nasa.gov/srtm/

Continuous Metres

Topographic Position

Index

Index representing the position of each

pixel regarding the mean elevation of a

neighbourhood within a 0.05º radius

(Jenness, 2007; Soares, 2017)

Categorical

Class 1- Flat Plain Areas

Class 2 - Valleys

Class 3 - Middle Slope

Class 4 - Upper Slope

Class 5 - Ridges

Ruggedness Ruggedness Index calculated from the

Digital Elevation Model Continuous -

Slope Slope calculated from the Digital

Elevation Model Continuous Decimal Degrees

Land-use type

Land use map built from satellite images,

field information, 1970 historical land use

map and military maps

Categorical

Class 1 - Native Forest

Class 2 - Secondary Forest

Class 3 - Shade Plantation

Class 4 - Non- Forested

Areas

Rainfall

Vectorised map obtained from a map with

30 years of mean annual precipitation

compiled data throughout the island and

later smoothed with a circular filter of 20

pixels radius

Continuous Millimetres

Distance to Coast

Minimum linear distance between each

pixel and the nearest point in coast line

(Soares, 2017)

Continuous Decimal Degrees

Remoteness Index

Cost accumulated surface created with a

friction surface derived from slope and

weighted by the population density

(Soares, 2017)

Continuous -

Distance to rivers Minimum linear distance between each

pixel and the nearest river Continuous Decimal Degrees

50

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

51

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

52

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

code

Village Number of

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

53

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

disappear in the proximity of the village?

When does the invasive snail appeared in

the proximity of the village?

Village

(Code)

Number of

interviews

Number of

answers

Year of

decline

(average)

Year of

decline

(standard

deviation)

Number of

answers

Year of

appearance

(average)

Year of

appearance

(standard

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

54

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

species)

Residual df Residual

deviance

Change in

deviance

p

Best model without invasive 947 529.68

Best model with the invasive

species occurrence

946 505.44 24.241 <0.001

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-

value)

0,001 0,001

55

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.

56

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

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

57

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

58

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

59

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

60

Table S16b – Chapter 2, transects, invasive species

Table S17a – Chapter 2, transects, endemic species

Table S17b – Chapter 2, transects, endemic species

61

FIGURES

62

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.

63

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).

64

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.

65

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.

66

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.

67

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.

68

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) )

69

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,


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,


prob=predict(modam1t, type=c("response"))

prob

library(pROC)

g <- roc(testA$Am ~ prob)

plot(g)

70

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()


#Am distribution map

AMdis <- predict(rasters, modam , type="response")

plot(AMdis, xaxt='n', yaxt='n', main = "Archachatina marginata")

writeRaster(AMdis, 'AM6var.tif')

71

#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)

72

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


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


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


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

73


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


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

74


#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)

75

#ACTIVITY PATTERNS

#Import act.csv

names(act)


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")

biological invasion and the conservation of endemic island

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